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Biophysics Theory and Experiment

Almost every area of modern biology, from molecular genetics to neuroscience, is being revolutionized by large scale, quantitative experiments. At the same time, developments in statistical mechanics and dynamical systems have prepared the physics community to address theoretical questions posed by more complex systems. From observing the dynamics of single biological molecules to building theories for the neural networks that make possible our perception of the world, there are myriad challenges for physicists and biologists willing to explore the boundary between their disciplines. We believe that the opportunities extend far beyond the application of known physical principles and experimental techniques to biological systems: biology offers us examples of very special physical systems, in which the state of the system represents information that has meaning to the organism and the dynamics of the system implements an "algorithm for living" that embodies functions essential for survival in a complex, fluctuating environment. We would like to make precise these intuitive notions of meaning and functionality. Our work is animated by the belief that, as in other areas of physics, the striking qualitative phenomena of life should have correspondingly deep theoretical explanations, and that this understanding ultimately will be tested only by a new generation of quantitative experiments.

Princeton University offers a unique environment for research and education at the interface between physics and biology. The Department of Physics has several faculty members with interests in biology, the Department of Molecular Biology has several faculty members who were educated as physicists, and many traditional biology laboratories on campus have students or postdocs with physics backgrounds. Barriers between departments are low, and reduced still further by multidisciplinary initiatives such as the Lewis-Sigler Institute, the Neuroscience graduate program, and the Biophysics certificate program for undergraduates. In just the past few years, the number of Princeton faculty with interests at the physics/biology interface has grown enormously, creating new opportunities for graduate students and postdoctoral fellows.


Robert H. Austin: What physics can do for biology, what biology can do for physics. Dynamics of proteins, DNA and cells William Bialek: Coding, computation and learning in the nervous system; noise and the physical limits to biological functions; statistical mechanics and information theory
Curtis G. Callan: Theoretical problems in genomics Michael Desai: Population genetics, evolutionary theory, and experimental evolution.
Thomas Gregor: Physical principles governing embryogenesis and cell communication from a systems perspective; measurement and analysis of protein and signaling molecule concentrations in living fruit fly embryos and amoebae communities (Arriving Feburary 2009) Matthias Kaschube: Collective behavior and emergent phenomena in biological systems; function and development of the nervous system; design principles in the visual cortex
William Ryu: Experimental biophysics, C. Elegans behavior Eva-Maria Schoetz: Biophysics of development and regeneration.
Joshua Shaevitz: How do bacteria acquire their specific shapes and structural properties? How do cells transport and organize their contents? Dynamic force measurements of proteins and living cells. David Tank: measurement and analysis of electrochemical signaling in the nervous system; neural integrators and short term memory; role of feedback in neural circuit dynamics.
A S S O C I A T E D    F A C U L T Y:
Michael J. Berry Information, efficiency, and adaptation in the neural code of the retina; statistical structure of natural scenes; eye movements and visual behavior Edward Cox The genesis of large scale spatial patterns in self-organizing biological systems
John Hopfield Statistical dynamics in biology: Algorithms, biophysics, and 'hardware' in neurobiological computation Saeed Tavazoie Studies of connectivity, dynamics, and evolution in biological networks through whole-genome and computational approaches
Samuel Wang Neurobiology:
(1) optical approaches to understandinglearning rules in single synapses, and
(2) theoretical approaches to understanding scaling relationships in brain anatomy
Ned Wingreen Modeling intracellular networks in bacteria: chemotaxis, quorum sensing, cell division, metabolism, circadian rhythms.
Leonid Kruglyak Experimental, theoretical and computational approaches using genome- scale data to understand how variation in DNA sequence is created by evolutionary forces and how this variation leads to all the observable differences among individuals within a species.  


 
 

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