Superessential reactions in metabolic networks

Barve, Aditya; Rodrigues, João F. Matias; Wagner, Andreas

doi:10.1073/pnas.1113065109

Cited by 51 publications

(70 citation statements)

References 75 publications

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“…A more recent computational study used flux balance analysis [422] and random re-wiring of a realistic model metabolic network [423] to study evolution of a model cell under a selection pressure to survive on a given carbon source [378]. The simulation showed that selection for one carbon source also allows the model cell to survive on a number of other carbon sources that were not selected for.…”

Section: Protein Evolution In the Context Of Functional Networkmentioning

confidence: 99%

Biophysics of protein evolution and evolutionary protein biophysics

Sikosek

Chan

2014

J. R. Soc. Interface.

209

194

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The study of molecular evolution at the level of protein-coding genes often entails comparing large datasets of sequences to infer their evolutionary relationships. Despite the importance of a protein's structure and conformational dynamics to its function and thus its fitness, common phylogenetic methods embody minimal biophysical knowledge of proteins. To underscore the biophysical constraints on natural selection, we survey effects of protein mutations, highlighting the physical basis for marginal stability of natural globular proteins and how requirement for kinetic stability and avoidance of misfolding and misinteractions might have affected protein evolution. The biophysical underpinnings of these effects have been addressed by models with an explicit coarse-grained spatial representation of the polypeptide chain. Sequence-structure mappings based on such models are powerful conceptual tools that rationalize mutational robustness, evolvability, epistasis, promiscuous function performed by 'hidden' conformational states, resolution of adaptive conflicts and conformational switches in the evolution from one protein fold to another. Recently, protein biophysics has been applied to derive more accurate evolutionary accounts of sequence data. Methods have also been developed to exploit sequence-based evolutionary information to predict biophysical behaviours of proteins. The success of these approaches demonstrates a deep synergy between the fields of protein biophysics and protein evolution.

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Section: Protein Evolution In the Context Of Functional Networkmentioning

confidence: 99%

Biophysics of protein evolution and evolutionary protein biophysics

Sikosek

Chan

2014

J. R. Soc. Interface.

209

194

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“…By contrast, it decreases with their genotypic diversity and with their phenotypic complexity (the number of carbon sources on which they are viable). Moreover, we find that a class of metabolic reactions that we refer to as superessential plays an important role in metabolic innovation [45].…”

Section: Introductionmentioning

confidence: 99%

“…For example, among three recombinant offspring that had independently gained viability on acetate, all three had gained the phosphoglycerate kinase reaction. Likewise, the five recombinant offspring that had independently gained viability on pyruvate achieved this gain through addition of the ribu- What is more, these reactions tend to share a property that we refer to as their superessentiality [45]. The superessentiality index (I SE [45].…”

Section: (B) Superessential Reactions Play An Important Role In Metabmentioning

confidence: 99%

“…Likewise, the five recombinant offspring that had independently gained viability on pyruvate achieved this gain through addition of the ribu- What is more, these reactions tend to share a property that we refer to as their superessentiality [45]. The superessentiality index (I SE [45]. It can be computed from randomly sampled metabolic networks viable on that carbon source.…”

Section: (B) Superessential Reactions Play An Important Role In Metabmentioning

confidence: 99%

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Phenotypic innovation through recombination in genome-scale metabolic networks

2016

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Recombination is an important source of metabolic innovation, especially in prokaryotes, which have evolved the ability to survive on many different sources of chemical elements and energy. Metabolic systems have a wellunderstood genotype-phenotype relationship, which permits a quantitative and biochemically principled understanding of how recombination creates novel phenotypes. Here, we investigate the power of recombination to create genome-scale metabolic reaction networks that enable an organism to survive in new chemical environments. To this end, we use flux balance analysis, an experimentally validated computational method that can predict metabolic phenotypes from metabolic genotypes. We show that recombination is much more likely to create novel metabolic abilities than random changes in chemical reactions of a metabolic network. We also find that phenotypic innovation is more likely when recombination occurs between parents that are genetically closely related, phenotypically highly diverse, and viable on few rather than many carbon sources. Survival on a new carbon source preferentially involves reactions that are superessential, that is, essential in many metabolic networks. We validate our observations with data from 61 reconstructed prokaryotic metabolic networks. Our systematic and quantitative analysis of metabolic systems helps understand how recombination creates innovation.

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“…The high position nodes in hierarchical networks can be identified by the random upstream regions [52] . Recent studies on the connections of essential reactions and on super-essential reactions indicate that essential reactions form a core of metabolic networks and that superessential reactions are needed in all organisms [53,54] . The cancer-specific targeting of signaling networks is mostly used in current anticancer strategies.…”

Section: Hitting Parallel Pathwaysmentioning

confidence: 99%

Diverse array-designed modes of combination therapies in Fangjiomics

Wang

2015

Acta Pharmacol Sin

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In line with the complexity of disease networks, diverse combination therapies have been demonstrated potential in the treatment of different patients with complex diseases in a personal combination profile. However, the identification of rational, compatible and effective drug combinations remains an ongoing challenge. Based on a holistic theory integrated with reductionism, Fangjiomics systematically develops multiple modes of array-designed combination therapies. We define diverse "magic shotgun" vertical, horizontal, focusing, siege and dynamic arrays according to different spatiotemporal distributions of hits on targets, pathways and networks. Through these multiple adaptive modes for treating complex diseases, Fangjiomics may help to identify rational drug combinations with synergistic or additive efficacy but reduced adverse side effects that reverse complex diseases by reconstructing or rewiring multiple targets, pathways and networks. Such a novel paradigm for combination therapies may allow us to achieve more precise treatments by developing phenotype-driven quantitative multi-scale modeling for rational drug combinations.

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Superessential reactions in metabolic networks

Cited by 51 publications

References 75 publications

Biophysics of protein evolution and evolutionary protein biophysics

Biophysics of protein evolution and evolutionary protein biophysics

Phenotypic innovation through recombination in genome-scale metabolic networks

Diverse array-designed modes of combination therapies in Fangjiomics

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