Compartmental and Spatial Rule-Based Modeling with Virtual Cell

Abstract

In rule-based modeling, molecular interactions are systematically specified in the form of reaction rules that serve as generators of reactions. This provides a way to account for all the potential molecular complexes and interactions among multivalent or multistate molecules. Recently, we introduced rule-based modeling into the Virtual Cell (VCell) modeling framework, permitting graphical specification of rules and merger of networks generated automatically (using the BioNetGen modeling engine) with hand-specified reaction networks. VCell provides a number of ordinary differential equation and stochastic numerical solvers for single-compartment simulations of the kinetic systems derived from these networks, and agent-based network-free simulation of the rules. In this work, compartmental and spatial modeling of rule-based models has been implemented within VCell. To enable rule-based deterministic and stochastic spatial simulations and network-free agent-based compartmental simulations, the BioNetGen and NFSim engines were each modified to support compartments. In the new rule-based formalism, every reactant and product pattern and every reaction rule are assigned locations. We also introduce the rule-based concept of molecular anchors. This assures that any species that has a molecule anchored to a predefined compartment will remain in this compartment. Importantly, in addition to formulation of compartmental models, this now permits VCell users to seamlessly connect reaction networks derived from rules to explicit geometries to automatically generate a system of reaction-diffusion equations. These may then be simulated using either the VCell partial differential equations deterministic solvers or the Smoldyn stochastic simulator.

The Interplay of Structural and Cellular Biophysics Controls Clustering of Multivalent Molecules

“…Biophysical models of these systems may serve to address the interplay of valency and geometry by systematically varying the cellular and molecular features underlying these properties. Stochastic reactiondiffusion modeling at the cellular scale cannot fully account for steric effects or molecular flexibility because molecules are typically modeled as infinitesimal points in space (17)(18)(19). In principle, multimolecular/multistate interactions could be modeled with atomistic molecular dynamics simulations, but the large sizes of these systems and the need to simulate on the second timescale make such simulations computationally impractical.…”

A unifying framework disentangles genetic, epigenetic, and stochastic sources of drug-response variability in anin vitromodel of tumor heterogeneity

“…Several studies have also demonstrated that alternative drug scheduling regimens may have benefits compared to traditional single-agent applications or up-front combinations 45,126 . Our work suggests extending these approaches to account for all levels of heterogeneity in tumors, i.e., genetic profiling to enumerate dominant genetic states, followed by characterization of the associated epigenetic landscapes using single-cell experimental approaches, possibly aided by advanced theoretical and computational modeling techniques 41,64,65,73,[127][128][129][130][131][132] . These landscapes can then be screened against batteries of available drugs, in vitro and in silico, to devise individualized treatments that can be tested against patient-derived xenografts in vivo before being administered clinically.…”

Pathway Commons: 2019 Update

“…A number of analysis packages that make use of PC data have been developed (42)(43)(44)(45)(46)(47)(48)(49)(50)(51)(52) . Here we briefly describe several tools developed by the PC team.…”