Current state and challenges for dynamic metabolic modeling

Vasilakou, Eleni; Machado, Daniel; Theorell, Axel; Rocha, Isabel; Nöh, Katharina; Oldiges, Marco; Wahl, A.

doi:10.1016/j.mib.2016.07.008

Cited by 43 publications

(26 citation statements)

References 82 publications

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“…During the last decade, fostered by the greater availability of the necessary experimental data, the development of large (up to genome-scale) kinetic models has become one of the main objectives in the field, as well as in related areas such as synthetic biology, metabolic engineering, or industrial biotechnology [1–10]. More recently, the first steps towards comprehensive whole-cell models have been taken [11], which has great potential for applications e.g.…”

Section: Introductionmentioning

confidence: 99%

Parameter identifiability analysis and visualization in large-scale kinetic models of biosystems

2017

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BackgroundKinetic models of biochemical systems usually consist of ordinary differential equations that have many unknown parameters. Some of these parameters are often practically unidentifiable, that is, their values cannot be uniquely determined from the available data. Possible causes are lack of influence on the measured outputs, interdependence among parameters, and poor data quality. Uncorrelated parameters can be seen as the key tuning knobs of a predictive model. Therefore, before attempting to perform parameter estimation (model calibration) it is important to characterize the subset(s) of identifiable parameters and their interplay. Once this is achieved, it is still necessary to perform parameter estimation, which poses additional challenges.MethodsWe present a methodology that (i) detects high-order relationships among parameters, and (ii) visualizes the results to facilitate further analysis. We use a collinearity index to quantify the correlation between parameters in a group in a computationally efficient way. Then we apply integer optimization to find the largest groups of uncorrelated parameters. We also use the collinearity index to identify small groups of highly correlated parameters. The results files can be visualized using Cytoscape, showing the identifiable and non-identifiable groups of parameters together with the model structure in the same graph.ResultsOur contributions alleviate the difficulties that appear at different stages of the identifiability analysis and parameter estimation process. We show how to combine global optimization and regularization techniques for calibrating medium and large scale biological models with moderate computation times. Then we evaluate the practical identifiability of the estimated parameters using the proposed methodology. The identifiability analysis techniques are implemented as a MATLAB toolbox called VisId, which is freely available as open source from GitHub (https://github.com/gabora/visid).ConclusionsOur approach is geared towards scalability. It enables the practical identifiability analysis of dynamic models of large size, and accelerates their calibration. The visualization tool allows modellers to detect parts that are problematic and need refinement or reformulation, and provides experimentalists with information that can be helpful in the design of new experiments.Electronic supplementary materialThe online version of this article (doi:10.1186/s12918-017-0428-y) contains supplementary material, which is available to authorized users.

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Section: Introductionmentioning

confidence: 99%

Parameter identifiability analysis and visualization in large-scale kinetic models of biosystems

2017

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“…Since conversion of substrate into biofuels involves a complex metabolic network, theoretical approach is needed as a tool to study the complex system to complete the experimental study. Theoretical studies provide high predictive power to design cells with desirable properties cheaply and reliably according to the needs of biotechnology production [5,6]. Some researchers used advanced mathematical model to capture the dynamical behaviour of a complex metabolic system such as the fermentation system [7][8][9][10][11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

Mathematical study of feedback inhibition effects on the dynamics of metabolites on the central metabolism of a yeast cell: a combination of kinetic model and metabolic control analysis

Kasbawati

Agnes

Kalondeng

et al. 2019

Biotechnology & Biotechnological Equipment

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The fermentation system is a metabolic system that can be considered as a complex system, since it involves metabolites linked by different reactions. Some outputs of the reaction act as input in the other reactions and they control the behaviour of the system. Glucose as the main carbon source of the yeast cells plays an important role in the cell's reproduction cycle and product synthesis. Glucose promotes negative feedback to some reactions in the central metabolism of the yeast cell. In this paper we mathematically study the effects of the negative feedback (inhibition) by glucose and other metabolites on the dynamics of the fermentation system. We also study the sensitivity of the flux in the presence of inhibition. We found that high inhibition by glucose affects the concentration of acetyl-CoA, which will lead to the respiration pathway of the yeast cells. High inhibition by acetaldehyde affects the concentration of all metabolites, including the maximal concentration of ethanol. Based on the metabolic control analysis results, we found that glucose can be considered as the external regulating point in increasing the flux of ethanol. Although glucose acts as a negative feedback, it can also be used to promote certain processes in the metabolic pathway since it gives the highest positive control in increasing the flux of ethanol as the desired product. For the internal regulation point, the maximal concentration of ethanol can be increased significantly by regulating the maximal activity of alcohol dehydrogenase and acetaldehyde dehydrogenase simultaneously.

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“…The enzyme-based formulation describes an entire microbial community as a single unit that responds to the environment by regulating its metabolism through expression of genes and enzymes. As a key advantage, this approach facilitates the application of existing dynamic modeling platforms that have been developed to predict metabolic behaviors of single organisms (Song et al, 2014 ; Vasilakou et al, 2016 ). In this regard, the cybernetic approach developed by Ramkrishna and co-workers (Ramkrishna, 1983 ; Ramkrishna and Song, 2012 ; Young, 2015 ) provides an ideal platform for the enzyme-based microbial community modeling due to its rational description of metabolic regulation through the control of enzyme syntheses and activities.…”

Section: Introductionmentioning

confidence: 99%

Regulation-Structured Dynamic Metabolic Model Provides a Potential Mechanism for Delayed Enzyme Response in Denitrification Process

et al. 2017

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In a recent study of denitrification dynamics in hyporheic zone sediments, we observed a significant time lag (up to several days) in enzymatic response to the changes in substrate concentration. To explore an underlying mechanism and understand the interactive dynamics between enzymes and nutrients, we developed a trait-based model that associates a community's traits with functional enzymes, instead of typically used species guilds (or functional guilds). This enzyme-based formulation allows to collectively describe biogeochemical functions of microbial communities without directly parameterizing the dynamics of species guilds, therefore being scalable to complex communities. As a key component of modeling, we accounted for microbial regulation occurring through transcriptional and translational processes, the dynamics of which was parameterized based on the temporal profiles of enzyme concentrations measured using a new signature peptide-based method. The simulation results using the resulting model showed several days of a time lag in enzymatic responses as observed in experiments. Further, the model showed that the delayed enzymatic reactions could be primarily controlled by transcriptional responses and that the dynamics of transcripts and enzymes are closely correlated. The developed model can serve as a useful tool for predicting biogeochemical processes in natural environments, either independently or through integration with hydrologic flow simulators.

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Current state and challenges for dynamic metabolic modeling

Cited by 43 publications

References 82 publications

Parameter identifiability analysis and visualization in large-scale kinetic models of biosystems

Parameter identifiability analysis and visualization in large-scale kinetic models of biosystems

Mathematical study of feedback inhibition effects on the dynamics of metabolites on the central metabolism of a yeast cell: a combination of kinetic model and metabolic control analysis

Regulation-Structured Dynamic Metabolic Model Provides a Potential Mechanism for Delayed Enzyme Response in Denitrification Process

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