unlike foxes or hedgehogs, our approach is that of the slime mold - spreading wide and seemingly thin; in search of food and fun. the connections, often unseen at first, emerge naturally.
Our research program aims to understand the organization and function of living matter using the tools and perspectives of complexity science, disordered systems, statistical mechanics and dynamical systems. We view biological entities as a truly unique state of active, adaptive matter; cells, biological tissues, coordinated animal groups and interacting populations (ecology) are a form of complex material and dynamical system with emergent properties that arise from mechanical, biochemical and socially mediated interactions between individuals.
We pursue this perspective via two complementary approaches:
(i) We construct de novo, synthetic mimics of living matter to study the minimal ingredients for self- assembly, computation, feedback, and evolvability. This serves as a kind of synthetic biology from a physical viewpoint and is likely to shed light on early evolution and the transitions therein. The focus is on understanding and duplicating, using synthetic inanimate components, the emergence of specific, quantifiable, and characteristic properties of living matter, rather than understanding how life itself emerges from its basic molecular building blocks. This is an important distinction from in vitro reconstitution approaches that have characterized many biophysical studies. The problem then naturally lends itself to the framework of statistical physics in which the key properties of the organization of living matter may be described.
(ii) We probe the physical basis of organization in biological systems. This represents a kind of physical biology which will allow us to quantitatively identify the broadly universal features of biological systems, similar to statistical mechanics approaches in physics. The approach is to make careful measurements on well-chosen biological phenomena and to use theories rooted in non-equilibrium statistical physics and dynamical systems to understand the data. Often, the experimental data requires the development of new theoretical ideas.
Our ultimate goal is to weave these two threads into a tapestry that captures many essential features of a theory of the organization and dynamics of living matter.
We pursue this perspective via two complementary approaches:
(i) We construct de novo, synthetic mimics of living matter to study the minimal ingredients for self- assembly, computation, feedback, and evolvability. This serves as a kind of synthetic biology from a physical viewpoint and is likely to shed light on early evolution and the transitions therein. The focus is on understanding and duplicating, using synthetic inanimate components, the emergence of specific, quantifiable, and characteristic properties of living matter, rather than understanding how life itself emerges from its basic molecular building blocks. This is an important distinction from in vitro reconstitution approaches that have characterized many biophysical studies. The problem then naturally lends itself to the framework of statistical physics in which the key properties of the organization of living matter may be described.
(ii) We probe the physical basis of organization in biological systems. This represents a kind of physical biology which will allow us to quantitatively identify the broadly universal features of biological systems, similar to statistical mechanics approaches in physics. The approach is to make careful measurements on well-chosen biological phenomena and to use theories rooted in non-equilibrium statistical physics and dynamical systems to understand the data. Often, the experimental data requires the development of new theoretical ideas.
Our ultimate goal is to weave these two threads into a tapestry that captures many essential features of a theory of the organization and dynamics of living matter.