Comprehensive review of models and methods for inferences in bio-chemical reaction networks
The key processes in biological and chemical systems are described by networks of
chemical reactions. From molecular biology to biotechnology applications, computational …
chemical reactions. From molecular biology to biotechnology applications, computational …
Combining formal methods and Bayesian approach for inferring discrete-state stochastic models from steady-state data
Stochastic population models are widely used to model phenomena in different areas such
as cyber-physical systems, chemical kinetics, collective animal behaviour, and beyond …
as cyber-physical systems, chemical kinetics, collective animal behaviour, and beyond …
Data-informed parameter synthesis for population Markov chains
Stochastic population models are widely used to model phenomena in different areas such
as chemical kinetics or collective animal behaviour. Quantitative analysis of stochastic …
as chemical kinetics or collective animal behaviour. Quantitative analysis of stochastic …
Bounding mean first passage times in population continuous-time Markov chains
We consider the problem of bounding mean first passage times and reachability
probabilities for the class of population continuous-time Markov chains, which capture …
probabilities for the class of population continuous-time Markov chains, which capture …
Variance reduction in stochastic reaction networks using control variates
Monte Carlo estimation in plays a crucial role in stochastic reaction networks. However,
reducing the statistical uncertainty of the corresponding estimators requires sampling a large …
reducing the statistical uncertainty of the corresponding estimators requires sampling a large …
Moment-based parameter inference with error guarantees for stochastic reaction networks
Inferring parameters of models of biochemical kinetics from single-cell data remains
challenging because of the uncertainty arising from the intractability of the likelihood function …
challenging because of the uncertainty arising from the intractability of the likelihood function …
Parameter estimation in biochemical models using moment approximations
K Hossain, RB Sidje - 2023 International Conference on …, 2023 - ieeexplore.ieee.org
Biochemical processes exhibiting stochastic fluctuations can be mathematically modeled
using the chemical master equation (CME). Because of the natural randomness of the …
using the chemical master equation (CME). Because of the natural randomness of the …
Neural Continuous-Time Markov Models
Continuous-time Markov chains are used to model stochastic systems where transitions can
occur at irregular times, eg, birth-death processes, chemical reaction networks, population …
occur at irregular times, eg, birth-death processes, chemical reaction networks, population …
Parameter synthesis and robustness analysis of rule-based models
Abstract We introduce the Quantitative Biochemical Space Language, a rule-based
language for a compact modelling of probabilistic behaviour of complex parameter …
language for a compact modelling of probabilistic behaviour of complex parameter …
Control variates for stochastic simulation of chemical reaction networks
Stochastic simulation is a widely used method for estimating quantities in models of
chemical reaction networks where uncertainty plays a crucial role. However, reducing the …
chemical reaction networks where uncertainty plays a crucial role. However, reducing the …