Theory of Biological Networks

Course with Participations of Group Members

Offers for Theses in the Group

Biophysics: mixing and transport in the gut

The gut microbiota has a direct impact on health, influencing how the gut digests nutrients. The microbiota itself and the nutrients are influenced by the gut contractions and the fluid flow produced by them. How do different contractions influence the dispersal of bacteria and nutrients? You will simulate the fluid flows and particle dispersal in a contracting tube (possible methodologies: COMSOL, Matlab, C, etc.). The aim is to quantify which type of contraction mixes the nutrients and the bacteria better, helping to understand the basic principle behind gut functioning.

suitable as

• Master’s Thesis Condensed Matter Physics
• Master’s Thesis Biophysics
• Master’s Thesis Applied and Engineering Physics
• Master’s Thesis Biomedical Engineering and Medical Physics
• Master’s Thesis Matter to Life
• Master’s Thesis Theoretical and Mathematical Physics

Supervisor: Karen Alim

Hidden order in living flow networks

Der intelligente Schleimpilz Physarum polycephalum ist dafür bekannt, dass er seinen netzförmigen Körper anpasst, um komplexe Probleme zu lösen, wie z.B. den kürzesten Weg durch ein Labyrinth zu finden oder das Problem des Handlungsreisenden zu lösen. Wie kann ein hirnloses Lebewesen solch komplexe Aufgaben bewältigen? Unsere Antwort: mit Hilfe der Physik von Strömungsnetzen. Finde heraus, wie Physarum sein Netzwerk anpasst, indem Du seine Netzwerkarchitektur quantifizierst und nach Skalierungsgesetzen suchst. Vorraussetzung: Statistische Physik. Zu verwendende und zu erlernende Werkzeuge: Matlab-Programmierung, Physik von laminaren Strömungen in Netzwerken, möglicherweise auch Zellkultur- und Hellfeldmikroskopie.

The smart slime mould Physarum polycephalum is renowned for adapting its network-shaped body to solve complex problems like finding the shortest path through a maze or solving the traveling salesman problem. How can a brainless critter accomplish such complex tasks? Our answer: by using the physics of flow networks. Find out how Physarum adapts its network by quantifying its network architecture searching for scaling laws in flow networks. Prerequisite: Statistical Physics. Tools to be used and learned: Matlab programming, physics of laminar flows in networks, potentially also cell culture and brightfieqld microscopy.

suitable as

• Bachelor’s Thesis Physics

Supervisor: Karen Alim

Strömung im Weißen Raucher

White smokers are likely the cradle of life. Their pores and tunnels allow for pockets of catalytic sites that fuel reactions at the very origin of life. How do these catalytic sites form and grow with the smoker? You will map out the structure of two-dimensional smoker models generated in William Orsi’s lab at LMU. You will then use your maps to simulate the flow and transport of reactants with the flow through the smoker. Methods: Matlab, Image Analysis, Statistical Physics, Stochastic Processes, Fluid Physics. Prerequisites: Statistical Physics and fascination for the marvels of nature.

suitable as

• Master’s Thesis Condensed Matter Physics
• Master’s Thesis Biophysics
• Master’s Thesis Applied and Engineering Physics
• Master’s Thesis Matter to Life
• Master’s Thesis Theoretical and Mathematical Physics

Supervisor: Karen Alim

Current and Finished Theses in the Group