Theory of Biological Networks

Course with Participations of Group Members

Offers for Theses in the Group

Active response by adaptation of mechanical properties?

The complex behavior of the giant cell Physarum polycephalum finds its origin in the versatile transformation of liquid cytoplasm to gel-like actin-myosin meshwork making up the tube walls and vice versa. These active mechanics allow the organism to recycle its’ gel-like tubes at its rear and move it in its fluid form to the front, where it grows. Also, responding to stimuli like food, touch, or light, a change in cytoplasm viscosity seems to initiate the response. Yet, what are the mechanical properties of the liquid cytoplasm, and how much do they change upon stimulation? Do the mechanical properties of the cytoplasm change with the location in the cell? The measure of the mechanical properties of cells is challenging, but one can probe their visco-elasticity by tracking injected micron-sized beads - a technique called microrheology. You will measure the mechanical properties of cytoplasm extracts and grown Physarum, and quantify how they change upon stimulation by passive and active microrheology. Task 1: Establish cytoplasm extraction following previous work in the literature. Task 2: Perform passive microrheology on cytoplasmic droplets without and with stimulation (light, food, drugs) and analyze your data quantitatively. Task 3: Establish active microrheology to extract cytoplasm viscosity in different parts of Physarum’s network.

suitable as

• Bachelor’s Thesis Physics

Supervisor: Karen Alim

Mapping network theory to network function

Networks exist as our social network, the world wide web, traffic routes but also as flow networks making up the vasculature of animals, plants, fungi and slime moulds. While a lot of measures have been developed to describe networks in general it is not clear how these measures are predicting network function via network architecture. You will quantify physical networks of the slime mould and numerically generated model networks with network theoretic measures. Mapping to slime mould behaviour and model network flow and transport function will allow you to identify predictive network theoretic measures. You will learn network theory, Matlab. Prerequisites: statistical physics. Task 1 Collect network theoretic measures from the literature and Matlab packages Task 2 Apply network theoretic measures on slime mould and model data and map their property onto the network architecture Task 3 Correlate link by link network theoretic measure and flow/transport in the link under inspection to identify measures of predictive power.

suitable as

• Bachelor’s Thesis Physics

Supervisor: Karen Alim

Current and Finished Theses in the Group