Research Figure

In order to survive, organisms must solve a wide range of physics problems. Understanding the phenomena of life means understanding the emergence and integration of essential biological functions. To search systematically for unifying physical principles, scientists must work together in a highly interactive environment that supports theorists working in concert with experimentalists on multiple relevant systems, rather than pursuing separate projects in disparate fields of biology. To this end, the Center is organized around four general questions, each of which is illustrated by different biological examples and explored through close collaboration between theory and experiment. These include examination of animal behavior from the development of organisms to the locomotion of worms and flies; the emergence of collective phenomena in groups of molecules, genes, neurons, and organisms; the role of physical limits on information transfer and processing in the genetic code, neural circuits, cellular sensors, and genetic and biochemical networks; and the mechanisms via which biological systems arrive at a particular operating point from protein number to adaptive immunity.

Behavior from cells to animals.

What fascinates us first about life is the macroscopic behavior of organisms—the beauty of the forms that emerge during development, the purposefulness and intelligence of actions. If there are general physical principles governing biological function, then we should be able to see signatures of these principles in behavior itself. We examine the full complexities of nearly natural behavior behaviors in animals from worms to flies to rodents, searching both for universal dynamics at long time scales and for the origins of individuality. We probe sensorimotor integration with increasingly complex stimuli, ultimately leading towards an understanding of the closed loop nature of social interactions. Finally, we explore the behavior of embryos, to understand the extraordinary precision and reproducibility of development.

Bridging scales.

From the spectacular aerial displays by flocks of birds down to the co- ordinated movements of cells in a gastrulating embryo, the most striking phenomena of life are the result of interactions among thousands of individual elements. We need, in addition to the re- ductionist program of finding life’s elementary constituents, a synthetic program of understanding how biological function emerges from interactions among these constituents. We investigate the connection between sequence variation and structure/interactions in proteins, the emergence of collective states in genetic networks, the ordered movements in large groups of cells and organisms, and the electrical activity in collections of neurons in the brains of worms, insects, and mammals. We work towards a statistical mechanics of all these networks, combining maximum entropy methods with renormalization group ideas.

Physical limits to biological information.

Life depends on information, but the laws of physics limit the quality and quantity of information available to the organism. We clarify the physical limits to the gathering and transmission of biologically relevant information, exploring a wide range of systems to see how close they come to these limits, and turn observations of optimality into principles from which the properties of information processing networks can be derived. There are tantalizing hints that the same physical principles can be used to understand, and even to predict, the architecture and dynamics of such a broad range of biological systems, from bacteria to brains.

Finding the right operating point.

Biological function must match the environment, and this necessitates dynamics that implement this match. In different contexts, these dynamics are embodied in different mechanisms, which are the subject of largely distinct literatures: adaptation, learning, and evolution. These processes are enormously efficient, from our brain’s ability to learn from just a few examples to the more disturbing ability of bacteria to evolve antibiotic resistance. We search for common principles governing these dynamics, in examples ranging from the control of protein copy numbers to bacterial evolution, and from learning to immunology.