Multi-Agent based modeling and simulation of metabolic networks

Abstract

The cardinal role of metabolic engineering in the field of biotechnology is increasing day-by-day, as biotechnology has become a vital tool for almost every industry, including chemical, pharmaceutical, health care, and food industries. Effective genetic manipulation of cell metabolism for performance enhancement is a critical step in obtaining low cost and high yield production. Increasingly, mathematical models play an important role in this field; examples include computational tools for simulation, data evaluation, design of experiments, systems analysis, prediction, design, and optimization. The first step in developing a comprehensive metabolic model of a microorganism is to identify all the metabolic pathways for the organism from available databases (such as KEGG). Often, the databases are incomplete which leads to incorrect results when the resulting model is simulated. In this work, we present an agent-based modeling and simulation (ABMS) approach to analyze metabolic pathways for inconsistencies. In the proposed approach, the metabolic system is modeled using three types of agents: Reaction agent, Cytoplasm agent, and Scheduler agent. Each metabolic reaction in the system is represented by a Reaction agent. The Cytoplasm agent resembles the cellular environment and the Scheduler agent regulates the execution of reactions. Starting from the substrate (or minimal nutrient condition), reactions are qualitatively executed by the Scheduler in a sequential manner. The reachability of the final product indicates the completeness of the pathway. In case of an incomplete network, the minimal set of reactions necessary to reach the final pathway can also be identified by this approach. The proposed approach thus identifies gaps in the network through qualitative simulation and would hence serve as a precursor to numerical modeling & simulation. We illustrate the approach using a metabolic model of E. coli, that includes Glycolysis, Pentose-Phosphate pathway, TCA cycle, Anaplerotic reactions, Pyruvate metabolism, Respiration and transport system reactions. We have also extended the same agent-based framework to perform dynamic simulation when kinetics of metabolic reactions are available. Simulation results are presented to illustrate the proposed modeling and simulation approach and its effectiveness is evaluated through comparison with published literature.