Titlebook: Computational Methods in Systems Biology; 14th International C Ezio Bartocci,Pietro Lio,Nicola Paoletti Conference proceedings 2016 Springe

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Generalized Method of Moments for Stochastic Reaction Networks in Equilibriumeither on statistical sampling or can only be applied to small systems. Here we present an inference procedure for stochastic models in equilibrium that is based on a moment matching scheme with optimal weighting and that can be used with high-throughput data like the one collected by flow cytometry

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Inference of Delayed Biological Regulatory Networks from Time Series Datal observations. But with the development of high-throughput data, there is a growing need for methods that automatically generate admissible models. Our research aim is to provide a logical approach to infer BRNs based on given time series data and known influences among genes. In this paper, we pro

CRAB

Matching Models Across Abstraction Levels with Gaussian Processesoften provide qualitatively concordant predictions over specific parametrisations, but it is generally unclear whether model predictions are quantitatively in agreement, and whether such agreement holds for different parametrisations. Here we present a generally applicable statistical machine learni

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Target Controllability of Linear Networkss like cancer. Recent research in the area of network science has shown that network control theory can be a powerful tool in the understanding and manipulation of such bio-medical networks. In 2011, Liu et al. developed a polynomial time optimization algorithm for computing the size of the minimal

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High-Performance Symbolic Parameter Synthesis of Biological Models: A Case Studynd therefore it is hard and computationally demanding to find admissible parameter values with respect to hypothesised constraints and wet-lab measurements. Recently, we have developed several high-performance techniques for parameter synthesis that are based on parallel coloured model checking. The

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Local Traces: An Over-Approximation of the Behaviour of the Proteins in Rule-Based Modelsuld be to understand how the behaviour of these systems emerges from these low-level interactions. Yet this is a quite long term challenge and it is desirable to offer intermediary levels of abstraction, so as to get a better understanding of the models and to increase our confidence within our mech

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