In this thesis we study biochemical reaction networks where various reactions between biochemical species occur.; In the first chapter we introduce a background for deterministic and stochastic formulations of biochemical reaction networks based on graph theory. We present motivating examples for multiscale reaction networks, which are studied in the third chapter.; In the second chapter we study a stochastic model for a general system of first-order reactions in which each reaction may be either a conversion reaction or a catalytic reaction. The governing master equation is formulated in a manner that explicitly separates the effects of network topology from other aspects, and the evolution equations for the first two moments are derived.; In the third chapter, we focus on a multiscale analysis of biochemical reaction networks where reactions between various molecular species occur on different time scales. With application of a perturbation method and an invariant theory based on the graph theory we eliminate fast kinetics and reduce the system in deterministic and stochastic descriptions.; In the fourth chapter we do a stochastic analysis of reaction-diffusion systems with linear reactions. The steady-state noise is computed explicitly for strongly connected closed and open systems.
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