William Bialek - US grants

The goal of the Methods in Computational Neuroscience Course at the Marine Biological Laboratory is to train 22 advanced students and post-doctoral fellows a year in the fundamental intellectual issues in computational neuroscience and to avail them with analytical and numerical tools to assist in their studies. The course has unique features that would be hard if not impossible to replicate in a university setting. First, it continues to attract and educate students from a broad range of established disciplines that impact upon neuroscience, including biology, cognitive science, computer science, engineering, mathematics and physics. Secondly, as a means to provide a coherent and relatively complete perspective on the growing field of computational neuroscience, students are exposed to a large visiting faculty in addition to receiving instruction and guidance from directors, scholars-in-residence and course assistants. It is unlikely that such a combination of faculty could be convened for an analogous course in a university setting. Lastly, the course provides students with access to state-of-the-art computational techniques and computer hardware fundamental to simulating neural systems from the detailed cellular through more abstracts systems level. The course is organized as an intensive four week lecture and laboratory series. The lectures progress along an increasing scale of computational complexity. They initially focus on the electrophysiology and chemical dynamics of individual neurons and synapses. This is followed by lectures on the dynamics and plasticity of local neuronal circuits and small nervous systems, topics that illustrate how single-cell properties shape computations by small networks. A final series of lectures addresses computations that involve sensory and perceptual issues in large neuronal systems, particularly cortex, and the emerging application of abstract models to provide a framework for understand these systems. The laboratory emphasizes the spectrum of tools necessary for successful research in computational neuroscience. On the one hand, the course provides training with general purpose simulation software as well as with simulation software that is specific to single cell dynamics, or to both single cells and large networks. On the other hand, students are trained in areas of applied mathematics, such as probability theory and matrix algebra, that are essential to formulating and solving problems in the field.

The overall goal of our research effort is to produce a quantitative description, and ultimately a mathematical theory, of how spatial patterns of gene expression are established in a developing embryo. Our experimental system is the Bicoid (Bed)morphogen gradient in the Drosophila embryo, the primary maternal determinant of the anterior-posterior axis. Our project brings together modern methods of experimental and theoretical biophysics with those of molecular biology and genetics to provide an integrated attack on (1) how the Bed gradient is established and maintained, (2) how is it scaled proportionately across embryos of different size, and (3) how is it read out to produce precise patterns of downstream gene expression. The dynamics of the formation and stabilization of the Bed gradient will be measured in living embryos expressing eGFP-Bcd. Image sequences from time-lapse two photon microscopy will be used, together with photobleaching methods and computational analysis, to assess passive and active contributions to gradient dynamics, to determine the protein half life of Bed, and to determine absolute concentrations of Bed in nuclei and cytoplasm at various stages of development. The scaling of Bed and gap gene expression patterns across closely related dipteran species that have bodies of different size but almost identical proportions will be analyzed using classical staining methods, extended by more sophisticated image processing methods. In addition, transformants expressing eGFP labeled-bicoid genes from different sized fly species will be expressed in Drosophla melanogaster to probe the biophysical mechanisms behind this scaling. Although genes appear to be activated by Bed at specific concentration thresholds along the length of the embryo, noise in transcriptional regulation places limits on the accuracy with which such thresholds can be marked. Theoretical work will define the nature of these limits in progressively more realistic models of each regulatory step. To test these models, the mean and variance of target genes (hunchback, orthodenticle) will be measured as functions of local concentration of Bed,both in wild type embryos, and in mutants and genetic mosaics where levels and activities can be artificially manipulated. Spatial correlations in the variance will also be measured, testing the hypothesis that communication among nuclei plays a role in suppressing noise and enhancing the precision of developmental boundaries. Our project addresses the fundamental question of how small changes in the concentration of signaling molecules produce robust control of cell fate. Precise read-out of such signaling pathways is required for normal development. Perturbations in signaling are associated with birth defects and cancer in adults.

Theoretical physics is the search for concise mathematical models of Nature. It has had great success in dealing with the inanimate world: we can now predict in quantitative detail what the most sensitive experiments will observe inside the nucleus and in the cosmos at large. By contrast, even as our ability to observe and measure improves dramatically, the phenomena of life remain largely unpredictable, even in their most qualitative aspects. In this project, a group of theoretical physicists will engage with students and postdoctoral scholars in an effort to close this gap; in short, to construct a theoretical physics of biological systems. The proponents have explored phenomena that span the tree of life: from metabolism in bacteria, through the determination of cell fate in embryonic development, to coding and computation of sensory information in brain. They have identified broad theoretical problems which cut across the traditional biological divisions of organism and system: Do living organisms operate near the limits set by the laws of physics as they gather and process information? Can we learn the detailed microscopic "model" of an organism, its "wiring diagram", from the finite set of observations we can make on how it behaves? How do organisms set the parameters that govern their function (i.e. how do they learn from experience)? These questions can all be given a mathematical form which guides a search for answers in terms of general principles, in the tradition of physics that will apply across disparate biological domains. The time is right to bring the beautiful phenomena of life under the powerful predictive umbrella of theoretical physics. Just as cosmology has progressed, in roughly one generation, from wild speculation to a precise framework for analyzing a rapidly expanding set of observations, the proponents believe that the intimate interaction between theory and experiment can lead to a new and deeper physics of biological systems. It is the creation of this scientific culture, where theory and experiment are equal partners in the exploration of life that is the fundamental intellectual merit of the project. It is not just the boundaries of academic disciplines, but our view of ourselves, which is at stake. A very important aspect of this project will be the training of a new generation of physicists for whom the development of a theoretical understanding of biological systems is a central part of their discipline. The graduate students and postdoctoral scholars who pass through the group will learn by example how to pursue that goal in a way consistent with the intellectual rigor and traditions of physics. They will eventually move on to faculty positions of their own, where they will transmit this attitude to new generations of students. More broadly, all project personnel are deeply engaged with new educational initiatives, addressing levels from the first year of college to advanced PhD students, which provide a more complete guide to the evolving, multidisciplinary intellectual landscape.

The participants in the project will assemble into subgroups to attack instances of these problems. The individual projects will have unusual scope: as an example, the question whether we can capture the complex statistics of biological behavior in a learnable mathematical model can be asked in very similar terms both of spiking retinal neurons, and of the antibody sequence repertoire of individual zebrafish. If the answer is yes and the models have similar mathematical structure, one will have learned something novel and deep about what makes evolved, living, systems different from the inanimate world. Since these questions can only be answered in the light of accurate data, the work will involve a close partnership with many experimental groups in fields ranging from bacteriology to human perceptual psychology. The product of these interactions will be the design of novel experiments and the creation of novel data analysis methods in order to address clearly formulated mathematical questions of broad significance.

This project is being jointly supported by the Physics of Living Systems program in the Division of Physics, the Cellular Cluster and the Systems and Synthetic Biology in the Division of Molecular and Cellular Biosciences, and the Neural Systems Cluster in the Division of Integrative Organismal Systems.

This collaborative research project, consisting of four institutions (Rice, Yale, UIUC and Princeton) aims to continue the Physics of Living Systems Student Research Network (PoLS SRN). This network has been in existence for four years and has had a dramatic impact on many graduate students, both in the US and abroad, working on the application of physical science techniques to living systems. These students now can participate in a global community that can help deal with the many complex issues involved in conducting research in such a new and inherently multidisciplinary field. These issues range from proper training, to gaining a broad perspective, to accessing technical expertise that may not be available at their home institution. In addition to the obvious broader impacts related to training of a research workforce, there are other broad impacts of this plan. Via the interaction of one of the PoLS nodes (Rice) with the biomedical community in Houston, students and faculty will be exposed to possible avenues whereby physics can contribute to human health issues. Funds to attract students from under-represented groups to network meetings will be available through the new funds administered by the newly proposed network coordinator. Also deas vetted by the PoLS SRN will be adapted to create student networks in other areas of science and engineering.

There is by now little disagreement with the general notion that concepts and methods from physics have been a critical contributor to the increased understanding of the living world, and that its importance will be growing as the scientific world moves toward an ever more quantitative and predictive form of biology. Thus, the physics community clearly needs to train a new generation of scientists who can lead this effort, scientists who have the right mix of physics/mathematics rigor and broad knowledge of living systems from molecular scales on up. The PoLS SRN aims at creating a community of graduate students who can collectively help themselves and their mentors accelerate and enhance this training process. This is being done by a mix of in-person and virtual modes of communication, and this proposal is a plan to continue and expand these efforts; it will reach more students, improve the social networking portals, and make use of the complementary research agendas of the different network nodes to provide broad technical expertise. Doing all of this, will boost the intellectual level of the entire research field and convince the best students that the Physics of Living Systems is truly the most exciting research frontier in 21st century science.

This project is being jointly supported by the Physics of Living Systems program in the Division of Physics, the Molecular Biophysics Cluster in the Division of Molecular and Cellular Biosciences, the Chemistry of Life Processes program in the Division of Chemistry, and the Cellular Dynamics and Function Program in the Division of Integrative Organismal Systems.

The development of quantitative theories of learning, memory, and prediction is fundamental to understanding human cognitive processing. This workshop, to take place in Arlington VA, May 8-9, 2014, tackles a key scientific need: to integrate modern complex systems and network approaches with understanding cognitive function. Predictive models of higher order cognitive processes could inform the development of neuroprosthetics, facilitate advances in brain-computer interfaces, and assist in the construction of intervention protocols for cognitive deficits that accompany neurological disorders and psychiatric disease.


Understanding how the human brain works has emerged as a major international focus of research in the coming decade, identified as such in President Obama's State of the Union Address in February 2013 and further developed in President Obama's BRAIN initiative announced on April 2, 2013. This workshop will bring together systems neuroscientists, cognitive scientists, applied mathematicians, and theoretical physicists. The aim is to identify a set of achievable goals that integrate dynamic, quantitative theories of cognition with neuroscientific and theoretical avenues of research.

Project: Our perception of the world seems a coherent whole, yet it is built out of the activities of many thousands or even millions of neurons, and similarly for our memories, thoughts, and actions. It seems difficult to understand the emergence of behavioral and phenomenal coherence unless the underlying neural activity also is coherent. But if the neurons are not independent, how do we describe their collective activity? In this work, The PI takes several approaches to this problem. In each case the PI is reaching for a theoretical framework with a generality that transcends the details of particular systems, but in each case the work is grounded by intimate collaboration with several experimental groups. It is now conventional to assert that the emergence of new, larger data sets in the study of biological systems creates a need for new and more efficient methods of data management and analysis. Thus, it is widely expected that the BRAIN initiative, which will provide substantial resources to advance our ability to record simultaneously from many neurons, will also have a significant "computational" component related to the storage, handling, and analysis of the huge bodies of data that will be generated. It is much less widely appreciated that making sense of the collective behavior in systems with many interacting degrees of freedom requires theory, not just analysis. The goal of this work is to develop such a theoretical framework.


The PI's collaborators are using multi-electrode arrays to monitor activity in populations of ganglion cells in the vertebrate retina, as well as optical methods to monitor activity in mammalian cortical and hippocampal circuits, and in the whole brain of the nematode C elegans. The PI will use maximum entropy ideas, which have had some success in such problems, to build models of the joint distribution of activity across the hundreds of neurons in each of these experiments. The entropy of this distribution determines the capacity of the network to carry information, and the geometry of the distribution captures intuition about network function. Beyond exploring the distribution of activity, the PI will construct a "thermodynamics" for these networks that will allow to place real networks in a phase diagram of possible networks. Finally, the PI will turn to dynamical aspects of network behavior, focusing on the encoding of predictive information. The PI's research is well integrated with teaching at the graduate and undergraduate level, and there will be a special effort to create a "current topics" course that brings issues at the interface of physics and biology to the attention of a broader group of students. In addition, the PI is engaged is a series of public outreach efforts, which will culminate in a book for a general audience.

A physical and quantitative understanding of the processes of life and living systems must describe biological functions in different contexts and across a wide range of systems. Through this Physics Frontiers Centers award the Center for the Physics of Biological Function (CPBF) will probe the unifying physical principles that govern the emergence of essential biological functions. A joint effort of Princeton University and the Graduate Center of the City University of New York, the CPBF will bring together theorists and experimentalists to study biological functions in a quantitative and systematic manner across a range of diverse systems. From cells to organisms to groups of organisms the CPBF will explore functional behavior, the mechanics of biological networks, the impacts of limited physical information within systems, and how biological systems set efficient parameters. The CPBF will foster the training and development of a new and diverse generation of scientists at the interface of physics and biology. It will expand successful existing interdisciplinary programs for undergraduates and graduate courses, and develop a dedicated undergraduate summer school. In addition, the center will host seminars and symposia that will connect CPBF with the larger scientific community and will engage in public lectures in New York City.

The CPBF research activities are organized around four themes, each of which will involve close collaboration between theory and experiment. The first focuses on animal behavior from the development of organisms to locomotion. The second considers the emergence of collective phenomena in groups of molecules, genes, neurons, and organisms. The third theme studies the role of physical limits on information transfer and processing in the genetic code, neural circuits, cellular sensors, and genetic and biochemical networks. The fourth theme examines the mechanisms through which biological systems arrive at a particular operating point from protein number to adaptive immunity.

This Physics Frontiers Centers award is co-funded by the Division of Physics and the Division of Chemistry within the Directorate of Mathematical and Physical Sciences, and by the Division of Molecular and Cellular Biosciences and the Division of Emerging Frontiers within the Directorate for Biological Sciences as part of Understanding the Brain activities.

Project Summary The theory hub put forward in this proposal will work to translate successful and powerful approaches to describing emergent collective behavior in physical systems so they can be applied to the brain. Working closely together, the three theorists will develop methods for finding and quantifying the relevant modes of population activity in the brain, both in instantaneous snapshots of activity and activity as it evolves in time. Methods will be tested in a wide range of neural systems at different processing stages and scales: from salamanders to rodents to humans, from the retina to the cortex, from tens to thousands of cells. The approach will be validated by checking that the neural code can be read out with high fidelity even after being compressed into a much smaller subspace. The project will produce data analysis code that will be made available for neuroscience researchers to use on their own data, in addition to the results of the analyses of the particular systems studied. The neural code is inherently collective; while single neurons execute sophisticated computations, hundreds to thousands of neurons are utilized to sense the environment and drive behavior in even the simplest organisms. Although the past hundred years have yielded substantial progress in neuroscience, only recently have researchers had the capacity to record from complete neural populations - that is, to view the collective behavior of a functioning neural network. With these rapid experimental advances, there is an urgent need for complementary theoretical and computational approaches to guide the exploration of emergent behavior in large groups of neurons, allowing one to turn `big data' into `big ideas'. This proposal outlines a path towards a new theoretical framework for finding and quantitatively analyzing collective phenomena in the brain that underlie sensory coding, the representation of space, prediction, and ultimately drive behavior. The project draws heavily on the success of so-called renormalization group approaches in theoretical physics that revolutionized the understanding of collective phenomena in physical systems, and sculpted much of the progress in statistical physics in the second half of the twentieth century. The methods explored in this proposal generalize such techniques so they can be applied to a much wider range of problems. The methods developed by this theory hub based on the renormalization group will be applicable to a wide range of neural data since they are explicitly designed to generalize techniques from theoretical physics to a much broader setting. Indeed, a larger goal of the approach is to search for universality in collective behavior in the neural code. The techniques proposed are relatively straightforward to execute and will provide a fundamental methodology for interrogating high-dimensional data in fields as diverse as behavioral neuroscience and biophysics. The new techniques will also be taught as part of the three theorists' ongoing efforts to expose incoming graduate students in biological sciences to quantitative methods in biology.