@book{
 title = {A numerical aggregation algorithm for the enzyme-catalyzed substrate conversion},
 type = {book},
 year = {2006},
 source = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
 keywords = {[Aggregation, Biochemical reactions, Markov chain,},
 volume = {4210 LNBI},
 id = {dd2626e5-2087-35ab-ac81-c422e3f89b75},
 created = {2017-01-02T09:34:04.000Z},
 file_attached = {false},
 profile_id = {bbb99b2d-2278-3254-820f-2de6d915ce63},
 last_modified = {2017-03-22T13:51:34.979Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {false},
 hidden = {false},
 private_publication = {false},
 abstract = {Computational models of biochemical systems are usually very large, and moreover, if reaction frequencies of different reaction types differ in orders of magnitude, models possess the mathematical property of stiffness, which renders system analysis difficult and often even impossible with traditional methods. Recently, an accelerated stochastic simulation technique based on a system partitioning, the slow-scale stochastic simulation algorithm, has been applied to the enzyme-catalyzed substrate conversion to circumvent the inefficiency of standard stochastic simulation in the presence of stiffness. We propose a numerical algorithm based on a similar partitioning but without resorting to simulation. The algorithm exploits the connection to continuous-time Markov chains and decomposes the overall problem to significantly smaller sub-problems that become tractable. Numerical results show enormous efficiency improvements relative to accelerated stochastic simulation. © Springer-Verlag Berlin Heidelberg 2006.},
 bibtype = {book},
 author = {Busch, H. and Sandmann, W. and Wolf, V.}
}

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