Titlebook: Computational Methods in Systems Biology; 17th International C Luca Bortolussi,Guido Sanguinetti Conference proceedings 2019 Springer Natur

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A Large-Scale Assessment of Exact Model Reduction in the BioModels Repositoryon based on ordinary differential equations and continuous-time Markov chains, respectively. In most cases, these models do not enjoy analytical solution, thus typically requiring expensive computational methods based on numerical solvers or stochastic simulations. Exact model reduction techniques c

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Computing Difference Abstractions of Metabolic Networks Under Kinetic Constraints prediction precision, however, depends heavily on which heuristics are applied in order to add linear consequences of the steady state equations of the metabolic network. In this paper we ask the question whether such heuristics can be avoided while obtaining the highest possible precision. This le

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BRE:IN - A Backend for Reasoning About Interaction Networks with Temporal Logicby the RE:IN tool, where an Abstract Boolean Network (ABN) specifies partial information about the network topology, and experimental observations are used to constrain the ABN, allowing to synthesize consistent models, or prove that no consistent model exists. RE:IN has been used successfully to de

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https://doi.org/10.1007/978-3-319-72532-1nstraints on the statistical moments of the stochastic process to reduce the estimators’ variances. We develop an algorithm that selects appropriate control variates in an on-line fashion and demonstrate the efficiency of our approach on several case studies.

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https://doi.org/10.1057/9780230510975lt in varying glycemic control. Such logical characterizations can provide feedback to clinicians and their patients about behavioral changes that patients may implement to improve T1D control. We present both individual- and population-level behavior patterns learned from a clinical dataset of 21 T1D patients.

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Control Variates for Stochastic Simulation of Chemical Reaction Networksnstraints on the statistical moments of the stochastic process to reduce the estimators’ variances. We develop an algorithm that selects appropriate control variates in an on-line fashion and demonstrate the efficiency of our approach on several case studies.

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