How to deal with parameters for whole-cell modelling

Babtie, Ann C.; Stumpf, Michael

doi:10.1098/rsif.2017.0237

Cited by 82 publications

(79 citation statements)

References 105 publications

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“…We anticipate that, with the advent of whole cell models [27,191,192] and other large-scale models [193,194], the demand for scalable methods will drastically increase in the coming years. However, already for medium-scale models, which are much more commonplace, parameter inference and in particular structure inference can be challenging.…”

Section: Discussionmentioning

confidence: 99%

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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Section: Discussionmentioning

confidence: 99%

Scalable Inference of Ordinary Differential Equation Models of Biochemical Processes

Fröhlich

Loos

Hasenauer

2018

Methods in Molecular Biology

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“…Model identifiability formulated as observability was considered in (Geffen et al, 2008) to replace traditional analytical approaches which often require model simplifications with more deterministic empirical methods. Changes in structural and practical identifiability of models with availability of new knowledge and data is studied in (Babtie and Stumpf, 2017). Global observability and detectability of reaction systems was studied in (Jaime and Denis, 2015).…”

Section: Review Of Parameter Estimation Strategies For Brnsmentioning

confidence: 99%

“…Cited by Abdullah et al (2013a) Cited by Abdullah et al (2013c) Cited by Alberton et al (2013) Cited by Ale et al (2013) Cited by Ali et al (2015) Cited by Amrein and Künsch (2012) Cited by Anai et al (2006) Cited by Andreychenko et al (2011) Cited by Andreychenko et al (2012) Cited by Andrieu et al (2010) Cited by Angius and Horváth (2011) Cited by Arnold et al (2014) Cited by Ashyraliyev et al (2009) Cited by Babtie and Stumpf (2017) Cited by Backenköhler et al (2016) Cited by Baker et al (133, 2010) Cited by Baker et al (2011) Cited by Baker et al (2013) Cited by Baker et al (2015) Cited by Banga and Canto (2008) Cited by Barnes et al (2011) Cited by Bayer et al (2015) Cited by Berrones et al (2016) Cited by Besozzi et al (2009) Cited by Bhaskar et al (2010) Cited Bogomolov et al (2015) Cited by Bouraoui et al (2015) Cited by Farza et al (2016) Cited by Boys et al (2008) Cited by Bronstein et al (2015) Cited by Brunel et al (2014) Cited by Busetto and Buhmann (2009) Cited by Camacho et al (2018) Cited by Balsa-Canto et al (2008) Cited by Carmi et al (2013) Cited by Cazzaniga et al (2015) Cited by Cedersund et al (2016) Cited by Ceska et al (2014) Cited by Ceška et al (2017) Cited by…”

Section: Abdullah Et Al (2013b)mentioning

confidence: 99%

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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“…Methods for uncertainty quantification and sensitivity analysis that take the entire parameter space into account are often called global methods (Borgonovo and Plischke, 2016;Babtie and Stumpf, 2017). Global methods are only occasionally used within the field of neuroscience (see e.g., Torres Valderrama et al (2015); Halnes et al (2009)).…”

Section: Introductionmentioning

confidence: 99%

Uncertainpy: A Python toolbox for uncertainty quantification and sensitivity analysis in computational neuroscience

Tennøe

Halnes

Einevoll

2018

Preprint

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Computational models in neuroscience typically contain many parameters that are poorly constrained by experimental data. Uncertainty quantification and sensitivity analysis provide rigorous procedures to quantify how the model output depends on this parameter uncertainty. Unfortunately, the application of such methods is not yet standard within the field of neuroscience.Here we present Uncertainpy, an open-source Python toolbox, tailored to perform uncertainty quantification and sensitivity analysis of neuroscience models. Uncertainpy aims to make it easy and quick to get started with uncertainty analysis, without any need for detailed prior knowledge. The toolbox allows uncertainty quantification and sensitivity analysis to be performed on already existing models without needing to modify the model equations or model implementation. Uncertainpy bases its analysis on polynomial chaos expansions, which are more efficient than the more standard Monte-Carlo based approaches.Uncertainpy is tailored for neuroscience applications by its built-in capability for calculating characteristic features in the model output. The toolbox does not merely perform a point-topoint comparison of the "raw" model output (e.g. membrane voltage traces), but can also calculate the uncertainty and sensitivity of salient model response features such as spike timing, action potential width, mean interspike interval, and other features relevant for various neural and neural network models. Uncertainpy comes with several common models and features built in, and including custom models and new features is easy.The aim of the current paper is to present Uncertainpy for the neuroscience community in a useroriented manner. To demonstrate its broad applicability, we perform an uncertainty quantification and sensitivity analysis on three case studies relevant for neuroscience: the original Hodgkin-Huxley point-neuron model for action potential generation, a multi-compartmental model of a thalamic interneuron implemented in the NEURON simulator, and a sparsely connected recurrent network model implemented in the NEST simulator. . 5, 2018; Keywords: uncertainty quantification, sensitivity analysis, features, polynomial chaos, quasi-Monte Carlo methods, stochastic modeling, computational modeling, PythonBY 4.0 International license not peer-reviewed) is the author/funder. It is made available under a The copyright holder for this preprint (which was . http://dx.doi.org/10.1101/274779 doi: bioRxiv preprint first posted online Mar SIGNIFICANCE STATEMENTA major challenge in computational neuroscience is to specify the often large number of parameters that define the neuron and neural network models. Many of these parameters have an inherent variability, and some may even be actively regulated and change with time. It is important to know how the uncertainty in model parameters affects the model predictions. To address this need we here present Uncertainpy, an open-source Python toolbox tailored to perform uncertainty quantification and sensiti...

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How to deal with parameters for whole-cell modelling

Cited by 82 publications

References 105 publications

Scalable Inference of Ordinary Differential Equation Models of Biochemical Processes

Scalable Inference of Ordinary Differential Equation Models of Biochemical Processes

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

Uncertainpy: A Python toolbox for uncertainty quantification and sensitivity analysis in computational neuroscience

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