Algebraic statistical model for biochemical network dynamics inference

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چکیده

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Algebraic Statistical Model for Biochemical Network Inference

We describe a statistical method for predicting most likely reactions in a biochemical reaction network from the longitudinal data on species concentrations. Such data is relatively easily available in biochemical laboratories, for instance, via the popular RTPCR technology. Under the assumed kinetics of the law of mass action, we also propose the data-based procedures for (i) estimating the pr...

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We describe a statistical method for predicting most likely reactions in a biochemical reaction network from the longitudinal data on species concentrations. Such data is relatively easily available in biochemical laboratories, for instance, via the popular RT-PCR technology. Under the assumed kinetics of the law of mass action, we also propose the data-based algorithms for estimating the predi...

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MOTIVATION Networks are widely used as structural summaries of biochemical systems. Statistical estimation of networks is usually based on linear or discrete models. However, the dynamics of biochemical systems are generally non-linear, suggesting that suitable non-linear formulations may offer gains with respect to causal network inference and aid in associated prediction problems. RESULTS W...

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ژورنال

عنوان ژورنال: Journal of Coupled Systems and Multiscale Dynamics

سال: 2013

ISSN: 2330-152X

DOI: 10.1166/jcsmd.2013.1032