Systems biology involves the quantitative and simultaneous integration of different and multiple biological components and their relationships with one another. For example, the components may be proteins, while their relationships may be described by signal transduction pathways. Unlike systems biology, molecular modeling focuses on a single complex between biomolecules and computes the interactions that exist in the complex. Although the two fields appear dissimilar, they are both quantitative in nature and involve many components and relationships. In the case of molecular modeling, the components are the atoms and their partial charges, and their relationships are the different interactions between them. Therefore, it’s no surprise that some molecular modeling methods are now being applied to systems biology. Moreover, there has been recent success in combining these two fields to rationally design effective therapeutics. In this program, we will bring together experts in these two fields of computational biology to discuss their frontier research.
Mathematical approaches: Differential equations, finite difference methods, Bayesian approaches, molecular dynamics, stochastic systems, clustering, nonlinear dynamics, Monte Carlo simulations, simulated annealing.
Daniel Kamei (UCLA)
Douglas Lauffenburger (Massachusetts Institute of Technology)
Ben Wu (UCLA)