MCell-R: A particle-resolution network-free spatial modeling framework

TL;DR

MCell-R is a novel framework combining rule-based modeling and network-free simulation to efficiently model spatial heterogeneity in complex biochemical systems at the particle level.

Contribution

It extends MCell with BioNetGen and NFsim capabilities, enabling efficient simulation of combinatorially complex, spatially-resolved biochemical networks.

Findings

Efficient simulation of multi-state, multi-component systems.

Handles combinatorial complexity without explicit network enumeration.

Applicable to biologically relevant spatial and temporal scales.

Abstract

Spatial heterogeneity can have dramatic effects on the biochemical networks that drive cell regulation and decision-making. For this reason, a number of methods have been developed to model spatial heterogeneity and incorporated into widely used modeling platforms. Unfortunately, the standard approaches for specifying and simulating chemical reaction networks become untenable when dealing with multi-state, multi-component systems that are characterized by combinatorial complexity. To address this issue, we developed MCell-R, a framework that extends the particle-based spatial Monte Carlo simulator, MCell, with the rule-based model specification and simulation capabilities provided by BioNetGen and NFsim. The BioNetGen syntax enables the specification of biomolecules as structured objects whose components can have different internal states that represent such features as covalent modification and conformation and which can bind components of other molecules to form molecular complexes. The network-free simulation algorithm used by NFsim enables efficient simulation of rule-based models even when the size of the network implied by the biochemical rules is too large to enumerate explicitly, which frequently occurs in detailed models of biochemical signaling. The result is a framework that can efficiently simulate systems characterized by combinatorial complexity at the level of spatially-resolved individual molecules over biologically relevant time and length scales.