Current Research Projects
Below you’ll find a list of research projects our team are currently working on. You may as well read about our past projects.
Biological cells live in a noisy world and process information in a stochastic manner. As model system, we study chemotactic search of biological cells to understand its robustness in the presence of sensing and motility noise. We are particularly interested in both the information theory of optimal sensing as well as the biochemical implementation of cellular decision making.
Motile cilia are slender cell appendages found on the surface of mammalian airways, brain ventricles, and the oviduct. These cilia beat rhythmically, often in a coordinated fashion, to pump fluids such as mucus or cerebrospinal fluid effectively. This metachronal coordination in cilia carpets , similar to a Mexican wave in a soccer stadium, presumably originate from hydrodynamic interactions between neighboring cilia. Yet, basic questions like “how does the direction of metachronal waves is set by the shape of the cilia beat” are open.
We study metachronal coordination in cilia carpets from a physicists' perspective, modeling each cilium as a noisy phase oscillator, coupled to its neighbors (similar to the Kuramoto model with local coupling). Using multi-scale simulations and theory, we answer questions of multi-stability of metachronal waves, as well as their robustness to active fluctuations.
Valve pattern formation in diatoms
Diatoms are a large group of single-celled algae that are prominent for their ability to deposit silicic acid into intricate patterns of biosilicified cell walls: diatoms live in glass houses. Their cell walls display a bewildering architectural complexity with hierarchical pore patterns, ribs and spikes. Yet, the physical mechanisms that shape these lightweight structures remain poorly understood. Recent computational models can explain biosilification at the micro-scale, but not yet the emergence of regular patterns on the meso-scale.
My project combines theory and experiment to understand the mechanism of valve pattern formation, using Thalassiosira pseudonana as a model organism. Currently, we investigate an unusual reaction-diffusion model that generates growing rib patterns as observed experimentally inside the silica deposition vesicle of dividing diatoms, where new valves form de novo. In parallel, we perform electron microscopy of developing valves and establish automated image analysis algorithms to characterize the morphodynamics of biosilica valves at different developmental stages.
Our aim is to identify possible mechanisms of self-organized pattern formation at the meso-scale during cell wall biogenesis in diatoms.
Mathematical modeling of developing muscles
How to build a muscle? Striated muscles are organized in pseudo-crystalline structures, called sarcomeres. They contain three different types of filaments. Sarcomeres consist of polar actin filaments crosslinked at their structural plus ends to the Z-disc, while their minus ends face the center of the sarcomere, where they interact with anchored bipolar myosin molecular motors. The giant protein Titin serves as an elastic spring that links the actin and myosin filaments. We do not understand how these elaborate structures self-assemble during muscle development.
We develop theoretical models of how cytoskeletal filaments in an initial disordered acto-myosin bundle interactions spontaneously form periodic patterns and, eventually, highly regular sarcomeres. Our approach combines mean-field models and agent-based simulations to investigate two putative mechanisms of myofibrillogenesis.
Figure from Dasbiswas et al., 2018
Image analysis of developing muscles
We are researching the development of myofibrils during myofibrillogenesis, when the proteins that make up muscles self-organise into sarcomeres, and eventually form in to highly ordered crystal-like structures.
In order to study the formation of myofibrils, we are developing Matlab-based feature detection algorithms. Existing feature detection algorithms work well to track fully developed muscles, however, these typically rely on the very regular final patterns to identify sarcomeres. This means these algorithms fail to detect sarcomeres well during myofibrillogenesis. Instead, we use a more general approach combining information from multiple channels from fluorescence microscopy to identify both individual sarcomeres and larger myofibrils. We apply our algorithms to multi-channel fluorescence images of Drosophila during myofibrillogenesis, provided by experimental collaboration partners from the Schnorrer lab (IBDM, Marseilles).
By tracking large numbers of sarcomeres in these images, we hope to identify key features revealing how muscles change during the fundamental formation stages, and identify the mechanisms used to build muscles.
Information processing in artificial cells
Can artificial cells process information? The PEN-DNA reaction provides a useful toolbox to create chemical reaction networks. While it can ultimately be used to create complex biological signal processing systems, we focus, as a start, on one single autocatalytic reaction inside a single compartment. Mathematical and computational modelling of the system can help to obtain a better understanding of the spatio-temporal dynamics and to calibrate model parameters. This can then be used to simulate larger networks in silico in the future. We are collaborating closely with the Tang group at the Max-Planck Institute of Molecular Cell Biology and Genetics Dresden, who perform the experiments.