Efficient modeling, simulation and coarse-graining of biological complexity with NFsim

Abstract

Managing the overwhelming numbers of molecular states and interactions is a fundamental obstacle to building predictive models of biological systems. Here we introduce the Network-Free Stochastic Simulator (NFsim), a general-purpose modeling platform that overcomes the combinatorial nature of molecular interactions. Unlike standard simulators that represent molecular species as variables in equations, NFsim uses a biologically intuitive representation: objects with binding and modification sites acted on by reaction rules. During simulations, rules operate directly on molecular objects to produce exact stochastic results with performance that scales independently of the reaction network size. Reaction rates can be defined as arbitrary functions of molecular states to provide powerful coarse-graining capabilities, for example to merge Boolean and kinetic representations of biological networks. NFsim enables researchers to simulate many biological systems that were previously inaccessible to general-purpose software, as we illustrate with models of immune system signaling, microbial signaling, cytoskeletal assembly and oscillating gene expression.

Propulsion and Chemotaxis in Bacteria‐Driven Microswimmers

“…Chemotaxis Signaling Pathway Model : The chemotaxis signaling pathway of E. coli was modeled with three major components, and their corresponding models were adapted from recent studies 37, 38, 39, 40, 41, 42, 43. The first component, MCP complex, was represented with a Monod–Wyman–Changeux (MWC) model52 to describe the allosteric effects of receptor clusters with identical receptors.…”

“…Thus far, the taxis‐based guiding method constitutes the major way to regulate the otherwise highly stochastic motion of bacteria‐driven microswimmers, which has been studied both in vitro17, 20, 21, 22, 23, 24, 25 and in vivo 14, 16. Chemotaxis, one of the most common taxis behaviors in bacteria, has been well understood36 and its signaling pathway has been mathematically modeled 37, 38, 39, 40, 41, 42, 43. In general, the chemotaxis of free‐swimming bacteria associates with a biased random walk, enabled by preferentially suppressed tumble tendency when the bacteria travel up a chemoattractant gradient; whereas in an uniform medium, the tumble tendency is isotropic over all swimming directions.…”

Complex Functional Rates in Rule-Based Languages for Biochemistry

“…The stochastic simulator NFsim [34], based on an extension of the BioNetGen language, allows the expression of rate functions which can depend on properties either global (at the level of the system) or "local" (at the level of the molecular complexes involved in the reaction). While the first kind of properties is not directly included in κ F and should be encoded manually by the modeler, the latter kind makes NFsim capabilities closer to κ F .…”

Lazy Updating of hubs can enable more realistic models by speeding up stochastic simulations

“…As a result, much effort has been invested in the speed-up of exact or approximate SSAs, 18,[23][24][25][26][27][28][29][30][31][32][33][34][35][36] producing different algorithms that require different tradeoffs. 18,[24][25][26][27][28][29][30][31][32][33][34][35][36][37][38][39][40] Tau-Leaping has emerged as an important approximate SSA. 21,24,37,[41][42][43] Below we present an algorithm that trades a very small amount of accuracy for a substantial reduction in computing time by skipping some updates in a special category of parts, which we call "hubs."…”