Identifying latent dynamic components in biological systems

Kondofersky, Ivan; Fuchs, Christiane; Theis, Fabian J.

doi:10.1049/iet-syb.2014.0013

Cited by 6 publications

(15 citation statements)

References 48 publications

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“…This assumption can be relaxed and we could allow for external catalysts, which were previously not part of the modelled species. We studied this network extension in another manuscript [13] and aim at combining both methods in the future.…”

Section: Discussionmentioning

confidence: 99%

“…In the first part of the proposed method, we estimate h h h(t) and interaction parameters k k k. Here, we approximate the observed time courses of x x x(t) by smoothing splines as done e. g. in [13] resulting in an estimtatex x x(t). This also presents an immediate approximation ofẋ x x(t) asx x x(t) = ∂ ∂tx x x(t).…”

Section: Estimation Of Hidden Catalystsmentioning

confidence: 99%

“…In applications, x obs i (t j ) often has a positive domain in which case (11) might be ill-defined. One possible solution for this might be log-normally distributed multiplicative noise as in [13]. Such error model is straightforward here, however, for reasons of notation simplicity, we only consider normally distributed errors in the manuscript and in the example section.…”

Section: Relating Hidden Catalysts To Network Componentsmentioning

confidence: 99%

“…with f θ θ θ (x obs i (t j )) representing the probability density function of the normal distribution in (13) In the next section, we will test the developed method on several artificial datasets and also apply it on real-world data from a biochemical pathway.…”

Section: Relating Hidden Catalysts To Network Componentsmentioning

confidence: 99%

“…We propose this approach as an extension of our previous work [13] where we considered a systematic network extension by by estimating the time courses of latent network components. The work in the present manuscript extends the available method in several ways.…”

Section: Introductionmentioning

confidence: 99%

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Inferring catalysis in biological systems

2016

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In systems biology, one is often interested in the communication patterns between several species, such as genes, enzymes or proteins. These patterns become more recognisable when temporal experiments are performed. This temporal communication can be structured by reaction networks such as gene regulatory networks or signalling pathways. Mathematical modelling of data arising from such networks can reveal important details, thus helping to understand the studied system. In many cases, however, corresponding models still deviate from the observed data. This may be due to unknown but present catalytic reactions. From a modelling perspective, the question of whether a certain reaction is catalysed leads to a large increase of model candidates. For large networks the calibration of all possible models becomes computationally infeasible. We propose a method which determines a substantially reduced set of appropriate model candidates and identifies the catalyst of each reaction at the same time. This is incorporated in a multiple-step procedure which first extends the network by additional latent variables and subsequently identifies catalyst candidates using similarity analysis methods. Results from synthetic data examples suggest a good performance even for non-informative data with few observations. Applied on CD95 apoptotic pathway our method provides new insights into apoptosis regulation.

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Section: Discussionmentioning

confidence: 99%

Section: Estimation Of Hidden Catalystsmentioning

confidence: 99%

Section: Relating Hidden Catalysts To Network Componentsmentioning

confidence: 99%

Section: Relating Hidden Catalysts To Network Componentsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Inferring catalysis in biological systems

2016

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Scalable Inference of Ordinary Differential Equation Models of Biochemical Processes

Fröhlich

Loos

Hasenauer

2018

Methods in Molecular Biology

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Ordinary differential equation models have become a standard tool for the mechanistic description of biochemical processes. If parameters are inferred from experimental data, such mechanistic models can provide accurate predictions about the behavior of latent variables or the process under new experimental conditions. Complementarily, inference of model structure can be used to identify the most plausible model structure from a set of candidates, and, thus, gain novel biological insight. Several toolboxes can infer model parameters and structure for small-to medium-scale mechanistic models out of the box. However, models for highly multiplexed datasets can require hundreds to thousands of state variables and parameters. For the analysis of such large-scale models, most algorithms require intractably high computation times. This chapter provides an overview of state-of-the-art methods for parameter and model inference, with an emphasis on scalability.

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Do-calculus enables estimation of causal effects in partially observed biomolecular pathways

et al. 2022

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Motivation Estimating causal queries, such as changes in protein abundance in response to a perturbation, is a fundamental task in the analysis of biomolecular pathways. The estimation requires experimental measurements on the pathway components. However, in practice many pathway components are left unobserved (latent) because they are either unknown, or difficult to measure. Latent variable models (LVMs) are well-suited for such estimation. Unfortunately, LVM-based estimation of causal queries can be inaccurate when parameters of the latent variables are not uniquely identified, or when the number of latent variables is misspecified. This has limited the use of LVMs for causal inference in biomolecular pathways. Results In this article, we propose a general and practical approach for LVM-based estimation of causal queries. We prove that, despite the challenges above, LVM-based estimators of causal queries are accurate if the queries are identifiable according to Pearl’s do-calculus and describe an algorithm for its estimation. We illustrate the breadth and the practical utility of this approach for estimating causal queries in four synthetic and two experimental case studies, where structures of biomolecular pathways challenge the existing methods for causal query estimation. Availability and implementation The code and the data documenting all the case studies are available at https://github.com/srtaheri/LVMwithDoCalculus. Supplementary information Supplementary data are available at Bioinformatics online.

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Identifying latent dynamic components in biological systems

Cited by 6 publications

References 48 publications

Inferring catalysis in biological systems

Inferring catalysis in biological systems

Scalable Inference of Ordinary Differential Equation Models of Biochemical Processes

Do-calculus enables estimation of causal effects in partially observed biomolecular pathways

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