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      https://www.ias.ac.in/article/fulltext/sadh/040/02/0411-0423

    • Keywords

       

      Cellular automata; enzyme kinetics; extended von-Neumann neighborhood.

    • Abstract

       

      Biochemical processes occur through intermediate steps which are associated with the formation of reaction complexes. These enzyme-catalyzed biochemical reactions are inhibited in a number of ways such as inhibitors competing for the binding site directly, inhibitors deforming the allosteric site or inhibitors changing the structure of active substrate. Using an in silico approach, the concentration of various reaction agents can be monitored at every single time step, which are otherwise difficult to analyze experimentally. Cell-based models with discrete state variables, such as Cellular Automata (CA) provide an understanding of the organizational principles of interacting cellular systems to link the individual cell (microscopic) dynamics wit a particular collective (macroscopic) phenomenon. In this study, a CA model representing a first order enzyme kinetics with inhibitor activity is formulated. The framework of enzyme reaction rules described in this study is probabilistic. An extended von Neumann neighborhood with periodic boundary condition is implemented on a two-dimensional (2D) lattice framework. The effect of lattice-size variation is studied followed by a sensitivity analysis of the model output to the probabilistic parameters which represent various kinetic reaction constants in the enzyme kinetic model. This provides a deeper insight into the sensitivity of the CA model to these parameters. It is observed that cellular automata can capture the essential features of a discrete real system, consisting of space, time and state, structured with simple local rules without making complex implementations but resulting in complex but explainable patterns.

    • Author Affiliations

       

      Abhishek Dutta1 2 Saurajyoti Kar3 Advait Apte4 Ingmar Nopens1 Denis Constales5

      1. BIOMATH, Department of Mathematical Modelling, Statistics and Bioinformatics, Ghent University, Coupure Links 653, 9000 Ghent, Belgium
      2. Departement Materiaalkunde, KU Leuven, Kasteelpark Arenberg 44 bus 2450, B-3001 Heverlee-Leuven, Belgium
      3. Department of Biotechnology, National Institute of Technology, Durgapur 713 209, India
      4. Biological Systems Engineering Department, Virginia Tech, Blacksburg, VA 24061, USA
      5. Department of Mathematical Analysis, Ghent University, Galglaan 2, B-9000 Ghent, Belgium
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