Research
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.
In plain EnglishBiological 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-organization 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. |
Tools and methods relevant for biologistsWe offer a variety of statistical and computational tools for experimental collaborators
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Genomic matter
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 detailed molecular states in many individual cells along the linear sequence of the DNA. In our group, we have the unique expertise to implement complex bioinformatics and machine-learning pipelines and can at the same time employ advanced methods from non-equilibrium statistical physics. This allows us to extend the boundaries of an emergent field of technology. We apply these theories to understand collective molecular processes underlying the behavior of cells with a current focus on the processes underlying aging. In synthetic biology, we also investigate how one can build information processing systems by coupling non-equilibrium processes to dynamic geometries.
Intelligent matter
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. These breakthroughs largely rely on deep neural networks comprising many interacting units. We use methods from statistical physics to understand emergent collective behavior in neural networks. We also study collective behavior and phase transitions in large ensembles of intelligent robots.
Multi-scale matter
Our understanding of physical matter relies on understanding how fluctuations propagate across spatial scales. Theorists have developed powerful tools, such as renormalization-group theory, that allow us to integrate out fluctuations on intermediary scales. In striking contrast, biological systems rely on a discrete hierarchy of dynamically coupled spatial scales, from molecules to mesoscopic condensates and cells in tissues. We develop theories of the emergent degrees of freedom that result from the multi-scale propagation of fluctuations. Applying these theories, we study how such systems control the propagation of fluctuations through spatial and temporal scales to perform biological tasks, such as signal and information processing.
From theory to clinical application
In personalized medicine, treatment decisions are tailored to individual patients. These decisions require making inferences on the stochastic evolution of the disease, such as the response of tumors to therapies. Together with clinical collaborators we study information-theoretic bounds on personalized treatment decisions and help develop therapeutic strategies in personalized cancer therapies.