David J. Schwab, Ph.D. - US grants

DESCRIPTION (provided by applicant): What basic mechanisms underlie development and how can we manipulate, disrupt, or correct them? I propose to study the collective behavior of Dictyostelium discoideum - a classic model organism for cell-cell signaling - focusing on how single-cell dynamics influence and give rise to the behavior of the aggregate. During starvation, Dictyostelium cells periodically secrete the chemoattractant cAMP, inducing production of cAMP in other cells. The result is a wavelike signal relay, and, ultimately, cellular aggregation. This transition from single-celled to multicellular life provides an ideal system to utilize my background in condensed matter physics to address a fundamental question in biology. At Princeton, I will collaborate closely with Thomas Gregor's lab, which developed the first FRET reporter of intracellular cAMP concentration. Their quantitative experiments provide a unique opportunity to connect the behavior of individual cells with the consequent fate of the population. The guidance of my mentor Ned Wingreen, an expert in bacterial chemotaxis, gradient sensing, and cell-cell communication, will be an invaluable resource. Through analysis of quantitative single-cell experiments, I have developed a model of the single-cell response to extracellular cAMP. My preliminary studies indicate that each cell behaves as an excitable system (a prime example is a spiking neuron). I will extend this model to study collective spatial dynamics mediated by diffusion of cAMP. In preliminary studies, I considered a mean-field situation, as in a well-mixed perfusion chamber, finding an intriguing dynamical quorum-sensing transition. To include spatial dynamics, I will first construct a model of spatial gradient sensing - unifying the concepts of excitability, adaptation, and directional sensing - guided by microfluidics-based experiments performed by the Gregor Lab. With this model, I will quantitatively reproduce aggregation through simulations of chemotactic cells. I will compare aggregation fidelity, measured by the size and spatial distribution of mound centers, against other chemotaxis mechanisms lacking excitable dynamics. I will then explore ways to disrupt faithful aggregation and make predictions for the behavior of various Dictyostelium mutants that can be tested in the Gregor Lab. Answering the questions in this proposal requires the right balance between using my background in condensed matter physics theory and engaging with the details of a complex biological system. The proposed project is therefore ideal for my transition to become an independent researcher working in the field of quantitative biology-it will allow me to use my established skills and to develop new ones. The environment at Princeton, both due to the guidance of my mentors, Professors Ned Wingreen and Thomas Gregor, and the greater community of quantitative biologists, provides an ideal setting to develop into an effective independent investigator. PUBLIC HEALTH RELEVANCE: Understanding how modifications of single cells alter population-level dynamics may lead to new ideas for drugs and therapies. My findings are likely to be applicable to systems related to Dictyostelium, e.g. neutrophils migrating collectively withi lymph nodes or self-organized tissue migration during embryogenesis. In summary, I propose to use insights into cellular dynamics to reprogram macroscopic behaviors, which are best controlled at molecular and cellular levels.

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.