The Department of Energy Microbial Cell Project: A 180° Paradigm Shift for Biology

Drell, Daniel

doi:10.1089/15362310252780799

Cited by 11 publications

(6 citation statements)

References 32 publications

(19 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the following years, with the development of sequencing and high-throughput technologies, the gene-protein-reaction (GPR) associations [16] have been improved and the number of sequenced genomes has sparked off [17,18]. This accumulation of knowledge eventually has led to the development of Genome scale metabolic networks (GEMs) [19,20], which encapsulate all the known biochemistry of organisms.…”

Section: Introductionmentioning

confidence: 99%

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

Ataman

Hatzimanikatis

2017

PLoS Comput Biol

View full text Add to dashboard Cite

In the post-genomic era, Genome-scale metabolic networks (GEMs) have emerged as invaluable tools to understand metabolic capabilities of organisms. Different parts of these metabolic networks are defined as subsystems/pathways, which are sets of functional roles to implement a specific biological process or structural complex, such as glycolysis and TCA cycle. Subsystem/pathway definition is also employed to delineate the biosynthetic routes that produce biomass building blocks. In databases, such as MetaCyc and SEED, these representations are composed of linear routes from precursors to target biomass building blocks. However, this approach cannot capture the nested, complex nature of GEMs. Here we implemented an algorithm, lumpGEM, which generates biosynthetic subnetworks composed of reactions that can synthesize a target metabolite from a set of defined core precursor metabolites. lumpGEM captures balanced subnetworks, which account for the fate of all metabolites along the synthesis routes, thus encapsulating reactions from various subsystems/pathways to balance these metabolites in the metabolic network. Moreover, lumpGEM collapses these subnetworks into elementally balanced lumped reactions that specify the cost of all precursor metabolites and cofactors. It also generates alternative subnetworks and lumped reactions for the same metabolite, accounting for the flexibility of organisms. lumpGEM is applicable to any GEM and any target metabolite defined in the network. Lumped reactions generated by lumpGEM can be also used to generate properly balanced reduced core metabolic models.

show abstract

Section: Introductionmentioning

confidence: 99%

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

Ataman

Hatzimanikatis

2017

PLoS Comput Biol

View full text Add to dashboard Cite

show abstract

“…Eight years later, the genomes of over 100 organisms from all major phylogenetic lineages have been sequenced, and sequencing of many more is currently under way (2,3). Disparities in the accuracy of genome annotation that were the subject of many heated discussions at the beginning of the genome era (4,5) are largely gone.…”

Section: Introductionmentioning

confidence: 99%

Identification and functional analysis of 'hypothetical' genes expressed in Haemophilus influenzae

Kolker¹,

Makarova

Shabalina

et al. 2004

Nucleic Acids Research

View full text Add to dashboard Cite

The progress in genome sequencing has led to a rapid accumulation in GenBank submissions of uncharacterized`hypothetical' genes. These genes, which have not been experimentally characterized and whose functions cannot be deduced from simple sequence comparisons alone, now comprise a signi®cant fraction of the public databases. Expression analyses of Haemophilus in¯uenzae cells using a combination of transcriptomic and proteomic approaches resulted in con®dent identi®ca-tion of 54`hypothetical' genes that were expressed in cells under normal growth conditions. In an attempt to understand the functions of these proteins, we used a variety of publicly available analysis tools. Close homologs in other species were detected for each of the 54`hypothetical' genes. For 16 of them, exact functional assignments could be found in one or more public databases. Additionally, we were able to suggest general functional characterization for 27 more genes (comprising~80% total). Findings from this analysis include the identi®cation of a pyruvate-formate lyase-like operon, likely to be expressed not only in H.in¯uenzae but also in several other bacteria. Further, we also observed three genes that are likely to participate in the transport and/or metabolism of sialic acid, an important component of the H.in¯uenzae lipo-oligosaccharide. Accurate functional annotation of uncharacterized genes calls for an integrative approach, combining expression studies with extensive computational analysis and curation, followed by eventual experimental veri®cation of the computational predictions.

show abstract

“…The use of genome-scale metabolic reconstructions of an organism may prove to be a valuable tool in attempts to account for biological complexity and to elucidate the genotype-phenotype relationship. The annotation of full microbial genome sequences (2,7) has enabled reconstruction of whole-cell metabolic networks (5,15,19,29). By using these reconstructed networks, detailed analyses of specific biological functions and system properties have been performed (11,12,21,25,27,31).…”

mentioning

confidence: 99%

Description and Interpretation of Adaptive Evolution of Escherichia coli K-12 MG1655 by Using a Genome-Scale In Silico Metabolic Model

2003

View full text Add to dashboard Cite

Genome-scale in silico metabolic networks of Escherichia coli have been reconstructed. By using a constraintbased in silico model of a reconstructed network, the range of phenotypes exhibited by E. coli under different growth conditions can be computed, and optimal growth phenotypes can be predicted. We hypothesized that the end point of adaptive evolution of E. coli could be accurately described a priori by our in silico model since adaptive evolution should lead to an optimal phenotype. Adaptive evolution of E. coli during prolonged exponential growth was performed with M9 minimal medium supplemented with 2 g of ␣-ketoglutarate per liter, 2 g of lactate per liter, or 2 g of pyruvate per liter at both 30 and 37°C, which produced seven distinct strains. The growth rates, substrate uptake rates, oxygen uptake rates, by-product secretion patterns, and growth rates on alternative substrates were measured for each strain as a function of evolutionary time. Three major conclusions were drawn from the experimental results. First, adaptive evolution leads to a phenotype characterized by maximized growth rates that may not correspond to the highest biomass yield. Second, metabolic phenotypes resulting from adaptive evolution can be described and predicted computationally. Third, adaptive evolution on a single substrate leads to changes in growth characteristics on other substrates that could signify parallel or opposing growth objectives. Together, the results show that genome-scale in silico metabolic models can describe the end point of adaptive evolution a priori and can be used to gain insight into the adaptive evolutionary process for E. coli.Biological systems are fundamentally complex, and thus a systems approach is necessary to account for the diversity of interactions that can occur among the myriad of molecular components that comprise living cells (1,5,14). The use of genome-scale metabolic reconstructions of an organism may prove to be a valuable tool in attempts to account for biological complexity and to elucidate the genotype-phenotype relationship. The annotation of full microbial genome sequences (2, 7) has enabled reconstruction of whole-cell metabolic networks (5,15,19,29). By using these reconstructed networks, detailed analyses of specific biological functions and system properties have been performed (11,12,21,25,27,31). In addition, numerous different in silico approaches have been developed and are available to analyze the properties of metabolic networks (11,16,24,28,34,36). While the rationales underlying the various methods are becoming widely accepted, there still has been limited prospective experimental verification of genome-scale in silico models with regard to their abilities to interpret and predict complex biological processes, such as adaptive evolution.In several studies the workers have productively combined computational and experimental approaches (4,17,32,33). In these studies, the in silico models were constructed and used to analyze specific metabolic subsystems accounting for ...

show abstract

The Department of Energy Microbial Cell Project: A 180° Paradigm Shift for Biology

Cited by 11 publications

References 32 publications

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

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

Identification and functional analysis of 'hypothetical' genes expressed in Haemophilus influenzae

Description and Interpretation of Adaptive Evolution of Escherichia coli K-12 MG1655 by Using a Genome-Scale In Silico Metabolic Model

Contact Info

Product

Resources

About