PROJECT TITLE :

Block Search Stochastic Simulation Algorithm (BlSSSA) A Fast Stochastic Simulation Algorithm for Modeling Large Biochemical Networks

ABSTRACT:

For the purpose of investigating the stochastic behavior of biochemical pathways and networks, stochastic simulation algorithms are utilized to a great extent. When simulating real biochemical systems, these algorithms have a high computational cost due to the large size, complex structure, and stiffness of the systems they are modeling. Several algorithms have been developed in order to cut down on the amount of work that needs to be done. It has been discovered that these algorithms are, in general, very quick when simulating networks with a weak coupling. In the case of strongly coupled networks, this results in the networks becoming slow because the amount of computational resources required to maintain their complex data structures is high. The Block Search Stochastic Simulation Algorithm that we develop here is presented ( BlSSSA ). Not only is BlSSSA quick at simulating networks with weakly coupled connections, but it is also quick at simulating networks with strongly coupled connections and stiff connections. We evaluate its performance in comparison to that of other algorithms that are already in use by utilizing two hypothetical networks, namely a linear chain network and a colloidal aggregation network, as well as three real biochemical networks, namely a stiff 1,3-Butadiene Oxidation network, B cell receptor signaling network, and FceRI signaling network. It has been demonstrated that BlSSSA is a faster algorithm than the others that were taken into consideration for this study.