Inferring Reaction Networks using Perturbation Data

Choi, Kiri; Hellerstein, Joseph L.; Wiley, H. Steven; Sauro, HM

doi:10.1101/351767

Cited by 6 publications

(7 citation statements)

References 9 publications

(11 reference statements)

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“…However, developing predictive mechanistic models of signaling pathways is difficult due to the large number of interactive components (including potentially unknown interactions) and the sparsity of suitable data to calibrate them. To address this need, we have been developing perturbation-based approaches to infer the underlying topology of signaling networks [17]. Synthetic networks would be useful in this effort, but they must recapitulate the biophysical constraints that govern signaling pathways.…”

Section: Introductionmentioning

confidence: 99%

“…These include: The number of species, , and maximum number of reactions . Randomly assigned ranges for species concentrations and rate constants can be modified via and . can be used to set the factor that perturbs the concentration at the input species. Default is set to 2. The number of random networks to generate can be set by changing the variable . The file is the dynamic library for libRoadRunner that is used to provide SBML simulation support [17].…”

mentioning

confidence: 99%

“…The file is the dynamic library for libRoadRunner that is used to provide SBML simulation support [17].…”

mentioning

confidence: 99%

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Generating synthetic signaling networks for in silico modeling studies

Xu¹,

Wiley

Sauro³

2020

Preprint

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Predictive models of signaling pathways have proven to be difficult to develop. Reasons include the uncertainty in the number of species, the complexity in species' interactions, and the sparseness and uncertainty in experimental data. Traditional approaches to developing mechanistic models rely on collecting experimental data and fitting a single model to that data. This approach works for simple systems but has proven unreliable for complex systems such as biological signaling networks. For example, uncertainty and sparseness of the data often result in overfitted models that have little predictive value beyond recapitulating the experimental data itself. Thus, there is a need to develop new approaches to create predictive mechanistic models of complex systems. However, to determine the effectiveness of any new algorithm, a baseline model is needed to test its performance. To meet this need, we developed a method for generating artificial synthetic networks that are reasonably realistic and thus can be treated as ground truth models. These synthetic models can then be used to generate synthetic data for developing and testing algorithms designed to recover the underlying network topology and associated parameters. Here, we describe a simple approach for generating synthetic signaling networks that can be used for this purpose.

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Section: Introductionmentioning

confidence: 99%

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confidence: 99%

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Generating synthetic signaling networks for in silico modeling studies

Xu¹,

Wiley

Sauro³

2020

Preprint

Self Cite

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“…Less work is devoted to the learning of reaction models and chemical reaction networks (CRNs). In [1], this problem is defined as the minimization of a fitness criterion based on the compatibility of the learned mechanistic model with the observed traces. An evolutionary algorithm is proposed via a two-step iterative procedure: first a set of reactions is inferred, then mass action law kinetic parameters are estimated.…”

Section: Introductionmentioning

confidence: 99%

On Inferring Reactions from Data Time Series by a Statistical Learning Greedy Heuristics

Martinelli

Grignard

Soliman

et al. 2019

Computational Methods in Systems Biology

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With the automation of biological experiments and the increase of quality of single cell data that can now be obtained by phosphoproteomic and time lapse videomicroscopy, automating the building of mechanistic models from these data time series becomes conceivable and a necessity for many new applications. While learning numerical parameters to fit a given model structure to observed data is now a quite well understood subject, learning the structure of the model is a more challenging problem that previous attempts failed to solve without relying quite heavily on prior knowledge about that structure. In this paper, we consider mechanistic models based on chemical reaction networks (CRN) with their continuous dynamics based on ordinary differential equations, and finite time series about the time evolution of concentration of molecular species for a given time horizon and a finite set of perturbed initial conditions. We present a greedy heuristics unsupervised statistical learning algorithm to infer reactions with a time complexity for inferring one reaction in O(t.n 2) where n is the number of species and t the number of observed transitions in the traces. We evaluate this algorithm both on simulated data from hidden CRNs, and on real videomicroscopy single cell data about the circadian clock and cell cycle progression of NIH3T3 embryonic fibroblasts. In all cases, our algorithm is able to infer meaningful reactions, though generally not a complete set for instance in presence of multiple time scales or highly variable traces.

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“…While dynamic mechanistic models have been suggested for predicting low-dimensional quantities that characterize cellular response [11,12], such as a scalar measure of proliferation, they face fundamental problems. These models cannot be easily formulated in a data-driven way and require temporal resolution of the experimental data.…”

Section: Introductionmentioning

confidence: 99%

Generative modeling and latent space arithmetics predict single-cell perturbation response across cell types, studies and species

Lotfollahi

Wolf

Theis

2018

Preprint

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Accurately modeling cellular response to perturbations is a central goal of computational biology. While such modeling has been proposed based on statistical, mechanistic and machine learning models in specific settings, no generalization of predictions to phenomena absent from training data (‘out-of-sample’) has yet been demonstrated. Here, we present scGen, a model combining variational autoencoders and latent space vector arithmetics for high-dimensional single-cell gene expression data. In benchmarks across a broad range of examples, we show that scGen accurately models dose and infection response of cells across cell types, studies and species. In particular, we demonstrate that scGen learns cell type and species specific response implying that it captures features that distinguish responding from non-responding genes and cells. With the upcoming availability of large-scale atlases of organs in healthy state, we envision scGen to become a tool for experimental design through in silico screening of perturbation response in the context of disease and drug treatment.

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Inferring Reaction Networks using Perturbation Data

Cited by 6 publications

References 9 publications

Generating synthetic signaling networks for in silico modeling studies

Generating synthetic signaling networks for in silico modeling studies

On Inferring Reactions from Data Time Series by a Statistical Learning Greedy Heuristics

Generative modeling and latent space arithmetics predict single-cell perturbation response across cell types, studies and species

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