A multi-objective constraint-based approach for modeling genome-scale microbial ecosystems

Budinich, Marko; Bourdon, Jérémie; Larhlimi, Abdelhalim; Eveillard, Damien

doi:10.1371/journal.pone.0171744

Cited by 48 publications

(47 citation statements)

References 68 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Overall, we let the PGA generate 384 populations containing 128 gene expression profiles each. The grey area represents the Pareto front identified from an alternative multi-optimization model by Budinich et al [37]. The plot shows that [37] covers a smaller area in the biomass-PHGDH metabolic space.…”

Section: C-edge: Effects Of Single-gene Perturbationsmentioning

confidence: 99%

Discovering Essential Multiple Gene Effects Through Large Scale Optimization: An Application to Human Cancer Metabolism

Occhipinti

Hamadi²,

Kugler

et al. 2021

IEEE/ACM Trans. Comput. Biol. and Bioinf.

View full text Add to dashboard Cite

Computational modelling of metabolic processes has proven to be a useful approach to formulate our knowledge and improve our understanding of core biochemical systems that are crucial to maintaining cellular functions. Towards understanding the broader role of metabolism on cellular decision-making in health and disease conditions, it is important to integrate the study of metabolism with other core regulatory systems and omics within the cell, including gene expression patterns.After quantitatively integrating gene expression profiles with a genome-scale reconstruction of human metabolism, we propose a set of combinatorial methods to reverse engineer gene expression profiles and to find pairs and higher-order combinations of genetic modifications that simultaneously optimize multiobjective cellular goals. This enables us to suggest classes of transcriptomic profiles that are most suitable to achieve given metabolic phenotypes.We demonstrate how our techniques are able to compute beneficial, neutral or "toxic" combinations of gene expression levels. We test our methods on nine tissue-specific cancer models, comparing our outcomes with the corresponding normal cells, identifying genes as targets for potential therapies. Our methods open the way to a broad class of applications that require an understanding of the interplay among genotype, metabolism, and cellular behaviour, at scale.

show abstract

Section: C-edge: Effects Of Single-gene Perturbationsmentioning

confidence: 99%

Discovering Essential Multiple Gene Effects Through Large Scale Optimization: An Application to Human Cancer Metabolism

Occhipinti

Hamadi²,

Kugler

et al. 2021

IEEE/ACM Trans. Comput. Biol. and Bioinf.

View full text Add to dashboard Cite

show abstract

“…The second strategy uses a multilevel objective function to simulate microbial communities. In this strategy, the overall growth of the community is optimized along with the individual growth rates of each species, but there is no biological basis for community objective functions …”

Section: Perspectivesmentioning

confidence: 99%

“…In this strategy, the overall growth of the community is optimized along with the individual growth rates of each species, but there is no biological basis for community objective functions. [104][105][106] One interesting example of the potential applicability of a community objective function comes not from the gut microbiome but from industrial biogas production. 107 Simulations of microbial methane production showed that a community of microbes produces maximum methane specific yield or production rate when at least one member of the community sacrifices optimal biomass yield for enhancing community methane production.…”

Section: Integrating Metabolic Modelsmentioning

confidence: 99%

Genome‐scale metabolic models applied to human health and disease

Cook

Nielsen

2017

WIREs Mechanisms of Disease

View full text Add to dashboard Cite

Advances in genome sequencing, high throughput measurement of gene and protein expression levels, data accessibility, and computational power have allowed genome-scale metabolic models (GEMs) to become a useful tool for understanding metabolic alterations associated with many different diseases. Despite the proven utility of GEMs, researchers confront multiple challenges in the use of GEMs, their application to human health and disease, and their construction and simulation in an organ-specific and disease-specific manner. Several approaches that researchers are taking to address these challenges include using proteomic and transcriptomic-informed methods to build GEMs for individual organs, diseases, and patients and using constraints on model behavior during simulation to match observed metabolic fluxes. We review the challenges facing researchers in the use of GEMs, review the approaches used to address these challenges, and describe advances that are on the horizon and could lead to a better understanding of human metabolism. WIREs Syst Biol Med 2017, 9:e1393. doi: 10.1002/wsbm.1393 For further resources related to this article, please visit the WIREs website.

show abstract

“…This has enabled the study of many aspects of evolution, including simulating the reduction of genome size in an individual (Batut et al, 2013). Multi-objective evolutionary algorithms (MOEA) have been used in many disciplines for solving problems that have two or more conflicting objectives (Budinich et al, 2017). The use of MOEA in combination with metabolic models has been implemented for the design of minimal genomes (Wang and Maranas, 2018) and for the production of industrially relevant molecules (Fong et al, 2005; Garcia and Trinh, 2019).…”

Section: Introductionmentioning

confidence: 99%

Simulating the evolutionary trajectories of metabolic pathways for insect symbionts in the Sodalis genus

Hall

Thorpe

Thomas

et al. 2019

Preprint

View full text Add to dashboard Cite

8 1 Abstract 9Insect-bacterial symbioses are ubiquitous, but there is still much to uncover about 10 how these relationships establish, persist and evolve. The tsetse endosymbiont So-11 dalis glossinidius displays intriguing metabolic adaptations to its microenvironment, 12 but the process by which this relationship evolved remains to be elucidated. The re-13 cent chance discovery of the free-living species of the Sodalis genus, S. praecaptivus, 14 provides a serendipitous starting point from which to investigate the evolution of 15 this symbiosis. Here, we present a flux balance model for S. praecaptivus. Metabolic 16 modelling is used in combination with a multi-objective evolutionary algorithm to 17 explore the trajectories that S. glossinidius may have undertaken after becoming 18 internalised. The time-dependent loss of key genes is shown to influence the evolved 19 populations, providing possible targets for future in vitro genetic manipulation. This 20 method provides an unusually detailed perspective on possible evolutionary trajec-21 tories for S. glossinidius in this fundamental process of evolutionary and ecological 22 change. 23 1 2 Introduction 24 Symbioses are both fundamental and ubiquitous in nature. Understanding their evo-25 lution poses an ongoing challenge, as well as an expanse of unresolved research ques-26 tions. Bacterial symbionts of insects provide a range of benefits including stress toler-27 ance (Dunbar et al., 2007; Wilcox et al., 2003), protection from predation (Nakabachi 28 et al., 2013; Oliver et al., 2003; Wilcox et al., 2003) and the provision of metabo-29 lites (Aksoy, 1995; Hrusa et al., 2015; Manzano-Marín et al., 2015; Shigenobu et al., 30 2000; Snyder and Rio, 2015; Thomas et al., 2009). The latter forms arguably the 31 strongest link within the symbioses. Host and symbiont frequently share metabolic 32 substrates, as well as the products and components of individual biosynthetic path-33 ways (McCutcheon et al., 2009a,b; McCutcheon and Moran, 2007; McCutcheon and 34 von Dohlen, 2011; Thomas et al., 2009; Wilson et al., 2010). These relationships 35 typically enable the host to survive on a nutritionally restricted diet, such as the 36 blood meal of the tsetse (Michalkova et al., 2014; Rio et al., 2003; Snyder and Rio, 37 2015) or the plant sap that feeds the aphid (Akman Gündüz and Douglas, 2009; 38 Baumann et al., 1995; Richards et al., 2010). 39 Deciphering the evolutionary pressures that affect the organisms within a symbiosis 40 is an essential part of understanding the relationship. This includes establishing 41 how the symbioses develop over time and the way in which the metabolism of the 42 individuals is intertwined. It is, however, often hindered by biological difficulties.43

show abstract

A multi-objective constraint-based approach for modeling genome-scale microbial ecosystems

Cited by 48 publications

References 68 publications

Discovering Essential Multiple Gene Effects Through Large Scale Optimization: An Application to Human Cancer Metabolism

Discovering Essential Multiple Gene Effects Through Large Scale Optimization: An Application to Human Cancer Metabolism

Genome‐scale metabolic models applied to human health and disease

Simulating the evolutionary trajectories of metabolic pathways for insect symbionts in the Sodalis genus

Contact Info

Product

Resources

About