Currently available Master projects in SV or STI-IBI laboratories

This page contains a list of Master projects that are currently available in Life Sciences (SV) or in STI-IBI laboratories. These projects can be searched by keywords. If you are interested or would like to obtain further information from the Lab, please use the contact email listed.
Note that if you are interested in an interdisciplinary project (one where you will be co-supervised by two labs from different EPFL Schools), you can type ‘interdisciplinary’ in the search box.

Laboratory Title Category
Lutolf Development of next-generation organoid technology

Organoids form through poorly understood morphogenetic processes in which initially homogeneous ensembles of stem cells spontaneously self-organize in suspension or within permissive three-dimensional extracellular matrices. Yet, the absence of virtually any predefined patterning influences such as morphogen gradients or mechanical cues results in an extensive heterogeneity. Moreover, the current mismatch in shape, size and lifespan between native organs and their in vitro counterparts hinders their even wider applicability. The goal of this Master project is the development of next-generation gastrointestinal organoids that are assembled by guiding cell-intrinsic self-patterning through engineered stem cell microenvironments. The student will employ advanced biomaterials and/or microtechnology approaches to control the behaviour of organoid-forming stem cells.
Keywords: Organoids, stem cells, self-organization, patterning, hydrogel, microfluidics, biofabrication

Supervisor:  Matthias Lutolf
Co-supervisor: tbd   
Contact: [email protected]

Required: Experience in mammalian cell culture
Availability: Spring 2020, Fall 2020, Spring 2021, Fall 2021
Bioengineering
Interdisciplinary
Lemaitre Drosophila Immunity

The Drosophila antimicrobial response at the time of the Cas9/CRISPR gene targeting revolution The application of Drosophila genetics has generated insights into insect immunity and uncovered general principles of animal host defense. These studies have shown that Drosophila has multiple defense “modules” that can be deployed in a coordinated response against distinct pathogens. Today, Drosophila can be considered as having one of the best-characterized host defense systems among the metazoan. The Cas9/CRISPR revolution offers new opportunities to revisit in a systematic manner Drosophila immunity. At the interface between large-scale genomic studies that lack resolution and individual gene analysis that lack breadth, our laboratory has undertaken a meso-scale ‘skilled’ analysis of immune modules, notably by addressing the individual and overlapping function of large immune gene family. The aim of the master project is to characterize the function of Drosophila immune modules (ex. antimicrobial peptides, phagocytosis,….) using powerful genetic approaches. Methods: Drosophila genetics, molecular biology, genomic, histology, microbiology, bioinformatic.
Keywords: immunology, genetics, drosophila, antimicrobials

Supervisor:  Bruno Lemaitre   
Contact: [email protected]

Required: A background in biology
Availability: Spring 2020, Fall 2020, Spring 2021, Fall 2021
Infectious diseases
Wet
Lemaitre Insect endosymbiosis

The foreign within: Drosophila-Spiroplasma interaction as a model of insect endosymbiosis Virtually every species of insect harbors facultative bacterial endosymbiotic bacterium (endosymbiont) that are transmitted from females to their offspring. Many manipulate host reproduction in order to spread within host populations. Others increase the fitness of their hosts by protecting their hosts against parasites. In spite of growing interest in endosymbionts, very little is known about the molecular mechanisms underlying endosymbiont-insect interactions. To fill this gap, we are dissecting the interaction between Drosophila and its native endosymbiont Spiroplasma poulsonii. The master project will use a broad range of approaches (molecular genetics, histology, microbiology, genomics….) to dissect the molecular mechanisms underlying key features of the symbiosis, including vertical transmission, regulation of symbiont growth, and symbiont-mediated protection against parasites. We believe that the fundamental knowledge generated on the Drosophila-Spiroplasma interaction will serve as a paradigm for other endosymbiont-insect interactions.
Keywords: symbiosis, drosophila, bacteria, genetics

Supervisor:  Bruno Lemaitre   
Contact: [email protected]

Required: a background in biology
Availability: Spring 2020, Fall 2020, Spring 2021, Fall 2021
Infectious diseases
Wet
Ramdya Apply deep learning to study animal behavior

Insects generate complex behaviors with a numerically simple nervous system. The objective of this project is to leverage computer vision and deep learning algorithms to study the leg positions of tethered behaving flies from high-resolution movies. These behavioral sequences can then be linked to simultaneously acquired neuroimaging data. This project at the interface of computer science, and neurobiology will be supervised at the Neuroengineering Laboratory in close interaction with computer science groups on campus.
Keywords:

Supervisor:  Pavan Ramdya
Co-supervisor: Fua (IC)   
Contact: [email protected]

Required:  C/C++, and/or Python
Availability: Spring 2020, Fall 2020, Spring 2021, Fall 2021
Neuroscience
Interdisciplinary
Ramdya Build a neuromechanical insect simulation

To understand the behavior of complex systems it is often necessary to build a model. The goal of this project is to develop a biorealistic 3D simulation of Drosophila. This model will be used to test bioinspired neural networks limb controllers. The project at the interface between robotics, computer science, and neurobiology will be supervised at the Neuroengineering Laboratory in close collaboration with the Biorobotics Laboratory.
Keywords:

Supervisor:  Pavan Ramdya
Co-supervisor: Ijspeert (Robotics)   
Contact: [email protected]

Required: C/C++, and/or Python
Availability: Spring 2020, Fall 2020, Spring 2021, Fall 2021
Neuroscience
Interdisciplinary
Ramdya Apply deep learning to study neural circuits

A central goal of neuroscience is to link neural activity and behavior. The objective of this project is to use computer vision and deep learning approaches to extract neural activity patterns during behavior and to make accurate predictions behavioral and internal states. This project at the interface between computer science and neurobiology will be supervised at the Neuroengineering Laboratory in close interaction with computer science and image processing groups on campus.
Keywords:

Supervisor:  Pavan Ramdya
Co-supervisor: Fua (IC)   
Contact: [email protected]

Required: C/C++, and/or Python
Availability: Spring 2020, Fall 2020, Spring 2021, Fall 2021
Neuroscience
Interdisciplinary
Ramdya Build a robotic fly

Nature has solved numerous challenges associated with autonomous behavioral control. We hope to leverage these solutions in robotics. The goal of this project is to construct an insect-inspired robot and to test bioinspired algorithms of limb control. This project at the interface of robotics and biology will be supervised at the Neuroengineering Laboratory in collaboration with EPFL robotics groups.
Keywords:

Supervisor:  Pavan Ramdya
Co-supervisor: Ijspeert (Robotics)   
Contact: [email protected]

Required: Microfabrication and/or Electronics experience
Availability: Spring 2020, Fall 2020, Spring 2021, Fall 2021
Neuroscience
Interdisciplinary
Ramdya Build robotic systems to automate neuroscience microsurgery

Surgical interventions commonly performed in medicine and research require skill and extensive training. The objective of this project is to democratize and automate microsurgery by developing automated vision-guided robotic systems that dissect and prepare animals for neural recordings. This project at the interface between robotics and neurobiology will be supervised at the Neuroengineering Laboratory in collaboration with the Microrobotics Laboratory.
Keywords:

Supervisor:  Pavan Ramdya
Co-supervisor: Sakar (Robotics)   
Contact: [email protected]

Required: Robotics / electronics experience
Availability: Spring 2020, Fall 2020, Spring 2021, Fall 2021
Bioengineering
Interdisciplinary
Oricchio Cancer Genomics data portal development

The goal of this project is to organize and link cancer genomics data obtained from different sources and in different formats within a browsable data portal accessible to people in the lab.
Keywords: Big genomic data, data portal, data integration

Supervisor:  Elisa Oricchio
Co-supervisor: Giovanni Ciriello, UNIL   
Contact: [email protected]

Required: python language required, knowledge of html/php preferred
Availability: Spring 2020, Fall 2020
Computational Biology
Interdisciplinary
Duboule Hox gene regulation in cultured embryoids

We have recently shown the implementation of collinear Hox genes expression in “gastruloids”, a mouse embryonic stem (mES) cells-based organoid that mimic early embryonic spatial and temporal genes expression [1]. The aim of this project is to generate stable deletions and knockin in mES cells in order to study the underlying mechanisms of Hox genes expression in this gastruloids.
Keywords: Gene regulation, chromatin, organoids, CRISPR/cas9

Supervisor:  Denis Duboule   
Contact: [email protected]

Required: Some experience in cell culture and molecular biology is a plus
Availability: Spring 2020, Fall 2020
Molecular biology
Dry and wet
D’Angelo Regulation of Lipid metabolism in Cell Growth

While proliferating, cells need to duplicate not just their DNA but also their cytosolic and membrane components. Cell growth is indeed the outcome of multiple processes, including the de novo synthesis of proteins and lipids. To this purpose, during phases of active cell division, the rate of growth is matched to the availability of nutrients required for the coordinated protein biosynthesis and membrane expansion. How the lipid synthetic system is regulated in response to growth stimuli or nutrients availability and how lipid remodelling influences growth is partially understood. Here we will evaluate changes in lipid metabolism as a consequence of different growth conditions and the impact of lipid synthetic inhibition to cell growth.
Keywords: lipid metabolism, proliferation and cancer, intracellular trafficking

Supervisor:  Giovanni D’Angelo   
Contact: [email protected]

Required:  –
Availability: Spring 2020, Fall 2020, Spring 2021, Fall 2021
Cell biology
Wet
D’Angelo Role of the Sphingolipid processing machinery in neural differentiation

Starting from the 1970s, a number of studies have reported that the plasma membrane composition in terms of sphingolipids is subjected to remodelling during neural development. These changes are accompanied by a reprogramming in the expression of genes encoding the sphingolipid synthetic enzymes. Our preliminary data indicate that post-translational regulation of the sphingolipid synthetic machinery synergizes with transcriptional programs to assist GSL remodelling. Specifically we find that the localization and stability of key enzymes involved in the metabolic rewiring are inversely affected in the course of neural differentiation. Here we want to study the molecular mechanisms accounting for this post-translational regulation.
Keywords: neural tube organoids, neural differentiation, lipid metabolism

Supervisor:  Giovanni D’Angelo   
Contact: [email protected]

Required:  –
Availability: Spring 2020, Fall 2020, Spring 2021, Fall 2021
Developmental biology
Wet
D’Angelo Single-Cell in situ Lipidomics

Lipids are fundamental constituents of all living beings. They participate in energy metabolism, account for the assembly of biological membranes, act as signalling molecules, and interact with proteins to influence their function and intracellular distribution. Eukaryotic cells produce thousands of different lipids each endowed with peculiar structural features and contributing to specific biological functions. Cellular lipidomes vary among cell types and are reprogrammed in differentiation events. Recent contributions including from our group have shown that lipid composition is subjected to remarkable cell-to-cell variation in syngeneic, homogeneous cell populations suggesting that cell-to-cell lipid heterogeneity contributes to the emergence of multicellular patterns. Lipid biologists have so far addressed lipidomes in bulk cell extracts or selected lipids at the single-cell level. Thus, how lipidomes vary form one cell to another and which cell-to-cell lipid variations have biological meaning remains to be defined. Here, we will develop an integrated pipeline coupling high-resolution mass spectrometry imaging, single-cell multi-omics and lipid probes to attain Single-Cell in situ Lipidomics analysis of cell populations. We will use this approach to interrogate the role of single-cell lipidome variations in the medically relevant case of dermal fibroblast heterogeneity..
Keywords: single cell omics, cell identity, senescence, cancer

Supervisor:  Giovanni D’Angelo   
Contact: [email protected]

Required:  –
Availability: Spring 2020, Fall 2020, Spring 2021, Fall 2021
Cell biology
Wet
Petersen Brain wide single cell anatomy

The goal of this project is to digitally reconstruct the axons and dendrites of cortical neurons in the mouse brain. Single neurons labelled with fluorescence will be imaged across the entire mouse brain at high resolution. The axons and dendrites will be traced through semi-automated procedures and quantified in the context of a standard mouse brain atlas.
Keywords: mouse neocortex, axon, dendrite, 3D imaging

Supervisor:  Carl Petersen   
Contact: [email protected]

Required:  –
Availability: Spring 2020, Fall 2020, Spring 2021, Fall 2021
Neuroscience
Dry and wet
Petersen Brain wide maps of inhibitory neurons

The goal of this project is to comprehensively map the locations and numbers of genetically-defined types of inhibitory neurons in the mouse neocortex. Genetically-engineered mice will be used to label specific types of GABAergic neurons with fluorescent proteins, and then the entire mouse brain will be imaged at high resolution. The student will develop image analysis methods to locate each neuron within a standard mouse brain atlas.
Keywords: Mouse neocortex, imaging, image analysis

Supervisor:  Carl Petersen   
Contact: [email protected]

Required: Image processing
Availability: Spring 2020, Fall 2020, Spring 2021, Fall 2021
Neuroscience
Dry and wet
Petersen Data analysis: electrophysiological recordings in behaving mice

The goal is to analyse multi-site extracellular recordings of neuronal activity in mice performing a goal-directed behavior learned through reward-based training. The neurophysiological data will be correlated with behavior quantified from high-speed videography. Specifically, we will investigate how neuronal circuits in the mouse brain learn to transform whisker sensory information into goal-directed licking motor output through reward-based learning.
Keywords: sensory processing, sensorimotor transformation, motor control, reward-based learning

Supervisor:  Carl Petersen   
Contact: [email protected]

Required: Coding (Matlab or Python)
Availability: Spring 2020, Fall 2020, Spring 2021, Fall 2021
Neuroscience
Dry
Petersen Data analysis: functional imaging in behaving mice

The goal of this project is to analyse functional calcium imaging data in behaving mice. We will correlate the dynamic calcium signals with behavior quantified from high-speed video filming. The aim is to investigate how neuronal circuits in the mouse brain learn to transform whisker sensory information into goal-directed licking motor output through reward-based learning.
Keywords: sensory processing, sensorimotor transformation, motor control, reward-based learning

Supervisor:  Carl Petersen   
Contact: [email protected]

Required: Image processing, Coding (Matlab or Python)
Availability: Spring 2020, Fall 2020, Spring 2021, Fall 2021
Neuroscience
Dry
Petersen Two-photon calcium imaging in behaving mice

The goal of this project is to image neuronal activity in behaving head-restrained mice. Mice will be trained through reward-based learning to carry out a goal-directed sensorimotor transformation. Two-photon microscopy will be used to image neurons expressing genetically-encoded calcium indicators with cellular resolution. We will correlate neuronal activity with sensory stimuli and behavior quantified from high-speed filming. Students will need to follow Module 1 of the animal experimentation course or equivalent. Minimum project duration 6 months.
Keywords: cellular imaging, sensory processing, motor control, reward-based learning

Supervisor:  Carl Petersen   
Contact: [email protected]l.ch

Required:  –
Availability: Spring 2020, Fall 2020, Spring 2021, Fall 2021
Neuroscience
Dry and wet
D’Angelo Molecular Basis of COL4A3BP/CERT1 syndrome

The human Ceramide Transfer Protein CERT (encoded by the gene COL4A3BP presently referred to as CERT1) is a cytosolic lipid transfer protein responsible for the non-vesicular transport of ceramide from the endoplasmic reticulum (ER) to the Golgi apparatus during sphingolipid biosynthesis. CERT1 activity is mediated by several functional motifs including an N-terminal pleckstrin homology (PH) domain that binds to phosphoinositide phosphatidylinositol-4-phosphate in the trans-Golgi and a C-terminal steroidogenic acute regulatory protein-related lipid transfer (START) domain that serves as a binding domain for ceramide. Genome-wide studies have suggested a putative association between CERT1 and intellectual disability (ID), still validation in a large cohort and dissection of the molecular etiology of the disease are lacking. We searched for human patients with CERT1 mutations and identified 19 individuals with de novo missense variants who suffer an infantile-onset developmental syndrome with severe ID, seizure and autism spectrum disorder. Here we want to address the molecular bases of the disease by studying the effects of patient mutations on CERT lipid transfer activity and on overall lipid metabolism.
Keywords: Inborn errors of lipid metabolism, lipid transfer proteins, Membrane contact sites

Supervisor:  Giovanni D’Angelo   
Contact: [email protected]

Required:  –
Availability: Spring 2020, Fall 2020, Spring 2021, Fall 2021
Cell biology
Wet
Sakar Engineering active biomaterials to study mechanical regulation of collective cell behavior

Morphogenetic movements are generally believed to be guided by mechanical forces along with morphogen gradients. Likewise, emerging data show that cells respond to various mechanical signals including ECM stiffening due to deposition or remodeling of collagen fibers and compressive stress generated by confined growth. However, the physical mechanism causing such multicellular movements remains unknown. MICROBS Laboratory has been developing small scale soft actuated biomaterials that can transduce electromagnetic energy into mechanical work. We have recently introduced optomechanical microactuators that can apply physiologically relevant forces within a three-dimensional workspace. The objective of this project is the integration of these microactuators within biological models, by embedding into ECM with cells that form higher order structures such as spheroids and organoids. The student is going to work on the surface chemistry of the actuators to optimize the transmission of forces. A time-lapse fluorescence imaging methodology will be established that involves i) finding the excitation parameters (i.e. duration, frequency, amplitude of actuator contraction) that leads to a physiological (or pathological) multi-cellular response ii) development of techniques for high-throughput, multi-agent actuation schemes for the generation of arbitrary stress profiles, and finally iii) investigation of techniques for the mapping of stress throughout the tissue.
Keywords: Bioengineering, mechnaotransduction, cell biology

Supervisor:  Selman Sakar   
Contact: [email protected]

Required: Wet-lab experience
Availability: Spring 2020, Fall 2020
Bioengineering
Wet
Van De Ville Exploring Brain Dynamics during Meditation

The benefits of meditation have been explored and described from a behavioral perpective but little is known on how brain function supports transition between normal and meditative states. We propose to explore brain dynamics during metitation using fMRI and EEG data. More precisely, we will compute the dominant dynamic modes in normal and meditative conditions in order to better understand the spatio-temporal properties of brain function in these two states.
Keywords: Human neuroimaging, computational approaches, dynamical models, autoregressive models

Supervisor:  Raphael Liegeois   
Contact: [email protected]

Required: Programming (Matlab or Python)
Availability: Spring 2020, Fall 2020, Spring 2021, Fall 2021
Neuroscience
Dry
Van De Ville BOLD signal variability and clinical outcomes in preterm-born children

Your task will be to analyse a recently acquired dataset including resting-state fMRI data and behavioural measures for 39 preterm children and 27 term-born controls, aged 9–13 years old. The main goal will be to compare the relationship between brain function, behavioural measures, and clinical assessments in the two groups, and interpret the results in view of the existing literature. If you would like to dive deeper into this subject, we also have a second resting-state recording, as well as behavioural data, for the preterm group after 12 weeks of mindfulness meditation training.
Keywords: Human neuroimaging, subspace methods

Supervisor:  Lorena Freitas   
Contact: [email protected]

Required: MATLAB; experience with fMRI analysis and Partial Least Squares method is a plus
Availability: Spring 2020, Fall 2020
Neuroscience
Dry
Van De Ville Structure-function relationships in patients with the agenesis of the corpus callosum

The corpus callosum is the largest white matter pathway connecting homologous structures of the two cerebral hemispheres and crucial for the transfer and integration of information across the brain. Developmental absence (agenesis) of the corpus callosum (AgCC) is a congenital brain malformation resulting from disruption of corpus callosum formation. Evidence suggests the existence of structural and functional neuroplastic response to preserve interhemispheric transfer in AgCC. In a cohort of children with AgCC that is much larger than previously explored samples of this rare clinical condition, we want to apply a newly-developed method for combining structural data from Diffusion Spectrum Imaging (DSI) and the functional data from functional Magnetic Resonance Imaging (fMRI). We integrate the two modalities by using state-of-the-art techniques borrowed from the field of graph signal processing. The aim would be to understand the difference in the existing structure-functional relationships between patients with AgCC versus healthy controls. How do AgCC patients manage to recuperate with the absence of the corpus callossum?
Keywords: MRI, functional and structural connectivity

Supervisor:  Anjali Tarun   
Contact: [email protected]

Required: Matlab
Availability: Spring 2020, Fall 2020
Neuroscience
Dry
Sakar Engineering control over 3D morphogenesis

During embryonic development flat, polarized epithelia sheets morph into complex three-dimensional structures and eventually form specialized compartments and organs. Initial bending and invagination events in epithelia are triggered by cellular contractile forces that lead to local cell shape changes and rearrangements. In principle, one can rationally shape biological tissues by controlling the location and magnitude of these mechanical forces. In this project you will work with engineered epithelial tissues and epithelial-mesenchymal multilayered constructs. First part of the project involves characterization of tissue composition and morphology using wide-field, confocal and light-sheet microscopy. In the second part, you will perturb cell mechanics by applying state-of-the-art manipulation technology and investigate local tissue architecture. Gained knowledge will be used to design and sculpt engineered 3D bodies. An in-house computational model will aid the exploration of the design space.
Keywords: tissue engineering, microscopy, microtechnology, optogenetics

Supervisor:  Selman Sakar   
Contact: [email protected]

Required: mammalian cell culture
Availability: Spring 2020, Fall 2020
Bioengineering
Dry and wet
Fellay Modelling Host-Pathogen Genomic Interactions

The genomes of hosts and pathogens are partially shaped by co-evolution. For example, positive selection forces are applied on both pathogen genetic variations, which evade the host immune system, and on host genetic variants, which decrease susceptibility to infections. Leveraging paired host-pathogen genome sequencing data, the student will utilize computational methods to explore the imprints of the co-evolution process on both genomes. Moreover, the student will also develop statistical methods to disentangle the complex interplay between host and pathogen genetics and to model the joint effects which contribute to the inter-individual heterogeneity in clinical outcome. The project will contribute to a deeper understanding of the co-evolution process, and shed light on the genetic underpinnings of infection severity and treatment responses.
Keywords: genomics, bioinformatics, statistics, infectious diseases

Supervisor:  Jacques Fellay   
Contact: [email protected]

Required: statistics and programming skills
Availability: Spring 2020, Fall 2020, Spring 2021, Fall 2021
Computational Biology
Dry
Constam Functional analysis of Activin-binding proteins

The TGFβ-related prohormone Activin-A is frequently upregulated in solid tumors and mediates tumor-induced muscle wasting as well as oncogenic or tumor-suppressive functions, depending on the context. The goal of this project is to validate binding of Activin-A to specific proteins that we found in an interactome screen for factors that preferentially bind mature Activin-A or precursor processing intermediates, respectively, and to analyze their functions in regulating the bioavailability and local signaling of Activin-A in the tumor microenvironment to promote tumor immune evasion, or endocrine Activin-A signals that mediate muscle wasting.
Keywords: TGFβ signaling, tumor immune evasion, proteomics, protein trafficking, proteases

Supervisor:  Daniel Constam   
Contact: [email protected]

Required: Molecular or cell biology, or (bio)chemistry
Availability: Spring 2020, Fall 2020
Cancer biology
Wet
Fellay Genomic investigation of severe viral respiratory infections in children

A robust immune response is required for clearance of viral respiratory infections. To uncover susceptibility to severe disease, we will investigate genomic variation in 120 children requiring intensive care support upon infection by a respiratory virus. The effect of both structural and functional genetic variation will be examined by (i) quantifying large deletions and copy number variants and (ii) gene burden testing for pathogenic variants from affected cases compared to healthy controls. Methods: exome sequencing and genotyping datasets have been prepared for 400 cases and controls. Command line bioinformatic tools will be used to process data. Methods will include the use of GATK command line tools for exome analysis [1], R data analysis for association testing [2] and Plink for genotyping data analysis [3]. Visualization will be used to interpret statistically relevant findings.
Keywords: bioinformatics, immunity, visualization, statistics

Supervisor:  Jacques Fellay   
Contact: [email protected]

Required: Experience with programming languages R, python, or unix shell scripting (bash) are beneficial for data manipulation. A biological sciences background will benefit the interpretation of biological-relevance.
Availability: Spring 2020, Fall 2020, Spring 2021, Fall 2021
Computational Biology
Dry
Schneggenburger Recapitulating De Novo α-synuclein Aggregations in Astrocytes

Astrocytes are known to provide various essential complex functions including structural and trophic support, primary roles in synaptic transmission, and maintenance of ionic homeostasis that allow efficient information processing by neuronal circuits (Nedergaard et al., 2003; Sofroniew and Vinters, 2010). Although alpha-synuclein (α-syn) is expressed at very low levels in astrocytes, α-syn inclusions are found in astrocytes under pathological conditions in the postmortem Parkinson’s disease brain (Wakabayashi et al., 2000; Song et al., 2009). As of today, there are no cellular or animal models that truly recapitulates astrocytic α-syn pathology as seen in postmortem PD brain. In the presence of α-syn overexpression, abundant α-syn aggregations could be detected (Lee et al., 2010; Sacino et al, 2014; Sorrentino et al., 2018) but the biochemical and ultrastructural properties of these aggregates and their relation to α-syn astrocytic pathology remains poorly understood. Since α-syn is expressed by human astrocytes and its protein level is a key determinant of aggregation, pathology formation and predisposition to developing PD, we plan to investigate conditions that could potentially induce changes in α-syn expression and its protein levels in primary astrocytes, with a greater emphasis on natural mechanisms rather than α-syn overexpression.
Keywords: Astrocyte, Parkinson’s disease, Synuclein, Inclusion

Supervisor:  Hilal Lashuel   
Contact: [email protected]

Required: Ideally basic knowledge with protein expression, primary neuron or astrocyte cultures
Availability: Spring 2020, Fall 2020
Neuroscience
Wet
Fellay Genome-wide copy-number variation association study of cardiovascular disease

Copy number variation (CNV) is an important form of genetic variation that has been suggested as potentially responsible for a significant proportion of the yet unexplained human phenotypic variability. For this project, you will investigate the contribution of CNVs as additional molecular markers to single nucleotide polymorphisms (SNPs) to cardiovascular disease. Using samples and data collected in the context of the CoLaus study, a large cohort of over 5’000 individuals from the general population of Lausanne, you will first harness the power of large-scale genomics and bioinformatics tools to call CNVs from SNP genotyping arrays. The called CNVs are then used for genome-wide association analyses of multiple cardiovascular traits. All together, these analyses have the potential to elucidate the pathogenesis of cardiovascular diseases, and outcomes of this project could include better prediction models and innovative targets for diagnostic or therapeutic development.
Keywords: Bioinformatics, human genomics, copy number variation, cardiovascular diseases

Supervisor:  Jacques Fellay   
Contact: [email protected]

Required: Programming (GNU Bash, R) and statistical skills
Availability: Spring 2020, Fall 2020, Spring 2021, Fall 2021
Computational Biology
Dry
Lashuel Expression patterns of alpha synuclein and its pathological forms and their role in the pathogenesis of Parkinson’s disease

A master student’s project is available in the Laboratory of Molecular and Chemical Biology of Neurodegeneration at the Swiss Federal Institute of Technology in Lausanne (EPFL), Switzerland (www.epfl.ch). The selected candidates will work on a project at the Laboratory of Molecular and Chemical Biology of Neurodegeneration (http://lashuel-lab.epfl.ch/) aimed to evaluate the expression patterns of alpha synuclein and its pathological forms in different brain areas and their role in the pathogenesis of Parkinson’s disease. To achieve this goal, the applicant will perform cutting-edge techniques such as iDISCO, that permits whole-mount immunolabeling with volume imaging of large cleared samples ranging from perinatal mouse embryos to adult organs, such as brains or kidneys. The desired applicant must be highly motivated to work as members of an interdisciplinary group working at the interface of chemistry and biology in collaboration with world-renowned research groups at the frontiers of neuroscience and brain research. The qualified candidate will benefit from working with very dynamic and multidisciplinary groups in a highly collaborative and stimulating environment and access to state-of-the-art laboratories and core-facilities. For more information about our groups, please visit our website and review our recent publications at http://www.ncbi.nlm.nih.gov/pubmed/?term=Lashuel.
Keywords: parkinson’s disease, idisco, alpha-synuclein

Supervisor:  Hila Ashuel   
Contact: [email protected]

Required:  –
Availability: Spring 2020, Fall 2020
Neuroscience
Dry and wet
Constam Regulation of mRNA translation by mechanosensory primary cilia

Normal development and function of the gut and other internal organs such as our lungs and the heart depend on asymmetric activation of the Nodal cascade. In most deuterostomes, this process is governed by a directional flow of extracellular fluid that stimulates specific ion channels in primary cilia to repress translation of the secreted Nodal antagonist Dand5 specifically on the prospective left side. However, the mechanism that uncouples the regulation of mRNA translation from mechanosensory cilia in patients with laterality defects and other ciliopathies is unknown. The goal of this project is to elucidate how primary cilia activate the RNA-binding protein Bicc1 to repress Dand5 mRNA translation.
Keywords: mechanosensation, cilia, translational regulation,

Supervisor:  Daniel Constam
Co-supervisor: Andy Oates (SV)   
Contact: [email protected]

Required: Molecular and cell biology, and/or confocal microscopy
Availability: Spring 2020, Fall 2020
Developmental biology
Interdisciplinary
Constam Analysis of a novel consensus RNA binding motif of Bicc1

A tandem repeat of three RNA-binding K homology domains in Bicc1 can bind specific target mRNAs that are implicated in polycystic kidney diseases (PKD) and other ciliopathies. However, until recently, a consensus RNA sequence mediating these interactions and its role in Bicc1-mediated translational repression have remained unknown. This project will validate the importance of novel 4-nucleotide consensus motifs that emerged from an unbiased screen for Bicc1-interacting sequences in mediating the binding of reporter RNAs to recombinant KH domains, and in enabling Bicc1-mediated mRNA localization and translational repression in cell-free assays and in cultured cell lines.
Keywords: translational regulation, RNA binding, reporter assays

Supervisor:  Daniel Constam   
Contact: [email protected]

Required: Molecular biology
Availability: Spring 2020, Fall 2020
Molecular biology
Wet
Constam Regulation of mRNA translation by inducible liquid-liquid phase transitioning

Translational repression of specific target mRNAs by Bicc1 critically depends on its Sterile Alpha Motif (SAM) that mediates head-to-tail self-association of structurally well-defined protein surfaces in helical polymers. The goal of this project is to test whether the helical organisation is necessary for polymers to translationally repress Bicc1-associated mRNAs (e.g. by enabling Bicc1 to control RNA folding), and whether it can be engineered to regulate polymerisation at will, or whether the essential contribution of the SAM domain to mRNA silencing can be mimicked by alternative means of inducible self-aggregation other than SAM-mediated liquid-liquid phase transitions.
Keywords: protein engineering, cell biology

Supervisor:  Daniel Constam   
Contact: [email protected]

Required: (bio)chemistry and/or live imaging
Availability: Spring 2020, Fall 2020
Bioengineering
Wet
Lashuel Investigation of seeding properties of mutant HTT proteins

Huntington’s Disease (HD) is a genetic and progressive neurodegenerative disorder characterized by motor, cognitive and psychiatric symptoms. Despite the fact that the gene responsible of HD is known, the underlying mechanisms leading to huntingtin (HTT) aggregation, link to neurodegeneration and death is still not clear. Different neurodegenerative disease causing proteins are known to have prion like property. Using seeding property of recombinant alpha-Synuclein proteins, our lab was able to generate a neuronal model of PD and study process of Lewy formation in details (Anne-Laure et al. Biorxv, 2019). The objective of the project is to investigate the seeding property of mutant HTT proteins and the generation of a neuronal model of HD. For this, we will use either Pre Formed HTT Fibrils (PFF) or purified HTT inclusions from primary neurons (native seeds) and add them into primary neuronal culture to assess their ability to be uptaken and to seed the aggregation of endogenous HTT in neurons. The aggregation will need to be characterized by Immunocytochemistry (ICC) and biochemistry depending on different conditions: 1) Use of different HTT protein fragments; 2) Use of different polyglutamines repeats within the Htt protein; 3) Use of HTT protein with and without the Nt17 domain; and 4) The influence of PTMs on HTT protein.
Keywords: Huntington’s disease, Primary neurons, aggregates, seeding

Supervisor:  Hilal Lashuel   
Contact: [email protected]

Required: Ideally basic knowledge with Biochemistry, imaging and cell culture.
Availability: Spring 2020, Fall 2020
Neuroscience
Wet
Sakar Mechanics of tail elongation in zebrafish embryos

The morphogenesis of vertebrate embryos is well-studied in terms of genetics and biochemical signaling. However, the role of mechanics is not well understood, primarily due to lack of tools and methods to apply forces at relevant spatiotemporal scales. To have a better understanding of the role of mechanics, we have recently developed an ex-vivo assay. Objective of this project is to perturb and probe samples with various micromanipulation systems. The systems will enable application of physiologically relevant stresses on the explants and quantify the results on somitogenesis and morphing. Optimizing the platforms, development of proper imaging tools for recording oscillatory gene signals, and post processing the data for quantification are the main elements of this project.
Keywords: mechanobiolgy, somitogenesis, zebrafish, microengineering, robotics`

Supervisor:  Selman Sakar
Co-supervisor: Andy Oates, SV   
Contact: [email protected]

Required: basic knowledge in solid mechanics
Availability: Spring 2020, Fall 2020, Spring 2021
Developmental biology
Interdisciplinary
Schneggenburger Investigating the effect of GCaMP6 expression on plasticity in the neural circuits involved in fear learning

Genetically encoded calcium indicators (GECIs) are powerful tools for monitoring intracellular calcium concentration and thus, indirectly, neuronal spiking activity. The family of GCaMP6 GECIs has become instrumental in neuroscience, particularly in in-vivo recordings. However, it is often disregarded that GECIs, like other Ca2+-indicators, are calcium buffers, usually of high affinity, which in typical experiment are strongly overexpressed in every cellular compartment. The presence of a strong buffer is likely to affect naturally occurring calcium-dependent plasticity mechanisms. Our laboratory is setting up an in-vivo calcium imaging approach using a microendoscope to study population dynamics in the insula cortex and amygdala during fear learning. It is therefore important to know if/how GCaMP6 expression influences the function of studied neurons in memory-related tasks. The project will first focus on estimating the added calcium buffering capacity in neurons of lateral amygdala (LA) expressing GCaMP6m using fluorescent calcium imaging and patch-clamp in brain slices. Second, the long-term plasticity of synaptic inputs from the insula cortex will be investigated in the LA neurons expressing GCaMP6m using patch-clamp electrophysiology. These experiments will allow the MA student to investigate quantitative aspects of GCaMP6m overexpression and to study how this affects neuronal plasticity.
Keywords: synaptic plasticity, calcium imaging, patch-clamp electrophysiology, brain slices

Supervisor:  Ralf Schneggenburger   
Contact: [email protected]

Required: completion of neuroscience/neurobiology course
Availability: Spring 2020, Fall 2020
Neuroscience
Wet
Schneggenburger Development of an automated pipeline for brain-wide counting and reference atlas registration of fluorescently labelled neurons

In our laboratory, we study the neural networks involved in associative learning of threat in auditory fear conditioning. One of approaches studies the distribution of neurons in the brain that undergo increase in activity upon specific sensory experience. This is achieved using a fluorescent reporter mouse line combined with timed expression of Cre-recombinase downstream of an immediate early response gene cFos. A typical outcome is a set of hundreds of images of histological sections of the mouse brain, in which the distribution of “activity-labeled” neurons needs to be quantified. This is an extremely workload-demanding task in case of manual analysis. There have been multiple recent attempts to automate the detection of single cells and registration of the images onto a reference atlas using advanced image analysis and deep learning. However, there is no single comprehensive pipeline accepting an image stack at its input and producing the cell density table at its output with minimal human input. The project is devoted to a design of such a workflow based on further development of interfaces between some of the published routines. The resulting toolbox will be very instrumental for our lab in particular, and for neuroscience community in general.
Keywords: image analysis, object detection, image registration, automation

Supervisor:  Ralf Schneggenburger   
Contact: [email protected]

Required: advanced programming in MATLAB, basic knowledge of image analysis
Availability: Spring 2020, Fall 2020
Computational Biology
Dry
Hummel Investigation of resting-state fMRI network changes following noninvasive brain stimulation during sleep in elderly and stroke patients

Objective: Sleep is essential for learning new skills at any age and for relearning functional capacities (such as motor skills) after a brain lesion (stroke). In the context of an ongoing project aiming at investigating the effects of non-invasive brain stimulation during sleep to enhance learning, the master student will analyse a resting-state fMRI dataset of healthy older / stroke patients. The project will consist in acquisition of data and analyses of structural and functional MRI data. Furthermore, the analysis will include electric field modelling of the applied brain stimulation to gain insight on the stimulation effect and topography.
Keywords: Resting-state fMRI, Noninvasive brain stimulation, Electric field simulation, Multimodal study

Supervisor:  Friedhelm Hummel   
Contact: [email protected]

Required:  –
Availability: Spring 2020, Fall 2020
Neuroscience
Dry
Hummel fMRI default mode network changes after stroke.

The brain is never at rest. Whatever action we may undertake, the brain is processing sensory information to act on the environment. Particularly, it was discovered in recent years that even by being at rest specific areas in the brain are active within a default mode network (DMN) comprising of the posterior cingulate cortex (PCC) or the ventral Anterior Cingulate Cortex (vACC), thought to play a role in different brain functions, as self-reference, social evaluation or episodic memory. However, the relationship of the DMN with different pathological clinical states after stroke is not well defined. After a stroke, lesions may cause damages at different cognitive levels: motor, language, attention etc. In a longitudinal study after stroke, multimodal data are collected from patients suffering from upper limb deficit after stroke. During 4 time points from 1 week to 1 year patients well be characterized with MRI scans for structural and functional MRI analyses. The goal of the study is to evaluate the correlation of the DMN activity with stroke outcome and functional reorganization informed by the structural connectome of each individual patient.
Keywords: stroke, fMRI, brain imaging, default mode network

Supervisor:  Friedhelm Hummel   
Contact: [email protected]

Required:  –
Availability: Spring 2020, Fall 2020
Neuroscience
Dry
Hummel Longitudinal stroke evaluation and graph analysis at rest from EEG

Stroke injury leads to disability up to 75% of stroke survivors. The understanding of the processes underlying recovery of stroke and the plastic correlates in the brain are not sufficiently understood. Electroencephalography (EEG) provides excellent information about brain connectivity with a high temporal resolution and will add to the understanding of the brain correlates of recovery from stroke. Advanced analytical techniques based on graph analysis will be used to investigate brain activity at rest. To this end, in a longitudinal study multimodal data are collected from stroke patients suffering from upper limb deficit. Patients will be evaluated from the acute (1 week) to the chronic stage (1 year) at 4 time points, brain activity is recorded by means of EEG. The goal of the study is to characterize the changes of the functional connectome during the process of recovery
Keywords: stroke, EEG, graph analysis, longitudinal study

Supervisor:  Friedhelm Hummel   
Contact: [email protected]

Required:  –
Availability: Spring 2020, Fall 2020
Neuroscience
Dry
Fellay Genetic targets of embryonic selection in humans

How strong is embryonic selection in humans? What are the targets of this selection? The best way to address these questions is to compare parental genomes with genomes of their offspring. Using polygenic scores, which provide integral genomic predisposition to different phenotypes, it is possible to analyse the transmission of multiple alleles from parents to offspring. A deviation of the observed score in the offspring from the expected one (mid-parent score) means over- or under- inheritance of the specific set of alleles. This deviation, if statistically robust, means embryonic selection. Here, using genotyped human trios with healthy or affected offspring, we plan to uncover the strength and direction of the human embryonic selection.
Keywords: transmission disequilibrium test, polygenic score, selection

Supervisor:  Jacques Fellay   
Contact: [email protected]

Required: statistics, R
Availability: Spring 2020, Fall 2020
Computational Biology
Dry
McCabe Robogenetics: Genome engineering for robotics

The goal of this interdisciplinary joint lab project is to automate Drosophila genetics and mutant selection through a combination of robotics with new genetic tools designed for machine rather than human manipulation. Studies of the fruit fly Drosophila melanogaster have made foundational contributions to the understanding of development, neuroscience and human disease. Existing proof-of-concept robotic systems can anesthetize, transfer and manipulate individual Drosophila. The student will build and adapt such a system to develop a new generation of ‘smart’ robots with sensors, which together with new genetic tools, will allow for robotic selection of animals of the desired genotype or mutation. This combination of novel genetic tools designed specifically for machine utility with robotics should facilitate an exponential increase in the throughput of genetic manipulation of this important model organism.
Keywords: genetics, robotics, machine vision, genome engineering

Supervisor:  Brian McCabe
Co-supervisor: Pavan Ramdya (IBI, SV)   
Contact: [email protected]

Required:  –
Availability: Spring 2020, Fall 2020, Spring 2021, Fall 2021
Neuroscience
Interdisciplinary
McCabe Non-invasive assays of neurodegeneration in models of human disease

Loss of synaptic connections and disruption of neuronal circuits is thought to be an early consequence of neurodegenerative disorders such as Alzheimer’s disease. However, assaying these early aspects of neurodegeneration is difficult and currently cannot be achieved in vivo. In this project, the student will use genome engineering methods to construct new genetic tools for the model organism Drosophila melanogaster designed to measure neural circuit integrity non-invasively from outside the animal. They will then deploy these tools in a humanised model of Alzheimer’s disease to assay the progressive disruption of neuronal circuits as neurodegeneration advances.
Keywords: genome engineering, neurodegeneration, disease, Drosophila

Supervisor:  Brian McCabe   
Contact: [email protected]

Required:  –
Availability: Spring 2020, Fall 2020, Spring 2021, Fall 2021
Neuroscience
Wet
La Manno Extensions of the RNA velocity algorithm

Background An ambitious goal of single-cell analyses is describing dynamical biological processes and shedding light on gene regulation mechanisms. However, because of the destructive nature of single-cell measurements, they can only provide a static snapshot of the cell at a given point of embryonic development. To overcome this fundamental limit, I recently developed a novel method, named “RNA velocity” as it estimates the first derivative of gene expression for each gene in a cell (RNA velocity of single cells, Nature 2018). Measuring the abundance of both unspliced and spliced RNA in the same cell, we can estimate the rate of change of gene expression and predict the future expression levels of a single cell. Activities This project will start by introducing a series of improvements to the current RNA velocity algorithm and software and it will proceed towards an extension of the model to a more general framework. (1) The student will implement in our software velocyto with the dynamical model proposed recently by Fabian Theis lab. (2) The student will analyze situations where the original assumptions of the method are not met and remediate fitting an alternative model (3) The student will use a latent variable model to decompose the velocity vector in interpretable components (e.g. cell cycle, maturation, and response to signals).
Keywords: machine learning, developmental biology, single-cell RNAseq

Supervisor:  Gioele La Manno   
Contact: [email protected]

Required: Experience in numerical python programming (numpy, scipy, matplotlib), at least a course related to either multivariate statistics or machine learning
Availability: Spring 2020, Fall 2020, Spring 2021, Fall 2021
Computational Biology
Dry
La Manno Implementation and extension of multimodal data integration algorithms

Background The scientific community is generating a great volume of datasets that record many features from single cells. These datasets survey the same population but each dataset is measuring different features: transcript, chromatin accessibility, DNA-methylation, surface protein concentration. These measurements are disjoint and do not come from the same cell. However, since the datasets are collected from the same organ/tissue/process it should be possible to align this data to obtain a more complete description of the molecular state of each cell. This kind of approach has proved to be possible through a series of multivariate statistics/machine learning procedures (Stuart et al. 2019). Activities The project is divided into two parts an implementation and a method extension part. In the first part, the student will individuate in the literature methods for multimodal data integration and batch correction and reimplement the best of them in python. In the second part, the student will identify common and different procedures in those methods and attempt to combine different them so as to propose a new improved version. Finally, the different methods including the improved one will be benchmarked using different reference datasets.
Keywords: single-cell transcriptomics, bioinformatics, data analysis

Supervisor:  Gioele La Manno   
Contact: [email protected]

Required: Knowledge of python (good) and R (basic) languages. Some experience with multivariate data analysis. Having implemented a machine learning method.
Availability: Spring 2020, Fall 2020, Spring 2021, Fall 2021
Computational Biology
Dry
La Manno Data-aware models for the design of maximally informative high throughput experiments

Background Single-cell RNA-sequencing (scRNAseq) yields high-quality transcriptomic data from a single cell suspension, but it needs to sacrifice information on cell localization. Conversely, multiplexed single-molecule FISH (osmFISH) has only medium-throughput but allows measurements in situ. Usually, after scRNAseq has been used to define and discover cell types, one would like to map back this knowledge to the tissue using osmFISH. In doing so, it is important to be able to optimally select a gene set that maximizes the information obtainable experimentally. For example, choosing 30 highly specific markers to detect 30 cell types might not be the best option, because, without redundancy, the failure of a probe could mean completely missing a population. We want to design a method that automatically chooses a subset of features that can be highly informative while robust to this kind of experimental uncertainty. Activities The student will (1) Build a model that predicts dense low-throughput measurements from sparse high-throughput data using machine learning and statistical modeling. (2) Design a strategy that optimally selects a set of features to measure by a low-throughput method. (3) Evaluate performances of the method considering uncertainties (4) Incorporate experimenter knowledge in the selection in the form of statistical priors. (5) integrating the procedure in a command-line tool.
Keywords: machine learning, statistical modeling, algorithms

Supervisor:  Gioele La Manno   
Contact: [email protected]

Required: python programming, familiarity with statistical modeling and multivariate regression
Availability: Spring 2020, Fall 2020, Spring 2021, Fall 2021
Computational Biology
Dry
La Manno Probing the influence of organizers on the transcriptome of neural stem cells

Background It is becoming increasingly evident that radial glia-like cells, the stem cells of the nervous system, are not a homogeneous population. In particular, using single-cell RNAseq we have identified different molecularly distinct states corresponding to spatiotemporal patterning of the brain. We would like to understand both the causes and the functional implications of the pattern observed. We are interested in investigating how the microenvironment and specific organizers influence or determine this pattern. We expect that paracrine signals are important in inducing those non-autonomous expression changes. Activities The student will culture primary cells obtained from embryonic mice brains at different ages and regions, and evaluate the composition of the cell types generated in vitro. Cocultures will be set up with “majority” cell population from a region, and a “minority” cell population derived from another region. The idea is that the bigger fraction of cells will provide a dominating amount of signaling molecules. Single-cell RNA sequencing will be performed on these cells, allowing a thorough comparison at the transcriptome level.
Keywords: single-cell transcriptomics, neuroscience, stem cells

Supervisor:  Gioele La Manno   
Contact: [email protected]

Required: Some experience with culturing cells, basic python programming
Availability: Spring 2020, Fall 2020, Spring 2021, Fall 2021
Developmental biology
Dry and wet
van der Goot Microtubules and organelle architecture

We are interested in understanding how the complex architecture of the endoplasmic reticulum is established and maintained. We recently identified a novel protein, through a proteomics approach, that connects the ER to microtubules. The project will be to investigate how this protein contributes to controlling ER shape and ER dynamics. It will involve using a variety of approaches, in particular super resolution microscopy, molecular biology and biochemistry.
Keywords: cell biology, molecular biology, bio engineering

Supervisor:  Gisou van der Goot
Co-supervisor: [email protected]   
Contact: [email protected]

Required:  –
Availability: Spring 2020, Fall 2020
Cell biology
Interdisciplinary
van der Goot Understanding the cellular dynamics of endoplasmic reticulum (ER) shaping proteins

CLIMP63 is a crucial regulator of ER architecture. It controls the dynamics and the compartmentalisation of the Endoplasmic reticulum. Using high resolution microscopy we intend to follow the dynamic of various CLIMP63 mutants to understand how the modification of CLIMP63 by lipids can reversibly tune ER morphology and possibly interfere with ER interactions with other cellular organelles.
Keywords: Cell and molecular biology, Bioengineering, microscopy

Supervisor:  Gisou van der Goot
Co-supervisor: [email protected]   
Contact: [email protected]

Required:  –
Availability: Spring 2020, Fall 2020
Cell biology
Interdisciplinary
van der Goot Structural determination of a key ER shaping protein

The ER forms a dynamic network of morphologically distinct compartments, including the nuclear envelop, the rough ER and smooth ER tubes. The morphology of the rough ER is to a large extent controlled by the transmembrane protein CLIMP63. To understand how CLIMP63 shapes membranes, we will study its 3D structure and its quaternary assembly. We will investigate both the lumen domain and the full length protein, which will be reconstituted into artificial lipid bilayers. The project will involve various techniques: protein purification, electron microscopy, biochemistry
Keywords: Cell and Molecular biology, Bioengineering, Biophysics

Supervisor:  Gisou van der Goot
Co-supervisor: [email protected]   
Contact: [email protected]

Required:  –
Availability: Spring 2020, Fall 2020
Cell biology
Interdisciplinary
Persat The mechanics of the gut microbiome

The main goal of this project is to determine if and what gene expression changes after bacterial cells contact the surface that resemble the ones of human cells. In this project, we will consider a variety of bacterial species and identify their response to surface contact. We will both subject bacterial populations of synthetic surfaces such as hydrogels but also to biological tissue-like systems generated from organoids. You will then extract RNA from these cells and measure their gene expression using state-of-the-art RNA-seq technology. Finally, you will use bioinformatic tools and statistics to determine how different species might adapt to the surface attachment. Overall, you will work with a wide variety of tools and technologies, including next-generation sequencing, bioinformatics, microfluidics and tissue-engineering.
Keywords: gut microbiome, organoids, mucus, mechanics

Supervisor:  Alexandre Persat   
Contact: [email protected]

Required:  –
Availability: Spring 2020, Fall 2020, Spring 2021, Fall 2021
Bioengineering
Wet
Persat Characterizing mechanosensation by cryo electron microscopy

Living systems sense and respond to mechanical cues present in their environments. For example, mechanics influence stem cell differentiation, development, motility and cancer progression. In bacteria, mechanics play an important role in regulating pathogenicity. Our lab focuses on understanding how bacteria sense and respond to forces. We have identified bacterial structures that are sensitive to forces, and now must capture the structural changes in these structures. In this project, you will design a new technique to apply forces on bacteria directly on electron microscopy grids, ultimately activating mechanosensors and allowing us to decipher their structural dynamics. This is an exciting project that uses methods from engineering (microfluidics, materials, afm), physics (light and electron microscopy) and biology (microbiology, mechanobiology), but no prior knowledge is required.
Keywords:

Supervisor:  Alexandre Persat   
Contact: [email protected]

Required:  –
Availability: Spring 2020, Fall 2020, Spring 2021, Fall 2021
Bioengineering
Wet
Biorobotics Laboratory Neuromechanical simulations of animal and human locomotion

The biorobotics laboratory has several semester and master projects in neuromechanical simulations of animals and humans. The project descriptions can be found here: https://biorob2.epfl.ch/pages/projects/
Keywords: locomotion, numerical models, biomechanics

Supervisor:  Auke Ijspeert   
Contact: [email protected]

Required: Programming in Python and C
Availability: Spring 2020, Fall 2020, Spring 2021, Fall 2021
Neuroscience
Dry
Suter Quantitative analysis of protein synthesis and degradation in Zebrafish by fluorescence microscopy

How protein levels are regulated by the interplay of protein synthesis and degradation is still poorly understood. We recently developed a fluorescent timer-based approach allowing to simultaneously monitor protein synthesis and degradation in individual, living cells. This project will be based on the expression of this fluorescent timer in zebrafish to monitor protein synthesis and degradation by quantitative fluorescence imaging. It will involve various techniques such as DNA cloning, zebrafish transgenesis, conventional/light sheet fluorescence microscopy and quantitative image analysis.
Keywords: protein homeostasis, fluorescent timer, zebrafish, live imaging

Supervisor:  David Suter
Co-supervisor: Andy Oates   
Contact: [email protected]

Required: The student should be highly interested in microscopy image analysis and in gaining a quantitative understanding of gene expression.
Availability: Spring 2020, Fall 2020
Cell biology
Interdisciplinary
Herzog Serial dependence in visual perception

Our recent experience has a strong impact on how we perceive the present: the daylight appears brighter after leaving a dark room, a white surface appears greener after staring at a red image. In some cases, prior events may even deceive us to perceive the present as more similar to the past than it actually is, a phenomenon known as serial dependence. The objective of this project is to investigate the role of prior experience and temporal dependencies in human vision by combining behavioral psychophysics and computational modeling. The student will be involved in the collection and analysis of psychophysical data with the primary objective to develop a physiologically plausible computational model of serial dependence in human vision.
Keywords: vision, computational modeling, psychophysics

Supervisor:  David Pascucci
Co-supervisor: David Pascucci   
Contact: [email protected]

Required: Good analytical and computational skills, ideal for students in: Computer Sciences, Life Sciences, Bioengeneering
Availability: Spring 2020, Fall 2020, Spring 2021, Fall 2021
Neuroscience
Interdisciplinary
Radenovic 3D parameter free resolution estimation

Recently we published a new method for image resolution estimation. The method is parameter free and exploits the phase information contained in the Fourier space of the image. Through the calculation of many partial phase correlation of the image with a filtered version, we are able to reliably extract the frequency support of any image, that is the highest frequency with significant contrast with respect to noise. So far we demonstrated the ability of the method to estimate the global, local and sectorial resolution of various cell samples and we would like to extend the method in the third dimension. This extension presents several problems that the student will have to understand and overcome, such as limited axial sampling, plane to plane coregistration, increased computational complexity, etc… The task of the student will be to implement the 3D version of the algorithm based on the current implementation (first in Matlab and then Java). The student will also have to investigate several options for processing optimization (code refactoring, GPU implementation, minimizing the number of correlations to be computed) due to the large amount of data to be processed inherent to volumetric imaging.
Keywords: Resolution estimation of 3D imaging datasets

Supervisor:  Aleksandra Radenovic   
Contact: [email protected]

Required: Knowledge in signal and image processing, Matlab, Java and basics of optical ima
Availability: Spring 2020, Fall 2020, Spring 2021, Fall 2021
Bioengineering
Dry and wet
Radenovic Deep learning assisted segmentation and mapping of DNA molecules

DNA analysis methods have evolved tremendously over the past decade. One of the goal of such techniques is to be able to recognize the species of origin. As an alternative to DNA sequencing (i.e. reading the whole DNA sequence), we have developed in our lab a way to map the DNA to its corresponding species while avoiding complicated PCR reactions and DNA sequencing. The method is based on sequence specific labelling of DNA and subsequent stretching on a glass surface. The stretched DNA is then imaged with a super-resolution microscope resulting in a sort of bar-code image (Figure). The intensity profile of each DNA molecules is extracted and matched against a database of species. 1,2 In order to study the entire microbiome, we need to analyse thousands of images, extract all the individual DNA molecules and match them to their sequences. This is too much for manual selection, a method is needed to automatically detect the DNA strands and extract their intensity profile. The task of the student will be to optimize a new approach to DNA segmentation based on machine learning and work on the automatisation of the full pipeline, from raw images to meaningful information. The second task of the student (Master project) will be to train another neural network to classify the segmented DNA. We will provide the student with experimental images, supervision and expertise to develop the algorithm (Python and Matlab). The student will be able to work in a highly interdisciplinary group with backgrounds ranging from polymer physics, image analysis, microscopy to molecular biology.
Keywords: Image processing of super-resolved stretched DNA

Supervisor:  Aleksandra Radenovic   
Contact: [email protected]

Required: Knowledge in signal and image processing, machine learning and basics of optical imaging,
Availability: Spring 2020, Fall 2020, Spring 2021, Fall 2021
Bioengineering
Dry and wet
Radenovic 3D Super-Resolution Optical Fluctuation Imaging for Molecular Parameter Estimation

Several techniques have been developed in order to overcome the diffraction limit in fluorescence microscopy. They rely on exploiting a priori knowledge about the quantum mechanical properties of fluorophores. For super-resolution optical fluctuation imaging (SOFI), higher order statistics (cumulants) of a time series of blinking fluorescence emitters are computed. SOFI analysis yields essentially background free images and is ideally suited for fast, 3D imaging using a multi-plane microscope. Balanced (b)SOFI deals with the nonlinear image contrast and enables up to fivefold improved spatial resolution in 2D. It allows extraction of molecular parameter maps such as the density and state lifetime of molecules. Molecular counting (or density estimation) based on SOFI is robust against imaging artifacts, which makes SOFI a unique tool in the super-resolution microscopy research field . We recently used bSOFI to investigate clustering of membrane proteins in T cells with an aim to understand their response In this project, you will explore the extension of bSOFI parameter estimation to 3D imaging. Based on an existing 2D simulation framework, different multi-plane configurations and fluorophore properties can be simulated (brightness/ signal-to-noise, blinking parameters, photo-bleaching) and evaluated using SOFI processing. You will perform super-resolution imaging of cells and/or DNA origami/protein calibration standards on our custom multi-plane microscopes. We aim to demonstrate multi-plane bSOFI parameter estimation for the first time. Depending on your skills and interests, the in silico or experimental aspects of the project can be emphasized.
Keywords: Super-resolution imaging

Supervisor:  Aleksandra Radenovic   
Contact: [email protected]

Required: Optics, microscopy, data analysis (Matlab), signal processing, cell culture, labelling of cells and preparation for super-resolution microscopy
Availability: Spring 2020, Fall 2020, Spring 2021, Fall 2021
Bioengineering
Dry and wet
Radenovic Optical Projection Tomography to Elucidate Neurodegeneration

Our research is set in the context of studying Alzheimer’s disease and observing Amyloid-beta plaques and immune cells using the 3D whole-tissue imaging modality known as OPT (see Nguyen, D. et al. 2017 Biomed Opt Express). The project’s aim is to apply OPT imaging to identifying amyloidosis levels and immune response in the brain and the gastro-intestinal tract of the 5xFAD mouse model. The task of the student will entail collaborating with microscopists and biologists to optimize the resolution optics of our existing in-house OPT setup while in parallel testing various antibody-mediated staining of tissue sections and whole tissues. All in vivo work will be performed by appropriately licensed staff members, and thus will not be part of this student project. The student will be able to work in a highly interdisciplinary group with backgrounds ranging from polymer physics, image analysis, microscopy to molecular biology.
Keywords: study of neurodegeneration in a mouse model of Alzheimer’s disease using OPT

Supervisor:  Aleksandra Radenovic   
Contact: [email protected]

Required: Basics of optical imaging, basic engineering techniques (e.g. soldering), general biology knowledge
Availability: Spring 2020, Fall 2020, Spring 2021, Fall 2021
Bioengineering
Dry and wet
Mathis Anatomical connectivity of the neural circuits required for learning motor skills

The lab studies the neural basis of motor learning by training mice to play skilled games. We have shown that cortex is important for this type of learning (Mathis et al 2017 Neuron). We now aim to study the underlying anatomical connections between cortex, brainstem, cerebellum, and the spinal cord. This project will leverage cutting-edge imaging of neural circuits (mesoSPIM) and anatomical tracing coupled with deep learning tools for efficient data analysis to elucidate the interconnectivity of important nodes for motor learning. Experience with stereotaxic surgery, viral injections, and tissue processing is highly desired. The deep learning-based tools can be learned. Minimum project duration of 6 months.
Keywords: stereotaxic surgery, anatomy, deep learning, neuroscience

Supervisor:  Mackenzie Mathis   
Contact: [email protected]

Required: Experience with mice, programming with Python
Availability: Fall 2020, Spring 2021, Fall 2021
Neuroscience
Dry and wet
Bitbol Understanding how optimization and phylogeny shape protein sequences

Proteins play crucial roles in our cells. The amino-acid sequence of a protein encodes its function, including its structure and its possible interactions. In evolution, random mutations affect the sequence, while natural selection acts at the level of function. Shedding light on the sequence-function mapping of proteins is central to a systems-level understanding of cells, and has far-reaching applications in synthetic biology and drug targeting. The current explosion of available sequence data enables data-driven approaches to discover the principles of protein operation. In alignments of homologous protein sequences, correlations exist between certain amino-acid sites. We aim to establish a full decomposition of these correlations, dissecting signatures from functional optimization, and from evolutionary history. We also aim to make new predictions for protein-protein interactions from sequence data, and to understand whether real proteins are mechanically optimized. Several directions are possible, but the project will be data-driven and involve analyzing real protein sequence data and/or protein structure data.
Keywords:

Supervisor:  Anne-florence Bitbol   
Contact: [email protected]

Required:  –
Availability: Spring 2020, Fall 2020, Spring 2021, Fall 2021
Computational Biology
Dry
Mathis Task-driven hierarchical deep neural networks model the proprioceptive pathway

Proprioception is critical for sensing and controlling the body, yet no canonical hierarchical model of the system exists. Task-driven modeling has provided important insights into other sensory systems. However, unlike for vision and audition, databases of relevant proprioceptive stimuli are not readily available. For this project the goal is to design different tasks (and create training datasets) as well as train models of the proprioceptive pathway on various behavioral tasks. We will then analyze the networks’ units as well as emerging computations to gain insights into proprioception. We are specifically interested in understanding the implications of different tasks and network architectures.
Keywords: task-driven modeling, deep learning, proprioception, motor control

Supervisor:  Alexander Mathis   
Contact: [email protected]

Required: Python programming skills • Knowledge in TensorFlow/PyTorch would be great (not required)
Availability: Fall 2020, Spring 2021
Neuroscience
Dry
Mathis Deep action recognition networks for animal behavior analysis

We strive to develop tools for the analysis of animal behavior. Behavior is a complex reflexion of an animal’s goals, state and individuality. Thus, accurately measuring behavior is crucial for advancing basic neuroscience, as well as the study of various neural and psychiatric disorders. The goal of the master thesis project is to optimize deep neural network architectures to predict the behavioral state of animals in various experiments with limited amounts of data. For this project various datasets from our collaborators will be utilized.
Keywords: Action recognition, deep learning, Behavior

Supervisor:  Alexander Mathis   
Contact: [email protected]

Required: Python programming skills • Knowledge in TensorFlow/PyTorch would be great (not required)
Availability: Fall 2020, Spring 2021
Neuroscience
Dry
Jaksic Simulating experimental evolution of cognition

Experimental evolution is a long-term experiment that is highly dependent on the initial experimental design. Hence, optimizing the experimental design for most efficient selection outcome is imperative. Using population genetics theory and forward simulations we can predict the quantitative outcomes of different experimental designs. This allows us to choose the most fitting experimental design while also producing an empirical null expectation from the experiment for future hypothesis testing. Optimal evolution experiment design yields highest resolution for identifying adaptive loci in the shortest amount of time. This is especially important for complex phenotypes that are expected to be influenced by many genetic loci of small effect which obfuscate the signal of selection. In our lab, we are interested in the genetic basis of evolution of cognition, arguably one of the most complex traits we know of. In this project you will use genotype data from sequenced Drosophila melanogaster lines to create a virtual population and simulate its different evolution scenarios. The results will help us decide on the design and interpretation of the first evolution experiment for cognitive ability.
Keywords: population genetics, experimental evolution, simulation

Supervisor:  Ana Jaksic   
Contact: [email protected]

Required: R; Basic command line scripting; Data formatting using awk, grep or similar
Availability: Fall 2020
Computational Biology
Dry
Jaksic Genetic basis of the interplay between dopamine and octopamine

Dopamine is a neurotransmitter important for regulating many behavioral traits, including locomotion. We previously screened for genome-wide genetic variation that underlies natural variation in locomotor ability in Drosophila caused by dopamine level perturbation. This uncovered a potential interplay between octopamine and dopamine in balancing external perturbations to these neurotransmitters. The aim of this project is to functionally validate the effects of octopamine signaling genes on locomotor variation caused by imbalance in dopamine levels. You will use Drosophila melanogaster GAL4>UAS transgenic system for targeted knock down of candidate gene expression in combination of pharmacological perturbation of neurotransmitter levels, and assay the effects on locomotor ability of flies. This outcome of this project will allow us to explore how the two neuronal signaling systems interact on a genetic level.
Keywords: genetics, neurobiology, drosophila

Supervisor:  Ana Jaksic   
Contact: [email protected]

Required: basic genetics, willingness to work with flies, willingness to learn
Availability: Fall 2020, Spring 2021
Neuroscience
Wet
Lingner telomere biology

The projects are linked to our ongoing research work on telomeres, involving molecular biological, biochemical and cell biological techniques and human cell culture. See https://www.epfl.ch/labs/lingner-lab/research-activities/
Keywords: telomeres, human cells, biochemistry, molecular genetics

Supervisor:  Joachim Lingner   
Contact: [email protected]

Required:  –
Availability: Spring 2021, Fall 2021
Molecular biology
Wet
Manley The when and wheres of mitochondrial gene expression

Mitochondria are the reason you breathe. They enable efficient burning of sugars and fat to produce ATP by cellular respiration. This in turn depends on the expression and maintenance of their own DNA (mtDNA). In this project you will aim to elucidate the distribution of mtDNA and how its replication and transcription are regulated. You will engineer cells to express transcription and replication markers, perform live and fixed cell confocal and super-resolution fluorescence microscopy, and finally analyse and evaluate the image-data. Ultimately, a better understanding of mtGene expression might provide the basis for to target many mitochondria-related diseases, such as f.i. Alzheimers.
Keywords: mitochondrial DNA, fluorescence microscopy, human cells

Supervisor:  Suliana Manley   
Contact: [email protected]

Required: enthusiasm, independence, self-discipline, capable of having fun
Availability: Fall 2020
Molecular biology
Dry and wet
De Los Rios Improving ribosomal proteins across the tree of life.

The ribosome is the protein synthesis factory of the cell. It is composed by tens of proteins with different functions. Here we want to explore the repertoire of known protein sequences (available on Uniprot) and of known protein domains (available for example on Pfam) to explore how different organisms decorate their ribosomal proteins to add further functions (e.g. improve co-translational protein folding or degradation etc).
Keywords: ribosomal proteins, bioinformatics, machine learning

Supervisor:  Paolo De Los Rios   
Contact: [email protected]

Required: bioinformatics
Availability: Fall 2020
Computational Biology
Dry
De Los Rios Exploiting evolution to predict and design protein structures

As Alphafold has proven recently, the information contained in the ensemble of sequences from a given protein family is sufficient to reliably predict the typical three dimensional fold of the proteins in that family. The goal of this project is to improve the techniques to extract this information exploiting interpretable machine learning approaches. In turn we want to turn these methods into generative algorithms that can be used to design proteins with a desired function.
Keywords: bioinformatics, structural biology, machine learning

Supervisor:  Paolo De Los Rios   
Contact: [email protected]

Required: bioinformatics, machine learning
Availability: Fall 2020, Spring 2021
Computational Biology
Dry
Sandi Analytical approaches to data from human virtual reality (VR) and neuro-physiology studies

The combination of Virtual Reality (VR), motion tracking, autonomic response recording and EEG provides a versatile and information enriched way to study human behavior and neurophysiology in a laboratory setting. The analysis of the resulting multivariate datasets is a challenging task requiring a combined knowledge from different areas like signal processing, machine learning, statistics and data visualization.
Keywords:

Supervisor:  Carmen Sandi
Co-supervisor: joã[email protected]   
Contact: [email protected]

Required: Both pre-processing and analysis of the output multivariate datasets comprised of full body motion, eye tracking, pupil dilation, respiration, heart rate, skin conductance, EMG, and EEG. And programming skills (Matlab and/or R and/or Python).
Availability: Fall 2020, Spring 2021
Neuroscience
Interdisciplinary
Sandi Development of an application for multimodal autonomic biofeedback

The student will contribute to the development and piloting of a biofeedback application that integrates physiological signals (heart rate, heart rate variability, skin conductance and breathing rate). The aim is to study the effectiveness of our protocol to reduce anxious behavior or stress responses. Feedback can be delivered on a computer screen or later, via an immersive environment through a head mounted display, depending on the student’s progress.
Keywords:

Supervisor:  Carmen Sandi
Co-supervisor: [email protected]   
Contact: [email protected]

Required: Basic knowledge in the aforementioned techniques and programming skills (Matlab and/or R and/or Python)
Availability: Fall 2020, Spring 2021
Neuroscience
Interdisciplinary
Sandi Effects of stress and anxiety on motivated behaviour – neurobiological mechanisms

Impairments in motivated behaviour are a key feature in many stress-related disorders. Here, we investigate the effects of stress on motivated behaviour in rats differing in anxiety. We use a combination of techniques including, but not limited to, behavioural analyses (EPM, operant conditioning), microdialysis, immunofluorescent detection and quantification of protein expression, characterization of mitochondrial function and antisense-mediated modulation of gene expression.
Keywords:

Supervisor:  Carmen Sandi   
Contact: [email protected]

Required:  –
Availability: Fall 2020, Spring 2021, Fall 2021
Neuroscience
Wet
La Manno Cerebellar development modeling in 4d

Background: Current paradigms of studying the brain involve carrying out various kinds of measurements on tissue sections using different techniques. These measurements reveal different aspects of the brain complexity, such as its connectivity, electrophysiology properties, and anatomical relationships but also the gene expression, lineage relationship, and epigenetic states. These properties are all important and, in concert, give rise to complex emergent behavior. The advent of high throughput technologies has meant that data is being generated faster than ever. In this context, it becomes essential to integrate data from different modalities in an automated manner and study them in their spatial context to gain a fuller understanding of the brain and its function. Activities: The student will work to improve and refine methods to create a reference 3D atlas of a mouse brain for different ages. The volumetric reference generated will be used for the visualization and rendering of different kinds of data. Different kinds of omics and functional data will be integrated into this reference and correlated to generate new hypotheses and discoveries. In particular, we will focus on the cerebellum and Purkinje cells, that are an anatomically well-organized cell population, and it will facilitate the dynamical modeling of molecular changes through time during development.
Keywords: Neuroscience, Data Science, Image analysis, Development

Supervisor:  Gioele La Manno
Co-supervisor: Ludovic Telley (UNIL)   
Contact: [email protected]

Required: Python or R programming. some experience with single processing or image analysis
Availability: Spring 2020, Fall 2020, Spring 2021, Fall 2021
Computational Biology
Interdisciplinary
Schürmann Inference of ion-channel models using neural networks

A central aspect of morphologically detailed neuron models is to accurately capture membrane mechanism dynamics. Ion channel models typically are described using a set of ordinary-differential equations and a set of parameters, which are fitted to experimental measurements. The parameter fitting process has traditionally been done using expert knowledge and parameter optimization methods, which find optimal parameters by comparing the electrical traces from experiment and simulation. With the advent of automated experimental methods for isolating and describing neuron ion channels, it becomes clear that a more automated, yet robust method for the generation of mechanism models, and their parameters, is needed. Neural networks may offer a new approach for accomplishing these tasks. Deep-learning approaches have been used in many instances for learning of parameters as well as the mathematical model itself (physics-informed neural networks). In this project we would like to explore in a first step the inference of optimal channel parameters by learning from the existing body of mechanism models using neural networks. Going further, the lessons learned could then be applied to designing neural networks for channel model inference.
Keywords: deep neural networks, mathematical modelling, ion-channels

Supervisor:  Felix Schürmann   
Contact: [email protected]

Required: machine learning, mathematical modelling, python programming, notions of membrane mechanism / ion-channel models
Availability: Fall 2020, Spring 2021, Fall 2021
Neuroscience
Dry
Schürmann Modeling energy-efficiency of neuron simulations

Modelling and simulation of morphologically detailed neuronal circuits enables us to gain a deeper understanding of biological processes on multiple scales of the brain. In order to study complex phenomena such as neuronal plasticity we need to be able to run simulations at an increased scale, both in space and time. Understanding the various aspects of a neuron simulation that influence simulation performance and parallel scalability allows us to take decisions on hardware choice and improvements in algorithms and data structures, which are necessary to push the envelope of simulation scale. An important measure that has not been considered for this type of simulations is power consumption. In this project we would like to extend our performance models for neuroscience simulations with a model for power consumption. Existing power consumption models (e.g. based on ECM) should be explored and extended to fit our case. The developed model can then be incorporated into the performance model, allowing us to give more detailed insights into the expected feasibility and efficiency of simulations on various hardware platforms.
Keywords: computational biology, simulation, mathematical modelling

Supervisor:  Felix Schürmann   
Contact: [email protected]

Required: python programming, mathematical modelling, computer hardware architecture, basic understanding of morphologically detailed neuron simulations
Availability: Fall 2020, Spring 2021, Fall 2021
Neuroscience
Dry
Gönczy Investigating centriole number control mechanisms in human cells

Objective: combine experiments and mathematical modeling to investigate how Plk4, STIL and HsSAS-6 together ensure assembly of a single new centriole next to each resident centriole, once per cell cycle; monitor fate of cells with altered centriole number in long term live imaging experiments. Approaches: human cell culture, live cell imaging, super-resolution microscopy (STED), mathematical modeling.
Keywords: centriole, number control, live imaging, modeling

Supervisor:  Pierre Gönczy   
Contact: [email protected]

Required: Ideal for students in: Life Sciences, Physics, Mathematics.
Availability: Fall 2020, Spring 2021, Fall 2021
Cell biology
Dry and wet
Gönczy Re-engineering SAS-6 proteins

Objective: test whether centrioles can form in human cells with a SAS-6 protein that assembles into a spiral rather than a ring-bearing structure, as well as into structures with altered fold symmetries. Approaches: site directed mutagenesis, protein expression and purification, cryo-electron microscopy, atomic force microscopy (AFM), human cell culture, CRISPR/Cas9-mediated engineering, super-resolution microscopy (STED).
Keywords: centriole, bioengineering, AFM, CRISPR/Cas9

Supervisor:  Pierre Gönczy   
Contact: [email protected]

Required: Ideal for students in: Life Sciences, Bioengineering.
Availability: Fall 2020, Spring 2021, Fall 2021
Cell biology
Wet
Gönczy Centriole inheritance in sexual and asexual reproduction

Objective: investigate how centrioles are inherited/formed in embryos generated through asexual reproduction, where oocytes develop without fertilization by sperm. Approaches: live imaging of early embryogenesis in the nematode Panagrolaimus, identification of homologues of C. elegans centriolar proteins, protein expression and purification, antibody generation, immunofluorescence analysis, confocal imaging.
Keywords: centriole, C. elegans, parthenogenesis, cell biology

Supervisor:  Pierre Gönczy   
Contact: [email protected]

Required: Ideal for students in: Life Sciences
Availability: Fall 2020, Spring 2021, Fall 2021
Developmental biology
Wet
Gönczy Evolutionary diversity and origin of centriolar proteins

Objective: identify homologues of fundamental centriolar proteins across the domains of life and thus help trace the origin of the centriole organelle; test newly identified candidates in cell free assay. Approaches: computational biology, structural prediction, cell biology
Keywords: centriole, computational biology, evolution of protein families

Supervisor:  Pierre Gönczy
Co-supervisor: Anne-Florence Bitbol   
Contact: [email protected]

Required: Ideal for students in: Computer Sciences, Life Sciences
Availability: Fall 2020, Spring 2021, Fall 2021
Computational Biology
Interdisciplinary
Gönczy Analyzing genes that set organismal thermal limits

Objective: analyze outcome of a screen performed in the yeast S. cerevisiae to identify genes important for determining organismal thermal range; investigate whether their function is conserved in the nematode C. elegans. Approaches: computational biology, functional genomics, microscopy.
Keywords: yeast, worms, thermal range, computational biology, functional genomics

Supervisor:  Pierre Gönczy   
Contact: [email protected]

Required: Ideal for students in: Life Sciences, Computer Sciences
Availability: Fall 2020, Spring 2021, Fall 2021
Computational Biology
Dry and wet
Gönczy Analysis of the flagellum in a new model organism for multicellularity

Objective: characterize the life cycle of the Ichthyosporean Chromosphaera perkinsii, identifying when and where flagellated cells appear; describe flagellar organization and analyse centrioles using immunofluorescence and electron microscopy. Approaches: conduct live cell imaging of C. perkinsii in different growth conditions, establish synchronization protocol using cell sorting, perform immunofluorescence analysis, as well as transmission and scanning electron microscopy.
Keywords: cell-cycle, centriole, EM, protist

Supervisor:  Pierre Gönczy
Co-supervisor: Omaya Dudin   
Contact: [email protected]

Required: Ideal for students in: Life Sciences
Availability: Fall 2020, Spring 2021, Fall 2021
Cell biology
Interdisciplinary
Blanke Neural correlates of memory, navigation, and bodily self

In this project, we will investigate how bodily self-consciousness influences episodic autobiographical memory and spatial navigation using Virtual Reality (VR) tasks. Two projects, using immersive virtual reality will be conducted. The first investigates the neural correlates of spatial navigation using EEG and the second the neural correlates of sensorimotor modulation of episodic autobiographical memory using fMRI.
Keywords: memory, navigation, virtual reality, eeg, fmri, self

Supervisor:  Olaf Blanke   
Contact: [email protected]

Required:  –
Availability: Fall 2020, Spring 2021, Fall 2021
Neuroscience
Dry
Blanke Investigating bodily self-conciousness using virtual reality

This project will combine virtual reality with neuroimaging and/or psychophysiology to explore the mechanisms underpinning the different dimensions of bodily self-conciousness, in healthy or clinical population.
Keywords: multisensory integration, interoception, self-consciousness, virtual reality

Supervisor:  Olaf Blanke   
Contact: [email protected]

Required:  –
Availability: Fall 2020, Spring 2021, Fall 2021
Neuroscience
Dry
Blanke Vestibular contributions to bodily self-consciousness (motion platform)

In this project you will combine virtual reality with the use of a motion platform in order to (1) induce virtual out-of-body experience in health subjects and (2) to explore the mechanisms underpinning such phenomenological experience.
Keywords: vestibular processing, out-of-body experience, virtual reality

Supervisor:  Olaf Blanke   
Contact: [email protected]

Required:  –
Availability: Fall 2020, Spring 2021, Fall 2021
Neuroscience
Dry
Blanke Investigating hallucinations in Parkinson’s Disease

The project will combine robotic technology, VR, psychophysics, and brain imaging technics to caracterize the brain mechanisms of hallucinations in PD. In particular, we aim at investigating how specific robotic sensorimotor stimulation can induce abberrant perceptions and modulate neural processes such as vision or audition.
Keywords: parkinson’s disease, robotics, hallucinations, virtual reality

Supervisor:  Olaf Blanke   
Contact: [email protected]

Required:  –
Availability: Fall 2020, Spring 2021, Fall 2021
Neuroscience
Dry
Mathis Tackling animal cognition tests with RL to gain insights into brain function

All animals possess some sort of intelligence, which manifests itself in more or less complex behavior, which typically allows them to solve specific tasks. To gain insights into brain function neuroscientists have devised a wide range of careful experiments to assess the potential and the limits of intelligence in different species, producing an extensive literature on the subject. Recently this literature inspired the Animal-AI testbed (http://animalaiolympics.com/AAI/), a framework to train and test autonomous agents to solve various classes of tasks inspired by the ones that animals face in laboratories. In 2019, tens of researchers have picked up this challenge, which focusses on training an autonomous agent to excel at as many animal-like skills as possible. Specifically, the agents are trained at solving typical laboratory problems, mostly related to acquiring food in various settings. However, not even the best submitted agents proved able to find a solution to the most complex tasks robustly. This confirms the suspicion that learning advanced animal behavior with Reinforcement Learning is still an open problem. The core of this thesis will be to tackle this challenge with the hope to push the envelope on one or more of the main forms of intelligence under examination in the testbed (spatial memory, dealing with transparent objects, object permanence, internal models, using tools and creativity).
Keywords: RL, deep learning, behavior

Supervisor:  Alexander Mathis   
Contact: [email protected]

Required: Python programming
Availability: Spring 2021, Fall 2021
Neuroscience
Dry
Suter Protein homeostasis during early development

How cells coordinate protein synthesis and degradation rates to regulate protein levels is poorly understood. We know very little on how different cell types vary in their strategies to regulate their proteome. Using a single cell, live approach that we pioneered (Alber et al., Molecular Cell 2018), this project will focus on how protein synthesis and degradation are modulated during embryonic stem (ES) cell differentiation, and how these depend on cell proliferation. The project will involve differentiation of various ES cell lines expressing a fluorescent timer, immunofluorescence analysis to determine the identity of differentiated cells, fluorescence microscopy, quantitative image analysis, and computational modelling of proteostasis.
Keywords: developmental biology, stem cells, quantitative imaging, protein homeostasis

Supervisor:  David Suter   
Contact: [email protected]

Required: None
Availability: Spring 2021, Fall 2021
Cell biology
Dry and wet
Suter Quantitative analysis of transcription factor activity

The activity of transcription factors (TFs) on cell fate decisions depends on their cumulative concentration over time. However monitoring their accumulation over time in single cells is challenging. The project will aim at developing a strategy allowing progressive changes in the expression levels of two spectrally distinct fluorescent proteins as a function of TF levels. The project will first provide a proof-of-concept of this strategy using in vitro experiments on mouse embryonic stem (ES) cells genetically modified with the reporter constructs. If time permits, genome editing will be performed to control the quantitative transcription factor readout by endogenous expression of Pax6 (a TF gene involved in neuronal cell fate determination). This will allow to track its cumulative expression during targeted in vitro differentiation of ES cells. This strategy will open the door to next generation, quantitative lineage tracing approaches and should have a broad range of applications to track the fate of cells with different expression levels of TFs or other genes in various organisms.
Keywords: molecular biology, transcription factors, stem cells, quantitative imaging,

Supervisor:  David Suter   
Contact: [email protected]

Required:  –
Availability: Spring 2021, Fall 2021
Cell biology
Dry and wet
Turcatti Classifying drugs according to their mode-of-action through High Content Screening

High content analysis using automated fluorescence microscopy is widely applied in our lab for morphological profiling of cells upon interaction with drugs or candidate chemical compounds. Highly informative data extracted from fluorescence microscopy imaging of cells allows identifying and classifying a certain number of phenotypes clustered according to the biological signature obtained In order to increase the level of information related to intracellular events triggered by chemical interference, the morphological profiling assay ‘cell painting’ will be implemented. In this multiplexed method, six fluorescent probes are used simultaneously for revealing cellular compartments or organelles under chemical perturbation. By automated image analysis and extraction of hundreds of features, and using machine learning algorithms, compounds will be clustered according to the phenotypic profile they trigger. For this multidisciplinary chemical biology project, experiments will initially be performed with one cell line at a fixed time point and at a single concentration. For deeper characterization and for few selected compounds, fluorescence-imaging time-lapse experiments can be envisioned for tracking the phenotypic evolution over time. Open source supervised machine learning software and solutions such as Cell profiler or Cell cognition will be used for image analysis and throughout the project.
Keywords: Chemical Biology, Drug Discovery, Phenotypic screening, Image analysis

Supervisor:  Gerardo Turcatti   
Contact: [email protected]

Required: Some experience in mammalian cell culture and/or fluorescence microscopy
Availability: Spring 2021, Fall 2021
Bioengineering
Dry and wet
Deplancke Genetic basis of tissue-specific expression variation in Drosophila melanogaster.

One of the major research pillars of the Deplancke lab is examining how genomic variation affects molecular and organismal diversity. In this project, we suggest studying the genetic variability of Drosophila melanogaster using eQTL mapping based on data from four tissues and 140 strains (genotypes). Background: One of the great challenges in computational biology is the assessment of how genomic variation contributes to complex phenotypic traits or disease susceptibility. Thus, understanding the molecular basis of genetic variation within the same species has not only evolutionary significance but is also crucial for medical applications. The approach that is widely put into practice in evolutionary studies is Quantitative Trait Loci (QTL*) mapping. It aims to identify regions that correlate with a variation of a quantitative trait in some phenotype in a group of organisms, which could provide an understanding of whether the observed variation is determined by one or a few loci, or by many of them. Interestingly, however, many of these mapped QTLs seem not to fall within coding regions of the genome, which suggests that these variants may contribute to phenotypic variation by affecting gene regulation rather than protein function. How these “non-coding variants” may do so is still very poorly understood though, which is why great efforts are dedicated to characterizing these variants, and this project will contribute to these efforts. One of the approaches to characterize non-coding variants is to study whether they impact on variation of global mRNA levels between individuals by carrying out expression quantitative trait loci (eQTL) studies. Because of external influences (diet, environment…) that are difficult to control in humans, such eQTL mapping studies are especially effective in model organisms such as Drosophila for which such influences can be standardized across different genotypes. Your goal will thus be to quantify genome-wide variation in gene expression in multiple Drosophila melanogaster lines, characterize population-scale diversity of transcriptomes and its genetic basis across tissues, and provide a biological interpretation of the obtained results. Useful links: https://advances.sciencemag.org/content/7/5/eabc3781 https://www.pnas.org/content/112/44/e6010  https://genomebiology.biomedcentral.com/articles/10.1186/s13059-019-1918-6
Keywords:

Supervisor:  Bart Deplancke   
Contact: [email protected]

Required: • At least 1 semester of Molecular Biology and Linear Algebra • Knowledge of bioinformatics & genomics basics (DE, GWAS, etc.) • Programming (GNU Bash, R and/or Python) and statistical skills • Strong motivation for learning new things
Availability: Spring 2021, Fall 2021
Computational Biology
Dry
Jakšić Cognitive fitness from a genetic, metabolic, and evolutionary perspective

The risk for developing Alzheimer’s disease (AD) is inversely correlated with the degree of educational attainment: The higher your educational achievement, the lower your risk for AD. This association is called the “cognitive reserve hypothesis” and, although epidemiologically supported by multiple studies, the physiological and molecular mechanisms behind this association are poorly understood. In this project, we set out to test the cognitive reserve hypothesis from an energy demand perspective. Since both learning and neurotoxicity in AD pose a high demand on cellular energy consumption, we hypothesize that well-trained brains are conditioned to utilize energy more economically. In other words, energy saved by a well-trained brain on learning can be re-routed to protect against neuronal cell death. To experimentally assess this hypothesis, you will use the fruitfly D. melanogaster we well as murine models of AD in a joint project between the Jaksic and Gräff labs. Specifically, you will first assess the genetic basis for the relationship between energy consumption, learning-dependent metabolic rates, lifetime learning, and neurodegeneration in a screen of genetically characterized D. melanogaster lines. These results will then be validated by whole transcriptome sequencing and functional manipulations in different mouse models of AD. Knowing the genetic architecture underlying metabolism, lifetime learning and the risk for AD will yield important insights into molecular basis of the cognitive reserve hypothesis. Interested? Please contact us at [email protected] or [email protected]
Keywords: Genomics, Neurobiology, Bioinformatics, Cognition

Supervisor:  Ana Jaksic
Co-supervisor: Gräff   
Contact: [email protected]

Required: R and/or Python, willingness to learn to work with the fly model
Availability: Fall 2020, Spring 2021
Neuroscience
Interdisciplinary
Aztekin Limb regeneration associated cell type cultures

Unlike mammals, certain species can regrow their lost limbs. Limb regeneration involves complex cell-cell interactions mediating proliferation, migration, and cell-fate decisions. Notably, upon limb amputations, Xenopus laevis tadpoles form an epithelial cell type (Apical-ectodermal-ridge, AER cells) that expresses genes influencing stem and progenitor cells for cellular mechanisms to regrow lost limbs. However, how AER coordinates multiple dynamic behaviors of stem and progenitor cells for morphogenesis is unclear. To overcome this, we will establish an in vitro system to investigate AER cells and their interaction with other populations, where imaging and molecular biology approaches could be readily employed. The student will focus on isolating and establishing in vitro culture protocol for AER cells. Upon successfully propagating these cells, the student will implement high-throughput live-imaging, gene-editing, and co-culture studies (AER cells with stem/progenitor cell types) to interrogate cell-cell interactions and dynamic cellular behaviors.
Keywords: regeneration, stem cells, signaling centers, AER, development, limb

Supervisor:  Can Aztekin   
Contact: [email protected]

Required: Experience in cell culture
Availability: Fall 2021
Developmental biology
Wet
Courtine SPINAL CORD STIMULATION TO RESTORE CYCLING AFTER PARALYSIS

We are interfacing the stimulation with recumbent bike that enables outdoor cycling.Your objective is to contribute to this integration and its evaluation. You will: – Develop stimulation protocols to adapt muscle activity to the cycling movement on a trike and personalize stimulation in patients with spinal cord injury. – Identify measures to evaluate the use of the trike with and without stimulation (trike logs and stimulation logs) – Analyze outcome measures from the trike (force sensors, IMU’s, power measures, etc…) to quantify the effect of training with the trike with and without stimulation over time. – Identify and analyze physiological changes related to the use of the trike (EMGs, cardiac frequency, generated force). – Adapt testing to an indoor cycling device for rehabilitation (BiPed Trike) in collaboration with a research team and UNIL (Dr. Jérôme Barral) – Evaluate the movement dependencies and performance to optimize the combinations of movements and stimulation.
Keywords: spinal cord injury, spinal cord stimulation, cycling

Supervisor:  Leonie Asboth   
Contact: [email protected]

Required: Basic neuroscience skills, mechanical skills, informatics skills
Availability: Fall 2021, Spring 2022, Fall 2022
Neuroscience
Dry
Courtine SINGLE CELL TRANSCRIPTOMIC PACKAGE DEVELOPMENT

NeuroRestore is a research and innovation center spanning EPFL and the University Hospital of Lausanne (CHUV) that develops and applies medical therapies aimed to restore neurological functions. We integrate implantable neurotechnologies with innovative treatments developed through rigorous preclinical and clinical studies. These developments have led to breakthroughs for the treatment of paraplegia, tetraplegia, Parkinson’s disease, stroke, and traumatic brain injuries. The position: Single-cell genomics is one of the most exciting techniques in current biology. By enabling large scale comparisons of cell populations transcriptomes, it allows for characterization of previously undefined cell types, discovery of regulatory pathways involved in various diseases. At NeuroRestore we have now fully implemented this cutting edge technology to reveal the mechanisms underlying functional recovery after paralysis at a molecular level. We are looking for a motivated and dynamic candidate for an internship focused on development of a R package that will encompass our various pipelines and newly developed methods for single cell biology. They will learn how to conduct single cell transcriptomics analysis, from the alignment of raw sequencing data up to the identification of each cell population in spinal cord. The candidate will work in a highly attractive, multidisciplinary and international environment spanning high-tech medical industry, academic labs, and clinical centers. Duration of the internship:3-6 months Location: Campus Biotech, Chemin des Mines 9, 1202 Geneva
Keywords:

Supervisor:  Matthieu Gautier   
Contact: [email protected]

Required: Experience with R language, Fluent use of English, • Experience with team software development and relevant tools (SVN, GIT, etc.), Ability to learn fast, Affinity to work in a dynamic and effective team. Ideally: Experience in Python and bash, Experience in package development
Availability: Fall 2021, Spring 2022, Fall 2022
Neuroscience
Dry and wet
Deplancke Studying the genetic basis of variation in mosquito vectorial capacity in Rio de Janeiro

Dengue and chikungunya are mosquito (Aedes aegypti)-borne arboviral diseases that affect millions of people, resulting in substantial hospitalization and death. To combat these diseases, we need to achieve a deeper understanding of A. aegypti biology and the impact of viral infection on the life-history traits of this insect, as this should have important implications for predicting the evolution of mosquito-parasite relationships and their role in the emergence and maintenance of arboviruses. To achieve such understanding, the Laboratories of Systems Biology and Genetics (EPFL) and Physiology and Control of Vector Arthropods (FIOCRUZ, Rio de Janeiro) have already generated a panel of already >60 recombinant A. aegypti inbred lines. We have begun to systematically sequence the genomes of these lines and we are now looking for a Master’s student who will engage in bioinformatic analyses to map genomic variation across these >60 lines. In parallel, the student will work in a fully equipped insectary at Fiocruz, performing phenotyping experiments on these inbred lines including the number of eggs deposited by females, egg viability, adult longevity, larval development time, insecticide resistance etc. Finally, the student may engage in some first genotype-phenotype analyses, linking specific mosquito traits to genetic variants.
Keywords: Mosquito, infection, vectorial capacity, viral disease, genomic variation, systems genetics, bioinformatics

Supervisor:  Bart Deplancke
Co-supervisor: FIOCRUZ, Rio de Janeiro   
Contact: [email protected]

Required: Skills in R / Python
Availability: Fall 2021, Spring 2022, Fall 2022
Infectious diseases
Interdisciplinary
Dal Peraro Simulating Nanopore Transport as a Heteropolymer Sequencing Technique

Nanopores are molecular assemblies that form cylindrical holes in a cell membrane or artificial surface. When a heteropolymer such as a protein translocates through a nanopore, the transient blockage of the channel depends on the sequence of monomers and can be quantified by the variation in an ionic current through the pore. The time series of the current provides a readout of the polymer’s sequence. The complex interactions of the polymer with the pore’s inner surface and the slow (millisecond) timescale of the process make experimental optimisation of nanopore behaviour slow and expensive. Molecular simulations provide near-atomistic detail on the length scale of amino acids, and can reach time scales of hundreds of microseconds. This project will use the established technique of dissipative particle dynamics simulations to simulate a heteropolymer translocating through a nanopore in a lipid membrane. The monomer-dependent variation in occlusion will be read out and its relation to the sequence of the polymer determined. The influence of pore parameters — width, surface structure — and a range of polymer sequences will be explored. The results are expected to shed light on the sensitivity and precision with which a polymer’s identity can be determined from its translocation through the pore.
Keywords: nanopore, aerolysin, sequence, membrane, simulation

Supervisor:  Matteo Dal Peraro   
Contact: [email protected]

Required: Good familiarity with molecular simulations; knowledge of C++ a bonus
Availability: Fall 2021, Spring 2022, Fall 2022
Bioengineering
Dry
Courtine Development of gait analysis pipeline for mice

The project is on developing a marker-less kinematic tracking platform in python that involves deep learning (adapting DeepLabCut, Mathis et al, 2018 Nature Neuroscience) and creating tools to analyze gait parameters. The aim is to develop a pipeline from setting up multiple cameras for capturing locomotion in 3D, performing marker-less tracking with DeepLabCut and computing gait parameters for comparing different behavioural abilities. The prospective student should already have knowledge of python and is able to create GUIs. Basics for the code have been already implemented in either Matlab, R or python /spyder. The idea is to combine the code, clean-up the code, add a filter option for false labelling and produce an easy-to-use user interface. Additionally, the student will adapt the code for specific experiments looking at the effect of spinal cord injury on mouse hindlimb locomotion and for other species. Besides this, the student will learn how to / assist with behavioural experiments with mice.
Keywords: gait analysis, spinal cord injury, pre-clinical models

Supervisor:  Gregoire Courtine
Co-supervisor: N/A   
Contact: [email protected]

Required: Matlab, R or python skills, making GUIs, available for a minimum of 3 months
Availability: Spring 2022, Fall 2022
Neuroscience
Interdisciplinary
Courtine Spinocerebellar tract after SCI for locomotor recovery after SCI

Determine the importance of the spinocerebellar tract after SCI for locomotor recovery after SCI. Modulating the activity of this tract will allow us to study how it orchestrates the plasticity of spared ascending and descending pathways. Transgenic and viral vector mediated chemogenetics will be combined with behavioural kinematic recordings to assess changes in locomotor recovery after SCI. This will demonstrate the importance of nonconscious proprioceptive input to the cerebellum for recovery of voluntary locomotor function. 3D marker-less kinematic recordings will be employed to analyse hindlimb locomotion in healthy mice and recovery of hindlimb function after SCI. Viral vector mediated neuronal tracing will be used to retrogradely label supraspinal motor centers combined with histological techniques to assess changes in neuronal plasticity. These experiments will establish the purpose and importance of the spinocerebellar proprioceptive input to the cerebellum for altering cerebellar output and recovery of motor function.
Keywords: spinal cord injury, pre-clinical models, wetlab work

Supervisor:  Gregoire Courtine
Co-supervisor: N/A   
Contact: [email protected]

Required: minimum 3 months, ideally some previous exposure to animal experimentation
Availability: Spring 2022, Fall 2022
Neuroscience
Interdisciplinary
Radtke Tracking the colorectal cancer microenvironment

Colorectal cancer (CRC) is one of the major forms of cancer and one of the leading causes of death in adults. It usually develops from benign precursor lesions that continue to accumulate mutations over time, correlating with progression of disease. Although the sequence of events of mutational activation of oncogenes and mutational loss of tumor suppressors is rather well characterized in human CRC, it is clear that the progression of the disease is not solely a cell autonomous process. Extrinsic factors such as the tumor microenvironment, including tumor-infiltrating immune cells, affect disease progression. We utilize three mouse models of CRC with accumulating oncogenic-driver mutations which exhibit variable tumor T cell infiltration and activation, dependent on the genotype of the mouse. For this purpose, we performed single-cell RNA sequencing (scRNAseq) to uncover the molecular mechanisms by which T cells are recruited or excluded from tumors, factors which may affect their response to immunotherapeutics. The aim of this master project is to participate in the investigation of how colon tumors progress and impact the tumor microenvironment over time by scRNAseq combined with flow cytometric and immunohistochemical analysis.
Keywords: Colorectal cancer, tumor microenvironment, immunotherapy, validation of scRNAseq, genetically engineered mouse models

Supervisor:  Amber Bowler   
Contact: [email protected]

Required: Knowledge in cell and molecular biology and basic understanding of flow cytometric analysis.
Availability: Fall 2021, Spring 2022
Cancer biology
Wet
Dal Peraro Developing biological nanopores for molecular sensing

Evolution has found countless ways to transport material across cells and cellular compartments separated by membranes. Protein assemblies that form channels and pores regulate the passage of molecules in and out of cells, contributing to maintain most of the fundamental processes that sustain living organisms. We are taking advantage of the natural properties of these biological systems to push technology forward and to develop the single-molecule sensing devices of tomorrow. Since we solved the structure of aerolysin, we are working to characterize and engineer this and other similar pores in order to enhance their native molecular sensing capabilities. Current fields of application that we are exploring in the lab include protein sequencing, biomarkers identification, polymer reading for long-term data storage and detection of environmental chemicals. Moreover, we strive to improve the current instrumentation and the signal processing pipeline using artificial intelligence. Multiple master projects are available in this domain both on the experimental or computational side depending on the skills and inclination of the candidate.
Keywords: nanopore sensing, protein sequencing, protein engineering, data storage

Supervisor:  Matteo Dal Peraro
Co-supervisor: Chan Cao, SV   
Contact: [email protected]

Required: Some of these are preferable : protein production, nanopore sensing, signal processing, structural biology
Availability: Fall 2021, Spring 2022, Fall 2022
Bioengineering
Interdisciplinary