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https://www.selleckchem.com/products/msu-42011.html Stochastic simulation algorithms are extensively used for exploring stochastic behavior of biochemical pathways/networks. Computational cost of these algorithms is high in simulating real biochemical systems due to their large size, complex structure and stiffness. In order to reduce the computational cost, several algorithms have been developed. It is observed that these algorithms are basically fast in simulating weakly coupled networks. In case of strongly coupled networks, they become slow as their computational cost become high in maintaining complex data structures. Here, we develop Block Search Stochastic Simulation Algorithm (BlSSSA). BlSSSA is not only fast in simulating weakly coupled networks but also fast in simulating strongly coupled and stiff networks. We compare its performance with other existing algorithms using two hypothetical networks, viz., linear chain and colloidal aggregation network, and three real biochemical networks, viz., B cell receptor signaling network, FceRI signaling network and a stiff 1,3-Butadiene Oxidation network. It has been shown that BlSSSA is faster than other algorithms considered in this study.Recent advances of microelectrode-dot-array (MEDA) based Biochips have revolutionized the application of Lab-on-chip devices. New techniques for MEDA based biochips confide on the concepts on computer-aided design automation and cyberphysical integration to provide ease of use, higher throughput and reliability. One of the major security concerns in MEDA based biochips is actuation tempering attacks targeted to change control sequence daisy chain input resulting in incorrect bioassays. In this paper, we attempted to identify different types of actuation tampering attacks specific to MEDA based biochips. We proposed one technique to detect errors in order to secure the biochips against actuation tempering attacks. This proposed technique is able to monitor such malicious operations

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