Addressing uncertainty in genome-scale metabolic model reconstruction and analysis

Bernstein, David B.; Sulheim, Snorre; Almaas, Eivind; Segrè, Daniel

doi:10.1186/s13059-021-02289-z

Cited by 115 publications

(100 citation statements)

References 214 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Moreover, even these high-quality flux balance models have limitations, which stem from a variety of sources. These include (i) a lack of mechanistic knowledge of reaction-specific uptake rates and kinetic parameters [87,88], (ii) the potential for non-unique FBA solutions and the choice of multiple objective functions [89] and (iii) a limited ability to directly model non-metabolic modes of interaction (e.g. via secondary metabolites) and additional environmental parameters (e.g.…”

Section: Discussionmentioning

confidence: 99%

An evolutionary algorithm for designing microbial communities via environmental modification

Pacheco

Segrè

2021

J. R. Soc. Interface.

Self Cite

View full text Add to dashboard Cite

Despite a growing understanding of how environmental composition affects microbial communities, it remains difficult to apply this knowledge to the rational design of synthetic multispecies consortia. This is because natural microbial communities can harbour thousands of different organisms and environmental substrates, making up a vast combinatorial space that precludes exhaustive experimental testing and computational prediction. Here, we present a method based on the combination of machine learning and metabolic modelling that selects optimal environmental compositions to produce target community phenotypes. In this framework, dynamic flux balance analysis is used to model the growth of a community in candidate environments. A genetic algorithm is then used to evaluate the behaviour of the community relative to a target phenotype, and subsequently adjust the environment to allow the organisms to approach this target. We apply this iterative process to thousands of in silico communities of varying sizes, showing how it can rapidly identify environments that yield desired taxonomic compositions and patterns of metabolic exchange. Moreover, this combination of approaches produces testable predictions for the assembly of experimental microbial communities with specific properties and can facilitate rational environmental design processes for complex microbiomes.

show abstract

Section: Discussionmentioning

confidence: 99%

An evolutionary algorithm for designing microbial communities via environmental modification

Pacheco

Segrè

2021

J. R. Soc. Interface.

Self Cite

View full text Add to dashboard Cite

show abstract

“…As shown in Figure 2, this database contained the data of expression, distribution, function and endogenous substrates for general transporter, which gives functional family, transporter phylogenetic classification, and structure of endogenous compounds (Table 3). TransportDB was suggested to be adopted for characterizing transporters of specific functions (Frioux et al, 2020;Bernstein et al, 2021).…”

Section: Systematic Classification Of the General Protein Transportersmentioning

confidence: 99%

Feature, Function, and Information of Drug Transporter–Related Databases

Yin¹,

Li²,

et al. 2021

Drug Metab Dispos

View full text Add to dashboard Cite

With the rapid progress in pharmaceutical experiments and clinical investigations, extensive knowledge of drug transporters (DTs) has accumulated, which is valuable data for the understanding of drug metabolism and disposition. However, such data is largely dispersed in the literature, which hampers its utility and significantly limits its possibility for comprehensive analysis. A variety of databases have, therefore, been constructed to provide DT-related data, and they were reviewed in this study. First, several knowledge bases providing data regarding clinically important drugs and their corresponding transporters were discussed, which constituted the most important resources of DT-centered data. Second, some databases describing the general transporters and their functional families were reviewed. Third, various databases offering transporter information as part of their entire data collection were described.Finally, customized database functions that are available to facilitate DT-related research were discussed. This review provided an overview of the whole collection of DT-related databases, which might facilitate research on precision medicine and rational drug use. Significant StatementA collection of well-established databases related to DTs were comprehensively reviewed, which were organized according to their importance in drug ADME research. These databases could collectively contribute to the research on rational drug use.

show abstract

“…However, model reconstruction and manual curation are still a time-consuming process. Simpler and more coarse-grained models were proven to be useful, as they have less accumulated biases and are more readily to be applied [8][9][10]. Both approaches rely on functional annotations predicted from genomic data.…”

Section: Introductionmentioning

confidence: 99%

Using Functional Annotations to Study Pairwise Interactions in Urinary Tract Infection Communities

Lara

Windt

Molenaar

et al. 2021

Genes

View full text Add to dashboard Cite

The behaviour of microbial communities depends on environmental factors and on the interactions of the community members. This is also the case for urinary tract infection (UTI) microbial communities. Here, we devise a computational approach that uses indices of complementarity and competition based on metabolic gene annotation to rapidly predict putative interactions between pair of organisms with the aim to explain pairwise growth effects. We apply our method to 66 genomes selected from online databases, which belong to 6 genera representing members of UTI communities. This resulted in a selection of metabolic pathways with high correlation for each pairwise combination between a complementarity index and the experimentally derived growth data. Our results indicated that Enteroccus spp. were most complemented in its metabolism by the other members of the UTI community. This suggests that the growth of Enteroccus spp. can potentially be enhanced by complementary metabolites produced by other community members. We tested a few putative predicted interactions by experimental supplementation of the relevant predicted metabolites. As predicted by our method, folic acid supplementation led to the increase in the population density of UTI Enterococcus isolates. Overall, we believe our method is a rapid initial in silico screening for the prediction of metabolic interactions in microbial communities.

show abstract

Addressing uncertainty in genome-scale metabolic model reconstruction and analysis

Cited by 115 publications

References 214 publications

An evolutionary algorithm for designing microbial communities via environmental modification

An evolutionary algorithm for designing microbial communities via environmental modification

Feature, Function, and Information of Drug Transporter–Related Databases

Using Functional Annotations to Study Pairwise Interactions in Urinary Tract Infection Communities

Contact Info

Product

Resources

About