A Multi-agent approach for $\textit{in silico}$ simulations of micro-biological systems

TL;DR

This paper presents a multi-agent computational testbed simulating the social aggregation behavior of Dictyostelium discoideum, focusing on agent interactions, physical variables, and robustness to noise, providing a new tool for biological studies.

Contribution

It introduces a novel multi-agent simulation framework for micro-biological systems, specifically modeling social amoeba aggregation, with validation and analysis of physical and noise effects.

Findings

Aggregation patterns depend on amoeba density and number.

The dynamics show robustness against various noise sources.

The model offers a validated methodology for future biological simulations.

Abstract

Using a Multi-agent systems paradigm, the present project develops, validates and exploits a computational $\textit{testbed}$ that simulates micro-biological complex systems, namely the aggregation patterns of the social amoeba $\textit{Dyctiostelium discoideum}$. We propose a new design and implementation for managing discrete simulations with autonomous agents on a microscopic scale, thus focusing on their social behavior and mutual interactions. Then, the dependence on the main physical variables is tested, namely density and number of amoebas; in addition, we analyze the robustness of the dynamics against various noise sources. Along with these results, we suggest a methodology for further studies that make use of our validated model.