Abstract
Mathematical models based on Ordinary Differential Equations (ODEs) are frequently used to describe and simulate biological systems. Nevertheless, such models are often difficult to understand. Unlike ODE models, Rule-Based Models (RBMs) utilise formal language to describe reactions as a cumulative number of statements that are easier to understand and correct. They are also gaining popularity because of their conciseness and simulation flexibility. However, RBMs generally lack tools to perform further analysis that requires simulation. This situation arises because exact and approximate simulations are computationally intensive. Translating RBMs into ODEs is commonly used to reduce simulation time, but this technique may be prohibitive due to combinatorial explosion. Here, we present the software called Pleione to calibrate RBMs. Parameter calibration is essential given the incomplete experimental determination of reaction rates and the goal of using models to reproduce experimental data. The software distributes stochastic simulations and calculations and incorporates equivalence tests to determine the fitness of RBMs compared with data. The primary features of Pleione were thoroughly tested on a model of gene regulation in Escherichia coli. Pleione yielded satisfactory results regarding calculation time and error reduction for multiple simulators, models, parameter search strategies, and computing infrastructures.
Highlights
Mathematical models based on Ordinary Differential Equations (ODEs) are frequently used to describe and simulate biological systems
BNF address the need for a calibration tool, they support only Rule-Based Models (RBMs) written in BioNetGen Language (BNGL) and SBML20 and rely on algebraic equations to compare experimental data and simulation that may be of special concern depending on the nature of the modelled phenomena
Pleione takes less time to calibrate as the increasing availability of CPUs reduces the burden of multiple stochastic simulations, leveraging the need for deterministic simulations of RBMs
Summary
Mathematical models based on Ordinary Differential Equations (ODEs) are frequently used to describe and simulate biological systems. Systems biology studies the behaviour of biological systems by determining and quantifying all of the molecular interactions that characterise them[1] This area of science relies on different experimental, mathematical, and computational tools to address system generalities such as robustness and specific details such as bi-stability[2,3]. These computational approaches can be classified into two primary types: those that aim to determine cell component interactions (e.g., methods to infer Gene Regulatory Networks GRNs, from expression data4) and those used to study the dynamical properties that define such systems[1,2]. We support parallelised calculations without SLURM using the python multiprocessing package
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