Gene expression for simulation of biological tissue

TL;DR

This paper introduces analytical gene expression models using differential equations within BioDynaMo, comparing Euler and Runge-Kutta methods for accuracy and performance in simulating biological tissues.

Contribution

It implements and evaluates differential equation-based gene regulatory models in BioDynaMo, a biological tissue simulator, advancing the simulation of gene expression processes.

Findings

Runge-Kutta method offers higher accuracy than Euler.

Both methods are feasible for integration into BioDynaMo.

Performance trade-offs depend on simulation complexity.

Abstract

BioDynaMo is a biological processes simulator developed by an international community of researchers and software engineers working closely with neuroscientists. The authors have been working on gene expression, i.e. the process by which the heritable information in a gene - the sequence of DNA base pairs - is made into a functional gene product, such as protein or RNA. Typically, gene regulatory models employ either statistical or analytical approaches, being the former already well understood and broadly used. In this paper, we utilize analytical approaches representing the regulatory networks by means of differential equations, such as Euler and Runge-Kutta methods. The two solutions are implemented and have been submitted for inclusion in the BioDynaMo project and are compared for accuracy and performance.