Literature DB >> 28687715

Resource allocation in living organisms.

Anne Goelzer1, Vincent Fromion2.   

Abstract

Quantitative prediction of resource allocation for living systems has been an intensive area of research in the field of biology. Resource allocation was initially investigated in higher organisms by using empirical mathematical models based on mass distribution. A challenge is now to go a step further by reconciling the cellular scale to the individual scale. In the present paper, we review the foundations of modelling of resource allocation, particularly at the cellular scale: from small macro-molecular models to genome-scale cellular models. We enlighten how the combination of omic measurements and computational advances together with systems biology has contributed to dramatic progresses in the current understanding and prediction of cellular resource allocation. Accurate genome-wide predictive methods of resource allocation based on the resource balance analysis (RBA) framework have been developed and ensure a good trade-off between the complexity/tractability and the prediction capability of the model. The RBA framework shows promise for a wide range of applications in metabolic engineering and synthetic biology, and for pursuing investigations of the design principles of cellular and multi-cellular organisms.
© 2017 The Author(s); published by Portland Press Limited on behalf of the Biochemical Society.

Keywords:  constraint-based modelling; optimality principle; resource allocation

Mesh:

Year:  2017        PMID: 28687715     DOI: 10.1042/BST20160436

Source DB:  PubMed          Journal:  Biochem Soc Trans        ISSN: 0300-5127            Impact factor:   5.407


  14 in total

Review 1.  Pseudomonad reverse carbon catabolite repression, interspecies metabolite exchange, and consortial division of labor.

Authors:  Heejoon Park; S Lee McGill; Adrienne D Arnold; Ross P Carlson
Journal:  Cell Mol Life Sci       Date:  2019-11-25       Impact factor: 9.261

2.  Elementary Growth Modes provide a molecular description of cellular self-fabrication.

Authors:  Daan H de Groot; Josephus Hulshof; Bas Teusink; Frank J Bruggeman; Robert Planqué
Journal:  PLoS Comput Biol       Date:  2020-01-27       Impact factor: 4.475

3.  Automatic construction of metabolic models with enzyme constraints.

Authors:  Pavlos Stephanos Bekiaris; Steffen Klamt
Journal:  BMC Bioinformatics       Date:  2020-01-14       Impact factor: 3.169

Review 4.  Quantitative metabolic fluxes regulated by trans-omic networks.

Authors:  Satoshi Ohno; Saori Uematsu; Shinya Kuroda
Journal:  Biochem J       Date:  2022-03-31       Impact factor: 3.766

Review 5.  In a quest for engineering acidophiles for biomining applications: challenges and opportunities.

Authors:  Yosephine Gumulya; Naomi J Boxall; Himel N Khaleque; Ville Santala; Ross P Carlson; Anna H Kaksonen
Journal:  Genes (Basel)       Date:  2018-02-21       Impact factor: 4.096

6.  Division rate, cell size and proteome allocation: impact on gene expression noise and implications for the dynamics of genetic circuits.

Authors:  François Bertaux; Samuel Marguerat; Vahid Shahrezaei
Journal:  R Soc Open Sci       Date:  2018-03-21       Impact factor: 2.963

7.  A Protocol for Generating and Exchanging (Genome-Scale) Metabolic Resource Allocation Models.

Authors:  Alexandra-M Reimers; Henning Lindhorst; Steffen Waldherr
Journal:  Metabolites       Date:  2017-09-06

8.  Modelling overflow metabolism in Escherichia coli with flux balance analysis incorporating differential proteomic efficiencies of energy pathways.

Authors:  Hong Zeng; Aidong Yang
Journal:  BMC Syst Biol       Date:  2019-01-10

Review 9.  Constraint-based modeling in microbial food biotechnology.

Authors:  Martin H Rau; Ahmad A Zeidan
Journal:  Biochem Soc Trans       Date:  2018-03-27       Impact factor: 5.407

Review 10.  Model-based metabolism design: constraints for kinetic and stoichiometric models.

Authors:  Egils Stalidzans; Andrus Seiman; Karl Peebo; Vitalijs Komasilovs; Agris Pentjuss
Journal:  Biochem Soc Trans       Date:  2018-02-22       Impact factor: 5.407

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