Besides the below offered topics for master and semester projects, we welcome you to bring your own ideas and elaborate a project together with us.
Whether you are interested in one listed topics or you bring some ideas of your own, we strongly encourage you to meet with members of the lab to learn about the research in LCSB.
To get the most of your internship or master project we recommend reading the following article:
Ten simple rules for getting the most out of a summer laboratory internship
Cell culture optimization and production of biopharmaceuticals using a CHO genome-scale model.
Biopharmaceuticals are medical drugs produced through genetic manipulation of living cells. They are mainly produced using mammalian cell lines given their similitude with human cells. The Chinese hamster ovary (CHO) are the most used cell lines to produce therapeutic proteins.
The reconstruction of high-quality curated genome scale metabolic models (GEMs) for CHO cells, enables the application of modern Systems Biology for the analysis and understanding of culture behavior under different bioprocessing conditions. The consideration of constraint-based mathematical models facilitates the integration of available experimental data to analyze and understand the relationships between the different elements of the system and predict the response to perturbations.
The objective of this project is to study CHO cells metabolism under different environmental and bioprocessing conditions (media composition, feeding schedule). We will use the CHO GEM and constraint-based methods such as flux balance analysis or thermodynamic-base balance analysis to examine the alternative culture media, to study the effect of the medium composition on intracellular reactions, and to reveal metabolic differences among cell lines.
|Type:||Master or semester project|
|Keywords:||CHO genome scale, cell culture optimization, biopharmaceuticals|
|Recommended knowledge:||Chemistry/Chemical engineering, Bio-Informatics|
|Suggested reading:||Rejc Z, et al. Computational modelling of genome-scale metabolic networks and its application to CHO cell cultures. Computers in Biology and Medicine 88 (2017) 150–160
H. Hefzi, et al., A consensus genome-scale reconstruction of Chinese hamster ovary cell metabolism, Cell Syst. 3 (5) (2016) 434–443.
|Contact:||M. Masid , V. Hatzimanikatis|
Generative Neural Networks for Characterization of Kinetic Metabolic Models
Kinetic models are indispensable in systems and synthetic biology studies of metabolism as they allow us to capture the dynamic behavior of metabolism and to predict dynamic responses of living organisms to genetic and environmental changes. The scientific community has recognized the utility and potential of kinetic models, and efforts towards building large-scale and genome-scale kinetic models are recently intensified. Nevertheless, the development of these models is still facing the challenges such as difficult characterization of the parameter space. Despite the abundantly available data, it is difficult to determine the parameters accurately describing the observed physiology. The reason for this are intrinsic nonlinearities and complex dynamic behavior of living organisms.
In this Master project, we propose to address this problem by employing a class of generative neural networks to construct a population of large-/genome- scale kinetic models consistent with the experiments. The project will be conducted in several phases: training of the neural network, validation of results, sensitivity analysis/hyperparameter tuning, application to a wider class of problems. In the first phase, using the set of parameters, devised from our in-house developed method for construction of kinetic models, a generative neural network will be trained and further used to generate the desired parameters. The obtain models will then be validated against the experimental data. Then, the student will work on improving the performance of the neural network by analyzing its structure. Finally, we will explore the application of such fine-tuned neural networks to a set of experimental data that describes alternative physiologies.
|Type:||Master or semster project|
|Keywords:||Machine learning, neural networks, kinetic models, metabolism|
|Recommended knowledge:||Basics of Chemistry/Chemical engineering/Biology, Data analysis|
|Suggested reading:||Andreozzi et al., iSCHRUNK – In Silico Approach to Characterization and Reduction of Uncertainty in the Kinetic Models of Genome-scale Metabolic Networks, Metabolic Engineering, (33), pp 158-168 (2016).
Miskovic & Hatzimanikatis, Production of biofuels and biochemicals: in need of an ORACLE, Trends in Biotechnology, 8(28), pp 391-397 (2010).
Mirza & Osindero, Conditional Generative Adversarial Nets, Arxiv.org, (2014).
Classification of Reaction Kinetics in Metabolic Networks
Stoichiometric models of large-scale metabolic networks are widely used to perform flux balance analysis, and explore different steady state characteristics of complex biochemical systems. However, these models cannot be used the study the dynamic properties of these systems, such as transitions between steady states. For this purpose it is required to take into account the information about the kinetic properties of these reaction networks. Incorporating this information for the generation of kinetic models requires the formulation of the reaction rate expressions for each and every individual biochemical reaction in the system. To build the rate expressions we need to consider the properties of each reaction regarding its molecularity, its thermodynamic and kinetic properties. Although the kinetic mechanism of many reactions is well studied, in most cases the exact kinetic mechanism is not known. Therefore, under different assumptions, distinct generalized approximations are often used.
|Recommended knowledge:||Chemistry/Chemical engineering, Bio-Informatics|
|Suggested reading:||Heinrich, R., & Schuster, S. (2012). The regulation of cellular systems. Springer Science & Business Media.|
|Segel, I. H. (1975). Enzyme kinetics (Vol. 957). Wiley, New York.|
Comparison of the metabolism between various malaria parasites
Malaria is a major global health care concern, and kills half a million people each year. It is caused by protozoan parasites of the genus Plasmodium. There are five parasites capable of infecting humans and their metabolism is slightly different. The eradication of malaria requires the identification of drug targets in all of the malaria parasites.
In this project, we aim to perform comparative analysis of the malaria parasites’ metabolism for the identification of common drug targets. We will use genome-scale metabolic models (GEMs) of the malaria parasites and apply constrained-based approaches like Flux Balance Analysis (FBA) and Thermodynamics-based Flux Analysis (TFA). These tools serve to perform mass and energy balances on the GEMs of the parasites. We will investigate in-depth the metabolic networks and identify the metabolic differences that define the characteristic behavior of each Plasmodium species.
||Semester or master project|
|Keywords:||Cellular metabolism, genome-scale metabolic model, malaria, drug targets, Plasmodium species|
|Recommended knowledge:||Background on mass and energy balances, general background on biochemistry, MATLAB programming|
Discovery and exploration of novel biotransformation networks
Today’s knowledge of biochemistry is far from being complete, especially when it comes to the more exotic biosynthesis routes of secondary metabolites. The pathways of peripheral metabolism produce highly complex molecules, some of which have valuable pharmaceutical or industrial properties. Many of these highly interesting metabolites have been extracted from plants and characterized, but their complex biosynthesis pathways remain unknown.
Using the computational tool BNICE.ch we can predict novel biotransformations and novel metabolites, which helps us to complement existing knowledge and to expand metabolic networks towards novel, hypothetical biosynthesis routes. We further use network analysis tools in combination with biological and chemical databases to explore and analyze, and evaluate possible biosynthesis routes that produce interesting pharmaceutical compounds. The output of our analysis will shed light on the metabolic processes and, at the same time, help metabolic engineers to modify organisms for the controlled production of these chemicals.
|Type:||Semester or master project|
|Keywords:||Novel biotransformations, biosynthetic reaction networks|
|Recommended knowledge:||Background in biochemistry and/or bioinformatics|
|Suggested reading:||Hadadi, N., et al., ATLAS of Biochemistry: A Repository of All Possible Biochemical Reactions for Synthetic Biology and Metabolic Engineering Studies. Acs Synthetic Biology, 2016. 5(10): p. 1155-1166|