Dynamics of Biological Systems: Protein Folding, Allosteric Signal Transmission and Epigenetic Circuits.

摘要

Biological systems are often modeled as networks to study the dynamics which is critically important in cellular mechanism. The model based on master equation was successfully used in studying protein folding dynamics, allosteric signal transmission, and epigenetic circuit.;Allosteric communications between different parts of molecular machines play critical roles in cellular signaling. Perturbation-based Markovian Transmission model is developed to study globally the dynamic responses of the macromolecular assemblies. By monitoring simultaneous responses of all residues across many decades of time span from the initial perturbation until reaching equilibrium, we show that this approach can yield rich information. With criteria based on quantitative measurements, a set of functionally important residues that are important for macromolecular movement, signal mediating, and binding interactions are identified. Additionally, allosteric signal transmission pathways were identified by analyzing the dynamic responses upon the binding of allosteric effectors by entropy methods. Our predictions are important for the allosteric transition reported by biochemical experiments and evolutionary data.;Cellular processes can be described as networks consisting of biomolecules in which the reactions involved. Models based on the chemical master equation provides a fundamental framework for studying stochasticity which is critical for biomolecular networks with small copy numbers of species. An epigenetic circuit of phage lambda switch in E. coli cells is one of these molecular networks. We compute directly the full steady-state probability landscape of the lysogeny maintenance network in phage lambda. Results show the importance of cooperative binding of repressors and double positive regulations. Our computation faithfully reproduces the hair triggers for UV-induced lysis observed in mutants and the limitation in robustness against mutations. The landscape approach computed from chemical master equation is general and can be applied to study broad issues in systems biology.