Session L7: Multiscale Phenomena in Biological Physics

Self assembly of natural and synthetic membranes using coarse grain models

The talk will review recent work by the Klein group in deriving force fields for natural and synthetic membrane forming systems. Molecular dynamics studies of the self-assembly will be presented for both types of systems.   

Bridging time-scale gaps via reaction path optimization

In this talk I will present a series of new computational methodologies that can be applied to systematically investigate the mechanism, free energy profiles, and rates of large-scale conformational changes of biomolecules. First, we enhance the efficiency of reaction path optimization methods, which use a series of duplicated systems, or replicas, to represent a discrete path by using holonomic constraints instead of reparametrization or using penalty potential functions that may require force projections to maintain equal distances between replicas. As a result, this formulation allows a straightforward application of super-linear optimization schemes such as the Adopted Basis Newton Raphson method, which uses much fewer energy and force evaluations to optimize a path. Novel objective functions, such as Hamiltonian and action, have also been designed for the search of novel pathways in addition to minimum energy paths. We have also generalized this approach to compute minimum free energy paths of a reaction. Second, constraints for sampling on the hyper-planes along an optimized path have been developed for computing the potential of mean force using the blue- moon approach. For obtaining rate information, we propose to solve the time-dependent Fokker-Planck equation by using the free energy profiles along a path as input. I will present the studies of two important conformational changes using these methods: the cis-to- trans isomerization of an alanine dipeptide and the helix-to-hairpin transition of an amyloid beta peptide.   

A Novel Empirical Potential Function and A Monte Carlo Sampling Technique

In protein folding study, two major issues are effective potential function and powerful sampling technique. In this meeting, recent results in both directions will be presented. In terms of potential function, we have developed an orientation-dependent statistical all-atom potential derived from side-chain packing. Test of the new potential on decoy set recognition indicates that it outperforms all the known statistical potential functions in the literature. Applications of this potential in substantially improving side-chain modeling will also be discussed. In terms of sampling technique, I will discuss some new results of a novel Monte Carlo sampling technique that performs simulation via direct computation of partition functions. The results will be compared with those of the well-known Wang-Landau sampling scheme. Application of this new MC method in studying protein folding will also be discussed.   

Energy Landscape of Cellular Networks

Cellular Networks are in general quite robust and perform their biological functions against the environmental perturbations. Progresses have been made from experimental global screenings, topological and engineering studies. However, there are so far few studies of why the network should be robust and perform biological functions from global physical perspectives. In this work, we will explore the global properties of the network from physical perspectives. The aim of this work is to develop a conceptual framework and quantitative physical methods to study the global nature of the cellular network. The main conclusion of this presentation is that we uncovered the underlying energy landscape for several small cellular networks such as MAPK signal transduction network and gene regulatory networks, from the experimentally measured or inferred inherent chemical reaction rates. The underlying dynamics of these networks can show bi-stable as well as oscillatory behavior. The global shapes of the energy landscapes of the underlying cellular networks we have studied are robust against perturbations of the kinetic rates and environmental disturbances through noise. We derived a quantitative criterion for robustness of the network function from the underlying landscape. It provides a natural explanation of the robustness and stability of the network for performing biological functions. We believe the robust landscape is a global universal property for cellular networks. We believe the robust landscape is a quantitative realization of Darwinian principle of natural selection at the cellular network level. It may provide a novel algorithm for optimizing the network connections, which is crucial for the cellular network design and synthetic biology. Our approach is general and can be applied to other cellular networks.