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Sambamoorthy G, Sinha H, Raman K. Evolutionary design principles in metabolism. Proc Biol Sci 2019; 286:20190098. [PMID: 30836874 PMCID: PMC6458322 DOI: 10.1098/rspb.2019.0098] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2019] [Accepted: 02/14/2019] [Indexed: 12/28/2022] Open
Abstract
Microorganisms are ubiquitous and adapt to various dynamic environments to sustain growth. These adaptations accumulate, generating new traits forming the basis of evolution. Organisms adapt at various levels, such as gene regulation, signalling, protein-protein interactions and metabolism. Of these, metabolism forms the integral core of an organism for maintaining the growth and function of a cell. Therefore, studying adaptations in metabolic networks is crucial to understand the emergence of novel metabolic capabilities. Metabolic networks, composed of enzyme-catalysed reactions, exhibit certain repeating paradigms or design principles that arise out of different selection pressures. In this review, we discuss the design principles that are known to exist in metabolic networks, such as functional redundancy, modularity, flux coupling and exaptations. We elaborate on the studies that have helped gain insights highlighting the interplay of these design principles and adaptation. Further, we discuss how evolution plays a role in exploiting such paradigms to enhance the robustness of organisms. Looking forward, we predict that with the availability of ever-increasing numbers of bacterial, archaeal and eukaryotic genomic sequences, novel design principles will be identified, expanding our understanding of these paradigms shaped by varied evolutionary processes.
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Affiliation(s)
- Gayathri Sambamoorthy
- Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai 600036, India
- Initiative for Biological Systems Engineering (IBSE), Indian Institute of Technology Madras, Chennai 600036, India
- Robert Bosch Centre for Data Science and Artificial Intelligence (RBCDSAI), Indian Institute of Technology Madras, Chennai 600036, India
| | - Himanshu Sinha
- Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai 600036, India
- Initiative for Biological Systems Engineering (IBSE), Indian Institute of Technology Madras, Chennai 600036, India
- Robert Bosch Centre for Data Science and Artificial Intelligence (RBCDSAI), Indian Institute of Technology Madras, Chennai 600036, India
| | - Karthik Raman
- Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai 600036, India
- Initiative for Biological Systems Engineering (IBSE), Indian Institute of Technology Madras, Chennai 600036, India
- Robert Bosch Centre for Data Science and Artificial Intelligence (RBCDSAI), Indian Institute of Technology Madras, Chennai 600036, India
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Abstract
Microbial communities are widespread in the environment, and to isolate and identify species or to determine relations among microorganisms, some 'omics methods like metagenomics, proteomics, and metabolomics have been used. When combined with various 'omics data, models known as artificial microbial ecosystems (AME) are powerful methods that can make functional predictions about microbial communities. Reconstruction of an AME model is the first step for model analysis. Many techniques have been applied to the construction of AME models, e.g., the compartmentalization approach, community objectives method, and dynamic analysis approach. Of these approaches, species compartmentalization is the most relevant to genetics. Besides, some algorithms have been developed for the analysis of AME models. In this chapter, we present a general protocol for the use of the species compartmentalization method to reconstruct a model of microbial communities. Then, the analysis of an AME is discussed.
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Pál C, Papp B. Evolution of complex adaptations in molecular systems. Nat Ecol Evol 2017; 1:1084-1092. [PMID: 28782044 PMCID: PMC5540182 DOI: 10.1038/s41559-017-0228-1] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2016] [Accepted: 05/02/2017] [Indexed: 12/31/2022]
Abstract
A central challenge in evolutionary biology concerns the mechanisms by which complex adaptations arise. Such adaptations depend on the fixation of multiple, highly specific mutations, where intermediate stages of evolution seemingly provide little or no benefit. It is generally assumed that the establishment of complex adaptations is very slow in nature, as evolution of such traits demands special population genetic or environmental circumstances. However, blueprints of complex adaptations in molecular systems are pervasive, indicating that they can readily evolve. We discuss the prospects and limitations of non-adaptive scenarios, which assume multiple neutral or deleterious steps in the evolution of complex adaptations. Next, we examine how complex adaptations can evolve by natural selection in changing environment. Finally, we argue that molecular 'springboards', such as phenotypic heterogeneity and promiscuous interactions facilitate this process by providing access to new adaptive paths.
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Affiliation(s)
- Csaba Pál
- Synthetic and Systems Biology Unit, Biological Research Center, Szeged, 6726, Hungary.
| | - Balázs Papp
- Synthetic and Systems Biology Unit, Biological Research Center, Szeged, 6726, Hungary
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Bahramali G, Goliaei B, Minuchehr Z, Marashi SA. A network biology approach to understanding the importance of chameleon proteins in human physiology and pathology. Amino Acids 2016; 49:303-315. [DOI: 10.1007/s00726-016-2361-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2016] [Accepted: 11/05/2016] [Indexed: 12/20/2022]
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Szappanos B, Fritzemeier J, Csörgő B, Lázár V, Lu X, Fekete G, Bálint B, Herczeg R, Nagy I, Notebaart RA, Lercher MJ, Pál C, Papp B. Adaptive evolution of complex innovations through stepwise metabolic niche expansion. Nat Commun 2016; 7:11607. [PMID: 27197754 PMCID: PMC5411730 DOI: 10.1038/ncomms11607] [Citation(s) in RCA: 45] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2015] [Accepted: 04/12/2016] [Indexed: 11/09/2022] Open
Abstract
A central challenge in evolutionary biology concerns the mechanisms by which complex metabolic innovations requiring multiple mutations arise. Here, we propose that metabolic innovations accessible through the addition of a single reaction serve as stepping stones towards the later establishment of complex metabolic features in another environment. We demonstrate the feasibility of this hypothesis through three complementary analyses. First, using genome-scale metabolic modelling, we show that complex metabolic innovations in Escherichia coli can arise via changing nutrient conditions. Second, using phylogenetic approaches, we demonstrate that the acquisition patterns of complex metabolic pathways during the evolutionary history of bacterial genomes support the hypothesis. Third, we show how adaptation of laboratory populations of E. coli to one carbon source facilitates the later adaptation to another carbon source. Our work demonstrates how complex innovations can evolve through series of adaptive steps without the need to invoke non-adaptive processes.
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Affiliation(s)
- Balázs Szappanos
- Synthetic and Systems Biology Unit, Institute of Biochemistry, Biological Research Centre of the Hungarian Academy of Sciences, Temesvári krt. 62, Szeged H-6726, Hungary
| | - Jonathan Fritzemeier
- Department for Computer Science, Heinrich Heine University, Universitätsstraße 1, Düsseldorf D-40221, Germany
| | - Bálint Csörgő
- Synthetic and Systems Biology Unit, Institute of Biochemistry, Biological Research Centre of the Hungarian Academy of Sciences, Temesvári krt. 62, Szeged H-6726, Hungary
| | - Viktória Lázár
- Synthetic and Systems Biology Unit, Institute of Biochemistry, Biological Research Centre of the Hungarian Academy of Sciences, Temesvári krt. 62, Szeged H-6726, Hungary
| | - Xiaowen Lu
- Department of Bioinformatics (CMBI), Radboud University Medical Centre, Geert Grooteplein Zuid 26–28, Nijmegen 6525 GA, The Netherlands
| | - Gergely Fekete
- Synthetic and Systems Biology Unit, Institute of Biochemistry, Biological Research Centre of the Hungarian Academy of Sciences, Temesvári krt. 62, Szeged H-6726, Hungary
| | - Balázs Bálint
- SeqOmics Biotechnology Ltd, Vállalkozók útja 7, Mórahalom H-6782, Hungary
| | - Róbert Herczeg
- SeqOmics Biotechnology Ltd, Vállalkozók útja 7, Mórahalom H-6782, Hungary
| | - István Nagy
- SeqOmics Biotechnology Ltd, Vállalkozók útja 7, Mórahalom H-6782, Hungary
- Sequencing Platform, Institute of Biochemistry, Biological Research Centre of the Hungarian Academy of Sciences, Temesvári krt. 62, Szeged H-6726, Hungary
| | - Richard A. Notebaart
- Department of Bioinformatics (CMBI), Radboud University Medical Centre, Geert Grooteplein Zuid 26–28, Nijmegen 6525 GA, The Netherlands
- Department of Internal Medicine, Radboud University Medical Center, Geert Grooteplein Zuid 8, Nijmegen 6525 GA, The Netherlands
| | - Martin J. Lercher
- Department for Computer Science, Heinrich Heine University, Universitätsstraße 1, Düsseldorf D-40221, Germany
| | - Csaba Pál
- Synthetic and Systems Biology Unit, Institute of Biochemistry, Biological Research Centre of the Hungarian Academy of Sciences, Temesvári krt. 62, Szeged H-6726, Hungary
| | - Balázs Papp
- Synthetic and Systems Biology Unit, Institute of Biochemistry, Biological Research Centre of the Hungarian Academy of Sciences, Temesvári krt. 62, Szeged H-6726, Hungary
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Hierarchical organization of fluxes in Escherichia coli metabolic network: Using flux coupling analysis for understanding the physiological properties of metabolic genes. Gene 2015; 561:199-208. [DOI: 10.1016/j.gene.2015.02.032] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2014] [Revised: 02/04/2015] [Accepted: 02/12/2015] [Indexed: 01/09/2023]
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Marashi SA, Hosseini Z. On correlated reaction sets and coupled reaction sets in metabolic networks. J Bioinform Comput Biol 2015; 13:1571003. [PMID: 25747383 DOI: 10.1142/s0219720015710031] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Two reactions are in the same "correlated reaction set" (or "Co-Set") if their fluxes are linearly correlated. On the other hand, two reactions are "coupled" if nonzero flux through one reaction implies nonzero flux through the other reaction. Flux correlation analysis has been previously used in the analysis of enzyme dysregulation and enzymopathy, while flux coupling analysis has been used to predict co-expression of genes and to model network evolution. The goal of this paper is to emphasize, through a few examples, that these two concepts are inherently different. In other words, except for the case of full coupling, which implies perfect correlation between two fluxes (R(2) = 1), there are no constraints on Pearson correlation coefficients (CC) in case of any other type of (un)coupling relations. In other words, Pearson CC can take any value between 0 and 1 in other cases. Furthermore, by analyzing genome-scale metabolic networks, we confirm that there are some examples in real networks of bacteria, yeast and human, which approve that flux coupling and flux correlation cannot be used interchangeably.
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Affiliation(s)
- Sayed-Amir Marashi
- Department of Biotechnology, College of Science, University of Tehran, Tehran, Iran.,Department of Bioinformatics, School of Computer Science, Institute for Research in Fundamental Sciences (IPM), Tehran, Iran
| | - Zhaleh Hosseini
- Department of Biotechnology, College of Science, University of Tehran, Tehran, Iran
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8
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Žitnik M, Zupan B. Data Fusion by Matrix Factorization. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2015; 37:41-53. [PMID: 26353207 DOI: 10.1109/tpami.2014.2343973] [Citation(s) in RCA: 90] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/05/2023]
Abstract
For most problems in science and engineering we can obtain data sets that describe the observed system from various perspectives and record the behavior of its individual components. Heterogeneous data sets can be collectively mined by data fusion. Fusion can focus on a specific target relation and exploit directly associated data together with contextual data and data about system's constraints. In the paper we describe a data fusion approach with penalized matrix tri-factorization (DFMF) that simultaneously factorizes data matrices to reveal hidden associations. The approach can directly consider any data that can be expressed in a matrix, including those from feature-based representations, ontologies, associations and networks. We demonstrate the utility of DFMF for gene function prediction task with eleven different data sources and for prediction of pharmacologic actions by fusing six data sources. Our data fusion algorithm compares favorably to alternative data integration approaches and achieves higher accuracy than can be obtained from any single data source alone.
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Ochoa D, Pazos F. Practical aspects of protein co-evolution. Front Cell Dev Biol 2014; 2:14. [PMID: 25364721 PMCID: PMC4207036 DOI: 10.3389/fcell.2014.00014] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2014] [Accepted: 04/02/2014] [Indexed: 11/15/2022] Open
Abstract
Co-evolution is a fundamental aspect of Evolutionary Theory. At the molecular level, co-evolutionary linkages between protein families have been used as indicators of protein interactions and functional relationships from long ago. Due to the complexity of the problem and the amount of genomic data required for these approaches to achieve good performances, it took a relatively long time from the appearance of the first ideas and concepts to the quotidian application of these approaches and their incorporation to the standard toolboxes of bioinformaticians and molecular biologists. Today, these methodologies are mature (both in terms of performance and usability/implementation), and the genomic information that feeds them large enough to allow their general application. This review tries to summarize the current landscape of co-evolution-based methodologies, with a strong emphasis on describing interesting cases where their application to important biological systems, alone or in combination with other computational and experimental approaches, allowed getting new insight into these.
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Affiliation(s)
- David Ochoa
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI) Hinxton, UK
| | - Florencio Pazos
- Computational Systems Biology Group, National Centre for Biotechnology (CNB-CSIC) Madrid, Spain
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Lu X, Kensche PR, Huynen MA, Notebaart RA. Genome evolution predicts genetic interactions in protein complexes and reveals cancer drug targets. Nat Commun 2014; 4:2124. [PMID: 23851603 PMCID: PMC3717498 DOI: 10.1038/ncomms3124] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2012] [Accepted: 06/07/2013] [Indexed: 12/05/2022] Open
Abstract
Genetic interactions reveal insights into cellular function and can be used to identify drug targets. Here we construct a new model to predict negative genetic interactions in protein complexes by exploiting the evolutionary history of genes in parallel converging pathways in metabolism. We evaluate our model with protein complexes of Saccharomyces cerevisiae and show that the predicted protein pairs more frequently have a negative genetic interaction than random proteins from the same complex. Furthermore, we apply our model to human protein complexes to predict novel cancer drug targets, and identify 20 candidate targets with empirical support and 10 novel targets amenable to further experimental validation. Our study illustrates that negative genetic interactions can be predicted by systematically exploring genome evolution, and that this is useful to identify novel anti-cancer drug targets. Genetic interactions can reveal insights into cellular functions. Here, Lu et al. show that negative genetic interactions in protein complexes can be predicted by systematically exploring the evolutionary history of genes, which may be useful for the identification of novel targets for anti-cancer drugs.
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Affiliation(s)
- Xiaowen Lu
- Department of Bioinformatics, Centre for Molecular Life Sciences, Radboud University Medical Centre, 6525GA Nijmegen, The Netherlands
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Marashi SA, Kouhestani H, Mahdavi M. Studying the relationship between robustness against mutations in metabolic networks and lifestyle of organisms. ScientificWorldJournal 2013; 2013:615697. [PMID: 24348175 PMCID: PMC3848384 DOI: 10.1155/2013/615697] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2013] [Accepted: 09/26/2013] [Indexed: 01/09/2023] Open
Abstract
Robustness is the key feature of biological networks that enables living organisms to keep their homeostatic state and to survive against external and internal perturbations. Variations in environmental conditions or nutrients and intracellular changes such as genetic mutations have the potential to change stability and efficiency of an organism. Structural robustness helps biological systems to choose alternative routes of adaptation to varying conditions. In this study, in order to estimate the structural robustness in metabolic networks we presented a novel flux balance-based approach inspired by bond percolation theory. Fourteen in silico metabolic models were studied in this work in order to examine the possible relationship between the lifestyle of organisms and their metabolic robustness. The results of this study confirm that in organisms which are highly adapted to their environment robustness to mutations may decrease compared to other organisms.
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Affiliation(s)
- Sayed-Amir Marashi
- Department of Biotechnology, College of Science, University of Tehran, Tehran 1417614411, Iran
- School of Computer Science, Institute for Research in Fundamental Sciences (IPM), P.O. Box 19395-5746, Tehran, Iran
| | - Hawa Kouhestani
- Department of Biology, Faculty of Natural Science, University of Tabriz, Tabriz 5166616471, Iran
| | - Majid Mahdavi
- Department of Biology, Faculty of Natural Science, University of Tabriz, Tabriz 5166616471, Iran
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12
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Shared protein complex subunits contribute to explaining disrupted co-occurrence. PLoS Comput Biol 2013; 9:e1003124. [PMID: 23874172 PMCID: PMC3715415 DOI: 10.1371/journal.pcbi.1003124] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2012] [Accepted: 05/17/2013] [Indexed: 11/19/2022] Open
Abstract
The gene composition of present-day genomes has been shaped by a complicated evolutionary history, resulting in diverse distributions of genes across genomes. The pattern of presence and absence of a gene in different genomes is called its phylogenetic profile. It has been shown that proteins whose encoding genes have highly similar profiles tend to be functionally related: As these genes were gained and lost together, their encoded proteins can probably only perform their full function if both are present. However, a large proportion of genes encoding interacting proteins do not have matching profiles. In this study, we analysed one possible reason for this, namely that phylogenetic profiles can be affected by multi-functional proteins such as shared subunits of two or more protein complexes. We found that by considering triplets of proteins, of which one protein is multi-functional, a large fraction of disturbed co-occurrence patterns can be explained. Every genome of current day species contains a very unique selection of genes. Why a specific genome is composed of exactly those genes is determined by many factors, but often not resolvable. It seems plausible that interacting genes would either occur together or be absent together, because if one of them is alone, it might not be able to perform its function properly, just as a bolt can only perform its function together with a nut and vice versa. However, it turns out that interacting genes very often do not nicely co-occur across a wide range of species, and frequently one gene can be found but the other not. In this study, we investigated the co-occurrences of multi-functional proteins and found that they are often maintained in a genome, even if one of their interaction partners is lost. This is because they can still perform some functions with other interaction partners that are still present. We can show that this has a noticeable effect on genome compositions and can explain otherwise surprisingly mismatching co-occurrence patterns of interacting genes.
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McCloskey D, Palsson BØ, Feist AM. Basic and applied uses of genome-scale metabolic network reconstructions of Escherichia coli. Mol Syst Biol 2013; 9:661. [PMID: 23632383 PMCID: PMC3658273 DOI: 10.1038/msb.2013.18] [Citation(s) in RCA: 229] [Impact Index Per Article: 20.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2012] [Accepted: 03/11/2013] [Indexed: 02/07/2023] Open
Abstract
The genome-scale model (GEM) of metabolism in the bacterium Escherichia coli K-12 has been in development for over a decade and is now in wide use. GEM-enabled studies of E. coli have been primarily focused on six applications: (1) metabolic engineering, (2) model-driven discovery, (3) prediction of cellular phenotypes, (4) analysis of biological network properties, (5) studies of evolutionary processes, and (6) models of interspecies interactions. In this review, we provide an overview of these applications along with a critical assessment of their successes and limitations, and a perspective on likely future developments in the field. Taken together, the studies performed over the past decade have established a genome-scale mechanistic understanding of genotype-phenotype relationships in E. coli metabolism that forms the basis for similar efforts for other microbial species. Future challenges include the expansion of GEMs by integrating additional cellular processes beyond metabolism, the identification of key constraints based on emerging data types, and the development of computational methods able to handle such large-scale network models with sufficient accuracy.
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Affiliation(s)
- Douglas McCloskey
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA
| | - Bernhard Ø Palsson
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Lyngby, Denmark
| | - Adam M Feist
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Lyngby, Denmark
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14
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A Lattice-Theoretic Framework for Metabolic Pathway Analysis. COMPUTATIONAL METHODS IN SYSTEMS BIOLOGY 2013. [DOI: 10.1007/978-3-642-40708-6_14] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
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Tervo CJ, Reed JL. FOCAL: an experimental design tool for systematizing metabolic discoveries and model development. Genome Biol 2012; 13:R116. [PMID: 23236964 PMCID: PMC4056367 DOI: 10.1186/gb-2012-13-12-r116] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2012] [Accepted: 12/13/2012] [Indexed: 01/05/2023] Open
Abstract
Current computational tools can generate and improve genome-scale models based on existing data; however, for many organisms, the data needed to test and refine such models are not available. To facilitate model development, we created the forced coupling algorithm, FOCAL, to identify genetic and environmental conditions such that a reaction becomes essential for an experimentally measurable phenotype. This reaction's conditional essentiality can then be tested experimentally to evaluate whether network connections occur or to create strains with desirable phenotypes. FOCAL allows network connections to be queried, which improves our understanding of metabolism and accuracy of developed models.
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Larhlimi A, David L, Selbig J, Bockmayr A. F2C2: a fast tool for the computation of flux coupling in genome-scale metabolic networks. BMC Bioinformatics 2012; 13:57. [PMID: 22524245 PMCID: PMC3515416 DOI: 10.1186/1471-2105-13-57] [Citation(s) in RCA: 48] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2011] [Accepted: 02/23/2012] [Indexed: 11/30/2022] Open
Abstract
Background Flux coupling analysis (FCA) has become a useful tool in the constraint-based analysis of genome-scale metabolic networks. FCA allows detecting dependencies between reaction fluxes of metabolic networks at steady-state. On the one hand, this can help in the curation of reconstructed metabolic networks by verifying whether the coupling between reactions is in agreement with the experimental findings. On the other hand, FCA can aid in defining intervention strategies to knock out target reactions. Results We present a new method F2C2 for FCA, which is orders of magnitude faster than previous approaches. As a consequence, FCA of genome-scale metabolic networks can now be performed in a routine manner. Conclusions We propose F2C2 as a fast tool for the computation of flux coupling in genome-scale metabolic networks. F2C2 is freely available for non-commercial use at https://sourceforge.net/projects/f2c2/files/.
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Affiliation(s)
- Abdelhalim Larhlimi
- Department of Bioinformatics, Institute for Biochemistry and Biology, University of Potsdam, Karl-Liebknecht-Str, 24-25, D-14476 Potsdam, Germany.
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Teusink B, Bachmann H, Molenaar D. Systems biology of lactic acid bacteria: a critical review. Microb Cell Fact 2011; 10 Suppl 1:S11. [PMID: 21995498 PMCID: PMC3231918 DOI: 10.1186/1475-2859-10-s1-s11] [Citation(s) in RCA: 51] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
Understanding the properties of a system as emerging from the interaction of well described parts is the most important goal of Systems Biology. Although in the practice of Lactic Acid Bacteria (LAB) physiology we most often think of the parts as the proteins and metabolites, a wider interpretation of what a part is can be useful. For example, different strains or species can be the parts of a community, or we could study only the chemical reactions as the parts of metabolism (and forgetting about the enzymes that catalyze them), as is done in flux balance analysis. As long as we have some understanding of the properties of these parts, we can investigate whether their interaction leads to novel or unanticipated behaviour of the system that they constitute. There has been a tendency in the Systems Biology community to think that the collection and integration of data should continue ad infinitum, or that we will otherwise not be able to understand the systems that we study in their details. However, it may sometimes be useful to take a step back and consider whether the knowledge that we already have may not explain the system behaviour that we find so intriguing. Reasoning about systems can be difficult, and may require the application of mathematical techniques. The reward is sometimes the realization of unexpected conclusions, or in the worst case, that we still do not know enough details of the parts, or of the interactions between them. We will discuss a number of cases, with a focus on LAB-related work, where a typical systems approach has brought new knowledge or perspective, often counterintuitive, and clashing with conclusions from simpler approaches. Also novel types of testable hypotheses may be generated by the systems approach, which we will illustrate. Finally we will give an outlook on the fields of research where the systems approach may point the way for the near future.
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Affiliation(s)
- Bas Teusink
- Systems Bioinformatics/NISB, Faculty of Earth and Life Sciences, VU University Amsterdam, De Boelelaan 1085, 1081 HV Amsterdam, The Netherlands.
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Abstract
Is evolution predictable at the molecular level? The ambitious goal to answer this question requires an understanding of the mutational effects that govern the complex relationship between genotype and phenotype. In practice, it involves integrating systems-biology modelling, microbial laboratory evolution experiments and large-scale mutational analyses - a feat that is made possible by the recent availability of the necessary computational tools and experimental techniques. This Review investigates recent progresses in mapping evolutionary trajectories and discusses the degree to which these predictions are realistic.
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Affiliation(s)
- Balázs Papp
- Synthetic and Systems Biology Unit, Institute of Biochemistry, Biological Research Center, Temesvári krt. 62, H-6726 Szeged, Hungary
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Yizhak K, Tuller T, Papp B, Ruppin E. Metabolic modeling of endosymbiont genome reduction on a temporal scale. Mol Syst Biol 2011; 7:479. [PMID: 21451589 PMCID: PMC3094061 DOI: 10.1038/msb.2011.11] [Citation(s) in RCA: 33] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2010] [Accepted: 02/09/2011] [Indexed: 11/23/2022] Open
Abstract
This study explores the order in which individual metabolic genes are lost in an in silico evolutionary process leading from the metabolic network of Eschericia coli to that of the genome-reduced endosymbiont Buchnera aphidicola. Simulating the reductive evolutionary process under several growth conditions, a remarkable correlation between in silico and phylogenetically reconstructed gene loss time is obtained. A gene's k-robustness (its depth of backups) is prime determinant of its loss time. In silico gene loss time is a better predictor of their actual loss times than genomic features and network properties. Simulating the reductive evolutionary process by the loss of large blocks followed by single-gene deletions, as known to occur in evolution, yields a remarkable correspondence with the phylogenetic reconstruction and the block loss reported in the literature.
An open fundamental challenge in Systems Biology is whether a genome-scale model can predict patterns of genome evolution by realistically accounting for the associated biochemical constraints. In this study, we explore the order in which individual genes are lost in an in silico evolutionary process, leading from the metabolic network of Eschericia coli to that of the endosymbiont Buchnera aphidicola. To evaluate the in silico gene loss time, we repeated the reductive evolutionary process introduced by Pál et al (2006), denoting the in silico deletion time of a gene in a single run of the reductive evolutionary process as the number of genes deleted before its own deletion occurred. By comparing the in silico evaluations of the gene loss time to that obtained by a phylogenetic reconstruction (Figure 1), we could evaluate the ability of an in silico process to predict temporal patterns of genome reduction. Applying this procedure on a literature-based viable media, we obtained a mean Spearman's correlation of 0.46 (53% of the maximal correlation, empirical P-value <9.9e−4) between in silico and phylogenetically reconstructed loss times. In order to provide an upper bound on evolutionary necessity stemming from metabolic constraints, we searched the space of potential growth media and biomass functions via a simulated annealing search algorithm aimed at identifying an environment/biomass function that maximizes the target correlation between in silico and reconstructed loss times. Simulating the reductive evolutionary process under the growth conditions and biomass function obtained in this process, we managed to improve the correlation between in silico and reconstructed loss times to a mean Spearman's correlation of 0.54 (63% of the maximal correlation, empirical P-value <9.9e−4, Figure 3). Examining the dependency of the predicted loss time of each gene on its intrinsic network-level properties we find a very strong inverse Spearman's correlation of −0.84 (empirical P-value <9.9e−4) between the order of gene loss predicted in silico and the k-robustness levels of the genes, the latter denoting the depth of their functional backups in the network (Deutscher et al, 2006). Moreover, in order to examine whether the relative loss time of a gene is influenced by its functional dependencies with other genes, we performed a flux-coupling analysis and identified pairs of reactions whose activities asymmetrically depend on each other, i.e., are directionally coupled (Burgard et al, 2004). We find that genes encoding reactions whose activity is needed for activating the other reaction (and not vice versa) have a tendency to be lost later, as one would expect (binomial P-value <1e−14). To assess the scale of these results, we examined as a control how well genomic features and network properties predict the phylogenetically reconstructed gene loss times. We examined the dependency of the latter on several factors that are known be inversely correlated with the propensity of a gene to be lost (Brinza et al, 2009; Delmotte et al, 2006; Tamames et al, 2007), including the genes' mRNA levels, tAI values (Covert et al, 2004; Reis et al, 2004; Sharp and Li, 1987; Tuller et al, 2010a) and the number of partners the gene products have in a protein–protein interaction network. Remarkably, these genomic features yield considerably lower Spearman's correlation than that obtained by the in silico simulations. Moreover, multiply regressing the loss times from the phylogenetic reconstruction on the in silico gene loss time predictions and the genomic and network variables, we found that the (normalized) coefficient of the in silico predictions in the regression is much higher than those of the genomic features, further testifying to the considerable independent predictive power of the metabolic model. Finally, simulating the evolutionary process as large block deletions at first followed by single-gene deletions as is thought to occur in evolution (Moran and Mira, 2001; van Ham et al, 2003), a remarkable correspondence with the phylogenetic reconstruction was found. Namely, we find that after a certain amount of genes are deleted from the genome, no further block deletions can occur due to the increasing density of essential genes. Notably, the maximum amount of genes that can be deleted in blocks (i.e., until no more blocks can be deleted) corresponds to the number of genes appearing in our phylogenetic reconstruction from the LCA (last common ancestor of Buchnera and E. coli) to the LCSA (last common symbiotic ancestor, nodes 1–3 in Figure 1A), as described in the literature. A fundamental challenge in Systems Biology is whether a cell-scale metabolic model can predict patterns of genome evolution by realistically accounting for associated biochemical constraints. Here, we study the order in which genes are lost in an in silico evolutionary process, leading from the metabolic network of Eschericia coli to that of the endosymbiont Buchnera aphidicola. We examine how this order correlates with the order by which the genes were actually lost, as estimated from a phylogenetic reconstruction. By optimizing this correlation across the space of potential growth and biomass conditions, we compute an upper bound estimate on the model's prediction accuracy (R=0.54). The model's network-based predictive ability outperforms predictions obtained using genomic features of individual genes, reflecting the effect of selection imposed by metabolic stoichiometric constraints. Thus, while the timing of gene loss might be expected to be a completely stochastic evolutionary process, remarkably, we find that metabolic considerations, on their own, make a marked 40% contribution to determining when such losses occur.
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Affiliation(s)
- Keren Yizhak
- The Blavatnik School of Computer Science, Tel Aviv University, Tel Aviv, Israel.
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David L, Marashi SA, Larhlimi A, Mieth B, Bockmayr A. FFCA: a feasibility-based method for flux coupling analysis of metabolic networks. BMC Bioinformatics 2011; 12:236. [PMID: 21676263 PMCID: PMC3144024 DOI: 10.1186/1471-2105-12-236] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2011] [Accepted: 06/15/2011] [Indexed: 12/20/2022] Open
Abstract
Background Flux coupling analysis (FCA) is a useful method for finding dependencies between fluxes of a metabolic network at steady-state. FCA classifies reactions into subsets (called coupled reaction sets) in which activity of one reaction implies activity of another reaction. Several approaches for FCA have been proposed in the literature. Results We introduce a new FCA algorithm, FFCA (Feasibility-based Flux Coupling Analysis), which is based on checking the feasibility of a system of linear inequalities. We show on a set of benchmarks that for genome-scale networks FFCA is faster than other existing FCA methods. Conclusions We present FFCA as a new method for flux coupling analysis and prove it to be faster than existing approaches. A corresponding software tool is freely available for non-commercial use at http://www.bioinformatics.org/ffca/.
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Teusink B, Westerhoff HV, Bruggeman FJ. Comparative systems biology: from bacteria to man. WILEY INTERDISCIPLINARY REVIEWS-SYSTEMS BIOLOGY AND MEDICINE 2011; 2:518-532. [PMID: 20836045 DOI: 10.1002/wsbm.74] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Comparative analyses, as carried out by comparative genomics and bioinformatics, have proven extremely powerful to obtain insight into the identity of specific genes that underlie differences and similarities across species. The central concept developed in this chapter is that important aspects of the functional differences between organisms derive not only from the differences in genetic components (which underlies comparative genomics) but also from dynamic, molecular (physical) interactions. Approaches that aim at identifying such network-based rather than component-based homologies between species we shall call Comparative Systems Biology. It will be illustrated by a number of examples from metabolic networks from prokaryotes, via yeast, to man. The potential for species comparisons, at the genome-scale using classical approaches and at the more detailed level of dynamic molecular networks will be illustrated. In our opinion, comparative systems biology, as a marriage between bioinformatics and systems biology, will offer new insights into the nature of organisms for the benefit of medicine, biotechnology, and drug design. As dynamic modeling is becoming more mainstream in cell biology, the potential of comparative systems biology will become more evident.
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Affiliation(s)
- Bas Teusink
- Systems BioInformatics, Center for Integrative Bioinformatics VU (IBIVU), VU University Amsterdam, The Netherlands.,Netherlands Institute Systems Biology (NISB), The Netherlands.,Kluyver Center for Genomics of Industrial Fermentation, The Netherlands
| | - Hans V Westerhoff
- Netherlands Institute Systems Biology (NISB), The Netherlands.,Molecular Cell Physiology, VU University Amsterdam, The Netherlands.,Manchester Centre for Integrative Systems Biology, University of Manchester, UK
| | - Frank J Bruggeman
- Systems BioInformatics, Center for Integrative Bioinformatics VU (IBIVU), VU University Amsterdam, The Netherlands.,Regulatory Networks Group, NISB, The Netherlands.,Life Sciences, Centre for Mathematics and Computer Science (CWI) Amsterdam, The Netherlands
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Abstract
One of the major aims of the nascent field of evolutionary systems biology is to test evolutionary hypotheses that are not only realistic from a population genetic point of view but also detailed in terms of molecular biology mechanisms. By providing a mapping between genotype and phenotype for hundreds of genes, genome-scale systems biology models of metabolic networks have already provided valuable insights into the evolution of metabolic gene contents and phenotypes of yeast and other microbial species. Here we review the recent use of these computational models to predict the fitness effect of mutations, genetic interactions, evolutionary outcomes, and to decipher the mechanisms of mutational robustness. While these studies have demonstrated that even simplified models of biochemical reaction networks can be highly informative for evolutionary analyses, they have also revealed the weakness of this modeling framework to quantitatively predict mutational effects, a challenge that needs to be addressed for future progress in evolutionary systems biology.
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Santos F, Boele J, Teusink B. A Practical Guide to Genome-Scale Metabolic Models and Their Analysis. Methods Enzymol 2011; 500:509-32. [DOI: 10.1016/b978-0-12-385118-5.00024-4] [Citation(s) in RCA: 43] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
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Rawat N, Biswas P. Shape, flexibility and packing of proteins and nucleic acids in complexes. Phys Chem Chem Phys 2011; 13:9632-43. [DOI: 10.1039/c1cp00027f] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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Marashi SA, Bockmayr A. Flux coupling analysis of metabolic networks is sensitive to missing reactions. Biosystems 2010; 103:57-66. [PMID: 20888889 DOI: 10.1016/j.biosystems.2010.09.011] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2010] [Revised: 07/16/2010] [Accepted: 09/24/2010] [Indexed: 11/29/2022]
Abstract
Genome-scale metabolic reconstructions are comprehensive, yet incomplete, models of real-world metabolic networks. While flux coupling analysis (FCA) has proved an appropriate method for analyzing metabolic relationships and for detecting functionally related reactions in such models, little is known about the impact of missing reactions on the accuracy of FCA. Based on an alternative characterization of flux coupling relations using elementary flux modes, this paper studies the changes that flux coupling relations may undergo due to missing reactions. In particular, we show that two uncoupled reactions in a metabolic network may be detected as directionally, partially or fully coupled in an incomplete version of the same network. Even a single missing reaction can cause significant changes in flux coupling relations. In case of two consecutive Escherichia coli genome-scale networks many fully coupled reaction pairs in the incomplete network become directionally coupled or even uncoupled in the more complete reconstruction. In this context, we found gene expression correlation values being significantly higher for the pairs that remained fully coupled than for the uncoupled or directionally coupled pairs. Our study clearly suggests that FCA results are indeed sensitive to missing reactions. Since the currently available genome-scale metabolic models are incomplete, we advise to use FCA results with care.
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Affiliation(s)
- Sayed-Amir Marashi
- International Max Planck Research School for Computational Biology and Scientific Computing, Max Planck Institute for Molecular Genetics, Ihnestrasse 63-73, Berlin, Germany.
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