Literature DB >> 26342586

Rites of passage: requirements and standards for building kinetic models of metabolic phenotypes.

Ljubisa Miskovic1, Milenko Tokic1, Georgios Fengos1, Vassily Hatzimanikatis2.   

Abstract

The overarching ambition of kinetic metabolic modeling is to capture the dynamic behavior of metabolism to such an extent that systems and synthetic biology strategies can reliably be tested in silico. The lack of kinetic data hampers the development of kinetic models, and most of the current models use ad hoc reduced stoichiometry or oversimplified kinetic rate expressions, which may limit their predictive strength. There is a need to introduce the community-level standards that will organize and accelerate the future developments in this area. We introduce here a set of requirements that will ensure the model quality, we examine the current kinetic models with respect to these requirements, and we propose a general workflow for constructing models that satisfy these requirements.
Copyright © 2015 The Authors. Published by Elsevier Ltd.. All rights reserved.

Mesh:

Year:  2015        PMID: 26342586     DOI: 10.1016/j.copbio.2015.08.019

Source DB:  PubMed          Journal:  Curr Opin Biotechnol        ISSN: 0958-1669            Impact factor:   9.740


  15 in total

1.  Analysis of human metabolism by reducing the complexity of the genome-scale models using redHUMAN.

Authors:  Maria Masid; Meric Ataman; Vassily Hatzimanikatis
Journal:  Nat Commun       Date:  2020-06-04       Impact factor: 14.919

2.  Quantitative modeling of pentose phosphate pathway response to oxidative stress reveals a cooperative regulatory strategy.

Authors:  Julien Hurbain; Quentin Thommen; Francois Anquez; Benjamin Pfeuty
Journal:  iScience       Date:  2022-06-28

3.  Characteristics of mathematical modeling languages that facilitate model reuse in systems biology: a software engineering perspective.

Authors:  Christopher Schölzel; Valeria Blesius; Gernot Ernst; Andreas Dominik
Journal:  NPJ Syst Biol Appl       Date:  2021-06-03

4.  Parameter estimation in large-scale systems biology models: a parallel and self-adaptive cooperative strategy.

Authors:  David R Penas; Patricia González; Jose A Egea; Ramón Doallo; Julio R Banga
Journal:  BMC Bioinformatics       Date:  2017-01-21       Impact factor: 3.169

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

Authors:  Attila Gábor; Alejandro F Villaverde; Julio R Banga
Journal:  BMC Syst Biol       Date:  2017-05-05

Review 6.  Applications of Genome-Scale Metabolic Models in Biotechnology and Systems Medicine.

Authors:  Cheng Zhang; Qiang Hua
Journal:  Front Physiol       Date:  2016-01-07       Impact factor: 4.566

7.  redGEM: Systematic reduction and analysis of genome-scale metabolic reconstructions for development of consistent core metabolic models.

Authors:  Meric Ataman; Daniel F Hernandez Gardiol; Georgios Fengos; Vassily Hatzimanikatis
Journal:  PLoS Comput Biol       Date:  2017-07-20       Impact factor: 4.475

8.  lumpGEM: Systematic generation of subnetworks and elementally balanced lumped reactions for the biosynthesis of target metabolites.

Authors:  Meric Ataman; Vassily Hatzimanikatis
Journal:  PLoS Comput Biol       Date:  2017-07-20       Impact factor: 4.475

9.  A design-build-test cycle using modeling and experiments reveals interdependencies between upper glycolysis and xylose uptake in recombinant S. cerevisiae and improves predictive capabilities of large-scale kinetic models.

Authors:  Ljubisa Miskovic; Susanne Alff-Tuomala; Keng Cher Soh; Dorothee Barth; Laura Salusjärvi; Juha-Pekka Pitkänen; Laura Ruohonen; Merja Penttilä; Vassily Hatzimanikatis
Journal:  Biotechnol Biofuels       Date:  2017-06-26       Impact factor: 6.040

10.  Network-level allosteric effects are elucidated by detailing how ligand-binding events modulate utilization of catalytic potentials.

Authors:  James T Yurkovich; Miguel A Alcantar; Zachary B Haiman; Bernhard O Palsson
Journal:  PLoS Comput Biol       Date:  2018-08-07       Impact factor: 4.475

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