Integrating Computational and Biochemical Studies to Explore Mechanisms in NF-κB Signaling

Abstract

Signal transduction networks regulate cellular function and respond to changes in intra- and intercellular environments. They control both homeostatic and stimulus-induced responses. However, the role and importance of individual network components in the function of the network are often difficult to discern. This is evident when a genetic deletion or mutation does not show an expected phenotype or when a pharmacological treatment has surprising effects. Computational simulations with networks reconstructed in silico as mathematical equations enable functional analysis of network behavior and are well suited to complement in vivo and in vitro studies (1–4). The utility of integrating experimental and computational methodologies derives from the iterative application of each approach to inform the other. Experimental analyses provide critical insight to select the components and reactions to include in the computational model and to constrain the simulation parameters. In turn, the computational analyses provide mechanistic insights to drive further experimental analyses. Studies of the lac operon in Escherichia coli are often cited as original examples of this integrated approach wherein a simple set of mathematical equations was sufficient to account for the transcriptional negative feedback of the lac operon (5). In eukaryotic systems, developmental, cell cycle control, and circadian rhythm processes in several organisms have been examined and validated in silico (6–10). Models of protein kinase cascades have produced important insights such as amplification and temporal fidelity, bistability, and signaling cross-talk (11). More recently, advances in systems biology have produced increasingly complete cellular parts lists that have enabled statistical modeling to reconstruct large scale molecular networks (12, 13). However, the resulting models are largely non-quantitative and do not consider the temporal dimension, although these aspects are essential to biological regulation (4). With top-down network reconstruction efforts identifying functional modules, traditional biochemical bottom-up approaches are critical for providing mechanistic detail. This is where kinetic computational modeling of molecular networks may function as an important bridge for these distinct approaches.