Theory of Biological Networks

Course with Participations of Group Members

Offers for Theses in the Group

Cooking up life in the white smoker

White smokers are likely the cradle of life. Their caves and tunnels allow reactants to accumulate at catalytic sites to start the reactions at the origin of life. How do these catalytic sites form and grow with the smoker? You will grow two-dimensional smokers on a microfluidic chip and measure the smoker geometry from your data. You will learn about microfluidics, microscopy, Matlab, image analysis and the fluid physics of laminar flow in flow networks. Prerequisites: Statistical physics and fascination for the wonders of nature. Task 1 Learn and improve the experimental setup to control the microfluidic device for smoker formation Task 2 Experimentally explore the morphologies of smokers as you vary inflow rate and inlet geometry Task 3 Quantify smoker morphology with respect to the flow network geometry within the smoker. Built a hypothesis which physical conditions favor the formation of caves as catalytic sites.

suitable as

• Master’s Thesis Biophysics

Supervisor: Karen Alim

Dynamic patterns of plant development

Plants mainly develop all their organs like leafs and flowers continuously at their stem. How can the patterning underlying the positioning of these organs at the plant shoot be so robust? You will investigate with numerical simulations the patterning at the tip of the plant shoot and study how variations in cell size and cell number at the shoot impact patterning. You will learn python, pattern formation, mechanics. Prerequisites: Enjoy programming Task 1 Generate different cell geometries on a spherical shell by getting to know our plant simulation code Task 2 Run our pattern formation simulation on the cells and program measures that characterize the patterns you observe Task 3 Quantify how cell geometry impacts pattern formation

suitable as

• Bachelor’s Thesis Physics

Supervisor: Karen Alim

How our veins adapt

Our vein network is important for transporting oxygen and other important resources in our body. Our vein network is not static but continuously adapts its structure and the thickness of individual veins. What laws govern the dynamics of individual veins? What role does the flow in the cores play? You will analyse data from vein networks on a microfluidic chip in order to quantitatively record the dynamics of the veins. For this purpose, you will further develop image analysis methods and adapt them to our completely new data of veins. Task 1 Learn how to work with the machine-learning based image analysis tool ilastik Task 2 Quantify vein diameters and vein network morphologies from existing vein microfluidic data

suitable as

• Master’s Thesis Applied and Engineering Physics

Supervisor: Karen Alim

In vitro and in silico assessment of the microvascular network under fluid flow

Blood vessels deliver oxygen and necessary nutrients to all tissues in the body. During embryonic development, formation of the first primitive vascular labyrinth through vasculogenesis is followed by vascular network expansion and maturation through angiogenesis. The latter comprises formation of new blood vessels out of pre-existing ones and the subsequent vascular remodeling in order to adapt the vascular network to the specific metabolic demands of the surrounding tissue. Onset of blood flow into the primary vascular network is known for having major impacts on vascular remodeling ensuring the network’s efficiency through structural normalization and hierarchy. Owing to the fact that vessel remodeling is often found impaired in pathological angiogenesis, many therapeutic methods have been developed to interfere with the vessel growth and normalization strictly affecting vascular morphology. However, the exact correlation between vessel morphological variations and alterations in blood flow dynamics has not been fully elucidated. Moreover, while many animal models have been developed to assess the effect of blood fluid flow on vascular morphology, translating their outcomes to human vascular system is often challenging. Employing the recent state-of-the-art microfluidic techniques for human organ-on-a-chip developments, one can grow perfusable human capillaries on polymeric chips for direct investigation of vascular structural development under flow through real time observations. Taking advantage of our in-house established human microvasculature on PDMS chip models, this project is aimed to assess the impact of fluid flow on the microvascular network architecture and vessel caliber. To this end you will be working with existing 2D network image datasets analysing the microvascular structural remodeling in response to the fluid flow.

suitable as

• Master’s Thesis Biomedical Engineering and Medical Physics

Supervisor: Karen Alim

Self-Organizing flow networks

Flow transport in complex networks is abundant in biology and engineering, from the vasculature of animals, to the hyphal networks of fungi, to the random networks making up batteries. Living systems continuously adapt their network morphology in response to stimuli; local feedback leads to self-organized structures optimal for fluid transport. In contrast, transport through engineered random networks is inefficient being limited to a few fast lanes. The current strategy to optimize flows in engineering is to build, branch by branch, an optimized network morphology. Here, we will lay the foundation for an entirely new bio-inspired approach namely to self-organize networks for optimal transport by local feedback. You will develop theoretical models to design feedback between chemical stimuli and network morphology that optimize for transport. The core idea is to optimize for transport by evening out flows as the network morphology is changed in response to a chemical transported by the flows pervading the network itself. Task 1 At first you will focus on simple network motifs as a Y-shaped and H-shaped structure. Solving theoretically for transport and absorption of chemical reagents in these network motifs will allow to deduce the physical parameters that allow to optimize transport in these network motifs. Task 2 You will then test your analytical predictions within a simulation of adaptive flow networks.

suitable as

• Master’s Thesis Theoretical and Mathematical Physics

Supervisor: Karen Alim

Storing memories in vascular architecture

Arteries making up our vasculature are dynamics and continuously change their diameters in response to the flows pervading them. In fact, those changes allow the vasculature to store memories about flows in the past. Yet, on which time scales are memories made and forgotten? You will numerically simulate memory formation in a model of adaptive networks. Numerically probing for memories at different time intervals you will determine the physical parameters that govern how long it takes to form a memory and when memories are forgotten. You will learn about statistical physics of disordered systems, Matlab. Prerequisite: Good memory :) Task 1 Implement local artery dynamics into our Matlab code Task 2 Numerically write and probe memory formation while varying model parameters Task 3 Derive scaling relation on how model parameters determine the time scales of memory formation and loss.

suitable as

• Bachelor’s Thesis Physics

Supervisor: Karen Alim

Structures for the Origin of Life

Weiße Raucher sind wahrscheinlich die Wiege des Lebens. Ihre Höhlen und Tunnel ermöglichen es Reaktanten an katalytischen Stellen anzusammeln, um damit die Reaktionen am Ursprung des Lebens in Gang setzen. Wie entstehen und wachsen diese katalytischen Stellen mit dem Smoker? Sie werden zweidimensionalen Smoker auf einem Mikrofluidik-Chip wachsen lassen und aus Ihren Daten die Rauchergeometrie vermessen und damit Strömung und Transport im Netzwerk berechnen und mit Ihren Daten vergleichen. Sie lernen Mikrofluidik, Mikroskopie, Matlab, Bildanalyse und die Strömungsphysik der laminaren Strömung in Strömungsnetzen kennen. Voraussetzungen: Statistische Physik und Faszination für die Wunder der Natur.

suitable as

• Master’s Thesis Applied and Engineering Physics

Supervisor: Karen Alim

Vasculature remodeling following a ‘stroke’

Our vasculature forms redundant connections to robustly provide supply via the stream pervading our vasculature. Yet, vessel occlusions, strokes, threaten the steady supply by interrupting transport. Which network geometry poses a vasculature at risk in the event of a stroke? You will quantify network dynamics in existing data of both or network model organism Physarum polycephalum and human vasculature culture on a chip. Mathematically estimating the importance of individual veins within observed networks will guide you to identify which network geometries pose risks. You will learn about fluid flows in networks, Matlab. Prerequisites: Electrodynamics Task 1 Learn how to calculate resistances and network equivalent resistances in flow networks Task 2 Quantify vein resistances in different experimental data of vasculature. Preliminary code is provided. Task 3 Compare your predictions to real data of vasculature remodelling. Built a hypothesis which network geometries are at risk. Calculations of network resistances on small model networks may help you here.

suitable as

• Master’s Thesis Biophysics

Supervisor: Karen Alim

Wie Adern wachsen und sich anpassen

Unser Adernetzwerk ist wichtig um Sauerstoff und andere wichtige Ressourcen in unserem Körper zu transportieren. Dabei ist unser Adernetzwerk nicht statisch sonder passt sich in seiner Struktur, in der Dicke einzelner Adern fortwährend an. Welche Gesetzmäßigkeiten folgt die Dynamik einzelner Adern? Welche Rolle spielt dabei die Strömung in den Adern? Du wirst Daten von Adernetzwerken auf einem Mikrofluidik Chip analysieren, um die Dynamik der Adern quantitativ zu erfassen. Dazu entwickelst Du Bildanalyseverfahren weiter und passt sich auf unsere ganz neuen Daten von Adern an.

suitable as

• Bachelor’s Thesis Physics

Supervisor: Karen Alim

Current and Finished Theses in the Group