A scalable method for parameter identification in kinetic models of metabolism using steady-state data

Abstract Motivation In kinetic models of metabolism, the parameter values determine the dynamic behaviour predicted by these models. Estimating parameters from in vivo experimental data require the parameters to be structurally identifiable, and the data to be informative enough to estimate these parameters. Existing methods to determine the structural identifiability of parameters in kinetic models of metabolism can only be applied to models of small metabolic networks due to their computational complexity. Additionally, a priori experimental design, a necessity to obtain informative data for parameter estimation, also does not account for using steady-state data to estimate parameters in kinetic models. Results Here, we present a scalable methodology to structurally identify parameters for each flux in a kinetic model of metabolism based on the availability of steady-state data. In doing so, we also address the issue of determining the number and nature of experiments for generating steady-state data to estimate these parameters. By using a small metabolic network as an example, we show that most parameters in fluxes expressed by mechanistic enzyme kinetic rate laws can be identified using steady-state data, and the steady-state data required for their estimation can be obtained from selective experiments involving both substrate and enzyme level perturbations. The methodology can be used in combination with other identifiability and experimental design algorithms that use dynamic data to determine the most informative experiments requiring the least resources to perform. Availability and implementation https://github.com/LMSE/ident. Supplementary information Supplementary data are available at Bioinformatics online

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Accelerating Kinetic Parameter Identification by Extracting Information from Transient Data: A Hydroprocessing Study Case

Catalysts ◽

10.3390/catal10040361 ◽

2020 ◽

Vol 10 (4) ◽

pp. 361

Author(s):

Ngoc-Yen-Phuong Cao ◽

Benoit Celse ◽

Denis Guillaume ◽

Isabelle Guibard ◽

Joris W. Thybaut

Keyword(s):

Steady State ◽

Experimental Design ◽

Kinetic Model ◽

Kinetic Modeling ◽

Sufficient Data ◽

Design Algorithm ◽

State Data ◽

Transient Data ◽

Study Case ◽

The One

Hydroprocessing reactions require several days to reach steady-state, leading to long experimentation times for collecting sufficient data for kinetic modeling purposes. The information contained in the transient data during the evolution toward the steady-state is, at present, not used for kinetic modeling since the stabilization behavior is not well understood. The present work aims at accelerating kinetic model construction by employing these transient data, provided that the stabilization can be adequately accounted for. A comparison between the model obtained against the steady-state data and the one after accounting for the transient information was carried out. It was demonstrated that by accounting for the stabilization, combined with an experimental design algorithm, a more robust and faster manner was obtained to identify kinetic parameters, which saves time and cost. An application was presented in hydrodenitrogenation, but the proposed methodology can be extended to any hydroprocessing reaction.

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On-Line Optimal Input Design Increases the Efficiency and Accuracy of the Modelling of an Inducible Synthetic Promoter

Processes ◽

10.3390/pr6090148 ◽

2018 ◽

Vol 6 (9) ◽

pp. 148 ◽

Cited By ~ 14

Author(s):

Lucia Bandiera ◽

Zhaozheng Hou ◽

Varun Kothamachu ◽

Eva Balsa-Canto ◽

Peter Swain ◽

...

Keyword(s):

Experimental Design ◽

A Priori ◽

Inducible Promoter ◽

Optimal Experimental Design ◽

Parameter Inference ◽

Synthetic Gene Circuits ◽

Input Design ◽

On Line

Synthetic biology seeks to design biological parts and circuits that implement new functions in cells. Major accomplishments have been reported in this field, yet predicting a priori the in vivo behaviour of synthetic gene circuits is major a challenge. Mathematical models offer a means to address this bottleneck. However, in biology, modelling is perceived as an expensive, time-consuming task. Indeed, the quality of predictions depends on the accuracy of parameters, which are traditionally inferred from poorly informative data. How much can parameter accuracy be improved by using model-based optimal experimental design (MBOED)? To tackle this question, we considered an inducible promoter in the yeast S. cerevisiae. Using in vivo data, we re-fit a dynamic model for this component and then compared the performance of standard (e.g., step inputs) and optimally designed experiments for parameter inference. We found that MBOED improves the quality of model calibration by ∼60%. Results further improve up to 84 % when considering on-line optimal experimental design (OED). Our in silico results suggest that MBOED provides a significant advantage in the identification of models of biological parts and should thus be integrated into their characterisation.

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Modelling Cell Metabolism: A Review on Constraint-Based Steady-State and Kinetic Approaches

Processes ◽

10.3390/pr9020322 ◽

2021 ◽

Vol 9 (2) ◽

pp. 322

Author(s):

Mohammadreza Yasemi ◽

Mario Jolicoeur

Keyword(s):

Parameter Estimation ◽

Steady State ◽

Mathematical Modelling ◽

Kinetic Models ◽

Metabolic Pathways ◽

Metabolic Networks ◽

Cell Metabolism ◽

Experimental Conditions ◽

Metabolic Modelling ◽

Modelling Approaches

Studying cell metabolism serves a plethora of objectives such as the enhancement of bioprocess performance, and advancement in the understanding of cell biology, of drug target discovery, and in metabolic therapy. Remarkable successes in these fields emerged from heuristics approaches, for instance, with the introduction of effective strategies for genetic modifications, drug developments and optimization of bioprocess management. However, heuristics approaches have showed significant shortcomings, such as to describe regulation of metabolic pathways and to extrapolate experimental conditions. In the specific case of bioprocess management, such shortcomings limit their capacity to increase product quality, while maintaining desirable productivity and reproducibility levels. For instance, since heuristics approaches are not capable of prediction of the cellular functions under varying experimental conditions, they may lead to sub-optimal processes. Also, such approaches used for bioprocess control often fail in regulating a process under unexpected variations of external conditions. Therefore, methodologies inspired by the systematic mathematical formulation of cell metabolism have been used to address such drawbacks and achieve robust reproducible results. Mathematical modelling approaches are effective for both the characterization of the cell physiology, and the estimation of metabolic pathways utilization, thus allowing to characterize a cell population metabolic behavior. In this article, we present a review on methodology used and promising mathematical modelling approaches, focusing primarily to investigate metabolic events and regulation. Proceeding from a topological representation of the metabolic networks, we first present the metabolic modelling approaches that investigate cell metabolism at steady state, complying to the constraints imposed by mass conservation law and thermodynamics of reactions reversibility. Constraint-based models (CBMs) are reviewed highlighting the set of assumed optimality functions for reaction pathways. We explore models simulating cell growth dynamics, by expanding flux balance models developed at steady state. Then, discussing a change of metabolic modelling paradigm, we describe dynamic kinetic models that are based on the mathematical representation of the mechanistic description of nonlinear enzyme activities. In such approaches metabolic pathway regulations are considered explicitly as a function of the activity of other components of metabolic networks and possibly far from the metabolic steady state. We have also assessed the significance of metabolic model parameterization in kinetic models, summarizing a standard parameter estimation procedure frequently employed in kinetic metabolic modelling literature. Finally, some optimization practices used for the parameter estimation are reviewed.

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K-FIT: An accelerated kinetic parameterization algorithm using steady-state fluxomic data

10.1101/612994 ◽

2019 ◽

Cited By ~ 3

Author(s):

Saratram Gopalakrishnan ◽

Satyakam Dash ◽

Costas Maranas

Keyword(s):

Steady State ◽

Kinetic Models ◽

Large Scale ◽

Metabolic Networks ◽

Decomposition Approach ◽

E Coli ◽

Environmental Perturbations ◽

Genetic Perturbations ◽

Gradient Based ◽

Speed Up

AbstractKinetic models predict the metabolic flows by directly linking metabolite concentrations and enzyme levels to reaction fluxes. Robust parameterization of organism-level kinetic models that faithfully reproduce the effect of different genetic or environmental perturbations remains an open challenge due to the intractability of existing algorithms. This paper introduces K-FIT, an accelerated kinetic parameterization workflow that leverages a novel decomposition approach to identify steady-state fluxes in response to genetic perturbations followed by a gradient-based update of kinetic parameters until predictions simultaneously agree with the fluxomic data in all perturbed metabolic networks. The applicability of K-FIT to large-scale models is demonstrated by parameterizing an expanded kinetic model forE. coli(307 reactions and 258 metabolites) using fluxomic data from six mutants. The achieved thousand-fold speed-up afforded by K-FIT over meta-heuristic approaches is transformational enabling follow-up robustness of inference analyses and optimal design of experiments to inform metabolic engineering strategies.

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A convenient computer program for fitting enzymatic rate laws to steady-state data

Computers and Biomedical Research ◽

10.1016/0010-4809(79)90032-6 ◽

1979 ◽

Vol 12 (5) ◽

pp. 461-469 ◽

Cited By ~ 48

Author(s):

Michael C. Kohn ◽

lawrence E. Menten ◽

David Garfinkel

Keyword(s):

Steady State ◽

Computer Program ◽

State Data ◽

Rate Laws

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Bayesian inference of metabolic kinetics from genome-scale multiomics data

10.1101/450163 ◽

2018 ◽

Author(s):

Peter C. St. John ◽

Jonathan Strutz ◽

Linda J. Broadbelt ◽

Keith E.J. Tyo ◽

Yannick J. Bomble

Keyword(s):

Steady State ◽

Bayesian Inference ◽

Kinetic Models ◽

Large Scale ◽

Biological Data ◽

Model Parameters ◽

Enzymatic Reactions ◽

Rate Laws ◽

Wide Range ◽

Computationally Intensive

SummaryModern biological tools generate a wealth of data on metabolite and protein concentrations that can be used to help inform new strain designs. However, integrating these data sources to generate predictions of steady-state metabolism typically requires a kinetic description of the enzymatic reactions that occur within a cell. Parameterizing these kinetic models from biological data can be computationally difficult, especially as the amount of data increases. Robust methods must also be able to quantify the uncertainty in model parameters as a function of the available data, which can be particularly computationally intensive. The field of Bayesian inference offers a wide range of methods for estimating distributions in parameter uncertainty. However, these techniques are poorly suited to kinetic metabolic modeling due to the complex kinetic rate laws typically employed and the resulting dynamic system that must be solved. In this paper, we employ linear-logarithmic kinetics to simplify the calculation of steady-state flux distributions and enable efficient sampling and variational inference methods. We demonstrate that detailed information on the posterior distribution of kinetic model parameters can be obtained efficiently at a variety of different problem scales, including large-scale kinetic models trained on multiomics datasets. These results allow modern Bayesian machine learning tools to be leveraged in understanding biological data and developing new, efficient strain designs.

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