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  • Open Daily: 10am - 10pm
    Alley-side Pickup: 10am - 7pm

    3038 Hennepin Ave Minneapolis, MN
    612-822-4611

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
3038 Hennepin Ave Minneapolis, MN
612-822-4611
6th Workshop on Computation of Biochemical Pathways and Genetic Networks

6th Workshop on Computation of Biochemical Pathways and Genetic Networks

Hubner, Katrin
Johann, Tim
Kummer, Ursula

Paperback

General ComputersGeneral Science

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ISBN10: 3832525874
ISBN13: 9783832525873
Publisher: Logos Verlag Berlin
Published: Sep 10 2010
Pages: 134
Weight: 0.52
Height: 0.20 Width: 6.60 Depth: 9.40
Language: English
This paper describes how to conduct Baysesian inference for fitting a dynamic stochastic process model using a stochastic simulator. In particular, we consider a complex nonlinear continuous time latent stochastic process of the celluar response to DNA damage. it is compared to time course data on the levels of two key proteins involved in this response, captured at the level of individual cells in a human cancer cell line. The primary goal is to calibratethe model by finding parameters of the model (kinetic rate constants) that are most consistent with the experimental data. Significant amounts of prior information are the model parameters. We use sophisticated Markov chain Monte Carlo methods to overcome the formidable computational challenges. Fuller details of the building and assessment of this model can be found in.

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