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Exploiting the information provided by the molecular noise of a biological process has proved to be valuable in extracting knowledge about the underlying kinetic parameters and sources of variability from single-cell measurements. However, quantifying this additional information a priori, to decide whether a single-cell experiment might be beneficial, is… (More)

Continuous-time Markov chains are commonly used in practice for modeling biochemical reaction networks in which the inherent randomness of the molecular interactions cannot be ignored. This has motivated recent research effort into methods for parameter inference and experiment design for such models. The major difficulty is that such methods usually… (More)

Systems biology rests on the idea that biological complexity can be better unraveled through the interplay of modeling and experimentation. However, the success of this approach depends critically on the informativeness of the chosen experiments, which is usually unknown a priori. Here, we propose a systematic scheme based on iterations of optimal… (More)

— In biochemical reaction networks stochasticity arising from molecular fluctuations often plays an important role. Recent years have seen an increasing number of studies which employed single-cell population experiments and used the measured stochasticity to estimate the parameters of stochastic kinetic models. Currently, there exist two approaches for… (More)

Citation: Parise F, Lygeros J and Ruess J (2015) Bayesian inference for stochastic individual-based models of ecological systems: a pest control simulation study. Mathematical models are of fundamental importance in the understanding of complex population dynamics. For instance, they can be used to predict the population evolution starting from different… (More)

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