Kinetic Modelling in Systems Biology by Oleg Demin download in iPad, ePub, pdf
Assume that we are studying a receptor R system that binds a ligand L. We follow that with a discussion of shortcomings and size scaling properties of existing stochastic simulation algorithms. It explores both the methods and applications of kinetic modeling in this emerging field. However, the resulting network model has a much higher dimensional state space and consists of a much larger set of reaction channels. We present the details of the existing kinetic simulation methods, and discuss their strengths and shortcomings.
In contrast, discrete stochastic simulations model each individual reaction event. Development of approximate algorithms was also pursued. The dimensionality of the state space and the number of chemical reaction channels have been increased by a factor of K, the number of subvolumes. Background and Introduction Reactions occurring among a set of reactants define a kinetic reaction network. We then conclude by briefly highlighting the future research trends from our perspective.
For example, energy metabolism of many bacteria requires cytochrome shuffling as part of the electron transfer mechanism. Abstract The dynamics of how the constituent components of a natural system interact defines the spatio-temporal response of the system to stimuli. However, a deterministic approach alone may not be sufficient for cellular systems when the underlying problem is inherently stochastic.
This is particularly true for molecular species involved in the transcriptional regulation of gene expression. Although it is still relatively uncommon, a hybrid approach which moves between deterministic and stochastic regimes is another possibility. Therefore, when feasible, stochastic simulation methods should be preferred. The relative concentration of the organisms and their geometrical arrangement can lead to heterogeneous distributions reflecting local variations in the microbial composition.
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