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García I, Chouaia B, Llabrés M, Simeoni M. Exploring the expressiveness of abstract metabolic networks. PLoS One 2023; 18:e0281047. [PMID: 36758030 PMCID: PMC9910719 DOI: 10.1371/journal.pone.0281047] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2022] [Accepted: 01/16/2023] [Indexed: 02/10/2023] Open
Abstract
Metabolism is characterised by chemical reactions linked to each other, creating a complex network structure. The whole metabolic network is divided into pathways of chemical reactions, such that every pathway is a metabolic function. A simplified representation of metabolism, which we call an abstract metabolic network, is a graph in which metabolic pathways are nodes and there is an edge between two nodes if their corresponding pathways share one or more compounds. The abstract metabolic network of a given organism results in a small network that requires low computational power to be analysed and makes it a suitable model to perform a large-scale comparison of organisms' metabolism. To explore the potentials and limits of such a basic representation, we considered a comprehensive set of KEGG organisms, represented through their abstract metabolic network. We performed pairwise comparisons using graph kernel methods and analyse the results through exploratory data analysis and machine learning techniques. The results show that abstract metabolic networks discriminate macro evolutionary events, indicating that they are expressive enough to capture key steps in metabolism evolution.
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Affiliation(s)
- Irene García
- Mathematics and Computer Science Department, University of the Balearic Islands, Palma, Spain
| | - Bessem Chouaia
- Dipartimento di Scienze Ambientali, Informatica e Statistica, Università Ca’ Foscari Venezia, Venice, Italy
| | - Mercè Llabrés
- Mathematics and Computer Science Department, University of the Balearic Islands, Palma, Spain
| | - Marta Simeoni
- Dipartimento di Scienze Ambientali, Informatica e Statistica, Università Ca’ Foscari Venezia, Venice, Italy
- European Centre for Living Technology (ECLT), Venice, Italy
- * E-mail:
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Latifi-Navid H, Soheili ZS, Samiei S, Sadeghi M, Taghizadeh S, Pirmardan ER, Ahmadieh H. Network analysis and the impact of Aflibercept on specific mediators of angiogenesis in HUVEC cells. J Cell Mol Med 2021; 25:8285-8299. [PMID: 34250732 PMCID: PMC8419159 DOI: 10.1111/jcmm.16778] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2021] [Revised: 05/25/2021] [Accepted: 06/11/2021] [Indexed: 12/31/2022] Open
Abstract
Angiogenesis, inflammation and endothelial cells’ migration and proliferation exert fundamental roles in different diseases. However, more studies are needed to identify key proteins and pathways involved in these processes. Aflibercept has received the approval of the US Food and Drug Administration (FDA) for the treatment of wet AMD and colorectal cancer. Moreover, the effect of Aflibercept on VEGFR2 downstream signalling pathways has not been investigated yet. Here, we integrated text mining data, protein‐protein interaction networks and multi‐experiment microarray data to specify candidate genes that are involved in VEGFA/VEGFR2 signalling pathways. Network analysis of candidate genes determined the importance of the nominated genes via different centrality parameters. Thereupon, several genes—with the highest centrality indexes—were recruited to investigate the impact of Aflibercept on their expression pattern in HUVEC cells. Real‐time PCR was performed, and relative expression of the specific genes revealed that Aflibercept modulated angiogenic process by VEGF/PI3KA/AKT/mTOR axis, invasion by MMP14/MMP9 axis and inflammation‐related angiogenesis by IL‐6‐STAT3 axis. Data showed Aflibercept simultaneously affected these processes and determined the nominated axes that had been affected by the drug. Furthermore, integrating the results of Aflibercept on expression of candidate genes with the current network analysis suggested that resistance against the Aflibercept effect is a plausible process in HUVEC cells.
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Affiliation(s)
- Hamid Latifi-Navid
- Department of Molecular Medicine, National Institute of Genetic Engineering and Biotechnology, Tehran, Iran
| | - Zahra-Soheila Soheili
- Department of Molecular Medicine, National Institute of Genetic Engineering and Biotechnology, Tehran, Iran
| | - Shahram Samiei
- Blood Transfusion Research Center, High Institute for Research and Education in Transfusion Medicine, Tehran, Iran
| | - Mehdi Sadeghi
- Department of Medical Genetics, National Institute for Genetic Engineering and Biotechnology, Tehran, Iran.,School of Biological Sciences, Institute for Research in Fundamental Sciences (IPM), Tehran, Iran
| | - Sepideh Taghizadeh
- Department of Molecular Medicine, National Institute of Genetic Engineering and Biotechnology, Tehran, Iran
| | - Ehsan Ranaei Pirmardan
- Ocular Tissue Engineering Research Center, Molecular Biomarkers Nano-Imaging Laboratory, Brigham and Women's Hospital, Boston, Massachusetts, USA.,Department of Radiology, Harvard Medical School, Boston, Massachusetts, USA
| | - Hamid Ahmadieh
- Ophthalmic Research Center, Research Institute for Ophthalmology and Vision Science, Shahid Beheshti University of Medical Sciences, Tehran, Iran
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Janwa H, Massey SE, Velev J, Mishra B. On the Origin of Biomolecular Networks. Front Genet 2019; 10:240. [PMID: 31024611 PMCID: PMC6467946 DOI: 10.3389/fgene.2019.00240] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2018] [Accepted: 03/04/2019] [Indexed: 12/17/2022] Open
Abstract
Biomolecular networks have already found great utility in characterizing complex biological systems arising from pairwise interactions amongst biomolecules. Here, we explore the important and hitherto neglected role of information asymmetry in the genesis and evolution of such pairwise biomolecular interactions. Information asymmetry between sender and receiver genes is identified as a key feature distinguishing early biochemical reactions from abiotic chemistry, and a driver of network topology as biomolecular systems become more complex. In this context, we review how graph theoretical approaches can be applied not only for a better understanding of various proximate (mechanistic) relations, but also, ultimate (evolutionary) structures encoded in such networks from among all types of variations they induce. Among many possible variations, we emphasize particularly the essential role of gene duplication in terms of signaling game theory, whereby sender and receiver gene players accrue benefit from gene duplication, leading to a preferential attachment mode of network growth. The study of the resulting dynamics suggests many mathematical/computational problems, the majority of which are intractable yet yield to efficient approximation algorithms, when studied through an algebraic graph theoretic lens. We relegate for future work the role of other possible generalizations, additionally involving horizontal gene transfer, sexual recombination, endo-symbiosis, etc., which enrich the underlying graph theory even further.
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Affiliation(s)
- Heeralal Janwa
- Department of Mathematics, University of Puerto Rico, San Juan, PR, United States
| | - Steven E Massey
- Department of Biology, University of Puerto Rico, San Juan, PR, United States
| | - Julian Velev
- Department of Physics, University of Puerto Rico, San Juan, PR, United States
| | - Bud Mishra
- Departments of Computer Science, Mathematics and Cell Biology, Courant Institute and NYU School of Medicine, New York University, New York City, NY, United States
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Bhardwaj T, Somvanshi P. A computational approach using mathematical modeling to assess the peptidoglycan biosynthesis of Clostridium botulinum ATCC 3502 for potential drug targets. GENE REPORTS 2018. [DOI: 10.1016/j.genrep.2018.07.002] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
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5
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Collet JM, McGuigan K, Allen SL, Chenoweth SF, Blows MW. Mutational Pleiotropy and the Strength of Stabilizing Selection Within and Between Functional Modules of Gene Expression. Genetics 2018; 208:1601-1616. [PMID: 29437825 PMCID: PMC5887151 DOI: 10.1534/genetics.118.300776] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2017] [Accepted: 01/30/2018] [Indexed: 11/18/2022] Open
Abstract
Variational modules, sets of pleiotropically covarying traits, affect phenotypic evolution, and therefore are predicted to reflect functional modules, such that traits within a variational module also share a common function. Such an alignment of function and pleiotropy is expected to facilitate adaptation by reducing the deleterious effects of mutations, and by allowing coordinated evolution of functionally related sets of traits. Here, we adopt a high-dimensional quantitative genetic approach using a large number of gene expression traits in Drosophila serrata to test whether functional grouping, defined by gene ontology (GO terms), predicts variational modules. Mutational or standing genetic covariance was significantly greater than among randomly grouped sets of genes for 38% of our functional groups, indicating that GO terms can predict variational modularity to some extent. We estimated stabilizing selection acting on mutational covariance to test the prediction that functional pleiotropy would result in reduced deleterious effects of mutations within functional modules. Stabilizing selection within functional modules was weaker than that acting on randomly grouped sets of genes in only 23% of functional groups, indicating that functional alignment can reduce deleterious effects of pleiotropic mutation but typically does not. Our analyses also revealed the presence of variational modules that spanned multiple functions.
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Affiliation(s)
- Julie M Collet
- School of Biological Sciences, The University of Queensland, Brisbane 4072, Queensland, Australia
| | - Katrina McGuigan
- School of Biological Sciences, The University of Queensland, Brisbane 4072, Queensland, Australia
| | - Scott L Allen
- School of Biological Sciences, The University of Queensland, Brisbane 4072, Queensland, Australia
| | - Stephen F Chenoweth
- School of Biological Sciences, The University of Queensland, Brisbane 4072, Queensland, Australia
| | - Mark W Blows
- School of Biological Sciences, The University of Queensland, Brisbane 4072, Queensland, Australia
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Chaiboonchoe A, Ghamsari L, Dohai B, Ng P, Khraiwesh B, Jaiswal A, Jijakli K, Koussa J, Nelson DR, Cai H, Yang X, Chang RL, Papin J, Yu H, Balaji S, Salehi-Ashtiani K. Systems level analysis of the Chlamydomonas reinhardtii metabolic network reveals variability in evolutionary co-conservation. MOLECULAR BIOSYSTEMS 2017; 12:2394-407. [PMID: 27357594 DOI: 10.1039/c6mb00237d] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
Metabolic networks, which are mathematical representations of organismal metabolism, are reconstructed to provide computational platforms to guide metabolic engineering experiments and explore fundamental questions on metabolism. Systems level analyses, such as interrogation of phylogenetic relationships within the network, can provide further guidance on the modification of metabolic circuitries. Chlamydomonas reinhardtii, a biofuel relevant green alga that has retained key genes with plant, animal, and protist affinities, serves as an ideal model organism to investigate the interplay between gene function and phylogenetic affinities at multiple organizational levels. Here, using detailed topological and functional analyses, coupled with transcriptomics studies on a metabolic network that we have reconstructed for C. reinhardtii, we show that network connectivity has a significant concordance with the co-conservation of genes; however, a distinction between topological and functional relationships is observable within the network. Dynamic and static modes of co-conservation were defined and observed in a subset of gene-pairs across the network topologically. In contrast, genes with predicted synthetic interactions, or genes involved in coupled reactions, show significant enrichment for both shorter and longer phylogenetic distances. Based on our results, we propose that the metabolic network of C. reinhardtii is assembled with an architecture to minimize phylogenetic profile distances topologically, while it includes an expansion of such distances for functionally interacting genes. This arrangement may increase the robustness of C. reinhardtii's network in dealing with varied environmental challenges that the species may face. The defined evolutionary constraints within the network, which identify important pairings of genes in metabolism, may offer guidance on synthetic biology approaches to optimize the production of desirable metabolites.
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Affiliation(s)
- Amphun Chaiboonchoe
- Laboratory of Algal, Systems, and Synthetic Biology, Division of Science and Math, New York University Abu Dhabi and Center for Genomics and Systems Biology (CGSB), New York University Abu Dhabi Institute, Abu Dhabi, UAE.
| | - Lila Ghamsari
- Center for Cancer Systems Biology (CCSB) and Department of Cancer Biology, Dana-Farber Cancer Institute, and Department of Genetics, Harvard Medical School, Boston, MA, USA
| | - Bushra Dohai
- Laboratory of Algal, Systems, and Synthetic Biology, Division of Science and Math, New York University Abu Dhabi and Center for Genomics and Systems Biology (CGSB), New York University Abu Dhabi Institute, Abu Dhabi, UAE.
| | - Patrick Ng
- Department of Biological Statistics and Computational Biology and Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, NY, USA.
| | - Basel Khraiwesh
- Laboratory of Algal, Systems, and Synthetic Biology, Division of Science and Math, New York University Abu Dhabi and Center for Genomics and Systems Biology (CGSB), New York University Abu Dhabi Institute, Abu Dhabi, UAE.
| | - Ashish Jaiswal
- Laboratory of Algal, Systems, and Synthetic Biology, Division of Science and Math, New York University Abu Dhabi and Center for Genomics and Systems Biology (CGSB), New York University Abu Dhabi Institute, Abu Dhabi, UAE.
| | - Kenan Jijakli
- Laboratory of Algal, Systems, and Synthetic Biology, Division of Science and Math, New York University Abu Dhabi and Center for Genomics and Systems Biology (CGSB), New York University Abu Dhabi Institute, Abu Dhabi, UAE.
| | - Joseph Koussa
- Laboratory of Algal, Systems, and Synthetic Biology, Division of Science and Math, New York University Abu Dhabi and Center for Genomics and Systems Biology (CGSB), New York University Abu Dhabi Institute, Abu Dhabi, UAE.
| | - David R Nelson
- Laboratory of Algal, Systems, and Synthetic Biology, Division of Science and Math, New York University Abu Dhabi and Center for Genomics and Systems Biology (CGSB), New York University Abu Dhabi Institute, Abu Dhabi, UAE.
| | - Hong Cai
- Laboratory of Algal, Systems, and Synthetic Biology, Division of Science and Math, New York University Abu Dhabi and Center for Genomics and Systems Biology (CGSB), New York University Abu Dhabi Institute, Abu Dhabi, UAE.
| | - Xinping Yang
- Center for Cancer Systems Biology (CCSB) and Department of Cancer Biology, Dana-Farber Cancer Institute, and Department of Genetics, Harvard Medical School, Boston, MA, USA
| | - Roger L Chang
- Department of Systems Biology, Harvard Medical School, Boston, MA, USA
| | - Jason Papin
- Department of Biomedical Engineering, University of Virginia, Charlottesville, VA, USA.
| | - Haiyuan Yu
- Department of Biological Statistics and Computational Biology and Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, NY, USA.
| | - Santhanam Balaji
- Laboratory of Algal, Systems, and Synthetic Biology, Division of Science and Math, New York University Abu Dhabi and Center for Genomics and Systems Biology (CGSB), New York University Abu Dhabi Institute, Abu Dhabi, UAE. and Center for Cancer Systems Biology (CCSB) and Department of Cancer Biology, Dana-Farber Cancer Institute, and Department of Genetics, Harvard Medical School, Boston, MA, USA and MRC Laboratory of Molecular Biology, Cambridge, UK.
| | - Kourosh Salehi-Ashtiani
- Laboratory of Algal, Systems, and Synthetic Biology, Division of Science and Math, New York University Abu Dhabi and Center for Genomics and Systems Biology (CGSB), New York University Abu Dhabi Institute, Abu Dhabi, UAE. and Center for Cancer Systems Biology (CCSB) and Department of Cancer Biology, Dana-Farber Cancer Institute, and Department of Genetics, Harvard Medical School, Boston, MA, USA
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Talikka M, Bukharov N, Hayes WS, Hofmann-Apitius M, Alexopoulos L, Peitsch MC, Hoeng J. Novel approaches to develop community-built biological network models for potential drug discovery. Expert Opin Drug Discov 2017; 12:849-857. [PMID: 28585481 DOI: 10.1080/17460441.2017.1335302] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
INTRODUCTION Hundreds of thousands of data points are now routinely generated in clinical trials by molecular profiling and NGS technologies. A true translation of this data into knowledge is not possible without analysis and interpretation in a well-defined biology context. Currently, there are many public and commercial pathway tools and network models that can facilitate such analysis. At the same time, insights and knowledge that can be gained is highly dependent on the underlying biological content of these resources. Crowdsourcing can be employed to guarantee the accuracy and transparency of the biological content underlining the tools used to interpret rich molecular data. Areas covered: In this review, the authors describe crowdsourcing in drug discovery. The focal point is the efforts that have successfully used the crowdsourcing approach to verify and augment pathway tools and biological network models. Technologies that enable the building of biological networks with the community are also described. Expert opinion: A crowd of experts can be leveraged for the entire development process of biological network models, from ontologies to the evaluation of their mechanistic completeness. The ultimate goal is to facilitate biomarker discovery and personalized medicine by mechanistically explaining patients' differences with respect to disease prevention, diagnosis, and therapy outcome.
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Affiliation(s)
- Marja Talikka
- a Philip Morris International R&D , Philip Morris Products S.A. , Neuchâtel , Switzerland
| | - Natalia Bukharov
- b Translational Data Management Services, Clarivate Analytics (Formerly the IP & Science Business of Thomson Reuters) , Boston , MA , USA
| | - William S Hayes
- c Data Sciences , Applied Dynamic Solutions, LLC , Rahway , NJ , USA
| | - Martin Hofmann-Apitius
- d Department of Bioinformatics , Fraunhofer Institute for Algorithms and Scientific Computing, Schloss Birlinghoven , Sankt Augustin , Germany
| | - Leonidas Alexopoulos
- e Systems Bioengineering Lab , National Technical University of Athens , Zografou , Greece.,f Protavio Ltd , Stevenage , UK
| | - Manuel C Peitsch
- a Philip Morris International R&D , Philip Morris Products S.A. , Neuchâtel , Switzerland
| | - Julia Hoeng
- a Philip Morris International R&D , Philip Morris Products S.A. , Neuchâtel , Switzerland
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Limitations of a metabolic network-based reverse ecology method for inferring host-pathogen interactions. BMC Bioinformatics 2017; 18:278. [PMID: 28545448 PMCID: PMC5445277 DOI: 10.1186/s12859-017-1696-7] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2017] [Accepted: 05/18/2017] [Indexed: 11/10/2022] Open
Abstract
Background Host–pathogen interactions are important in a wide range of research fields. Given the importance of metabolic crosstalk between hosts and pathogens, a metabolic network-based reverse ecology method was proposed to infer these interactions. However, the validity of this method remains unclear because of the various explanations presented and the influence of potentially confounding factors that have thus far been neglected. Results We re-evaluated the importance of the reverse ecology method for evaluating host–pathogen interactions while statistically controlling for confounding effects using oxygen requirement, genome, metabolic network, and phylogeny data. Our data analyses showed that host–pathogen interactions were more strongly influenced by genome size, primary network parameters (e.g., number of edges), oxygen requirement, and phylogeny than the reserve ecology-based measures. Conclusion These results indicate the limitations of the reverse ecology method; however, they do not discount the importance of adopting reverse ecology approaches altogether. Rather, we highlight the need for developing more suitable methods for inferring host–pathogen interactions and conducting more careful examinations of the relationships between metabolic networks and host–pathogen interactions. Electronic supplementary material The online version of this article (doi:10.1186/s12859-017-1696-7) contains supplementary material, which is available to authorized users.
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Gallagher AJ, Skubel RA, Pethybridge HR, Hammerschlag N. Energy metabolism in mobile, wild-sampled sharks inferred by plasma lipids. CONSERVATION PHYSIOLOGY 2017; 5:cox002. [PMID: 28852506 PMCID: PMC5570055 DOI: 10.1093/conphys/cox002] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/22/2016] [Revised: 12/28/2016] [Accepted: 01/05/2017] [Indexed: 05/30/2023]
Abstract
Evaluating how predators metabolize energy is increasingly useful for conservation physiology, as it can provide information on their current nutritional condition. However, obtaining metabolic information from mobile marine predators is inherently challenging owing to their relative rarity, cryptic nature and often wide-ranging underwater movements. Here, we investigate aspects of energy metabolism in four free-ranging shark species (n = 281; blacktip, bull, nurse, and tiger) by measuring three metabolic parameters [plasma triglycerides (TAG), free fatty acids (FFA) and cholesterol (CHOL)] via non-lethal biopsy sampling. Plasma TAG, FFA and total CHOL concentrations (in millimoles per litre) varied inter-specifically and with season, year, and shark length varied within a species. The TAG were highest in the plasma of less active species (nurse and tiger sharks), whereas FFA were highest among species with relatively high energetic demands (blacktip and bull sharks), and CHOL concentrations were highest in bull sharks. Although temporal patterns in all metabolites were varied among species, there appeared to be peaks in the spring and summer, with ratios of TAG/CHOL (a proxy for condition) in all species displaying a notable peak in summer. These results provide baseline information of energy metabolism in large sharks and are an important step in understanding how the metabolic parameters can be assessed through non-lethal sampling in the future. In particular, this study emphasizes the importance of accounting for intra-specific and temporal variability in sampling designs seeking to monitor the nutritional condition and metabolic responses of shark populations.
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Affiliation(s)
- Austin J. Gallagher
- Rosenstiel School of Marine and Atmospheric Science, University of Miami, Miami, FL, USA
- Fish Ecology and Conservation Physiology Laboratory, Department of Biology and Institute of Environmental Science, Carleton University, Ottawa, ON, Canada
- Beneath the Waves, Inc., Miami, FL, USA
| | - Rachel A. Skubel
- Rosenstiel School of Marine and Atmospheric Science, University of Miami, Miami, FL, USA
- Leonard and Jayne Abess Center for Ecosystem Science and Policy, University of Miami, Coral Gables, FL, USA
| | | | - Neil Hammerschlag
- Rosenstiel School of Marine and Atmospheric Science, University of Miami, Miami, FL, USA
- Leonard and Jayne Abess Center for Ecosystem Science and Policy, University of Miami, Coral Gables, FL, USA
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A Glimpse to Background and Characteristics of Major Molecular Biological Networks. BIOMED RESEARCH INTERNATIONAL 2015; 2015:540297. [PMID: 26491677 PMCID: PMC4605226 DOI: 10.1155/2015/540297] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/01/2015] [Revised: 07/22/2015] [Accepted: 08/18/2015] [Indexed: 12/11/2022]
Abstract
Recently, biology has become a data intensive science because of huge data sets produced by high throughput molecular biological experiments in diverse areas including the fields of genomics, transcriptomics, proteomics, and metabolomics. These huge datasets have paved the way for system-level analysis of the processes and subprocesses of the cell. For system-level understanding, initially the elements of a system are connected based on their mutual relations and a network is formed. Among omics researchers, construction and analysis of biological networks have become highly popular. In this review, we briefly discuss both the biological background and topological properties of major types of omics networks to facilitate a comprehensive understanding and to conceptualize the foundation of network biology.
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Quintero A, Ramírez J, Leal LG, López-Kleine L. Algorithm for the Construction of a Global Enzymatic Network to be Used for Gene Network Reconstruction. Curr Genomics 2014; 15:400-7. [PMID: 25435802 PMCID: PMC4245699 DOI: 10.2174/1389202915666140807004909] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2014] [Revised: 07/28/2014] [Accepted: 08/05/2014] [Indexed: 11/22/2022] Open
Abstract
Relationships between genes are best represented using networks constructed from information of different types, with metabolic information being the most valuable and widely used for genetic network reconstruction. Other types of information are usually also available, and it would be desirable to systematically include them in algorithms for network reconstruction. Here, we present an algorithm to construct a global metabolic network that uses all available enzymatic and metabolic information about the organism. We construct a global enzymatic network (GEN) with a total of 4226 nodes (EC numbers) and 42723 edges representing all known metabolic reactions. As an example we use microarray data for Arabidopsis thaliana and combine it with the metabolic network constructing a final gene interaction network for this organism with 8212 nodes (genes) and 4606,901 edges. All scripts are available to be used for any organism for which genomic data is available.
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Affiliation(s)
- Andrés Quintero
- Universidad Nacional de Colombia, Department of Biology, Master Student, Universidad Nacional de Colombia-Sede Bogotá
| | - Jorge Ramírez
- Universidad Nacional de Colombia, School of Mathematics, Associate Professor, Universidad Nacional de Colombia-Sede Medellín
| | - Luis Guillermo Leal
- Universidad Nacional de Colombia, Department of Statistics, Master Student, Universidad Nacional de Colombia-Sede Bogotá
| | - Liliana López-Kleine
- Universidad Nacional de Colombia, Department of Statistics, Associate Professor, Universidad Nacional de Colombia-Sede Bogotá, Colombia
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Das Roy R, Bhardwaj M, Bhatnagar V, Chakraborty K, Dash D. How do eubacterial organisms manage aggregation-prone proteome? F1000Res 2014; 3:137. [PMID: 25339987 PMCID: PMC4193397 DOI: 10.12688/f1000research.4307.1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 06/24/2014] [Indexed: 11/20/2022] Open
Abstract
Eubacterial genomes vary considerably in their nucleotide composition. The percentage of genetic material constituted by guanosine and cytosine (GC) nucleotides ranges from 20% to 70%. It has been posited that GC-poor organisms are more dependent on protein folding machinery. Previous studies have ascribed this to the accumulation of mildly deleterious mutations in these organisms due to population bottlenecks. This phenomenon has been supported by protein folding simulations, which showed that proteins encoded by GC-poor organisms are more prone to aggregation than proteins encoded by GC-rich organisms. To test this proposition using a genome-wide approach, we classified different eubacterial proteomes in terms of their aggregation propensity and chaperone-dependence using multiple machine learning models. In contrast to the expected decrease in protein aggregation with an increase in GC richness, we found that the aggregation propensity of proteomes increases with GC content. A similar and even more significant correlation was obtained with the GroEL-dependence of proteomes: GC-poor proteomes have evolved to be less dependent on GroEL than GC-rich proteomes. We thus propose that a decrease in eubacterial GC content may have been selected in organisms facing proteostasis problems.
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Affiliation(s)
- Rishi Das Roy
- GNR Knowledge Centre for Genome Informatics, Institute of Genomics and Integrative Biology, Council of Scientific and Industrial Research, Delhi, 110007, India ; Department of Biotechnology, University of Pune, Pune, 411007, India
| | - Manju Bhardwaj
- Department of Computer Science, Maitreyi College, Chanakyapuri, Delhi, 110021, India
| | - Vasudha Bhatnagar
- Department of Computer Science, Faculty of Mathematical Sciences, University of Delhi, Delhi, 110007, India
| | - Kausik Chakraborty
- GNR Knowledge Centre for Genome Informatics, Institute of Genomics and Integrative Biology, Council of Scientific and Industrial Research, Delhi, 110007, India
| | - Debasis Dash
- GNR Knowledge Centre for Genome Informatics, Institute of Genomics and Integrative Biology, Council of Scientific and Industrial Research, Delhi, 110007, India ; Department of Biotechnology, University of Pune, Pune, 411007, India
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Wang M, Zhang W, Ding W, Dai D, Zhang H, Xie H, Chen L, Guo Y, Xie J. Parallel clustering algorithm for large-scale biological data sets. PLoS One 2014; 9:e91315. [PMID: 24705246 PMCID: PMC3976248 DOI: 10.1371/journal.pone.0091315] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2013] [Accepted: 02/10/2014] [Indexed: 02/06/2023] Open
Abstract
BACKGROUNDS Recent explosion of biological data brings a great challenge for the traditional clustering algorithms. With increasing scale of data sets, much larger memory and longer runtime are required for the cluster identification problems. The affinity propagation algorithm outperforms many other classical clustering algorithms and is widely applied into the biological researches. However, the time and space complexity become a great bottleneck when handling the large-scale data sets. Moreover, the similarity matrix, whose constructing procedure takes long runtime, is required before running the affinity propagation algorithm, since the algorithm clusters data sets based on the similarities between data pairs. METHODS Two types of parallel architectures are proposed in this paper to accelerate the similarity matrix constructing procedure and the affinity propagation algorithm. The memory-shared architecture is used to construct the similarity matrix, and the distributed system is taken for the affinity propagation algorithm, because of its large memory size and great computing capacity. An appropriate way of data partition and reduction is designed in our method, in order to minimize the global communication cost among processes. RESULT A speedup of 100 is gained with 128 cores. The runtime is reduced from serval hours to a few seconds, which indicates that parallel algorithm is capable of handling large-scale data sets effectively. The parallel affinity propagation also achieves a good performance when clustering large-scale gene data (microarray) and detecting families in large protein superfamilies.
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Affiliation(s)
- Minchao Wang
- School of Computer Engineering and Science, Shanghai University, Shanghai, P.R.China
| | - Wu Zhang
- School of Computer Engineering and Science, Shanghai University, Shanghai, P.R.China
- High Performance Computing Center, Shanghai University, Shanghai, P.R.China
| | - Wang Ding
- School of Computer Engineering and Science, Shanghai University, Shanghai, P.R.China
| | - Dongbo Dai
- School of Computer Engineering and Science, Shanghai University, Shanghai, P.R.China
| | - Huiran Zhang
- School of Computer Engineering and Science, Shanghai University, Shanghai, P.R.China
| | - Hao Xie
- College of Stomatology, Wuhan University, Wuhan, P.R.China
| | - Luonan Chen
- School of Computer Engineering and Science, Shanghai University, Shanghai, P.R.China
- Key Laboratory of Systems Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, P.R.China
| | - Yike Guo
- School of Computer Engineering and Science, Shanghai University, Shanghai, P.R.China
- Department of Computing, Imperial College London, London, United Kingdom
| | - Jiang Xie
- School of Computer Engineering and Science, Shanghai University, Shanghai, P.R.China
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14
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Duardo-Sánchez A, Munteanu CR, Riera-Fernández P, López-Díaz A, Pazos A, González-Díaz H. Modeling Complex Metabolic Reactions, Ecological Systems, and Financial and Legal Networks with MIANN Models Based on Markov-Wiener Node Descriptors. J Chem Inf Model 2013; 54:16-29. [DOI: 10.1021/ci400280n] [Citation(s) in RCA: 20] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2023]
Affiliation(s)
- Aliuska Duardo-Sánchez
- Department
of Information and Communication Technologies, Computer Science Faculty, University of A Coruña, Campus de Elviña, 15071, A Coruña, A Coruña, Spain
- Department of Special Public Law, Financial
and Tributary Law Area, Faculty of Law, University of Santiago de Compostela (USC), 15782, Santiago de Compostela, A Coruña, Spain
| | - Cristian R. Munteanu
- Department
of Information and Communication Technologies, Computer Science Faculty, University of A Coruña, Campus de Elviña, 15071, A Coruña, A Coruña, Spain
| | - Pablo Riera-Fernández
- Department
of Information and Communication Technologies, Computer Science Faculty, University of A Coruña, Campus de Elviña, 15071, A Coruña, A Coruña, Spain
| | - Antonio López-Díaz
- Department of Special Public Law, Financial
and Tributary Law Area, Faculty of Law, University of Santiago de Compostela (USC), 15782, Santiago de Compostela, A Coruña, Spain
| | - Alejandro Pazos
- Department
of Information and Communication Technologies, Computer Science Faculty, University of A Coruña, Campus de Elviña, 15071, A Coruña, A Coruña, Spain
| | - Humberto González-Díaz
- Department of Organic Chemistry II, Faculty of Science and Technology, University of the Basque Country (UPV/EHU), 48940, Leioa, Bizkaia, Spain
- IKERBASQUE, Basque
Foundation for Science, 48011, Bilbao, Biscay, Spain
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15
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Integrative approaches for finding modular structure in biological networks. Nat Rev Genet 2013; 14:719-32. [PMID: 24045689 DOI: 10.1038/nrg3552] [Citation(s) in RCA: 351] [Impact Index Per Article: 31.9] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
A central goal of systems biology is to elucidate the structural and functional architecture of the cell. To this end, large and complex networks of molecular interactions are being rapidly generated for humans and model organisms. A recent focus of bioinformatics research has been to integrate these networks with each other and with diverse molecular profiles to identify sets of molecules and interactions that participate in a common biological function - that is, 'modules'. Here, we classify such integrative approaches into four broad categories, describe their bioinformatic principles and review their applications.
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16
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Turkarslan S, Wurtmann EJ, Wu WJ, Jiang N, Bare JC, Foley K, Reiss DJ, Novichkov P, Baliga NS. Network portal: a database for storage, analysis and visualization of biological networks. Nucleic Acids Res 2013; 42:D184-90. [PMID: 24271392 PMCID: PMC3964938 DOI: 10.1093/nar/gkt1190] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2023] Open
Abstract
The ease of generating high-throughput data has enabled investigations into organismal complexity at the systems level through the inference of networks of interactions among the various cellular components (genes, RNAs, proteins and metabolites). The wider scientific community, however, currently has limited access to tools for network inference, visualization and analysis because these tasks often require advanced computational knowledge and expensive computing resources. We have designed the network portal (http://networks.systemsbiology.net) to serve as a modular database for the integration of user uploaded and public data, with inference algorithms and tools for the storage, visualization and analysis of biological networks. The portal is fully integrated into the Gaggle framework to seamlessly exchange data with desktop and web applications and to allow the user to create, save and modify workspaces, and it includes social networking capabilities for collaborative projects. While the current release of the database contains networks for 13 prokaryotic organisms from diverse phylogenetic clades (4678 co-regulated gene modules, 3466 regulators and 9291 cis-regulatory motifs), it will be rapidly populated with prokaryotic and eukaryotic organisms as relevant data become available in public repositories and through user input. The modular architecture, simple data formats and open API support community development of the portal.
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Affiliation(s)
- Serdar Turkarslan
- Institute for Systems Biology, Seattle, WA 98109, USA and Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
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17
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Analyzing methods for path mining with applications in metabolomics. Gene 2013; 534:125-38. [PMID: 24230973 DOI: 10.1016/j.gene.2013.10.056] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2013] [Revised: 10/23/2013] [Accepted: 10/25/2013] [Indexed: 11/22/2022]
Abstract
Metabolomics is one of the key approaches of systems biology that consists of studying biochemical networks having a set of metabolites, enzymes, reactions and their interactions. As biological networks are very complex in nature, proper techniques and models need to be chosen for their better understanding and interpretation. One of the useful strategies in this regard is using path mining strategies and graph-theoretical approaches that help in building hypothetical models and perform quantitative analysis. Furthermore, they also contribute to analyzing topological parameters in metabolome networks. Path mining techniques can be based on grammars, keys, patterns and indexing. Moreover, they can also be used for modeling metabolome networks, finding structural similarities between metabolites, in-silico metabolic engineering, shortest path estimation and for various graph-based analysis. In this manuscript, we have highlighted some core and applied areas of path-mining for modeling and analysis of metabolic networks.
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18
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Takemoto K, Yoshitake I. Limited influence of oxygen on the evolution of chemical diversity in metabolic networks. Metabolites 2013; 3:979-92. [PMID: 24958261 PMCID: PMC3937826 DOI: 10.3390/metabo3040979] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2013] [Revised: 10/09/2013] [Accepted: 10/11/2013] [Indexed: 12/04/2022] Open
Abstract
Oxygen is thought to promote species and biomolecule diversity. Previous studies have suggested that oxygen expands metabolic networks by acquiring metabolites with different chemical properties (higher hydrophobicity, for example). However, such conclusions are typically based on biased evaluation, and are therefore non-conclusive. Thus, we re-investigated the effect of oxygen on metabolic evolution using a phylogenetic comparative method and metadata analysis to reduce the bias as much as possible. Notably, we found no difference in metabolic network expansion between aerobes and anaerobes when evaluating phylogenetic relationships. Furthermore, we showed that previous studies have overestimated or underestimated the degrees of differences in the chemical properties (e.g., hydrophobicity) between oxic and anoxic metabolites in metabolic networks of unicellular organisms; however, such overestimation was not observed when considering the metabolic networks of multicellular organisms. These findings indicate that the contribution of oxygen to increased chemical diversity in metabolic networks is lower than previously thought; rather, phylogenetic signals and cell-cell communication result in increased chemical diversity. However, this conclusion does not contradict the effect of oxygen on metabolic evolution; instead, it provides a deeper understanding of how oxygen contributes to metabolic evolution despite several limitations in data analysis methods.
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Affiliation(s)
- Kazuhiro Takemoto
- Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, Kawazu 680-4, Iizuka, Fukuoka 820-8502, Japan.
| | - Ikumi Yoshitake
- Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, Kawazu 680-4, Iizuka, Fukuoka 820-8502, Japan
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19
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Bryant WA, Faruqi AA, Pinney JW. Analysis of metabolic evolution in bacteria using whole-genome metabolic models. J Comput Biol 2013; 20:755-64. [PMID: 23992299 DOI: 10.1089/cmb.2013.0079] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Recent advances in the automation of metabolic model reconstruction have led to the availability of draft-quality metabolic models (predicted reaction complements) for multiple bacterial species. These reaction complements can be considered as trait representations and can be used for ancestral state reconstruction to infer the most likely metabolic complements of common ancestors of all bacteria with generated metabolic models. We present here an ancestral state reconstruction for 141 extant bacteria and analyze the reaction gains and losses for these bacteria with respect to their lifestyles and pathogenic nature. A simulated annealing approach is used to look at coordinated metabolic gains and losses in two bacteria. The main losses of Onion yellows phytoplasma OY-M, an obligate intracellular pathogen, are shown (as expected) to be in cell wall biosynthesis. The metabolic gains made by Clostridium difficile CD196 in adapting to its current habitat in the human colon is also analyzed. Our analysis shows that the capability to utilize N-Acetyl-neuraminic acid as a carbon source has been gained, rather than having been present in the Clostridium ancestor, as has the capability to synthesize phthiocerol dimycocerosate, which could potentially aid the evasion of the host immune response. We have shown that the availability of large numbers of metabolic models, along with conventional approaches, has enabled a systematic method to analyze metabolic evolution in the bacterial domain.
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Affiliation(s)
- William A Bryant
- Department of Life Sciences, Imperial College London , London, United Kingdom
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20
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Martínez-Núñez MA, Poot-Hernandez AC, Rodríguez-Vázquez K, Perez-Rueda E. Increments and duplication events of enzymes and transcription factors influence metabolic and regulatory diversity in prokaryotes. PLoS One 2013; 8:e69707. [PMID: 23922780 PMCID: PMC3726781 DOI: 10.1371/journal.pone.0069707] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2013] [Accepted: 06/13/2013] [Indexed: 11/18/2022] Open
Abstract
In this work, the content of enzymes and DNA-binding transcription factors (TFs) in 794 non-redundant prokaryotic genomes was evaluated. The identification of enzymes was based on annotations deposited in the KEGG database as well as in databases of functional domains (COG and PFAM) and structural domains (Superfamily). For identifications of the TFs, hidden Markov profiles were constructed based on well-known transcriptional regulatory families. From these analyses, we obtained diverse and interesting results, such as the negative rate of incremental changes in the number of detected enzymes with respect to the genome size. On the contrary, for TFs the rate incremented as the complexity of genome increased. This inverse related performance shapes the diversity of metabolic and regulatory networks and impacts the availability of enzymes and TFs. Furthermore, the intersection of the derivatives between enzymes and TFs was identified at 9,659 genes, after this point, the regulatory complexity grows faster than metabolic complexity. In addition, TFs have a low number of duplications, in contrast to the apparent high number of duplications associated with enzymes. Despite the greater number of duplicated enzymes versus TFs, the increment by which duplicates appear is higher in TFs. A lower proportion of enzymes among archaeal genomes (22%) than in the bacterial ones (27%) was also found. This low proportion might be compensated by the interconnection between the metabolic pathways in Archaea. A similar proportion was also found for the archaeal TFs, for which the formation of regulatory complexes has been proposed. Finally, an enrichment of multifunctional enzymes in Bacteria, as a mechanism of ecological adaptation, was detected.
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Affiliation(s)
- Mario Alberto Martínez-Núñez
- Departamento de Ingeniería de Sistemas Computacionales y Automatización, Instituto de Investigaciones en Matemáticas Aplicadas y en Sistemas, Universidad Nacional Autónoma de México, Ciudad Universitaria, México D.F., México
- * E-mail: (MMN); (EPR)
| | - Augusto Cesar Poot-Hernandez
- Departamento de Ingeniería Celular y Biocatálisis, Instituto de Biotecnología, Universidad Nacional Autónoma de México, Cuernavaca, Morelos, México
| | - Katya Rodríguez-Vázquez
- Departamento de Ingeniería de Sistemas Computacionales y Automatización, Instituto de Investigaciones en Matemáticas Aplicadas y en Sistemas, Universidad Nacional Autónoma de México, Ciudad Universitaria, México D.F., México
| | - Ernesto Perez-Rueda
- Departamento de Ingeniería Celular y Biocatálisis, Instituto de Biotecnología, Universidad Nacional Autónoma de México, Cuernavaca, Morelos, México
- * E-mail: (MMN); (EPR)
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21
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Does habitat variability really promote metabolic network modularity? PLoS One 2013; 8:e61348. [PMID: 23593470 PMCID: PMC3625173 DOI: 10.1371/journal.pone.0061348] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2012] [Accepted: 03/06/2013] [Indexed: 11/19/2022] Open
Abstract
The hypothesis that variability in natural habitats promotes modular organization is widely accepted for cellular networks. However, results of some data analyses and theoretical studies have begun to cast doubt on the impact of habitat variability on modularity in metabolic networks. Therefore, we re-evaluated this hypothesis using statistical data analysis and current metabolic information. We were unable to conclude that an increase in modularity was the result of habitat variability. Although horizontal gene transfer was also considered because it may contribute for survival in a variety of environments, closely related to habitat variability, and is known to be positively correlated with network modularity, such a positive correlation was not concluded in the latest version of metabolic networks. Furthermore, we demonstrated that the previously observed increase in network modularity due to habitat variability and horizontal gene transfer was probably due to a lack of available data on metabolic reactions. Instead, we determined that modularity in metabolic networks is dependent on species growth conditions. These results may not entirely discount the impact of habitat variability and horizontal gene transfer. Rather, they highlight the need for a more suitable definition of habitat variability and a more careful examination of relationships of the network modularity with horizontal gene transfer, habitats, and environments.
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22
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Srivastava A, Somvanshi P, Mishra BN. Reconstruction and visualization of carbohydrate, N-glycosylation pathways in Pichia pastoris CBS7435 using computational and system biology approaches. SYSTEMS AND SYNTHETIC BIOLOGY 2012; 7:7-22. [PMID: 24432138 DOI: 10.1007/s11693-012-9102-2] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Received: 09/06/2012] [Revised: 12/13/2012] [Accepted: 12/17/2012] [Indexed: 12/27/2022]
Abstract
Pichia pastoris is an efficient expression system for production of recombinant proteins. To understand its physiology for building novel applications it is important to understand and reconstruct its metabolic network. The metabolic reconstruction approach connects genotype with phenotype. Here, we have attempted to reconstruct carbohydrate metabolism pathways responsible for high biomass density and N-glycosylation pathways involved in the post translational modification of proteins of P. pastoris CBS7435. Both these metabolic pathways play a crucial role in heterologous protein production. We report novel, missing and unannotated enzymes involved in the target metabolic pathways. A strong possibility of cellulose and xylose metabolic processes in P. pastoris CBS7435 suggests its use in the area of biofuels. The reconstructed metabolic networks can be used for increased yields and improved product quality, for designing appropriate growth medium, for production of recombinant therapeutics and for making biofuels.
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Affiliation(s)
- Akriti Srivastava
- Department of Biotechnology, Institute of Engineering and Technology, G.B. Technical University, Sitapur Road, Lucknow, 226021 India
| | - Pallavi Somvanshi
- Department of Biotechnology, TERI University, 10 Institutional Area, Vasant Kunj, New Delhi, 110070 India
| | - Bhartendu Nath Mishra
- Department of Biotechnology, Institute of Engineering and Technology, G.B. Technical University, Sitapur Road, Lucknow, 226021 India
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23
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Abstract
Molecular network data are increasingly becoming available, necessitating the development of well performing computational tools for their analyses. Such tools enabled conceptually different approaches for exploring human diseases to be undertaken, in particular, those that study the relationship between a multitude of biomolecules within a cell. Hence, a new field of network biology has emerged as part of systems biology, aiming to untangle the complexity of cellular network organization. We survey current network analysis methods that aim to give insight into human disease.
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Affiliation(s)
- Vuk Janjić
- Department of Computing, Imperial College London, 180 Queen's Gate, SW7 2AZ London, UK
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24
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Takemoto K. Metabolic network modularity arising from simple growth processes. PHYSICAL REVIEW. E, STATISTICAL, NONLINEAR, AND SOFT MATTER PHYSICS 2012; 86:036107. [PMID: 23030980 DOI: 10.1103/physreve.86.036107] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/24/2012] [Indexed: 06/01/2023]
Abstract
Metabolic networks consist of linked functional components, or modules. The mechanism underlying metabolic network modularity is of great interest not only to researchers of basic science but also to those in fields of engineering. Previous studies have suggested a theoretical model, which proposes that a change in the evolutionary goal (system-specific purpose) increases network modularity, and this hypothesis was supported by statistical data analysis. Nevertheless, further investigation has uncovered additional possibilities that might explain the origin of network modularity. In this work we propose an evolving network model without tuning parameters to describe metabolic networks. We demonstrate, quantitatively, that metabolic network modularity can arise from simple growth processes, independent of the change in the evolutionary goal. Our model is applicable to a wide range of organisms and appears to suggest that metabolic network modularity can be more simply determined than previously thought. Nonetheless, our proposition does not serve to contradict the previous model; it strives to provide an insight from a different angle in the ongoing efforts to understand metabolic evolution, with the hope of eventually achieving the synthetic engineering of metabolic networks.
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Affiliation(s)
- Kazuhiro Takemoto
- Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, Iizuka Fukuoka 820-8502, Japan.
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25
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Takemoto K, Borjigin S. Metabolic network modularity in archaea depends on growth conditions. PLoS One 2011; 6:e25874. [PMID: 21998711 PMCID: PMC3188556 DOI: 10.1371/journal.pone.0025874] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2011] [Accepted: 09/12/2011] [Indexed: 11/30/2022] Open
Abstract
Network modularity is an important structural feature in metabolic networks. A previous study suggested that the variability in natural habitat promotes metabolic network modularity in bacteria. However, since many factors influence the structure of the metabolic network, this phenomenon might be limited and there may be other explanations for the change in metabolic network modularity. Therefore, we focus on archaea because they belong to another domain of prokaryotes and show variability in growth conditions (e.g., trophic requirement and optimal growth temperature), but not in habitats because of their specialized growth conditions (e.g., high growth temperature). The relationship between biological features and metabolic network modularity is examined in detail. We first show the absence of a relationship between network modularity and habitat variability in archaea, as archaeal habitats are more limited than bacterial habitats. Although this finding implies the need for further studies regarding the differences in network modularity, it does not contradict previous work. Further investigations reveal alternative explanations. Specifically, growth conditions, trophic requirement, and optimal growth temperature, in particular, affect metabolic network modularity. We have discussed the mechanisms for the growth condition-dependant changes in network modularity. Our findings suggest different explanations for the changes in network modularity and provide new insights into adaptation and evolution in metabolic networks, despite several limitations of data analysis.
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Affiliation(s)
- Kazuhiro Takemoto
- PRESTO, Japan Science and Technology Agency, Kawaguchi, Saitama, Japan.
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26
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Cloots L, Marchal K. Network-based functional modeling of genomics, transcriptomics and metabolism in bacteria. Curr Opin Microbiol 2011; 14:599-607. [DOI: 10.1016/j.mib.2011.09.003] [Citation(s) in RCA: 20] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2011] [Revised: 08/28/2011] [Accepted: 09/05/2011] [Indexed: 01/10/2023]
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27
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Recognition of DNA by the helix-turn-helix global regulatory protein Lrp is modulated by the amino terminus. J Bacteriol 2011; 193:3794-803. [PMID: 21642464 DOI: 10.1128/jb.00191-11] [Citation(s) in RCA: 11] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023] Open
Abstract
The AsnC/Lrp family of regulatory proteins links bacterial and archaeal transcription patterns to metabolism. In Escherichia coli, Lrp regulates approximately 400 genes, over 200 of them directly. In earlier studies, lrp genes from Vibrio cholerae, Proteus mirabilis, and E. coli were introduced into the same E. coli background and yielded overlapping but significantly different regulons. These differences were seen despite amino acid sequence identities of 92% (Vibrio) and 98% (Proteus) to E. coli Lrp, including complete conservation of the helix-turn-helix motifs. The N-terminal region contains many of the sequence differences among these Lrp orthologs, which led us to investigate its role in Lrp function. Through the generation of hybrid proteins, we found that the N-terminal diversity is responsible for some of the differences between orthologs in terms of DNA binding (as revealed by mobility shift assays) and multimerization (as revealed by gel filtration, dynamic light scattering, and analytical ultracentrifugation). These observations indicate that the N-terminal tail plays a significant role in modulating Lrp function, similar to what is seen for a number of other regulatory proteins.
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28
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González-Díaz H, Prado-Prado F, Sobarzo-Sánchez E, Haddad M, Maurel Chevalley S, Valentin A, Quetin-Leclercq J, Dea-Ayuela MA, Teresa Gomez-Muños M, Munteanu CR, José Torres-Labandeira J, García-Mera X, Tapia RA, Ubeira FM. NL MIND-BEST: A web server for ligands and proteins discovery—Theoretic-experimental study of proteins of Giardia lamblia and new compounds active against Plasmodium falciparum. J Theor Biol 2011; 276:229-49. [DOI: 10.1016/j.jtbi.2011.01.010] [Citation(s) in RCA: 24] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2010] [Revised: 12/02/2010] [Accepted: 01/10/2011] [Indexed: 10/18/2022]
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Pavlopoulos GA, Secrier M, Moschopoulos CN, Soldatos TG, Kossida S, Aerts J, Schneider R, Bagos PG. Using graph theory to analyze biological networks. BioData Min 2011; 4:10. [PMID: 21527005 PMCID: PMC3101653 DOI: 10.1186/1756-0381-4-10] [Citation(s) in RCA: 311] [Impact Index Per Article: 23.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2010] [Accepted: 04/28/2011] [Indexed: 11/10/2022] Open
Abstract
Understanding complex systems often requires a bottom-up analysis towards a systems biology approach. The need to investigate a system, not only as individual components but as a whole, emerges. This can be done by examining the elementary constituents individually and then how these are connected. The myriad components of a system and their interactions are best characterized as networks and they are mainly represented as graphs where thousands of nodes are connected with thousands of vertices. In this article we demonstrate approaches, models and methods from the graph theory universe and we discuss ways in which they can be used to reveal hidden properties and features of a network. This network profiling combined with knowledge extraction will help us to better understand the biological significance of the system.
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Affiliation(s)
- Georgios A Pavlopoulos
- Department of Computer Science and Biomedical Informatics, University of Central Greece, Lamia, 35100, Greece
- Faculty of Engineering - ESAT/SCD, Katholieke Universiteit Leuven, Kasteelpark Arenberg 10, 3001, Leuven-Heverlee, Belgium
| | - Maria Secrier
- Structural and Computational Biology Unit, EMBL, Meyerhofstrasse 1, 69117, Heidelberg, Germany
| | - Charalampos N Moschopoulos
- Department of Computer Engineering & Informatics, University of Patras, Rio, 6500, Patras, Greece
- Bioinformatics & Medical Informatics Team, Biomedical Research Foundation, Academy of Athens, Soranou Efessiou 4, 11527, Athens, Greece
| | | | - Sophia Kossida
- Bioinformatics & Medical Informatics Team, Biomedical Research Foundation, Academy of Athens, Soranou Efessiou 4, 11527, Athens, Greece
| | - Jan Aerts
- Faculty of Engineering - ESAT/SCD, Katholieke Universiteit Leuven, Kasteelpark Arenberg 10, 3001, Leuven-Heverlee, Belgium
| | - Reinhard Schneider
- Structural and Computational Biology Unit, EMBL, Meyerhofstrasse 1, 69117, Heidelberg, Germany
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Campus Limpertsberg, 162 A, avenue de la Faïencerie, L-1511 Luxembourg
| | - Pantelis G Bagos
- Department of Computer Science and Biomedical Informatics, University of Central Greece, Lamia, 35100, Greece
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30
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Tse H, Cai JJ, Tsoi HW, Lam EP, Yuen KY. Natural selection retains overrepresented out-of-frame stop codons against frameshift peptides in prokaryotes. BMC Genomics 2010; 11:491. [PMID: 20828396 PMCID: PMC2996987 DOI: 10.1186/1471-2164-11-491] [Citation(s) in RCA: 33] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2010] [Accepted: 09/09/2010] [Indexed: 12/03/2022] Open
Abstract
Background Out-of-frame stop codons (OSCs) occur naturally in coding sequences of all organisms, providing a mechanism of early termination of translation in incorrect reading frame so that the metabolic cost associated with frameshift events can be reduced. Given such a functional significance, we expect statistically overrepresented OSCs in coding sequences as a result of a widespread selection. Accordingly, we examined available prokaryotic genomes to look for evidence of this selection. Results The complete genome sequences of 990 prokaryotes were obtained from NCBI GenBank. We found that low G+C content coding sequences contain significantly more OSCs and G+C content at specific codon positions were the principal determinants of OSC usage bias in the different reading frames. To investigate if there is overrepresentation of OSCs, we modeled the trinucleotide and hexanucleotide biases of the coding sequences using Markov models, and calculated the expected OSC frequencies for each organism using a Monte Carlo approach. More than 93% of 342 phylogenetically representative prokaryotic genomes contain excess OSCs. Interestingly the degree of OSC overrepresentation correlates positively with G+C content, which may represent a compensatory mechanism for the negative correlation of OSC frequency with G+C content. We extended the analysis using additional compositional bias models and showed that lower-order bias like codon usage and dipeptide bias could not explain the OSC overrepresentation. The degree of OSC overrepresentation was found to correlate negatively with the optimal growth temperature of the organism after correcting for the G+C% and AT skew of the coding sequence. Conclusions The present study uses approaches with statistical rigor to show that OSC overrepresentation is a widespread phenomenon among prokaryotes. Our results support the hypothesis that OSCs carry functional significance and have been selected in the course of genome evolution to act against unintended frameshift occurrences. Some results also hint that OSC overrepresentation being a compensatory mechanism to make up for the decrease in OSCs in high G+C organisms, thus revealing the interplay between two different determinants of OSC frequency.
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Affiliation(s)
- Herman Tse
- Carol Yu Centre for Infection, Department of Microbiology, The University of Hong Kong, Hong Kong, China
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