A divide-and-conquer approach to analyze underdetermined biochemical models

Kotte, Oliver; Heinemann, Matthias

doi:10.1093/bioinformatics/btp004

Cited by 30 publications

(20 citation statements)

References 14 publications

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“…This idea has been cited in many publications and, unfortunately, has sometimes led to misinterpretations. Since parameter estimation is often an arduous task in practice, it is tempting to use the notion of sloppiness to argue that it is not necessary nor possible to uniquely determine the parameter values, thus justifying that no further efforts are invested to it (see for example [39,16,31,51,19]). The suggestion that sloppiness is a universal -or, more precisely, ubiquitous -property of systems biology models has spurred a debate: should modellers desist from trying to estimate precise values for the parameters and, instead, focus on characterizing model predictions?…”

Section: Introductionmentioning

confidence: 99%

On the relationship between sloppiness and identifiability

Chis

Villaverde

Banga

et al. 2016

Mathematical Biosciences

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Dynamic models of biochemical networks are often formulated as sets of non-linear ordinary differential equations, whose states are the concentrations or abundances of the network components. They typically have a large number of kinetic parameters, which must be determined by calibrating the model with experimental data. In recent years it has been suggested that dynamic systems biology models are universally sloppy, meaning that the values of some parameters can be perturbed by several orders of magnitude without causing significant changes in the model output. This observation has prompted calls for focusing on model predictions rather than on parameters. In this work we examine the concept of sloppiness, investigating its links with the long-established notions of structural and practical identifiability. By analysing a set of case studies we show that sloppiness is not equivalent to lack of identifiability, and that sloppy models can be identifiable. Thus, using sloppiness to draw conclusions about the possibility of estimating parameter values can be misleading. Instead, structural and practical identifiability analyses are better tools for assessing the confidence in parameter estimates. Furthermore, we show that, when designing new experiments to decrease parametric uncertainty, designs that optimize practical identifiability criteria are more informative than those that minimize sloppiness.

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Section: Introductionmentioning

confidence: 99%

On the relationship between sloppiness and identifiability

Chis

Villaverde

Banga

et al. 2016

Mathematical Biosciences

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“…In addition to data availability, there are two other factors, arising from the biology of the systems, that ease the construction of large-scale kinetic models [16]. The first one is the observation that the structure of a biological network (i.e., what the network components are and how they are connected) largely determines its function, as observed in constraint-based analyses [17].…”

Section: Introductionmentioning

confidence: 99%

Bridging the gap between gene expression and metabolic phenotype via kinetic models

2013

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BackgroundDespite the close association between gene expression and metabolism, experimental evidence shows that gene expression levels alone cannot predict metabolic phenotypes, indicating a knowledge gap in our understanding of how these processes are connected. Here, we present a method that integrates transcriptome, fluxome, and metabolome data using kinetic models to create a mechanistic link between gene expression and metabolism.ResultsWe developed a modeling framework to construct kinetic models that connect the transcriptional and metabolic responses of a cell to exogenous perturbations. The framework allowed us to avoid extensive experimental characterization, literature mining, and optimization problems by estimating most model parameters directly from fluxome and transcriptome data. We applied the framework to investigate how gene expression changes led to observed phenotypic alterations of Saccharomyces cerevisiae treated with weak organic acids (i.e., acetate, benzoate, propionate, or sorbate) and the histidine synthesis inhibitor 3-aminotriazole under steady-state conditions. We found that the transcriptional response led to alterations in yeast metabolism that mimicked measured metabolic fluxes and concentration changes. Further analyses generated mechanistic insights of how S. cerevisiae responds to these stresses. In particular, these results suggest that S. cerevisiae uses different regulation strategies for responding to these insults: regulation of two reactions accounted for most of the tolerance to the four weak organic acids, whereas the response to 3-aminotriazole was distributed among multiple reactions. Moreover, we observed that the magnitude of the gene expression changes was not directly correlated with their effect on the ability of S. cerevisiae to grow under these treatments. In addition, we identified another potential mechanism of action of 3-aminotriazole associated with the depletion of tetrahydrofolate.ConclusionsOur simulation results show that the modeling framework provided an accurate mechanistic link between gene expression and cellular metabolism. The proposed method allowed us to integrate transcriptome, fluxome, and metabolome data to determine and interpret important features of the physiological response of yeast to stresses. Importantly, given its flexibility and robustness, our approach can be applied to investigate the transcriptional-metabolic response in other cellular systems of medical and industrial relevance.

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“…Because proteins with a high degree of sequence similarity generate similar peptides upon tryptic digestion, their distinction by mass spectrometric analysis poses a particular analytical challenge. The quantification of the comprehensive set of enzymes and isoenzymes representing the central carbon and amino‐acid metabolism in yeast, under different metabolic states, would provide a unique opportunity to observe the change of the system as a whole and generate key information for the modeling of this system (Kotte and Heinemann, 2009; Oberhardt et al , 2009; Heinemann and Sauer, 2010).…”

Section: Introductionmentioning

confidence: 99%

Comprehensive quantitative analysis of central carbon and amino‐acid metabolism in Saccharomyces cerevisiae under multiple conditions by targeted proteomics

Costenoble

Picotti

Reiter

et al. 2011

Molecular Systems Biology

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109

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With a targeted proteomic approach, we could quantify 90% of the enzymes involved in carbon and amino-acid metabolism in yeast, including complete isoenzyme families, throughout a set of different metabolic states.The data, interpreted through flux balance modeling, indicate that S. cerevisiae expresses enzymes, not necessarily used in a particular metabolic condition.For many isoenzymes our data suggest functional diversification, which might explain their parallel presence in the S. cerevisiae genome.

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A divide-and-conquer approach to analyze underdetermined biochemical models

Cited by 30 publications

References 14 publications

On the relationship between sloppiness and identifiability

On the relationship between sloppiness and identifiability

Bridging the gap between gene expression and metabolic phenotype via kinetic models

Comprehensive quantitative analysis of central carbon and amino‐acid metabolism in Saccharomyces cerevisiae under multiple conditions by targeted proteomics

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