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Kinetic Monte Carlo Method for Rule-based Modeling of Biochemical Networks
Jin Yang, Michael I. Monine, James R. Faeder, William S. Hlavacek
TL;DR
Explicitly tracking the many species and reactions implied by biochemical reaction rules can make population-based simulation impractical. This paper extends a particle-based method with rule-conditioned rates and applies it to ligand–receptor aggregation, reproducing the equilibrium phase diagram and predicting distinct aggregation kinetics.
Problem
Population-based methods can be impractical for rule-derived networks because they must explicitly track potentially enormous numbers of chemical species and reactions.
Source: p. 2Method
Rule-based KMC extends DFFK by adjusting rule-defined rates for contextual constraints and sampling reaction classes without forming combinations of reactive components.
Source: pp. 3, 5Results
The simulations recapitulate the TLBR equilibrium phase diagram and predict transient small aggregates and distinct time courses for ligand doses with similar equilibrium aggregation.
Source: pp. 8, 12Takeaways & Limitations
Rule-based KMC can simulate aggregation models whose implied reaction networks become impractical to generate or simulate explicitly.
Source: pp. 2, 15The TLBR model excludes cyclic aggregates by disallowing binding between sites within the same ligand–receptor complex.
Source: p. 6
Abstract
from arXiv · showhide
We present a kinetic Monte Carlo method for simulating chemical transformations specified by reaction rules, which can be viewed as generators of chemical reactions, or equivalently, definitions of reaction classes. A rule identifies the molecular components involved in a transformation, how these components change, conditions that affect whether a transformation occurs, and a rate law. The computational cost of the method, unlike conventional simulation approaches, is independent of the number of possible reactions, which need not be specified in advance or explicitly generated in a simulation. To demonstrate the method, we apply it to study the kinetics of multivalent ligand-receptor interactions. We expect the method will be useful for studying cellular signaling systems and other physical systems involving aggregation phenomena.