# Adaptive Moment Closure for Parameter Inference of Biochemical Reaction Networks

@article{Schilling2015AdaptiveMC, title={Adaptive Moment Closure for Parameter Inference of Biochemical Reaction Networks}, author={Christian Schilling and Sergiy Bogomolov and Thomas A. Henzinger and Andreas Podelski and Jakob Ruess}, journal={Bio Systems}, year={2015}, volume={149}, pages={ 15-25 } }

## 22 Citations

daptive moment closure for parameter inference of biochemical eaction networks

- Computer Science
- 2016

A moment-based parameter inference method that automatically chooses the most appropriate moment closure method, and adaptively changes the approximation during the parameter inference to ensure that always the best approximation is used, even in cases where different approximations are best in different regions of the parameter space.

Generalized method of moments for estimating parameters of stochastic reaction networks

- BiologyBMC Systems Biology
- 2016

A generalized method of moments approach for inferring the parameters of reaction networks based on a sophisticated matching of the statistical moments of the corresponding stochastic model and the sample moments of population snapshot data is proposed.

Wasserstein Distances for Estimating Parameters in Stochastic Reaction Networks

- Computer ScienceCMSB
- 2019

Modelling and inference for stochastic models of reaction networks remains challenging due to additional complexities not present in the deterministic case.

Parameter estimation for biochemical reaction networks using Wasserstein distances

- Computer ScienceJournal of Physics A: Mathematical and Theoretical
- 2019

A method for estimating parameters in stochastic models of biochemical reaction networks by fitting steady-state distributions using Wasserstein distances usingBayesian optimization to find parameters minimizing this distance based on the trained Gaussian process.

Exact lower and upper bounds on stationary moments in stochastic biochemical systems.

- MathematicsPhysical biology
- 2017

A novel method to find exact lower and upper bounds on stationary moments for a given arbitrary system of biochemical reactions by exploiting the fact that statistical moments of any positive-valued random variable must satisfy some constraints that are compactly represented through the positive semidefiniteness of moment matrices is proposed.

Comprehensive Review of Models and Methods for Inferences in Bio-Chemical Reaction Networks

- Computer ScienceFront. Genet.
- 2019

The findings indicate that many combinations of models, tasks and methods are still relatively unexplored, and there are many new research opportunities to explore combinations that have not been considered-perhaps for good reasons.

Stochastic Modeling and Statistical Inference of Intrinsic Noise in Gene Regulation System via Chemical Master Equation

- Computer Science
- 2016

The principles in constructing a CME model for studying gene regulation system are explored, the popular approximations of CME are discussed, and the exiting statistical methods that can be used to infer the unknown parameters or structures in CMEmodel using single-cell-level gene expression data are summary.

On moments and timing: stochastic analysis of biochemical systems

- Mathematics
- 2018

At the level of individual living cells, key species such as genes, mRNAs, and proteins are typically present in small numbers. Consequently, the biochemical reactions involving these species are…

Formal language for statistical inference of uncertain stochastic systems

- Computer Science
- 2016

This thesis introduces ProPPA, a process algebra for the specification of stochastic systems with uncertain parameters, and describes a new mathematical object capable of capturing this information, the first time that uncertainty has been incorporated into the syntax and semantics of a formal language.

Set-Based Analysis for Biological Modeling

- BiologyAutomated Reasoning for Systems Biology and Medicine
- 2019

This chapter investigates the use of set-based analysis techniques, designed to compute on sets of behaviors, for the validation of biological models under uncertainties and perturbations, so that the execution of the considered biological model under the influence of the synthesized parameters is guaranteed to satisfy a given constraint or property.

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A generalized method of moments approach for inferring the parameters of reaction networks based on a sophisticated matching of the statistical moments of the corresponding stochastic model and the sample moments of population snapshot data is proposed.

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The initial motivation for the method was improving over the performance of stochastic simulation and moment closure methods, and it is demonstrated that it can be used in an experimental setting to estimate moments of species that cannot be measured directly from time course measurements of the moments of other species.