Modular Parameter Identification of Biomolecular Networks

Lang, Moritz; Stelling, Jörg

doi:10.1137/15m103306x

Cited by 7 publications

(5 citation statements)

References 31 publications

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“…(cont.) Kulikov and Kulikova (2015a) Cited by Kulikov and Kulikova (2017) Cited by Kutalik et al (2007) Cited by Kuwahara et al (2013) Cited by Lakatos et al (2015) Cited by Lang and Stelling (2016) Cited by Li and Vu (2013) Cited by Li and Vu (2015) Cited by Liao et al (2015a) Cited by Liao et al (2015b) Cited by Liepe et al (2014) Cited by Lillacci and Khammash (2010a) Cited by Lillacci and Khammash (2010b) Cited by Lillacci and Khammash (2012) Cited by Lindera and Rempala (2015) Cited by Liu et al (2006) Cited by Liu and Wang (2008b) Cited by Liu and Wang (2008a) Cited by Liu and Wang (2009) Cited by Liu et al (2012) Cited by Liu and Gunawan (2014) Cited by Loos et al (2016) Cited by Lück and Wolf (2016) Cited by Mancini et al (2015) Cited by Mannakee et al (2016) Cited by Mansouri et al (2014) Cited by Mansouri et al (2015) Cited by Matsubara et al (2006) Cited by Mazur (2012) Cited by Mazur and Kaderali (2013) Cited by McGoff et al (2015) Cited by Meskin et al (2011) Cited by Meskin et al (2013) Cited by Meyer et al (2014) Cited by Michailidis and dAlché Buc (2013) Cited by Michalik et al (2009) Cited by Mihaylova et al (2011) Cited by Mihaylova et al (2012) Cited by…”

Section: Abdullah Et Al (2013b)mentioning

confidence: 99%

“…Probabilistic model checking can be used to facilitate robustness analysis of stochastic biochemical models ( Česka et al, 2014). Iterative, feedback dependent modularization of models with parameters identification was devised in (Lang and Stelling, 2016). Selection among hierarchical models assuming Akaike information was studied in (Rodriguez-Fernandez et al, 2013).…”

Section: Review Of Modeling Strategies For Brnsmentioning

confidence: 99%

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

2019

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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.

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Section: Abdullah Et Al (2013b)mentioning

confidence: 99%

Section: Review Of Modeling Strategies For Brnsmentioning

confidence: 99%

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

2019

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“…predicted protein abundance, in response to varying a single parameter). In some cases, experimental measurements of species concentrations may allow us to effectively decompose our models into smaller modules for efficient parameter estimation [73]. Bayesian inference methods in particular are limited in terms of scale and are generally only feasible for models with up to tens to hundreds of species and parameters [74,75].…”

Section: Application To Large-scale Hybrid Modelsmentioning

confidence: 99%

How to deal with parameters for whole-cell modelling

Babtie¹,

Stumpf²

2017

J. R. Soc. Interface.

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Dynamical systems describing whole cells are on the verge of becoming a reality. But as models of reality, they are only useful if we have realistic parameters for the molecular reaction rates and cell physiological processes. There is currently no suitable framework to reliably estimate hundreds, let alone thousands, of reaction rate parameters. Here, we map out the relative weaknesses and promises of different approaches aimed at redressing this issue. While suitable procedures for estimation or inference of the whole (vast) set of parameters will, in all likelihood, remain elusive, some hope can be drawn from the fact that much of the cellular behaviour may be explained in terms of smaller sets of parameters. Identifying such parameter sets and assessing their behaviour is now becoming possible even for very large systems of equations, and we expect such methods to become central tools in the development and analysis of whole-cell models.

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“…Statistical model selection (Burnham & Anderson, 2013) provides the tools to make such decisions, balancing the ability of a model to fit the data with the model's complexity. As larger and larger models, even models for whole cells (Karr et al, 2012(Karr et al, , 2015Lang & Stelling, 2016;, are being considered model selection problems will presumably become the norm, especially when models are constructed exhaustively or automatically (Ma et al, 2009;Barnes et al, 2011;Szederkényi et al, 2011;Sunnåker et al, 2014;Babtie et al, 2014;Leon et al, 2016;Gerardin et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Multi-Model and Network Inference Based on Ensemble Estimates: Avoiding the Madness of Crowds

Stumpf

2019

Preprint

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The development of mathematical models of biological systems has largely relied on a mix of biological intuition, mathematical expediency, and comparisons with data. Models are hard to develop and hard to validate. Recent progress in theoretical systems biology, applied mathematics and computational statistics has, however, led to a proliferation of models and methods that allow us to compare quantitatively the performance of different candidate models at describing a particular biological system or process. Model selection has been applied with great success to problems where a small number -typically less than 10 -of models are compared, but recently studies have started to consider thousands and even millions of candidate models. As formal computational and automated systems increasingly aim to replace or augment biological intuition this trend is likely to continue. Often no single model is chosen as the best, but a large number of models is found to be compatible with data, and these are then often studied as an ensemble. In other situations several prediction algorithms are used to make ensemble predictions, for example, about the presence or absence of molecular interactions. Using a set of test-cases I outline when such ensemble estimators can be relied upon, and when they are likely to fail. If model or predictor ensembles are not carefully constructed and curated, predictions and estimates based on ensembles can perform worse than good individual predictors.

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Modular Parameter Identification of Biomolecular Networks

Cited by 7 publications

References 31 publications

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

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

How to deal with parameters for whole-cell modelling

Multi-Model and Network Inference Based on Ensemble Estimates: Avoiding the Madness of Crowds

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