Adaptive moment closure for parameter inference of biochemical reaction networks

Schilling, Christian; Bogomolov, Sergiy; Henzinger, Thomas A.; Podelski, Andreas; Ruess, Jakob

doi:10.1016/j.biosystems.2016.07.005

Cited by 8 publications

(5 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Consequently, as long as a suitable moment closure scheme can be used to establish quantitative relationships between moments and unknown parameters, the uncertainty of estimations can be quantified using either the frequentist or the Bayesian likelihood-based method. Similar ideas can also be found in the work of Ruess, Milias-Argeitis and Lygeros (2013), Ruess and Lygeros (2015), Fröhlich, et al,(2016) and Schilling, et al, (2016).…”

Section: Inference Approachsupporting

confidence: 74%

See 1 more Smart Citation

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

Du,

Wong

2016

Preprint

View full text Add to dashboard Cite

Intrinsic noise, the stochastic cell-to-cell fluctuations in mRNAs and proteins, has been observed and proved to play an important roles in cellular systems. Due to recent developments in single-cell-level measurement technology, the studies on intrinsic noise are becoming increasingly popular among scholars. The chemical master equation (CME) has been used to model the evolution of complex chemical and biological systems since 1940, and is often put forth as the standard tool for modeling intrinsic noise in gene regulation systems. A CME-based model can capture the discrete, stochastic, and dynamical nature of gene regulation systems, and may offer causal and physical explanations of the observed data at single-cell level. Nonetheless, the complexity of the CME also poses a serious challenge for researchers in proposing practical modeling and inference frameworks. In this article, we will review the existing works on the modeling and inference of intrinsic noise in gene regulation systems within the framework of the CME model. We will explore the principles of constructing a CME model for studying gene regulation systems and discuss the popular approximations of the CME. Then we will study the simulation simulation methods as well as the analytical and numerical approaches that can be used to solve the CME model. Finally we will summarize the existing statistical methods that can be used to infer unknown parameters or structures in the CME model using single-cell-level gene expression data.

show abstract

Section: Inference Approachsupporting

confidence: 74%

“…Unfortunately, such error is often hard to evaluate in practice. Schilling, et al, (2016) proposed an adaptive algorithm to handle this issue. Their approach utilizes a simulation algorithm to generate samples of the estimated CME model, where the parameter values are inferred based on various moment closure schemes.…”

Section: Inference Approachmentioning

confidence: 99%

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

Du,

Wong

2016

Preprint

View full text Add to dashboard Cite

show abstract

“…However, as pointed out in [13,15,16] these moment-based methods are not always suitable for inference. Computing the moments for reaction systems with bimolecular interactions usually necessitates the use of so-called moment closure approximations, validity conditions for which are not well-understood [17][18][19]. Given the wide variety of moment closure schemes it is not generally clear a priori which, if any, will prove suitable for a given reaction system, and the right method is usually chosen empirically based on its performance [20].…”

Section: Introductionmentioning

confidence: 99%

Parameter estimation for biochemical reaction networks using Wasserstein distances

Öcal¹,

Grima²,

Sanguinetti³

2019

J. Phys. A: Math. Theor.

View full text Add to dashboard Cite

We present a method for estimating parameters in stochastic models of biochemical reaction networks by fitting steady-state distributions using Wasserstein distances. We simulate a reaction network at different parameter settings and train a Gaussian process to learn the Wasserstein distance between observations and the simulator output for all parameters. We then use Bayesian optimization to find parameters minimizing this distance based on the trained Gaussian process. The effectiveness of our method is demonstrated on the three-stage model of gene expression and a genetic feedback loop for which moment-based methods are known to perform poorly. Our method is applicable to any simulator model of stochastic reaction networks, including Brownian Dynamics.

show abstract

“…These methods are popular, since they are easy to implement, efficient computationally, do not require complete statistical description, and also achieve good accuracy if species appear in large copy numbers (Schnoerr et al, 2017). Moment closure methods leading to coupled ODEs can approach CME solution with a low computational complexity (Fröhlich et al, 2016;Bogomolov et al, 2015;Schilling et al, 2016). Specifically, the n-th moment of population size depends on its (n + 1) moment.…”

Section: Modeling Brns By Approximationsmentioning

confidence: 99%

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

2019

View full text Add to dashboard Cite

The key processes in biological and chemical systems are described by networks of chemical reactions. From molecular biology to biotechnology applications, computational models of reaction networks are used extensively to elucidate their non-linear dynamics. The model dynamics are crucially dependent on the parameter values which are often estimated from observations. Over the past decade, the interest in parameter and state estimation in models of (bio-) chemical reaction networks (BRNs) grew considerably. The related inference problems are also encountered in many other tasks including model calibration, discrimination, identifiability, and checking, and optimum experiment design, sensitivity analysis, and bifurcation analysis. The aim of this review paper is to examine the developments in literature to understand what BRN models are commonly used, and for what inference tasks and inference methods. The initial collection of about 700 documents concerning estimation problems in BRNs excluding books and textbooks in computational biology and chemistry were screened to select over 270 research papers and 20 graduate research theses. The paper selection was facilitated by text mining scripts to automate the search for relevant keywords and terms. The outcomes are presented in tables revealing the levels of interest in different inference tasks and methods for given models in the literature as well as the research trends are uncovered. Our 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. The most common models of BRNs in literature involve differential equations, Markov processes, mass action kinetics, and state space representations whereas the most common tasks are the parameter inference and model identification. The most common methods in literature are Bayesian analysis, Monte Carlo sampling strategies, and model fitting to data using evolutionary algorithms. The new research problems which cannot be directly deduced from the text mining data are also discussed.

show abstract

Adaptive moment closure for parameter inference of biochemical reaction networks

Cited by 8 publications

References 32 publications

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

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

Parameter estimation for biochemical reaction networks using Wasserstein distances

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

Contact Info

Product

Resources

About