Layered decomposition for the model order reduction of timescale separated biochemical reaction networks

Prescott, Thomas P.; Papachristodoulou, Antonis

doi:10.1016/j.jtbi.2014.04.007

“…More recently, Prescott and Papachristodoulou have developed a variant of this approach (Prescott and Papachristodoulou 2013, 2014) that further generalises the process of dividing such systems based upon differences in reaction timescales and hence partitioning the columns of the stoichiometry matrix. This work yielded an automatable model decomposition method they term layering (Prescott and Papachristodoulou 2014).…”

Section: Model Reduction Methodsmentioning

confidence: 99%

Methods of Model Reduction for Large-Scale Biological Systems: A Survey of Current Methods and Trends

Snowden

¹

,

Graaf

²

,

Tindall

³

2017

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Complex models of biochemical reaction systems have become increasingly common in the systems biology literature. The complexity of such models can present a number of obstacles for their practical use, often making problems difficult to intuit or computationally intractable. Methods of model reduction can be employed to alleviate the issue of complexity by seeking to eliminate those portions of a reaction network that have little or no effect upon the outcomes of interest, hence yielding simplified systems that retain an accurate predictive capacity. This review paper seeks to provide a brief overview of a range of such methods and their application in the context of biochemical reaction network models. To achieve this, we provide a brief mathematical account of the main methods including timescale exploitation approaches, reduction via sensitivity analysis, optimisation methods, lumping, and singular value decomposition-based approaches. Methods are reviewed in the context of large-scale systems biology type models, and future areas of research are briefly discussed.Electronic supplementary materialThe online version of this article (doi:10.1007/s11538-017-0277-2) contains supplementary material, which is available to authorized users.

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“…A number of previous studies have addressed model reduction for biochemical systems, as reviewed in [10]. Some examples include reduction by topological modifications to resolve non-identifiability in models [11,12] and reduction by timescale partitioning [13][14][15]. Nonidentifiability arises when multiple unique parameterizations of a model give the same model output.…”

Section: Introductionmentioning

confidence: 99%

Parallel Tempering with Lasso for model reduction in systems biology

Gupta

¹

,

Lee

²

,

Faeder

³

2020

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Systems Biology models reveal relationships between signaling inputs and observable molecular or cellular behaviors. The complexity of these models, however, often obscures key elements that regulate emergent properties. We use a Bayesian model reduction approach that combines Parallel Tempering with Lasso regularization to identify minimal subsets of reactions in a signaling network that are sufficient to reproduce experimentally observed data. The Bayesian approach finds distinct reduced models that fit data equivalently. A variant of this approach that uses Lasso to perform selection at the level of reaction modules is applied to the NF-κB signaling network to test the necessity of feedback loops for responses to pulsatile and continuous pathway stimulation. Taken together, our results demonstrate that Bayesian parameter estimation combined with regularization can isolate and reveal core motifs sufficient to explain data from complex signaling systems. Author summaryCells respond to diverse environmental cues using complex networks of interacting proteins and other biomolecules. Mathematical and computational models have become invaluable tools to understand these networks and make informed predictions to rationally perturb cell behavior. However, the complexity of detailed models that try to capture all known biochemical elements of signaling networks often makes it difficult to determine the key regulatory elements that are responsible for specific cell behaviors. Here, we present a Bayesian computational approach, PTLasso, to automatically extract minimal subsets of detailed models that are sufficient to explain experimental data. The method simultaneously calibrates and reduces models, and the Bayesian approach samples globally, allowing us to find alternate mechanistic explanations for the data if present. We demonstrate the method on both synthetic and real biological data and show that PTLasso is an effective method to isolate distinct parts of a larger signaling model that are sufficient for specific data. PLOS COMPUTATIONAL BIOLOGYPLOS Computational Biology | https://doi.

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“…Previous studies have addressed model reduction for biochemical systems [10]. Some examples include reductions by topological modifications to resolve non-identifiability in models [11,12] and reductions by timescale partitioning [13,14]. Non-identifiability arises when multiple unique parameterizations of a model give the same model output.…”

Section: Introductionmentioning

confidence: 99%

Parallel Tempering with Lasso for Model Reduction in Systems Biology

Gupta

¹

,

Lee

²

,

Faeder

³

2019

Preprint

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Systems Biology models reveal relationships between signaling inputs and observable molecular or cellular behaviors. The complexity of these models, however, often obscures key elements that regulate emergent properties. We use a Bayesian model reduction approach that combines Parallel Tempering with Lasso regularization to identify minimal subsets of reactions in a signaling network that are sufficient to reproduce experimentally observed data. The Bayesian approach finds distinct reduced models that fit data equivalently. A variant of this approach based on Group Lasso is applied to the NF-κB signaling network to test the necessity of feedback loops for responses to pulsatile and continuous pathway stimulation. Taken together, our results demonstrate that Bayesian parameter estimation combined with regularization can isolate and reveal core motifs sufficient to explain data from complex signaling systems.August 1, 2019 1/23 separations in the reaction kinetics to apply model reduction based on quasi-steady-state and related approximations [10,14]. Both of these methods generate reduced models but do not carry out parameter estimation to fit experimental data. Another approach to model reduction that includes parameter estimation [15] uses mixed-integer nonlinear optimization to combine parameter estimation with model reduction by reaction elimination, a technique common in the field of chemical engineering [16,17]. Drawbacks of this approach are that it requires an additional binary parameter for every reaction in the model and that the genetic algorithms used for the optimization only provide point estimates of the parameters.Here, we develop reaction elimination in a Bayesian framework that combines parameter estimation and model reduction without requiring additional parameters. BPE has been shown to be useful to characterize high-dimensional, rugged, multimodal parameter landscapes common to systems biology models [1,[18][19][20][21], but it suffers from the drawback that the Markov Chain Monte Carlo (MCMC) methods commonly used to sample model parameter space are often slow to converge and do not scale well with the number of model parameters. We recently showed that Parallel Tempering (PT), a physics-based method for accelerating MCMC [22], outperforms conventional MCMC for systems biology models with up to dozens of parameters [18]. Here, we apply Lasso (also known as L1 regularization), a penalty on the absolute values of the parameters being optimized, to carry out model reduction. In the fields of statistics and machine learning, Lasso is widely used for variable selection to identify a parsimonious model -a minimal subset of variables required to explain the data [23]. In the context of biology, Lasso has been widely applied to gene expression and genomic data typically in combination with standard regression techniques [24][25][26][27][28] and less commonly in Bayesian frameworks [29,30]. In the mechanistic modeling context, Lasso regression has been used to predict cell type specific parameters in ...

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Layered decomposition for the model order reduction of timescale separated biochemical reaction networks

Cited by 35 publications

References 20 publications

Methods of Model Reduction for Large-Scale Biological Systems: A Survey of Current Methods and Trends

Methods of Model Reduction for Large-Scale Biological Systems: A Survey of Current Methods and Trends

Parallel Tempering with Lasso for model reduction in systems biology

Parallel Tempering with Lasso for Model Reduction in Systems Biology

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