Many of the most important protein complexes in cells, from the proteasome to the apoptosome, consist either of ring-like structures or of rings stacked on top of each other. Although ring-like structures can display incredible thermodynamic stability, their assembly dynamics can be quite complicated. We are actively investigating how biological systems can evolve ring-like complexes that assemble efficiently. Current work involves:
Studying the assembly dynamics of rings and stacked rings using deterministic and stochastic models
Determining structures and reactions that assemble with optimal efficiency
Determining structures that are robust to perturbations in the concentration of components
Analysis of solved structures of rings and stacked rings in order to test our predictions
The assembly of large macromolecular structures does not typically occur under circumstances in which the appropriate components are isolated from the rest of the cell. Rather, assembly must proceed in the context of large Protien-Protein Interaction (PPI) networks. The interactions in such networks are often characterized by conflicts; that is, proteins often participate in more than one functional complex, and thus the surfaces on these proteins often exhibit more than one specific binding partner. Large networks are also characterized by combinatorial complexity, which refers to the fact that PPI networks can often generate astronomical numbers of unique molecular species. Using rule- and agent-based modeling techniques, we have developed the first dynamical simulations of an entire PPI network. We are currently using these models to examine the unique problems that arise when assembly occurs in a network context. We are also investigating the extent to which these problems arise in signaling networks and how biological systems have evolved to overcome combinatorial complexity.
In pursuing this work we have become part of a large community pursuing the development and application of rule- and agent-based modeling in systems biology. We employ the kappa language and simulation tools; more information about these methods can be found at kappalanguage.org and rulebase.org.
Designing macromolecular structures
There is currently considerable interest in synthetic biology, which is the construction of novel devices from biological parts. Initiatives such as iGEM have proved very successful, and have prompted the need for theoretical and computational approaches aimed at the rational design of biological systems. We are currently building on our work described above to provide tools and techniques that can be used to design structures that will self-assemble rapidly and efficiently, even in the noisy context of a real living cell.
One of the greatest regularities in all of biology is the relationship between an organism’s mass and its metabolic rate. Until recently, it was assumed that this relationship took the form of a power law, with the exponent having a value of 2/3 or 3/4, depending on the data set. We were recently involved in work that demonstrated that the relationship between mass and metabolic rate is not actually a pure power law, but rather displays considerable curvature on a log-log scale. We are currently investigating theoretical models of metabolic scaling in order to determine the underlying source for this curvature.