To establish complex structures, biological systems rely on a constant flux of energy from their environment and they therefore operate far from thermal equilibrium. The ensuing phenomenology often is highly complex such that a deep going understanding poses significant challenges to theoretical physics. By collaborating with experimental groups we combine novel experimental possibilities, in particular in single-cell genomics, with the methodology of non-equilibrium statistical physics, data science and artificial intelligence to unveil mechanistic principles governing active biosystems. At the same time this research gives rise to interesting and challenging problems at the frontier of theoretical physics.
Non-equilibrium statistical physics of single-cell genomics
Recent technological breakthroughs in single-cell genomics now give rise to the opportunity to study biological systems with unprecedented molecular details. These technologies allow taking snapshots of molecular states in many individual cells with the resolution of single molecules along the linear sequence of the DNA. Biological function relies, however, on processes in space and time and the interaction between many loci on the mesoscopic scale.
In our group we have the unique expertise to implement complex statistical and machine learning pipelines necessary for the interpretation of such experiments and can at the same time employ advanced methods from non-equilibrium statistical physics. This allows us to use renormalisation group theory, field theory and spin glass theory to overcome conceptual limitations of this emerging technology and to understand the collective molecular processes underlying the behaviour of cells during development, regeneration and ageing.
In plain English
Biological systems continuously use energy to build complex structures in space and time. Examples are the complex interactions between molecules among each other and with the DNA or the self-organisation of cells into complex organs. Understanding the rules governing such systems is pivotal for developing therapeutic strategies for diseases related to the breaking of these rules. Together with experimental collaborators researchers in our group apply methods from theoretical physics, data science and artificial intelligence to understand the working principles underlying biological systems.
Active epigenetic processes in development, ageing and rejuvenation
During development, the differentiation of cells to different lineages is closely associated with the establishment of specific epigenetic marks along the sequence of the DNA. During ageing, temporal changes in these patterns have been found to be tightly correlated to biological age and predictive of the time of death. The mechanistic basis underlying these processes is, however, poorly understood. The recent advent of single-cell sequencing technologies for the first time give the opportunity to probe these processes with unprecedented molecular resolution in vivo. Biological function, however, relies on collective processes on the cellular scale which emerge from many interactions on the microscopic scale. But what can we learn about such collective processes from detailed empirical information on the molecular scale?
Concepts from non-equilibrium statistical physics provide a powerful framework to understand collective epigenetic processes. We combine data obtained from novel single-cell technologies with methods from non-equilibrium statistical physics to understand collective epigenetic processes regulating cellular behaviour during development and ageing.
Statistical physics of deep learning
In recent years, advances in our ability to train deep neural networks and to collect large amounts of data have led to breakthroughs in artificial intelligence. We are, however, lacking a general understanding of how and why deep learning works. We use methods from statistical physics allow us to unveil general principles underlying the functioning of deep neural networks.
Propagation of fluctuations through scales of biological organisation
Physical systems are defined by microscopic symmetries which are broken on the macroscopic scale. In striking contrast, biological systems rely on a hierarchy of dynamically coupled spatial scales, from molecules to mesoscopic condensates and cells in tissues, each giving rise to collective degrees of freedom. We are interested in how such systems control the propagation of fluctuations through spatial and temporal scales to perform biological tasks, such as sensing or decision making.
Together with our collaborators we apply these general concepts to fundamental questions in cell biology. We also draw on experiments on social insects exploiting the unique non-equilibrium phenomenology of such systems.
Morphogenesis and clonal evolution
The development and maintenance of tissues relies on a tightly balanced coordination of cell proliferation and differentiation. How do these cells collectively build and maintain complex structures such as the heart or the pancreas? With the recent advent of transgenic mouse models we are now in a position to study collective fate decisions on a functional level. On this level, stem cell populations are often considered as an ensemble of statistically equivalent cells, such that the methodology of non-equilibrium physics has been successfully employed to understand their behaviour.
However, major questions in stem cell biology remain elusive: How can we understand the surprising degree of heterogeneity that appears to be characteristic of many stem cell populations? How can we use lineage tracing to understand the morphogenesis of developing tissues, where tissue deformation and cell migration often lead to the fragmentation and merging of labelled clones? To answer these questions we develop theoretical frameworks to facilitate the application of functional assays in novel contexts and to unveil the functional regulation of stem cell behaviour. In collaboration with experimental groups we use these methods to unveil stem cell fate behaviour in various tissues and investigate universal principles of cell fate regulation.
Phenotypic heterogeneity and epigenetic plasticity
A high degree of specialization makes organisms vulnerable to environmental changes. How can organisms specialize to an ecological niche and at the same time be able to survive in a variety of different conditions? Many bacteria and viruses counteract specialization by switching between several phenotypic states, and even stem cell population seem to be much more heterogeneeous than previously thought. As an example, phenotypic switching is a strategy commonly employed by pathogens to evade a host's immune system. Some types of bacteria also stochastically switch between phenotypic states to minimize the risk of population extinction due to an attack with antibiotics, and syncitial stem cells in the murine testis stochastically interconvert between various stages of differentiation.
Inspired by these phenomena, we are interested in how genetic diversity, phenotypic heterogeneity and stem cell plasticity are regulated across scales.The apparent lack of microscopic symmetries in these systems give rise to challenging and interesting questions at the frontier of non-equilibrium statistical physics.