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Energy Transduction and Pattern Formation in Proteins, Cells, and Populations


Our objectives remain to provide mechanistic explanations for biological processes at the molecular, cell, and population levels. During this grant period we will focus our attention specifically on the following systems. <LI>PROPULSION AND PATTERN FORMATION IN BACTERIA. Previously, we have elucidated the propulsion mechanisms for E. coli swimming, Myxococcus xanthus gliding, and Listeria monocytogenes polymerization propulsion. Now we have begun investigating the propulsion mechanisms for the Mollicutes Spiroplasma (swimming) and Mycoplasma (gliding).<li> PROTEIN MOTORS. We have set about improving our model for the bacterial flagellar motor of E. coli in light of new experimental data, and continue our work on the packaging motor of PHI29 phage. <LI>MEMBRANE MECHANOCHEMISTRY. We have completed the first stage of our model for endocytosis in budding yeast, and now plan to extend the model to mammalian cells and bacteria. We also are continuing our modeling of the cubic to lamellar transition in membranes as applied to the formation of the endoplasmic reticulum in eukaryotes and the thylakoid membrane in plants. <LI>PATTERN FORMATION IN NEURAL NETWORKS. We have begun a major new direction to elucidate the neural mechanisms underlying the spatial patterning on the shells of aquatic mollusks and the mantles of cephalopods. This new research into neural pattern formation leads in an entirely novel direction for studying how neural activity controls complex physiological functions.

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NON-TECHNICAL SUMMARY: We propose to construct computational models for biophysical mechanisms at several levels of organization. At the molecular level we address the biomechanics of the motor that packages DNA into viruses, and the motors that propel the locomotion of several kinds of bacteria. At the cellular level, we model the process of endocytosis whereby cells ingest substances from their environment. At the population level, we shall model the patterns formed by gliding bacteria, and those generated by the neural network of mollusks that create the diverse patterns found on sea shells.<P>APPROACH: My laboratory is dedicated to constructing quantitative models of biological systems, and so our principle instrument is the computer. All of our work, however, is carried out in close collaboration with experimental laboratories, so we seldom work far from data. In all of these projects our procedure is the same. We construct a mathematical description of the model based on all of the experimental data. Our models are based on the physics and chemistry at the relevant level of organization. These models are generally complex, and must be simulated on computers. Because they necessarily contain many parameters, we must adhere closely to the experiments to ascertain the parameter values. Our approach to modeling biological systems is not to produce general theories, but to address specific experimentally determined systems. Thus all of our work is carried out in close collaboration with experimental laboratories. We first become intimately familiar with the experimental data. Then we formulate a provisional model that addresses the major features of the system. This takes one of several forms: analytic equations (e.g. stochastic differential equations, agent or mean field equations), or computer algorithms. The goal is to fit the major features of the data. There ensues several rounds of data comparisons that invariably requires successive modifications of the model. Ultimately, we hope to capture all the data in the model. However, this generally requires further experimentation. Indeed, one of the major contributions of the model, aside from providing a conceptual framework for understanding the data, is to suggest experiments yet undone that would not have been contemplated in the absence of the model.

Oster, George
University of California - Berkeley
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