Time series data on biochemical reactions reveal transient behavior, away from chemical equilibrium, and contain information on the dynamic interactions among reacting components. However, this information can be difficult to extract using conventional analysis techniques. We present a new method to infer biochemical pathway mechanisms from time course data using a global nonlinear modeling technique to identify the elementary reaction steps which constitute the pathway. The method involves the generation of a complete dictionary of polynomial basis functions based on the law of mass action. Using these basis functions, there are two approaches to model construction, namely the general to specific and the specific to general approach. We demonstrate that our new methodology reconstructs the chemical reaction steps and connectivity of the glycolytic pathway of Lactococcus lactis from time course experimental data.

Premature S-phase entry due to Cdh1 ablation results from premature loss of the CDK inhibitor p27 and a reduced requirement for cyclin E1. This prolonged S phase coincides with slowed replication fork elongation and fewer replication terminations, both of which could contribute to genome instability.

Virtual-tissue modeling is used to model emergent cyst growth in polycystic kidney disease. Model predictions, confirmed experimentally, show that decreased cell adhesion is necessary to produce stalk cysts, and loss of contact inhibition causes saccular cysts. Virtual-tissue modeling can be used to fully explore cell- and tissue-based behaviors.