Using the ant colony optimization algorithm in the network inference and parameter estimation of biochemical systems

Developing models that can represent biochemical systems is one of the hallmarks of systems biology. Scientists have been gathering data from actual experiments, but there is a lack in computer models that can be used by scientists in analysing the various biochemical systems more effectively. In this research, we propose to use an ant colony optimization (ACO) algorithm for the network inference and parameter estimation of biochemical systems, particularly S-systems. The ACO has been used for various problems, and with several improvements, it can also be used to solve the problems that we are considering. Since the ACO has discrete and continuous forms, we plan to use each form for the network inference and parameter estimation problems respectively. The results of our work show that the ACO can be effectively used in the formation of model for biochemical systems.