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
        }
}
daptive moment closure for parameter inference of biochemical eaction networks
TLDR
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
TLDR
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
TLDR
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
TLDR
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.
TLDR
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
TLDR
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
TLDR
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
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
TLDR
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
TLDR
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.
...
...

References

SHOWING 1-10 OF 32 REFERENCES
Moment-Based Methods for Parameter Inference and Experiment Design for Stochastic Biochemical Reaction Networks
TLDR
The theory behind moment-based methods for parameter inference and experiment design for continuous-time Markov chains is summarized and new case studies where their performance is investigated are provided.
Moment Fitting for Parameter Inference in Repeatedly and Partially Observed Stochastic Biological Models
TLDR
The potential of moment fitting for parameter inference by means of illustrative stochastic biological models from the literature is demonstrated and both deterministic and stochastics parameter inference algorithms are improved with respect to accuracy and efficiency.
Generalized method of moments for estimating parameters of stochastic reaction networks
TLDR
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.
Minimal moment equations for stochastic models of biochemical reaction networks with partially finite state space.
  • J. Ruess
  • Mathematics
    The Journal of chemical physics
  • 2015
TLDR
It is shown that the result that less moments provide more information can only stem from an unnecessarily complicated description of the system in the classical formulation, and a symbolic algorithm is provided that can be used to derive minimal systems of unconditional moment equations for models with partially finite state space.
Bayesian inference for stochastic individual-based models of ecological systems: a pest control simulation study
TLDR
Recent advances in parameter inference for continuous-time Markov chain models are presented, based on a moment closure approximation of the parameter likelihood, and how these results can help in understanding, and ultimately controlling, complex systems in ecology are investigated.
Moment-based inference predicts bimodality in transient gene expression
TLDR
This work demonstrates how cell-to-cell variability can be incorporated into the analysis obviating the need for the ubiquitous assumption that the measurements stem from a homogeneous cell population and proposes a new method that makes use of low-order moments of the measured distribution.
Markovian dynamics on complex reaction networks
Kinetic Theory Modeling and Efficient Numerical Simulation of Gene Regulatory Networks Based on Qualitative Descriptions
TLDR
Proper Generalized Decomposition (PGD) can be used to overcome the curse of dimensionality, providing fast and accurate solutions to an otherwise intractable problem.
Designing experiments to understand the variability in biochemical reaction networks
TLDR
An optimal experimental design framework is proposed which is employed to compare the utility of dual-reporter and perturbation experiments for quantifying the different noise sources in a simple model of gene expression and it is shown that well-chosen gene induction patterns may allow one to identify features of the system which remain hidden in unplanned experiments.
Moment estimation for chemically reacting systems by extended Kalman filtering.
TLDR
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.
...
...