CLS research focuses on research at the frontiers of physics with the living world that explore the nature of how living matter encodes and exploits adaptive responses from molecular to ecological length scales and physiological to evolutionary time scales. This requires the development of physics of adaptation in strongly driven systems. The research exploits recent capabilities to acquire large and high quality data in diverse biological systems and advances in artificial intelligence/machine learning. These approaches enable new strategies to study the physics of living systems. The research program is organized around three themes:
Origins of Adaptive Mechanisms
Genetic evolution plays out slowly over billions of generations yet builds flexible components that can adapt to perturbations on timescales as short as even a fraction of a generation. Traditional investigations into the structure of living systems have uncovered design principles in isolated examples of biological systems such as how circadian clocks regulate metabolism or how flagella provide motility in specific conditions. However, much less is known about the general principles underlying a system’s ability to reshape and acquire new functions through minimal mutations or physiological remodeling. We will exploit emerging technologies to study dynamical function across large libraries of mutationally accessible variants. Our goal is to create new physics, the statistical physics of evolving systems to understand mutability and flexibility.
Encoding information in morphogenetic systems
We seek to understand how developmental trajectories and physiological histories encode the shapes of living systems. In contrast to elastic solids, for example, the molecular interactions underlying cell and tissue mechanics are highly dynamic. These dynamics obscure how information can be preserved over time scales much longer than fluctuations in molecular expression and sub-cellular forces, and how information can be transmitted over the length scales of many cells. We argue that these hidden answers lie in the mechanochemical feedbacks that give rise to nonreciprocal interactions. These interactions enable living systems to exploit features of dynamical systems, such as slowing of dynamics near attractors and action-potential-like mechanochemical excitations, so that they function like mechanical analogs of neural networks. We seeks to identify new ways of looking at cell dynamics and the underlying mechanochemical feedbacks so that we may develop dynamical systems theory in the context of tissue morphodynamics. This theory will enable us to rationally engineer and control the shapes and dynamics of cells and tissues.
Computation in Complex Networks and Environments
Living systems perform computations to navigate an ever-changing dynamic world. The information that living systems receive from their external environment and their internal states can be high-dimensional, multifaceted, and presented over a wide range of time scales. The quintessential biological systems for processing information are networks––between molecules within cells, between cells within organisms, and between organisms within ecologies. These networks can be heterogeneous and multimodal, involving chemical, electrical, and mechanical signals. Computations must typically select among many possible actions in a timely manner to be effective. The complexity of inputs, computations, and outputs presents apparent challenges for organisms and real challenges for researchers trying to understand them. We seek to understand how living systems are adapted to perform computations in dynamic environments, and how learning shapes these solutions.