Computational models of molecular self-organization in cellular environments.
The cellular environment creates numerous obstacles to efficient chemistry, as molecular components must navigate through a complex, densely crowded, heterogeneous, and constantly changing landscape in order to function at the appropriate times and places. Such obstacles are especially challenging to self-organizing or self-assembling molecular systems, which often need to build large structures in confined environments and typically have high-order kinetics that should make them exquisitely sensitive to concentration gradients, stochastic noise, and other non-ideal reaction conditions. Yet cells nonetheless manage to maintain a finely tuned network of countless molecular assemblies constantly forming and dissolving with a robustness and efficiency generally beyond what human engineers currently can achieve under even carefully controlled conditions. Significant advances in high-throughput biochemistry and genetics have made it possible to identify many of the components and interactions of this network, but its scale and complexity will likely make it impossible to understand at a global, systems level without predictive computational models. It is thus necessary to develop a clear understanding of how the reality of cellular biochemistry differs from the ideal models classically assumed by simulation approaches and how simulation methods can be adapted to accurately reflect biochemistry in the cell, particularly for the self-organizing systems that are most sensitive to these factors. In this review, we present approaches that have been undertaken from the modeling perspective to address various ways in which self-organization in the cell differs from idealized models.
LeDuc
Philip
P
Department of Mechanical Engineering and Biomedical Engineering, Carnegie Mellon University, Pittsburgh, PA 15213, USA. prl@andrew.cmu.edu
Schwartz
Russell
R
D001703:Biophysics / Q000706:statistics & numerical data; D002468:Cell Physiological Phenomena; D019295:Computational Biology / Q000706:statistics & numerical data*; D046911:Macromolecular Substances; D008390:Markov Chains; D008954:Models, Biological; D009010:Monte Carlo Method; D049490:Systems Biology
D046911:Macromolecular Substances