Theoretical Condensed Matter Physics

Cornell University, Ithaca NY

Investigators

Abstract

This grant is co-funded by the Divisions of Materials Research, Molecular and Cellular Biology, and Physics. The grant supports theoretical research in biological physics. Many problems in biology have a component that involves statistical mechanics either directly because the question is on the molecular scale, or indirectly because a probablistic model is being used to decode a genomic sequence. The internal Watson-Crick base pairing of RNA defines its secondary structure which is better delineated energetically than the corresponding elements in proteins. A kinetic Monte Carlo code has recently been written to follow the formation of secondary structure for RNA's up to ~ 400 bases in length and includes non-nested configurations (pseudoknots). The code will be applied to the ends of the molecule as in recent experiments. Protein trafficing in cells involves numerous steps where proteins are sorted and targeted, e.g., in the Golgi substrate as processed in successive cisternae by different enzymes. In collaboration with a laboratory at NIH, models which allow for Golgi enzyme localization in a connected membrane system will be devised and tested. These are alternatives to the prevailing ideas that either the substrate lives in fixed compartments and the enzymes flux through the compartments via an active process of sorting and vesicle transport, or the enzymes are fixed and the substrates are actively transported. There are now multiple fluorescent proteins that can be expressed in live cells (sometimes as chimeras with native proteins), which allow for measurement of their number in one cell as a function of time. In conjunction with an experimental group at Rockefeller, we are modeling the stochastic noise (due to a small number of molecules) in transcription and translation. More generally we will collaborate on experiments and theory to assess whether stochastic effects impact the fitness of bacteria. The regulation of gene transcription by short, e.g., 10-30 bp, motifs to which proteins bind and integrate environmental signals is important to many areas of biology. An algorithm has been devised which finds improbable strings of bases, based on the frequency of shorter strings present in an iteratively constructed 'dictionary.' It yielded many significant motifs when applied to yeast, and we intend to apply the algorithm to other organisms, as well as extend it to recognize fuzzy patterns. The comparison of related species is another way to recover regulatory motifs and we intend to apply a probablistic alignment code both to fit the evolution rate in noncoding sequence and assign probabilities to the regions which are more conserved than chance and thus functional. An algorithm to relate gene expression data directly to sequence and assign statistical significance has been tried on yeast and will be extended to other multicellualr organisms. %%% This grant is co-funded by the Divisions of Materials Research, Molecular and Cellular Biology, and Physics. The grant supports theoretical research in biological physics. Many problems in biology have a component that involves statistical mechanics either directly because the question is on the molecular scale, or indirectly because a probablistic model is being used to decode a genomic sequence. This research will investigate a variety of problems of current biological interest using techniques from theoretical condensed matter physics. ***