A cellular network can be modelled mathematically using principles of chemical kinetics. These are particularly useful for studying the dynamic aspects of cells such as gene transcription, translation, regulation, and protein-protein interactions. Due to a large number of parameters, variables and constraints in cellular networks, numerical and computational techniques are often necessary. The development of computational approaches and analytical results to understand these dynamic processes is essential for the elucidation of cellular mechanisms. An increasing number of scientists are working to improve these approaches and to create, refine and test dynamical models in order to accurately reflect observations, and acquire predictive explanatory power. At the cellular level, chemical dynamics are often dominated by the action of regulatory molecules present at levels of only a few copies per cell. Intrinsic noise due to random fluctuations of these components appears to have significant consequences: the observed large variation in morphology, rates of development, physiological responses in a cell often lead to a randomization of phenotypic outcomes and non-genetic population heterogeneity. Hence, stochastic modeling of the molecular dynamics within a cell is necessary in order to fully describe a set of expected outcomes. In many cases of biological interest some of the chemical species in the network are present in much lower abundance than others and the reaction rate constants can var
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