Bulletin of the American Physical Society
2009 APS March Meeting
Volume 54, Number 1
Monday–Friday, March 16–20, 2009; Pittsburgh, Pennsylvania
Session Z8: Statistical Physics in Biology |
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Sponsoring Units: DBP Chair: Marek Cieplak, Johns Hopkins University Room: 414/415 |
Friday, March 20, 2009 11:15AM - 11:51AM |
Z8.00001: Allometric Scaling in Biology Invited Speaker: The unity of life is expressed not only in the universal basis of inheritance and energetics at the molecular level, but also in the pervasive scaling of traits with body size at the whole-organism level. More than 75 years ago, Kleiber and Brody and Proctor independently showed that the metabolic rates, B, of mammals and birds scale as the three-quarter power of their mass, M. Subsequent studies showed that most biological rates and times scale as $M^{-1/4}$ and $M^{1/4}$ respectively, and that these so called quarter-power scaling relations hold for a variety of organisms, from unicellular prokaryotes and eukaryotes to trees and mammals. The wide applicability of Kleiber's law, across the 22 orders of magnitude of body mass from minute bacteria to giant whales and sequoias, raises the hope that there is some simple general explanation that underlies the incredible diversity of form and function. We will present a general theoretical framework for understanding the relationship between metabolic rate, B, and body mass, M. We show how the pervasive quarter-power biological scaling relations arise naturally from optimal directed resource supply systems. This framework robustly predicts that: 1) whole organism power and resource supply rate, B, scale as $M^{3/4}$; 2) most other rates, such as heart rate and maximal population growth rate scale as $M^{-1/4}$; 3) most biological times, such as blood circulation time and lifespan, scale as $M^{1/4}$; and 4) the average velocity of flow through the network, $\bar {v}$, such as the speed of blood and oxygen delivery, scales as $M^{1/12}$. Our framework is valid even when there is no underlying network. Our theory is applicable to unicellular organisms as well as to large animals and plants. This work was carried out in collaboration with Amos Maritan along with Jim Brown, John Damuth, Melanie Moses, Andrea Rinaldo, and Geoff West. [Preview Abstract] |
Friday, March 20, 2009 11:51AM - 12:27PM |
Z8.00002: From gene expressions to genetic networks Invited Speaker: A method based on the principle of entropy maximization is used to identify the gene interaction network with the highest probability of giving rise to experimentally observed transcript profiles [1]. In its simplest form, the method yields the pairwise gene interaction network, but it can also be extended to deduce higher order correlations. Analysis of microarray data from genes in Saccharomyces cerevisiae chemostat cultures exhibiting energy metabollic oscillations identifies a gene interaction network that reflects the intracellular communication pathways. These pathways adjust cellular metabolic activity and cell division to the limiting nutrient conditions that trigger metabolic oscillations. The success of the present approach in extracting meaningful genetic connections suggests that the maximum entropy principle is a useful concept for understanding living systems, as it is for other complex, nonequilibrium systems. The time-dependent behavior of the genetic network is found to involve only a few fundamental modes [2,3]. \\[4pt] REFERENCES:\\[0pt] [1] T. R. Lezon, J. R. Banavar, M. Cieplak, A. Maritan, and N. Fedoroff, Using the principle of entropy maximization to infer genetic interaction networks from gene expression patterns, Proc. Natl. Acad. Sci. (USA) 103, 19033-19038 (2006) \\[0pt] [2] N. S. Holter, M. Mitra, A. Maritan, M. Cieplak, J. R. Banavar, and N. V. Fedoroff, Fundamental patterns underlying gene expression profiles: simplicity from complexity, Proc. Natl. Acad. Sci. USA 97, 8409-8414 (2000) \\[0pt] [3] N. S. Holter, A. Maritan, M. Cieplak, N. V. Fedoroff, and J. R. Banavar, Dynamic modeling of gene expression data, Proc. Natl. Acad. Sci. USA 98, 1693-1698 (2001) [Preview Abstract] |
Friday, March 20, 2009 12:27PM - 1:03PM |
Z8.00003: Non-equilibrium thermodynamic effects during cell division Invited Speaker: A mitotic spindle is a regular structure within a cell consisting of oriented microtubule fibers. It plays a fundamental role in chromosome separation during cell division. Forming a spindle pattern is a major structural step towards mitosis. We have developed biophysical non-equilibrium thermodynamic models to describe in vitro chromosome driven spindle formation experiments in Xenopus extracts. Our first 2D model calculations [1] successfully described the order of events seen in some of the Xenopus extracts experiments, where the chromosomes are replaced by chromatin-covered micrometer magnetic beads. I will describe more realistic 3D improvements in our modeling analysis, which include microtubule contact forces and excluded volume [2, 3]. There are, however, a number of challenges that must be addressed for spindle modeling to continue to be a useful tool for understanding this fundamental biological process, in particular the biophysical simulation times. In this talk I will describe some important problems needing better biological data and hypothesis. I will also discuss our most recent numerical algorithmic improvements that are expected to greatly increase the simulations speed and thus allowing a more realistic representation of the experimental situation in Xenopus extracts. [1] S. C. Schaffner and J. V. Jose, PNAS, 103, 11166 (2006), [2] ibid in ``Methods in Cell Biology'' (Elsevier- Academic Press)(2008)and [3]ibid(to be published). [Preview Abstract] |
Friday, March 20, 2009 1:03PM - 1:39PM |
Z8.00004: New Proposed Mechanism for Actin-Polymerization-Driven Motility Invited Speaker: When a cells crawls, its shape re-organizes via polymerization and depolymerization of a network of actin filaments. The growing ends of the filaments are localized near the leading edge of the crawling cell, and their polymerization, regulated by a host of proteins, pushes the cell membrane forwards in a biological model known as the dendritic nucleation model. We have performed Brownian dynamics simulations to see how the dendritic nucleation model leads to motion. Our results are not consistent with previous models of motility, and suggest a new picture for the physical mechanism underlying this form of motility. [Preview Abstract] |
Friday, March 20, 2009 1:39PM - 2:15PM |
Z8.00005: to be determined by you Invited Speaker: |
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