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Systems biology is a rapidly emerging eld that investigates intra- and intercellular dynamics from a system-oriented point of view As such, it is highly interdisciplinary and combines theoretical, experimental and computational techniques from mathematics, computer science, and engineering with those from physics, chemistry, molecular and cell biology Biological systems, in the same way as arti cial or technical ones, consist of mutually related components that interact with each other and the system environment They are formidably complex, and developing computationally tractable models is desirable and necessary in order to gain insights and predict their behavior The reader is referred to [3, 41, 42] for expositions of general concepts and methods in systems biology, and more speci cally to [11, 74] for a focus on computational modeling and system-theoretical aspects The basic building blocks at the molecular level of biological systems are coupled biochemical reactions between different molecular species The fundamental rules are given by stoichiometric equations de ning which molecular species may react in order to result in a certain product and how many molecules are involved in the reaction Quantitative timing aspects are speci ed by reaction rates assigned to each reaction Sets of such molecular reactions constitute biological and genetic networks, also referred to as pathways, which are enormously complex as the set of reactions is huge Different (but related) modeling paradigms re ect different viewpoints or focuses As usual, models are distinguished in terms of their states as well as by the rules driving the possible state changes and governing the system dynamics They may be state-continuous or state-discrete, continuous or discrete in time, deterministic or stochastic For a long time the most common, somehow canonical approach was deterministic, where the system state at any time is given by the concentrations (measured in moles per liter) of each molecular species By the law of mass action, expressing the system dynamics in terms of the chemical rate equations yields a system of ordinary differential equations (ODEs) for the concentrations of molecular species However, this properly re ects neither the discreteness of molecular quantities nor the stochastic nature of chemical reactions The evidence of inherent randomness has been reported and pointed out in many studies, and stochastic models are today well established in system biology; see [6, 9, 32, 46, 47, 75, 79] to mention but a few particularly notable publications dealing speci cally with stochastic models The most widespread stochastic modeling approach to biochemically reacting systems that we adopt in this chapter is by continuous-time Markov chains (CTMCs), whose dynamics are described in terms of the chemical master equation As in other domains, the largeness of the models makes stochastic simulation a particular prominent analysis technique Accelerating simulations is motivated by the fact that stochastic simulations in general are computationally costly, meaning that the advantage of being able to deal with large models has to be offset by the signi cant amount of computer time required Rare events. java ean 13 reader Java Barcode Reader Tutorial to scan, read linear, 2d barcodes in ...
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