A Random Walk in Physical Biology

Citation

Peterson, Eric Lee (2008) A Random Walk in Physical Biology. Dissertation (Ph.D.), California Institute of Technology. doi:10.7907/X00X-VC27. https://resolver.caltech.edu/CaltechETD:etd-05282008-152952

Abstract

Biology as a scientific discipline is becoming evermore quantitative as tools become available to probe living systems on every scale from the macro to the micro and now even to the nanoscale. In quantitative biology the challenge is to understand the living world in an in vivo context, where it is often difficult for simple theoretical models to connect with the full richness and complexity of the observed data. Computational models and simulations offer a way to bridge the gap between simple theoretical models and real biological systems; towards that aspiration are presented in this thesis three case studies in applying computational models that may give insight into native biological structures.

The first is concerned with soluble proteins; proteins, like DNA, are linear polymers written in a twenty-letter "language" of amino acids. Despite the astronomical number of possible proteins sequences, a great amount of similarity is observed among the folded structures of globular proteins. One useful way of discovering similar sequences is to align their sequences, as done e.g. by the popular BLAST program. By clustering together amino acids and reducing the alphabet that proteins are written in to fewer than twenty letters, we find that pairwise sequence alignments are actually more sensitive to proteins with similar structures.

The second case study is concerned with the measurement of forces applied to a membrane. We demonstrate a general method for extracting the forces applied to a fluid lipid bilayer of arbitrary shape and show that the subpiconewton forces applied by optical tweezers to vesicles can be accurately measured in this way.

In the third and final case study we examine the forces between proteins in a lipid bilayer membrane. Due to the bending of the membrane surrounding them, such proteins feel mutually attractive forces which can help them to self-organize and act in concert. These finding are relevant at the areal densities estimated for membrane proteins such as the MscL mechanosensitive channel. The findings of the analytical studies were confirmed by a Monte Carlo Markov Chain simulation using the fully two-dimensional potentials between two model proteins in a membrane.

Living systems present us with beautiful and intricate structures, from the helices and sheets of a folded protein to the dynamic morphology of cellular organelles and the self-organization of proteins in a biomembrane and a synergy of theoretical and it in silico approaches should enable us to build and refine models of in vivo biological data.

Thesis Files