On the Optimization of non-Dense Metabolic Networks in non-Equilibrium State Utilizing 2D-Lattice Simulation

TL;DR

This paper explores the optimization of dynamic, non-equilibrium metabolic networks with low molecule counts using 2D lattice simulation and swarm particle algorithms, addressing real-world biological complexity.

Contribution

It introduces a novel approach for optimizing non-equilibrium metabolic networks with low molecule counts using 2D lattice simulation and swarm algorithms.

Findings

Successful simulation of a prototyped network

Effective optimization achieved with swarm particle algorithm

Results demonstrate potential for realistic biological modeling

Abstract

Modeling and optimization of metabolic networks has been one of the hottest topics in computational systems biology within recent years. However, the complexity and uncertainty of these networks in addition to the lack of necessary data has resulted in more efforts to design and usage of more capable models which fit to realistic conditions. In this paper, instead of optimizing networks in equilibrium condition, the optimization of dynamic networks in non-equilibrium states including low number of molecules has been studied using a 2-D lattice simulation. A prototyped network has been simulated with such approach, and has been optimized using Swarm Particle Algorithm the results of which are presented in addition to the relevant plots.