We currently have several Master project topics available. Please find the details for each project below. If you have any questions or would like to learn more, feel free to contact Professor Sangwoo Kim ([email protected]) or the designated MESOBIO group member listed in each project description.
Understanding the mechanical properties of disordered networks is essential in diverse fields including materials science, architecture, and biophysics. These networks, composed of constituent elements with distinct mechanical properties, exhibit rich behavior and offer flexibility in tuning their macroscopic mechanical properties. The goal of this project is to investigate structure-property relationships in disordered two-dimensional network structures. To achieve this, we will develop a numerical simulation framework to analyze both linear and non-linear responses in these systems for varying network topology, pre-stress levels, and individual bond properties. The student will contribute to the development of in-house simulation codes and explore a range of model parameters to gain insights into the structure-property relationships.
Contacts
Roxane Ollivier [email protected]
Sangwoo Kim [email protected]
Biological tissues continuously consume energy through metabolic processes, leading to dynamical regulation of their structure and mechanical properties. This regulation involves various active processes, including fluctuations in junctional tension, cell crawling on the substrates, changes in cell size, and cell proliferation and apoptosis. The primary goal of this project is to investigate the emergent non-equilibrium dynamics of biological tissues in response to different types and magnitudes of active fluctuations. The student will implement diverse active processes in the Active Foam Model and explore how these fluctuations influence tissue properties. Each student will focus on a specific morphological phenomenon in connection with active fluctuations such as cell sorting, wound healing, and collective cell dynamics.
Contacts
Alessandro Rizzi [email protected]
Sangwoo Kim [email protected]
Bacterial cells employ diverse locomotion strategies to navigate complex environments and enhance their chances of survival. Certain species, such Pseudomonas Aeruginosa, use a mode of surface movement called twitching motility, which involves the extension and retraction of filamentous structures known as pili. These pili attach to the substrate and generate pulling forces that drive motion. In addition to locomotion, this mechanism incorporates mechano-sensing, enabling bacteria to efficiently explore their local environments. The primary goal of this project is to understand how the dynamics of pili in a single bacterial cell give rise to distinct patterns of locomotion. The student will develop a numerical framework that incorporates key aspects of pili dynamics, including extrusion rate, attachment rate, and pulling force magnitude. This work may be extended to investiage how individual twitching motility contributes to pattern formation in bacterial aggregates.
Contacts
Sangwoo Kim [email protected]
Many soft materials are composed of disordered assemblies of particles, where the spatial arrangement is closely linked to their emergent mechanical properties. Identifying low-energy configurations of these particle packings is importance both for fundamental physical principles and for the smart design of soft materials. However, the disordered nature of these materials gives rise to an exponential large number of metastable states, making the search for low-energy states, particularly the ground state, challenging. The primary goal of this project is to explore distinct annealing algorithms to minimize metastable energy and approach the ground state. We will focus on two complementary methods, a physics-based approach the leverage known correlations between structure and energ, and a machine-learning approach using custom-trained model. The student will implement particle packing simulations to generate large datasets across varying packing fraction and degrees of size polydispersity. These datasets will be analyzed to uncover structure-energy correlations and used to train in-house AI model to guide the search for the ground states.
Contacts
Sangwoo Kim [email protected]