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Dickinson TA. A növények evolúciója és osztályozása (Evolution and Systematics of Plants). — By J. Podani. Syst Biol 2018. [DOI: 10.1093/sysbio/syy025] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Affiliation(s)
- Timothy A Dickinson
- Green Plant Herbarium, Royal Ontario Museum, and Department of Ecology & Evolutionary Biology, University of Toronto, Canada
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2
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Rezvan A, Eslahchi C. Comparison of different approaches for identifying subnetworks in metabolic networks. J Bioinform Comput Biol 2017; 15:1750025. [DOI: 10.1142/s0219720017500251] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
A metabolic network model provides a computational framework for studying the metabolism of a cell at the system level. The organization of metabolic networks has been investigated in different studies. One of the organization aspects considered in these studies is the decomposition of a metabolic network. The decompositions produced by different methods are very different and there is no comprehensive evaluation framework to compare the results with each other. In this study, these methods are reviewed and compared in the first place. Then they are applied to six different metabolic network models and the results are evaluated and compared based on two existing and two newly proposed criteria. Results show that no single method can beat others in all criteria but it seems that the methods introduced by Guimera and Amaral and Verwoerd do better on among metabolite-based methods and the method introduced by Sridharan et al. does better among reaction-based ones. Also, the methods are applied to several artificial networks, each constructed from merging a few KEGG pathways. Then, their capability to recover those pathways are compared. Results show that among metabolite-based methods, the method of Guimera and Amaral does better again, however, no notable difference between the performances of reaction-based methods was detected.
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Affiliation(s)
- Abolfazl Rezvan
- Department of Computer Science, Faculty of Mathematical Sciences, Shahid Beheshti University, Tehran, Iran
| | - Changiz Eslahchi
- Department of Computer Science, Faculty of Mathematical Sciences, Shahid Beheshti University, Tehran, Iran
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3
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Pratas D, Pinho AJ. On the Approximation of the Kolmogorov Complexity for DNA Sequences. PATTERN RECOGNITION AND IMAGE ANALYSIS 2017. [DOI: 10.1007/978-3-319-58838-4_29] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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4
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Deyasi K, Banerjee A, Deb B. Phylogeny of metabolic networks: a spectral graph theoretical approach. J Biosci 2016; 40:799-808. [PMID: 26564980 DOI: 10.1007/s12038-015-9562-0] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Many methods have been developed for finding the commonalities between different organisms in order to study their phylogeny. The structure of metabolic networks also reveals valuable insights into metabolic capacity of species as well as into the habitats where they have evolved. We constructed metabolic networks of 79 fully sequenced organisms and compared their architectures. We used spectral density of normalized Laplacian matrix for comparing the structure of networks. The eigenvalues of this matrix reflect not only the global architecture of a network but also the local topologies that are produced by different graph evolutionary processes like motif duplication or joining. A divergence measure on spectral densities is used to quantify the distances between various metabolic networks, and a split network is constructed to analyse the phylogeny from these distances. In our analysis, we focused on the species that belong to different classes, but appear more related to each other in the phylogeny. We tried to explore whether they have evolved under similar environmental conditions or have similar life histories. With this focus, we have obtained interesting insights into the phylogenetic commonality between different organisms.
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Affiliation(s)
- Krishanu Deyasi
- Department of Mathematics and Statistics, Indian Institute of Science Education and Research, Kolkata, Mohanpur 741 246, India
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Singh PK, Shakya M. Comparative evolutionary analysis of cell cycle proteins networks in fission and budding yeast. Cell Biochem Biophys 2014; 70:1167-75. [PMID: 24906232 DOI: 10.1007/s12013-014-0037-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
Fission yeast and budding yeast are the two distantly related species with common ancestors. Various studies have shown significant differences in metabolic networks and regulatory networks. Cell cycle regulatory proteins in both species have differences in structural as well as in functional organization. Orthologous proteins in cell cycle regulatory protein networks seem to play contemporary role in both species during the evolution but little is known about non-orthologous proteins. Here, we used system biology approach to compare topological parameters of orthologous and non-orthologous proteins to find their contributions during the evolution to make an efficient cell cycle regulation. Observed results have shown a significant role of non-orthologous proteins in fission yeast in maintaining the efficiency of cell cycle regulation with less number of proteins as compared to budding yeast.
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Affiliation(s)
- Praveen K Singh
- Department of Bioinformatics, Maulana Azad National Institute of Technology, Bhopal, India,
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6
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Janjić V, Sharan R, Pržulj N. Modelling the yeast interactome. Sci Rep 2014; 4:4273. [PMID: 24589662 PMCID: PMC3940977 DOI: 10.1038/srep04273] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2013] [Accepted: 02/14/2014] [Indexed: 11/25/2022] Open
Abstract
The topology behind biological interaction networks has been studied for over a decade. Yet, there is no definite agreement on the theoretical models which best describe protein-protein interaction (PPI) networks. Such models are critical to quantifying the significance of any empirical observation regarding those networks. Here, we perform a comprehensive analysis of yeast PPI networks in order to gain insights into their topology and its dependency on interaction-screening technology. We find that: (1) interaction-detection technology has little effect on the topology of PPI networks; (2) topology of these interaction networks differs in organisms with different cellular complexity (human and yeast); (3) clear topological difference is present between PPI networks, their functional sub-modules, and their inter-functional “linkers”; (4) high confidence PPI networks have more “geometrical” topology compared to predicted, incomplete, or noisy PPI networks; and (5) inter-functional “linker” proteins serve as mediators in signal transduction, transport, regulation and organisational cellular processes.
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Affiliation(s)
- Vuk Janjić
- Department of Computing, Imperial College London, London, United Kingdom
| | - Roded Sharan
- Blavatnik School of Computer Science, Tel-Aviv University, Tel-Aviv 69978, Israel
| | - Nataša Pržulj
- Department of Computing, Imperial College London, London, United Kingdom
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7
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Vey G. Gene coexpression as Hebbian learning in prokaryotic genomes. Bull Math Biol 2013; 75:2431-49. [PMID: 24078338 DOI: 10.1007/s11538-013-9900-z] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2013] [Accepted: 08/27/2013] [Indexed: 10/26/2022]
Abstract
Biological interaction networks represent a powerful tool for characterizing intracellular functional relationships, such as transcriptional regulation and protein interactions. Although artificial neural networks are routinely employed for a broad range of applications across computational biology, their underlying connectionist basis has not been extensively applied to modeling biological interaction networks. In particular, the Hopfield network offers nonlinear dynamics that represent the minimization of a system energy function through temporally distinct rewiring events. Here, a scaled energy minimization model is presented to test the feasibility of deriving a composite biological interaction network from multiple constituent data sets using the Hebbian learning principle. The performance of the scaled energy minimization model is compared against the standard Hopfield model using simulated data. Several networks are also derived from real data, compared to one another, and then combined to produce an aggregate network. The utility and limitations of the proposed model are discussed, along with possible implications for a genomic learning analogy where the fundamental Hebbian postulate is rendered into its genomic equivalent: Genes that function together junction together.
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Hearnshaw EJ, Wilson MM. A complex network approach to supply chain network theory. INTERNATIONAL JOURNAL OF OPERATIONS & PRODUCTION MANAGEMENT 2013. [DOI: 10.1108/01443571311307343] [Citation(s) in RCA: 220] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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Ari E, Ittzés P, Podani J, Thi QCL, Jakó É. Comparison of Boolean analysis and standard phylogenetic methods using artificially evolved and natural mt-tRNA sequences from great apes. Mol Phylogenet Evol 2012; 63:193-202. [DOI: 10.1016/j.ympev.2012.01.010] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2011] [Revised: 11/17/2011] [Accepted: 01/11/2012] [Indexed: 11/28/2022]
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Sun MGF, Sikora M, Costanzo M, Boone C, Kim PM. Network evolution: rewiring and signatures of conservation in signaling. PLoS Comput Biol 2012; 8:e1002411. [PMID: 22438796 PMCID: PMC3305342 DOI: 10.1371/journal.pcbi.1002411] [Citation(s) in RCA: 28] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2011] [Accepted: 01/14/2012] [Indexed: 01/09/2023] Open
Abstract
The analysis of network evolution has been hampered by limited availability of protein interaction data for different organisms. In this study, we investigate evolutionary mechanisms in Src Homology 3 (SH3) domain and kinase interaction networks using high-resolution specificity profiles. We constructed and examined networks for 23 fungal species ranging from Saccharomyces cerevisiae to Schizosaccharomyces pombe. We quantify rates of different rewiring mechanisms and show that interaction change through binding site evolution is faster than through gene gain or loss. We found that SH3 interactions evolve swiftly, at rates similar to those found in phosphoregulation evolution. Importantly, we show that interaction changes are sufficiently rapid to exhibit saturation phenomena at the observed timescales. Finally, focusing on the SH3 interaction network, we observe extensive clustering of binding sites on target proteins by SH3 domains and a strong correlation between the number of domains that bind a target protein (target in-degree) and interaction conservation. The relationship between in-degree and interaction conservation is driven by two different effects, namely the number of clusters that correspond to interaction interfaces and the number of domains that bind to each cluster leads to sequence specific conservation, which in turn results in interaction conservation. In summary, we uncover several network evolution mechanisms likely to generalize across peptide recognition modules. Protein interaction networks control virtually all cellular processes. The rules governing their evolution have remained elusive, as comprehensive protein interaction data is available for only a small number of very distant species, making evolutionary network studies difficult. Here we attempt to overcome this limitation by computationally constructing protein interaction networks for 23 relatively tightly spaced yeast species. We focus on networks consisting of kinase and peptide binding domain interactions, which play central roles in signaling pathways. These networks enable us to investigate evolutionary network mechanisms. We are able, for the first time, to accurately quantify the contribution of different rewiring mechanisms. Interaction change appears to be mainly accomplished through binding site evolution rather than through gene gain or loss. This is in contrast to other evolutionary processes, where gene duplication or deletion is a major driving factor. Moreover, our analysis reveals that interaction changes are very fast – fast enough that the number of changes saturates, i.e., the actual rate of change has been strongly underestimated in previous studies. Our analysis also reveals different mechanisms by which certain interactions are conserved throughout evolution. Our results likely transfer to other species and networks, and will benefit future evolutionary studies of signaling pathways.
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Affiliation(s)
- Mark G. F. Sun
- Department of Computer Science, University of Toronto, Toronto, Canada
| | - Martin Sikora
- Banting and Best Department of Medical Research, University of Toronto, Toronto, Canada
- Terrence Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Canada
- Institut de Biologia Evolutiva (UPF-CSIC), CEXS-UPF-PRBB, Barcelona, Spain
| | - Michael Costanzo
- Banting and Best Department of Medical Research, University of Toronto, Toronto, Canada
- Terrence Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Canada
| | - Charles Boone
- Banting and Best Department of Medical Research, University of Toronto, Toronto, Canada
- Terrence Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Canada
| | - Philip M. Kim
- Department of Computer Science, University of Toronto, Toronto, Canada
- Banting and Best Department of Medical Research, University of Toronto, Toronto, Canada
- Terrence Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, Canada
- * E-mail:
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Elgoyhen AB, Langguth B, Vanneste S, De Ridder D. Tinnitus: network pathophysiology-network pharmacology. Front Syst Neurosci 2012; 6:1. [PMID: 22291622 PMCID: PMC3265967 DOI: 10.3389/fnsys.2012.00001] [Citation(s) in RCA: 42] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2011] [Accepted: 01/11/2012] [Indexed: 01/12/2023] Open
Abstract
Tinnitus, the phantom perception of sound, is a prevalent disorder. One in 10 adults has clinically significant subjective tinnitus, and for one in 100, tinnitus severely affects their quality of life. Despite the significant unmet clinical need for a safe and effective drug targeting tinnitus relief, there is currently not a single Food and Drug Administration (FDA)-approved drug on the market. The search for drugs that target tinnitus is hampered by the lack of a deep knowledge of the underlying neural substrates of this pathology. Recent studies are increasingly demonstrating that, as described for other central nervous system (CNS) disorders, tinnitus is a pathology of brain networks. The application of graph theoretical analysis to brain networks has recently provided new information concerning their topology, their robustness and their vulnerability to attacks. Moreover, the philosophy behind drug design and pharmacotherapy in CNS pathologies is changing from that of "magic bullets" that target individual chemoreceptors or "disease-causing genes" into that of "magic shotguns," "promiscuous" or "dirty drugs" that target "disease-causing networks," also known as network pharmacology. In the present work we provide some insight into how this knowledge could be applied to tinnitus pathophysiology and pharmacotherapy.
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Affiliation(s)
- Ana B. Elgoyhen
- Instituto de Investigaciones en Ingeniería Genética y Biología Molecular, Consejo Nacional de Investigaciones Científicas y Técnicas and Tercera Cátedra de Farmacología, Facultad de Medicina, Universidad de Buenos AiresBuenos Aires, Argentina
| | - Berthold Langguth
- Interdisciplinary Tinnitus Clinic, Departments of Psychiatry and Psychotherapy, University of RegensburgRegensburg, Germany
| | - Sven Vanneste
- TRI, BRAIN and Department of Neurosurgery, University Hospital AntwerpEdegem, Belgium
| | - Dirk De Ridder
- TRI, BRAIN and Department of Neurosurgery, University Hospital AntwerpEdegem, Belgium
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Belda E, Silva FJ, Peretó J, Moya A. Metabolic networks of Sodalis glossinidius: a systems biology approach to reductive evolution. PLoS One 2012; 7:e30652. [PMID: 22292008 PMCID: PMC3265509 DOI: 10.1371/journal.pone.0030652] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2011] [Accepted: 12/22/2011] [Indexed: 02/07/2023] Open
Abstract
BACKGROUND Genome reduction is a common evolutionary process affecting bacterial lineages that establish symbiotic or pathogenic associations with eukaryotic hosts. Such associations yield highly reduced genomes with greatly streamlined metabolic abilities shaped by the type of ecological association with the host. Sodalis glossinidius, the secondary endosymbiont of tsetse flies, represents one of the few complete genomes available of a bacterium at the initial stages of this process. In the present study, genome reduction is studied from a systems biology perspective through the reconstruction and functional analysis of genome-scale metabolic networks of S. glossinidius. RESULTS The functional profile of ancestral and extant metabolic networks sheds light on the evolutionary events underlying transition to a host-dependent lifestyle. Meanwhile, reductive evolution simulations on the extant metabolic network can predict possible future evolution of S. glossinidius in the context of genome reduction. Finally, knockout simulations in different metabolic systems reveal a gradual decrease in network robustness to different mutational events for bacterial endosymbionts at different stages of the symbiotic association. CONCLUSIONS Stoichiometric analysis reveals few gene inactivation events whose effects on the functionality of S. glossinidius metabolic systems are drastic enough to account for the ecological transition from a free-living to host-dependent lifestyle. The decrease in network robustness across different metabolic systems may be associated with the progressive integration in the more stable environment provided by the insect host. Finally, reductive evolution simulations reveal the strong influence that external conditions exert on the evolvability of metabolic systems.
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Affiliation(s)
- Eugeni Belda
- Institut Cavanilles de Biodiversitat i Biologia Evolutiva, Universitat de València, València, Spain
- Departament de Genètica, Universitat de València, València, Spain
| | - Francisco J. Silva
- Institut Cavanilles de Biodiversitat i Biologia Evolutiva, Universitat de València, València, Spain
- Departament de Genètica, Universitat de València, València, Spain
- Unidad Mixta de Investigación de Genómica y Salud (Centro Superior de Investigación en Salud Pública, CSISP/Institut Cavanilles), Universitat de València, València, Spain
| | - Juli Peretó
- Institut Cavanilles de Biodiversitat i Biologia Evolutiva, Universitat de València, València, Spain
- Departament de Bioquímica i Biologia Molecular, Universitat de València, València, Spain
| | - Andrés Moya
- Institut Cavanilles de Biodiversitat i Biologia Evolutiva, Universitat de València, València, Spain
- Departament de Genètica, Universitat de València, València, Spain
- Unidad Mixta de Investigación de Genómica y Salud (Centro Superior de Investigación en Salud Pública, CSISP/Institut Cavanilles), Universitat de València, València, Spain
- * E-mail:
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13
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Banerjee A. Structural distance and evolutionary relationship of networks. Biosystems 2011; 107:186-96. [PMID: 22133717 DOI: 10.1016/j.biosystems.2011.11.004] [Citation(s) in RCA: 21] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2011] [Revised: 11/04/2011] [Accepted: 11/06/2011] [Indexed: 10/15/2022]
Abstract
Exploring common features and universal qualities shared by a particular class of networks in biological and other domains is one of the important aspects of evolutionary study. In an evolving system, evolutionary mechanism can cause functional changes that forces the system to adapt to new configurations of interaction pattern between the components of that system (e.g. gene duplication and mutation play a vital role for changing the connectivity structure in many biological networks. The evolutionary relation between two systems can be retraced by their structural differences). The eigenvalues of the normalized graph Laplacian not only capture the global properties of a network, but also local structures that are produced by graph evolutions (like motif duplication or joining). The spectrum of this operator carries many qualitative aspects of a graph. Given two networks of different sizes, we propose a method to quantify the topological distance between them based on the contrasting spectrum of normalized graph Laplacian. We find that network architectures are more similar within the same class compared to between classes. We also show that the evolutionary relationships can be retraced by the structural differences using our method. We analyze 43 metabolic networks from different species and mark the prominent separation of three groups: Bacteria, Archaea and Eukarya. This phenomenon is well captured in our findings that support the other cladistic results based on gene content and ribosomal RNA sequences. Our measure to quantify the structural distance between two networks is useful to elucidate evolutionary relationships.
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Chang X, Wang Z, Hao P, Li YY, Li YX. Exploring mitochondrial evolution and metabolism organization principles by comparative analysis of metabolic networks. Genomics 2010; 95:339-44. [PMID: 20298776 DOI: 10.1016/j.ygeno.2010.03.006] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2009] [Revised: 12/20/2009] [Accepted: 03/08/2010] [Indexed: 01/12/2023]
Abstract
The endosymbiotic theory proposed that mitochondrial genomes are derived from an alpha-proteobacterium-like endosymbiont, which was concluded from sequence analysis. We rebuilt the metabolic networks of mitochondria and 22 relative species, and studied the evolution of mitochondrial metabolism at the level of enzyme content and network topology. Our phylogenetic results based on network alignment and motif identification supported the endosymbiotic theory from the point of view of systems biology for the first time. It was found that the mitochondrial metabolic network were much more compact than the relative species, probably related to the higher efficiency of oxidative phosphorylation of the specialized organelle, and the network is highly clustered around the TCA cycle. Moreover, the mitochondrial metabolic network exhibited high functional specificity to the modules. This work provided insight to the understanding of mitochondria evolution, and the organization principle of mitochondrial metabolic network at the network level.
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Affiliation(s)
- Xiao Chang
- Bioinformatics Center, Key Lab of Systems Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China.
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Prado-Prado FJ, Ubeira FM, Borges F, González-DÃaz H. Unified QSAR & network-based computational chemistry approach to antimicrobials. II. Multiple distance and triadic census analysis of antiparasitic drugs complex networks. J Comput Chem 2010; 31:164-73. [DOI: 10.1002/jcc.21292] [Citation(s) in RCA: 21] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
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Chou IC, Voit EO. Recent developments in parameter estimation and structure identification of biochemical and genomic systems. Math Biosci 2009; 219:57-83. [PMID: 19327372 DOI: 10.1016/j.mbs.2009.03.002] [Citation(s) in RCA: 298] [Impact Index Per Article: 19.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2008] [Revised: 03/06/2009] [Accepted: 03/15/2009] [Indexed: 01/16/2023]
Abstract
The organization, regulation and dynamical responses of biological systems are in many cases too complex to allow intuitive predictions and require the support of mathematical modeling for quantitative assessments and a reliable understanding of system functioning. All steps of constructing mathematical models for biological systems are challenging, but arguably the most difficult task among them is the estimation of model parameters and the identification of the structure and regulation of the underlying biological networks. Recent advancements in modern high-throughput techniques have been allowing the generation of time series data that characterize the dynamics of genomic, proteomic, metabolic, and physiological responses and enable us, at least in principle, to tackle estimation and identification tasks using 'top-down' or 'inverse' approaches. While the rewards of a successful inverse estimation or identification are great, the process of extracting structural and regulatory information is technically difficult. The challenges can generally be categorized into four areas, namely, issues related to the data, the model, the mathematical structure of the system, and the optimization and support algorithms. Many recent articles have addressed inverse problems within the modeling framework of Biochemical Systems Theory (BST). BST was chosen for these tasks because of its unique structural flexibility and the fact that the structure and regulation of a biological system are mapped essentially one-to-one onto the parameters of the describing model. The proposed methods mainly focused on various optimization algorithms, but also on support techniques, including methods for circumventing the time consuming numerical integration of systems of differential equations, smoothing overly noisy data, estimating slopes of time series, reducing the complexity of the inference task, and constraining the parameter search space. Other methods targeted issues of data preprocessing, detection and amelioration of model redundancy, and model-free or model-based structure identification. The total number of proposed methods and their applications has by now exceeded one hundred, which makes it difficult for the newcomer, as well as the expert, to gain a comprehensive overview of available algorithmic options and limitations. To facilitate the entry into the field of inverse modeling within BST and related modeling areas, the article presented here reviews the field and proposes an operational 'work-flow' that guides the user through the estimation process, identifies possibly problematic steps, and suggests corresponding solutions based on the specific characteristics of the various available algorithms. The article concludes with a discussion of the present state of the art and with a description of open questions.
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Affiliation(s)
- I-Chun Chou
- Integrative BioSystems Institute and The Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, 313 Ferst Drive, Atlanta, GA 30332, USA.
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Ding G, Yu Z, Zhao J, Wang Z, Li Y, Xing X, Wang C, Liu L, Li Y. Tree of life based on genome context networks. PLoS One 2008; 3:e3357. [PMID: 18852873 PMCID: PMC2566592 DOI: 10.1371/journal.pone.0003357] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2008] [Accepted: 09/11/2008] [Indexed: 11/18/2022] Open
Abstract
Efforts in phylogenomics have greatly improved our understanding of the backbone tree of life. However, due to the systematic error in sequence data, a sequence-based phylogenomic approach leads to well-resolved but statistically significant incongruence. Thus, independent test of current phylogenetic knowledge is required. Here, we have devised a distance-based strategy to reconstruct a highly resolved backbone tree of life, on the basis of the genome context networks of 195 fully sequenced representative species. Along with strongly supporting the monophylies of three superkingdoms and most taxonomic sub-divisions, the derived tree also suggests some intriguing results, such as high G+C gram positive origin of Bacteria, classification of Symbiobacterium thermophilum and Alcanivorax borkumensis in Firmicutes. Furthermore, simulation analyses indicate that addition of more gene relationships with high accuracy can greatly improve the resolution of the phylogenetic tree. Our results demonstrate the feasibility of the reconstruction of highly resolved phylogenetic tree with extensible gene networks across all three domains of life. This strategy also implies that the relationships between the genes (gene network) can define what kind of species it is.
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Affiliation(s)
- Guohui Ding
- Bioinformatics Center, Key Lab of Systems Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, People's Republic of China
- Graduate School of the Chinese Academy of Sciences, Shanghai, People's Republic of China
| | - Zhonghao Yu
- College of Life Science & Biotechnology, Shanghai Jiao Tong University, Shanghai, People's Republic of China
| | - Jing Zhao
- College of Life Science & Biotechnology, Shanghai Jiao Tong University, Shanghai, People's Republic of China
- Shanghai Center for Bioinformation Technology, Shanghai, People's Republic of China
| | - Zhen Wang
- Bioinformatics Center, Key Lab of Systems Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, People's Republic of China
- Graduate School of the Chinese Academy of Sciences, Shanghai, People's Republic of China
| | - Yun Li
- Bioinformatics Center, Key Lab of Systems Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, People's Republic of China
- Graduate School of the Chinese Academy of Sciences, Shanghai, People's Republic of China
| | - Xiaobin Xing
- Bioinformatics Center, Key Lab of Systems Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, People's Republic of China
- Graduate School of the Chinese Academy of Sciences, Shanghai, People's Republic of China
| | - Chuan Wang
- Bioinformatics Center, Key Lab of Systems Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, People's Republic of China
| | - Lei Liu
- Bioinformatics Center, Key Lab of Systems Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, People's Republic of China
- Shanghai Center for Bioinformation Technology, Shanghai, People's Republic of China
| | - Yixue Li
- Bioinformatics Center, Key Lab of Systems Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, People's Republic of China
- College of Life Science & Biotechnology, Shanghai Jiao Tong University, Shanghai, People's Republic of China
- Shanghai Center for Bioinformation Technology, Shanghai, People's Republic of China
- * E-mail:
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Nykter M, Price ND, Larjo A, Aho T, Kauffman SA, Yli-Harja O, Shmulevich I. Critical networks exhibit maximal information diversity in structure-dynamics relationships. PHYSICAL REVIEW LETTERS 2008; 100:058702. [PMID: 18352443 DOI: 10.1103/physrevlett.100.058702] [Citation(s) in RCA: 40] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/26/2007] [Indexed: 05/26/2023]
Abstract
Network structure strongly constrains the range of dynamic behaviors available to a complex system. These system dynamics can be classified based on their response to perturbations over time into two distinct regimes, ordered or chaotic, separated by a critical phase transition. Numerous studies have shown that the most complex dynamics arise near the critical regime. Here we use an information theoretic approach to study structure-dynamics relationships within a unified framework and show that these relationships are most diverse in the critical regime.
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Affiliation(s)
- Matti Nykter
- Institute of Signal Processing, Tampere University of Technology, Tampere, Finland
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GonzÁlez-DÍaz H, Prado-Prado FJ. Unified QSAR and network-based computational chemistry approach to antimicrobials, part 1: Multispecies activity models for antifungals. J Comput Chem 2007; 29:656-67. [DOI: 10.1002/jcc.20826] [Citation(s) in RCA: 71] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2023]
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Chambers RA, Bickel WK, Potenza MN. A scale-free systems theory of motivation and addiction. Neurosci Biobehav Rev 2007; 31:1017-45. [PMID: 17574673 PMCID: PMC2150750 DOI: 10.1016/j.neubiorev.2007.04.005] [Citation(s) in RCA: 41] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2006] [Revised: 04/03/2007] [Accepted: 04/09/2007] [Indexed: 11/24/2022]
Abstract
Scale-free organizations, characterized by uneven distributions of linkages between nodal elements, describe the structure and function of many life-based complex systems developing under evolutionary pressures. We explore motivated behavior as a scale-free map toward a comprehensive translational theory of addiction. Motivational and behavioral repertoires are reframed as link and nodal element sets, respectively, comprising a scale-free structure. These sets are generated by semi-independent information-processing streams within cortical-striatal circuits that cooperatively provide decision-making and sequential processing functions necessary for traversing maps of motivational links connecting behavioral nodes. Dopamine modulation of cortical-striatal plasticity serves a central-hierarchical mechanism for survival-adaptive sculpting and development of motivational-behavioral repertoires by guiding a scale-free design. Drug-induced dopamine activity promotes drug taking as a highly connected behavioral hub at the expense of natural-adaptive motivational links and behavioral nodes. Conceptualizing addiction as pathological alteration of scale-free motivational-behavioral repertoires unifies neurobiological, neurocomputational and behavioral research while addressing addiction vulnerability in adolescence and psychiatric illness. This model may inform integrative research in defining more effective prevention and treatment strategies for addiction.
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Affiliation(s)
- R. Andrew Chambers
- Assistant Professor of Psychiatry, Director, Laboratory for Translational Neuroscience of Dual Diagnosis Disorders, Institute of Psychiatric Research, Assistant Medical Director, Indiana Division of Mental Health and Addiction, Indiana University School of Medicine, 791 Union Drive, Indianapolis, IN 46202, Ph: (317) 278-1716, Fax: (317) 274-1365,
| | - Warren K. Bickel
- Professor of Psychiatry, Wilbur D. Mills Chair of Alcoholism and Drug Abuse Prevention, Director, Center for Addiction Research, College of Medicine, Director, Center for the Study of Tobacco, Fay W Boozeman College of Public Health, University of Arkansas for Medical Sciences, Little Rock, AR,
| | - Marc N. Potenza
- Associate Professor of Psychiatry, Director, Problem Gambling Clinic at Yale, Director, Women and Addictions Core of Women’s Health Research at Yale, Director of Neuroimaging, MIRECC VISN1, West Haven Veteran’s Administration Hospital, Yale University School of Medicine, New Haven, CT,
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22
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Daisuke T, Horton P. Inference of scale-free networks from gene expression time series. J Bioinform Comput Biol 2006; 4:503-14. [PMID: 16819798 DOI: 10.1142/s0219720006001886] [Citation(s) in RCA: 26] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2005] [Revised: 12/27/2005] [Accepted: 01/05/2006] [Indexed: 11/18/2022]
Abstract
Quantitative time-series observation of gene expression is becoming possible, for example by cell array technology. However, there are no practical methods with which to infer network structures using only observed time-series data. As most computational models of biological networks for continuous time-series data have a high degree of freedom, it is almost impossible to infer the correct structures. On the other hand, it has been reported that some kinds of biological networks, such as gene networks and metabolic pathways, may have scale-free properties. We hypothesize that the architecture of inferred biological network models can be restricted to scale-free networks. We developed an inference algorithm for biological networks using only time-series data by introducing such a restriction. We adopt the S-system as the network model, and a distributed genetic algorithm to optimize models to fit its simulated results to observed time series data. We have tested our algorithm on a case study (simulated data). We compared optimization under no restriction, which allows for a fully connected network, and under the restriction that the total number of links must equal that expected from a scale free network. The restriction reduced both false positive and false negative estimation of the links and also the differences between model simulation and the given time-series data.
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Affiliation(s)
- Tominaga Daisuke
- Computational Biology Research Center, National Institute of Advanced Industrial Science and Technology, Aomi 2-42, Koto, Tokyo 135-0064, Japan.
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Eom YH, Lee S, Jeong H. Exploring local structural organization of metabolic networks using subgraph patterns. J Theor Biol 2006; 241:823-9. [PMID: 16504210 DOI: 10.1016/j.jtbi.2006.01.018] [Citation(s) in RCA: 16] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2005] [Revised: 01/17/2006] [Accepted: 01/17/2006] [Indexed: 10/25/2022]
Abstract
Metabolic networks of many cellular organisms share global statistical features. Their connectivity distributions follow the long-tailed power law and show the small-world property. In addition, their modular structures are organized in a hierarchical manner. Although the global topological organization of metabolic networks is well understood, their local structural organization is still not clear. Investigating local properties of metabolic networks is necessary to understand the nature of metabolism in living organisms. To identify the local structural organization of metabolic networks, we analysed the subgraphs of metabolic networks of 43 organisms from three domains of life. We first identified the network motifs of metabolic networks and identified the statistically significant subgraph patterns. We then compared metabolic networks from different domains and found that they have similar local structures and that the local structure of each metabolic network has its own taxonomical meaning. Organisms closer in taxonomy showed similar local structures. In addition, the common substrates of 43 metabolic networks were not randomly distributed, but were more likely to be constituents of cohesive subgraph patterns.
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Affiliation(s)
- Young-Ho Eom
- Department of Physics, Korea Advanced Institute of Science and Technology, Daejeon 305-701, Korea
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24
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Oh SJ, Joung JG, Chang JH, Zhang BT. Construction of phylogenetic trees by kernel-based comparative analysis of metabolic networks. BMC Bioinformatics 2006; 7:284. [PMID: 16753070 PMCID: PMC1534063 DOI: 10.1186/1471-2105-7-284] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2005] [Accepted: 06/06/2006] [Indexed: 11/30/2022] Open
Abstract
Background To infer the tree of life requires knowledge of the common characteristics of each species descended from a common ancestor as the measuring criteria and a method to calculate the distance between the resulting values of each measure. Conventional phylogenetic analysis based on genomic sequences provides information about the genetic relationships between different organisms. In contrast, comparative analysis of metabolic pathways in different organisms can yield insights into their functional relationships under different physiological conditions. However, evaluating the similarities or differences between metabolic networks is a computationally challenging problem, and systematic methods of doing this are desirable. Here we introduce a graph-kernel method for computing the similarity between metabolic networks in polynomial time, and use it to profile metabolic pathways and to construct phylogenetic trees. Results To compare the structures of metabolic networks in organisms, we adopted the exponential graph kernel, which is a kernel-based approach with a labeled graph that includes a label matrix and an adjacency matrix. To construct the phylogenetic trees, we used an unweighted pair-group method with arithmetic mean, i.e., a hierarchical clustering algorithm. We applied the kernel-based network profiling method in a comparative analysis of nine carbohydrate metabolic networks from 81 biological species encompassing Archaea, Eukaryota, and Eubacteria. The resulting phylogenetic hierarchies generally support the tripartite scheme of three domains rather than the two domains of prokaryotes and eukaryotes. Conclusion By combining the kernel machines with metabolic information, the method infers the context of biosphere development that covers physiological events required for adaptation by genetic reconstruction. The results show that one may obtain a global view of the tree of life by comparing the metabolic pathway structures using meta-level information rather than sequence information. This method may yield further information about biological evolution, such as the history of horizontal transfer of each gene, by studying the detailed structure of the phylogenetic tree constructed by the kernel-based method.
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Affiliation(s)
- S June Oh
- Department of Pharmacology, Inje University College of Medicine, Busan, 614-735, Korea
| | - Je-Gun Joung
- Center for Bioinformation Technology, Seoul National University, Seoul, 151-742, Korea
- Graduate Program in Bioinformatics, Seoul National University, Seoul, 151-742, Korea
| | - Jeong-Ho Chang
- Biointelligence Laboratory, School of Computer Sci. and Eng., Seoul National University, Seoul, 151-742, Korea
| | - Byoung-Tak Zhang
- Center for Bioinformation Technology, Seoul National University, Seoul, 151-742, Korea
- Graduate Program in Bioinformatics, Seoul National University, Seoul, 151-742, Korea
- Biointelligence Laboratory, School of Computer Sci. and Eng., Seoul National University, Seoul, 151-742, Korea
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25
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Abstract
Water was called by Szent-Gyorgi "life's mater and matrix, mother and medium." This chapter considers both aspects of his statement. Many astrobiologists argue that some, if not all, of Earth's water arrived during cometary bombardments. Amorphous water ices of comets possibly facilitated organization of complex organic molecules, kick-starting prebiotic evolution. In Gaian theory, Earth retains its water as a consequence of biological activity. The cell cytomatrix is a proteinaceous matrix/lattice incorporating the cytoskeleton, a pervasive, holistic superstructural network that integrates metabolic pathways. Enzymes of metabolic pathways are ordered in supramolecular clusters (metabolons) associated with cytoskeleton and/or membranes. Metabolic intermediates are microchanneled through metabolons without entering a bulk aqueous phase. Rather than being free in solution, even major signaling ions are probably clustered in association with the cytomatrix. Chloroplasts and mitochondria, like bacteria and archaea, also contain a cytoskeletal lattice, metabolons, and channel metabolites. Eukaryotic metabolism is mathematically a scale-free or small-world network. Enzyme clusters of bacterial origin are incorporated at a pathway level that is architecturally archaean. The eucaryotic cell may be a product of serial endosymbiosis, a chimera. Cell cytoplasm is approximately 80% water. Water is indisputably a conserved structural element of proteins, essential to their folding, specificity, ligand binding, and to enzyme catalysis. The vast literature of organized cell water has long argued that the cytomatrix and cell water are an entire system, a continuum, or gestalt. Alternatives are offered to mainstream explanations of cell electric potentials, ion channel, enzyme, and motor protein function, in terms of high-order cooperative systems of ions, water, and macromolecules. This chapter describes some prominent concepts of organized cell water, including vicinal water network theory, the association-induction hypothesis, wave-cluster theory, phase-gel transition theories, and theories of low- and high-density water polymorphs.
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Affiliation(s)
- V A Shepherd
- Department of Biophysics, School of Physics, The University of NSW NSW 2052, Sydney, Australia
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26
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Abstract
The concept of the genome tree depends on the potential evolutionary significance in the clustering of species according to similarities in the gene content of their genomes. In this respect, genome trees have often been identified with species trees. With the rapid expansion of genome sequence data it becomes of increasing importance to develop accurate methods for grasping global trends for the phylogenetic signals that mutually link the various genomes. We therefore derive here the methodological concept of genome trees based on protein conservation profiles in multiple species. The basic idea in this derivation is that the multi-component “presence-absence” protein conservation profiles permit tracking of common evolutionary histories of genes across multiple genomes. We show that a significant reduction in informational redundancy is achieved by considering only the subset of distinct conservation profiles. Beyond these basic ideas, we point out various pitfalls and limitations associated with the data handling, paving the way for further improvements. As an illustration for the methods, we analyze a genome tree based on the above principles, along with a series of other trees derived from the same data and based on pair-wise comparisons (ancestral duplication-conservation and shared orthologs). In all trees we observe a sharp discrimination between the three primary domains of life: Bacteria, Archaea, and Eukarya. The new genome tree, based on conservation profiles, displays a significant correspondence with classically recognized taxonomical groupings, along with a series of departures from such conventional clusterings. Since Darwin's Origin of Species and Haeckel's Tree of Life, systematic biology has attempted to classify species into “family trees.” Genomics has provided a new framework permitting descriptions of sibling relations between species on the basis of their complete genetic blueprints. While trees based on single genes (rRNA), or limited numbers of genes have been useful, genome trees derived from complete genome comparisons should lead to more complete pictures of phylogenetic relations between various organisms. In order to reach such a global vision, procedures to establish sibling relationships should depend on an overall comparison that captures the evolutionary fates of proteins jointly in multiple genomes. This paper aims to establish a methodological basis to use genuine multidimensional procedures in the construction of genome trees. This approach completes the derivation of trees based on more classical techniques of pair-wise comparison between species. The authors survey classification schemes emerging from this approach, which either supports traditional views, such as the separation between the three phylogenetic domains Bacteria, Archaea, and Eukarya, or challenges them by suggesting, for example, intermingled clusterings of Proteobacteria with various other bacterial species.
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Affiliation(s)
- Fredj Tekaia
- Unité de Génétique Moléculaire des Levures (URA 2171 CNRS and UFR927 Univ. P.M. Curie), Institut Pasteur, Paris, France.
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Zhou T, Yan G, Wang BH. Maximal planar networks with large clustering coefficient and power-law degree distribution. PHYSICAL REVIEW. E, STATISTICAL, NONLINEAR, AND SOFT MATTER PHYSICS 2005; 71:046141. [PMID: 15903760 DOI: 10.1103/physreve.71.046141] [Citation(s) in RCA: 48] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/30/2004] [Revised: 12/21/2004] [Indexed: 05/02/2023]
Abstract
In this article, we propose a simple rule that generates scale-free networks with very large clustering coefficient and very small average distance. These networks are called random Apollonian networks (RANs) as they can be considered as a variation of Apollonian networks. We obtain the analytic results of power-law exponent gamma=3 and clustering coefficient C= (46/3)-36 ln 3/2 approximately 0.74, which agree with the simulation results very well. We prove that the increasing tendency of average distance of RANs is a little slower than the logarithm of the number of nodes in RANs. Since most real-life networks are both scale-free and small-world networks, RANs may perform well in mimicking the reality. The RANs possess hierarchical structure as C(k) approximately k(-1) that are in accord with the observations of many real-life networks. In addition, we prove that RANs are maximal planar networks, which are of particular practicability for layout of printed circuits and so on. The percolation and epidemic spreading process are also studied and the comparisons between RANs and Barabási-Albert (BA) as well as Newman-Watts (NW) networks are shown. We find that, when the network order N (the total number of nodes) is relatively small (as N approximately 10(4)), the performance of RANs under intentional attack is not sensitive to N , while that of BA networks is much affected by N. And the diseases spread slower in RANs than BA networks in the early stage of the susceptible-infected process, indicating that the large clustering coefficient may slow the spreading velocity, especially in the outbreaks.
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Affiliation(s)
- Tao Zhou
- Nonlinear Science Center and Department of Modern Physics, University of Science and Technology of China, Hefei Anhui, 230026, People's Republic of China
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28
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Zhu D, Qin ZS. Structural comparison of metabolic networks in selected single cell organisms. BMC Bioinformatics 2005; 6:8. [PMID: 15649332 PMCID: PMC549204 DOI: 10.1186/1471-2105-6-8] [Citation(s) in RCA: 60] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2004] [Accepted: 01/14/2005] [Indexed: 11/10/2022] Open
Abstract
Background There has been tremendous interest in the study of biological network structure. An array of measurements has been conceived to assess the topological properties of these networks. In this study, we compared the metabolic network structures of eleven single cell organisms representing the three domains of life using these measurements, hoping to find out whether the intrinsic network design principle(s), reflected by these measurements, are different among species in the three domains of life. Results Three groups of topological properties were used in this study: network indices, degree distribution measures and motif profile measure. All of which are higher-level topological properties except for the marginal degree distribution. Metabolic networks in Archaeal species are found to be different from those in S. cerevisiae and the six Bacterial species in almost all measured higher-level topological properties. Our findings also indicate that the metabolic network in Archaeal species is similar to the exponential random network. Conclusion If these metabolic network properties of the organisms studied can be extended to other species in their respective domains (which is likely), then the design principle(s) of Archaea are fundamentally different from those of Bacteria and Eukaryote. Furthermore, the functional mechanisms of Archaeal metabolic networks revealed in this study differentiate significantly from those of Bacterial and Eukaryotic organisms, which warrant further investigation.
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Affiliation(s)
- Dongxiao Zhu
- Bioinformatics Program, University of Michigan, Ann Arbor, MI 48109, USA.
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29
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Briones C, Manrubia SC, Lázaro E, Lazcano A, Amils R. Reconstructing evolutionary relationships from functional data: a consistent classification of organisms based on translation inhibition response. Mol Phylogenet Evol 2004; 34:371-81. [PMID: 15619448 DOI: 10.1016/j.ympev.2004.10.020] [Citation(s) in RCA: 12] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2003] [Revised: 09/06/2004] [Accepted: 10/19/2004] [Indexed: 10/26/2022]
Abstract
The last two decades have witnessed an unsurpassed effort aimed at reconstructing the history of life from the genetic information contained in extant organisms. The availability of many sequenced genomes has allowed the reconstruction of phylogenies from gene families and its comparison with traditional single-gene trees. However, the appearance of major discrepancies between both approaches questions whether horizontal gene transfer (HGT) has played a prominent role in shaping the topology of the Tree of Life. Recent attempts at solving this controversy and reaching a consensus tree combine molecular data with additional phylogenetic markers. Translation is a universal cellular function that involves a meaningful, highly conserved set of genes: both rRNA and r-protein operons have an undisputed phylogenetic value and rarely undergo HGT. Ribosomal function reflects the concerted expression of that genetic network and consequently yields information about the evolutionary paths followed by the organisms. Here we report on tree reconstruction using a measure of the performance of the ribosome: antibiotic sensitivity of protein synthesis. A large database has been used where 33 ribosomal systems belonging to the three major cellular lineages were probed against 38 protein synthesis inhibitors. Different definitions of distance between pairs of organisms have been explored, and the classical algorithm of bootstrap evaluation has been adapted to quantify the reliability of the reconstructions obtained. Our analysis returns a consistent phylogeny, where archaea are systematically affiliated to eukarya, in agreement with recent reconstructions which used information-processing systems. The integration of the information derived from relevant functional markers into current phylogenetic reconstructions might facilitate achieving a consensus Tree of Life.
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Affiliation(s)
- Carlos Briones
- Centro de Astrobiología, Carretera de Ajalvir Km. 4, 28850 Torrejón de Ardoz, Madrid, Spain.
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30
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Ma HW, Zeng AP. Phylogenetic comparison of metabolic capacities of organisms at genome level. Mol Phylogenet Evol 2004; 31:204-13. [PMID: 15019620 DOI: 10.1016/j.ympev.2003.08.011] [Citation(s) in RCA: 36] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2003] [Revised: 06/05/2003] [Indexed: 11/22/2022]
Abstract
Horizontal gene transfer (HGT) has been shown to widely spread in organisms by comparative genomic studies. However, its effect on the phylogenetic relationship of organisms, especially at a system level of different cellular functions, is still not well understood. In this work, we have constructed phylogenetic trees based on the enzyme, reaction, and gene contents of metabolic networks reconstructed from annotated genome information of 82 sequenced organisms. Results from different phylogenetic distance definitions and based on three different functional subsystems (i.e., metabolism, cellular processes, information storage and processing) were compared. Results based on the three different functional subsystems give different pictures on the phylogenetic relationship of organisms, reflecting the different extents of HGT in the different functional systems. In general, horizontal transfer is prevailing in genes for metabolism, but less in genes for information processing. Nevertheless, the major results of metabolic network-based phylogenetic trees are in good agreement with the tree based on 16S rRNA and genome trees, confirming the three domain classification and the close relationship between eukaryotes and archaea at the level of metabolic networks. These results strongly support the hypothesis that although HGT is widely distributed, it is nevertheless constrained by certain pre-existing metabolic organization principle(s) during the evolution. Further research is needed to identify the organization principle and constraints of metabolic network on HGT which have large impacts on understanding the evolution of life and in purposefully manipulating cellular metabolism.
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Affiliation(s)
- Hong-Wu Ma
- Department of Genome Analysis, GBF-German Research Center for Biotechnology, Mascheroder Weg 1, D-38124 Braunschweig, Germany
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31
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Boldogköi Z. Gene Network Polymorphism Is the Raw Material of Natural Selection: The Selfish Gene Network Hypothesis. J Mol Evol 2004; 59:340-57. [PMID: 15553089 DOI: 10.1007/s00239-004-2629-4] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
Abstract
Population genetics, the mathematical theory of modern evolutionary biology, defines evolution as the alteration of the frequency of distinct gene variants (alleles) differing in fitness over the time. The major problem with this view is that in gene and protein sequences we can find little evidence concerning the molecular basis of phenotypic variance, especially those that would confer adaptive benefit to the bearers. Some novel data, however, suggest that a large amount of genetic variation exists in the regulatory region of genes within populations. In addition, comparison of homologous DNA sequences of various species shows that evolution appears to depend more strongly on gene expression than on the genes themselves. Furthermore, it has been demonstrated in several systems that genes form functional networks, whose products exhibit interrelated expression profiles. Finally, it has been found that regulatory circuits of development behave as evolutionary units. These data demonstrate that our view of evolution calls for a new synthesis. In this article I propose a novel concept, termed the selfish gene network hypothesis, which is based on an overall consideration of the above findings. The major statements of this hypothesis are as follows. (1) Instead of individual genes, gene networks (GNs) are responsible for the determination of traits and behaviors. (2) The primary source of microevolution is the intraspecific polymorphism in GNs and not the allelic variation in either the coding or the regulatory sequences of individual genes. (3) GN polymorphism is generated by the variation in the regulatory regions of the component genes and not by the variance in their coding sequences. (4) Evolution proceeds through continuous restructuring of the composition of GNs rather than fixing of specific alleles or GN variants.
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Affiliation(s)
- Zsolt Boldogköi
- Laboratory of Neuromorphology, Department of Anatomy, Faculty of Medicine, University of Budapest, Budapest, Hungary.
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32
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Abstract
To study the evolution of the yeast protein interaction network, we first classified yeast proteins by their evolutionary histories into isotemporal categories, then analyzed the interaction tendencies within and between the categories, and finally reconstructed the main growth path. We found that two proteins tend to interact with each other if they are in the same or similar categories, but tended to avoid each other otherwise, and that network evolution mirrors the universal tree of life. These observations suggest synergistic selection during network evolution and provide insights into the hierarchical modularity of cellular networks.
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Affiliation(s)
- Hong Qin
- Department of Ecology and Evolution, University of Chicago, 1101 East 57th Street, Chicago, IL 60637; Institute of Statistics, National Chiao Tung University, 1001 Ta Hsueh Road, Hsinchu, Taiwan 30050, Republic of China; and Department of Statistics, University of Chicago, 5734 South University Avenue, Chicago, IL 60637
| | - Henry H. S. Lu
- Department of Ecology and Evolution, University of Chicago, 1101 East 57th Street, Chicago, IL 60637; Institute of Statistics, National Chiao Tung University, 1001 Ta Hsueh Road, Hsinchu, Taiwan 30050, Republic of China; and Department of Statistics, University of Chicago, 5734 South University Avenue, Chicago, IL 60637
| | - Wei B. Wu
- Department of Ecology and Evolution, University of Chicago, 1101 East 57th Street, Chicago, IL 60637; Institute of Statistics, National Chiao Tung University, 1001 Ta Hsueh Road, Hsinchu, Taiwan 30050, Republic of China; and Department of Statistics, University of Chicago, 5734 South University Avenue, Chicago, IL 60637
| | - Wen-Hsiung Li
- Department of Ecology and Evolution, University of Chicago, 1101 East 57th Street, Chicago, IL 60637; Institute of Statistics, National Chiao Tung University, 1001 Ta Hsueh Road, Hsinchu, Taiwan 30050, Republic of China; and Department of Statistics, University of Chicago, 5734 South University Avenue, Chicago, IL 60637
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33
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Spirin V, Mirny LA. Protein complexes and functional modules in molecular networks. Proc Natl Acad Sci U S A 2003; 100:12123-8. [PMID: 14517352 PMCID: PMC218723 DOI: 10.1073/pnas.2032324100] [Citation(s) in RCA: 744] [Impact Index Per Article: 35.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
Proteins, nucleic acids, and small molecules form a dense network of molecular interactions in a cell. Molecules are nodes of this network, and the interactions between them are edges. The architecture of molecular networks can reveal important principles of cellular organization and function, similarly to the way that protein structure tells us about the function and organization of a protein. Computational analysis of molecular networks has been primarily concerned with node degree [Wagner, A. & Fell, D. A. (2001) Proc. R. Soc. London Ser. B 268, 1803-1810; Jeong, H., Tombor, B., Albert, R., Oltvai, Z. N. & Barabasi, A. L. (2000) Nature 407, 651-654] or degree correlation [Maslov, S. & Sneppen, K. (2002) Science 296, 910-913], and hence focused on single/two-body properties of these networks. Here, by analyzing the multibody structure of the network of protein-protein interactions, we discovered molecular modules that are densely connected within themselves but sparsely connected with the rest of the network. Comparison with experimental data and functional annotation of genes showed two types of modules: (i) protein complexes (splicing machinery, transcription factors, etc.) and (ii) dynamic functional units (signaling cascades, cell-cycle regulation, etc.). Discovered modules are highly statistically significant, as is evident from comparison with random graphs, and are robust to noise in the data. Our results provide strong support for the network modularity principle introduced by Hartwell et al. [Hartwell, L. H., Hopfield, J. J., Leibler, S. & Murray, A. W. (1999) Nature 402, C47-C52], suggesting that found modules constitute the "building blocks" of molecular networks.
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Affiliation(s)
- Victor Spirin
- Harvard-MIT Division of Health Sciences and Technology, 16-343, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, MA 02139, USA
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34
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Abstract
The evolution of enzymes and pathways is under debate. Recent studies show that recruitment of single enzymes from different pathways could be the driving force for pathway evolution. Other mechanisms of evolution, such as pathway duplication, enzyme specialization, de novo invention of pathways or retro-evolution of pathways, appear to be less abundant. Twenty percent of enzyme superfamilies are quite variable, not only in changing reaction chemistry or metabolite type but in changing both at the same time. These variable superfamilies account for nearly half of all known reactions. The most frequently occurring metabolites provide a helping hand for such changes because they can be accommodated by many enzyme superfamilies. Thus, a picture is emerging in which new pathways are evolving from central metabolites by preference, thereby keeping the overall topology of the metabolic network.
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Affiliation(s)
- Steffen Schmidt
- European Molecular Biology Laboratory Heidelberg, Postfach 102209, Germany
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35
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Abstract
The topology of the proteome map revealed by recent large-scale hybridization methods has shown that the distribution of protein-protein interactions is highly heterogeneous, with many proteins having few edges while a few of them are heavily connected. This particular topology is shared by other cellular networks, such as metabolic pathways, and it has been suggested to be responsible for the high mutational homeostasis displayed by the genome of some organisms. In this paper we explore a recent model of proteome evolution that has been shown to reproduce many of the features displayed by its real counterparts. The model is based on gene duplication plus re-wiring of the newly created genes. The statistical features displayed by the proteome of well-known organisms are reproduced and suggest that the overall topology of the protein maps naturally emerges from the two leading mechanisms considered by the model.
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Affiliation(s)
- Romualdo Pastor-Satorras
- Dept. de Fisica, FEN, Universitat Politècnica de Catalunya, Campus Nord B4, 08034 Barcelona, Spain
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36
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Abstract
Engineering principles are used in the exploitation of biocatalysts derived from cells. The purity of reagents, catalysts and maintenance of operation variables are extremely important for bioengineering systems. Any change in the purity of reagents or in operation variables usually leads to a dramatic decrease in productivity. Cellular systems, however, are able to work with relatively high impure conditions and increase their productivity in response to external signals. Thus the seemingly disordered 'bag of juice' or cytoplasm has more order and much higher order of integration than first appears. Learning the semantics of this paradoxical ability of order and integration would help bioengineers to understand and enhance productivity even using impure reagents.
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Affiliation(s)
- Subhra Chakrabarti
- Environmental Biotechnology Division, ABRD Company LLC, 1555 Wood Road, Cleveland, Ohio 44121, USA
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Jiménez JL, Mitchell MP, Sgouros JG. Microarray analysis of orthologous genes: conservation of the translational machinery across species at the sequence and expression level. Genome Biol 2002; 4:R4. [PMID: 12537549 PMCID: PMC151285 DOI: 10.1186/gb-2002-4-1-r4] [Citation(s) in RCA: 17] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2002] [Revised: 08/28/2002] [Accepted: 10/31/2002] [Indexed: 12/04/2022] Open
Abstract
BACKGROUND Genome projects have provided a vast amount of sequence information. Sequence comparison between species helps to establish functional catalogues within organisms and to study how they are maintained and modified across phylogenetic groups during evolution. Microarray studies allow us to determine groups of genes with similar temporal regulation and perhaps also common regulatory upstream regions for binding of transcription factors. The integration of sequence and expression data is expected to refine our current annotations and provide some insight into the evolution of gene regulation across organisms. RESULTS We have investigated how well the protein subcellular localization and functional categories established from clustering of orthologous genes agree with gene-expression data in Saccharomyces cerevisiae. An increase in the resolution of biologically meaningful classes is observed upon the combination of experiments under different conditions. The functional categories deduced by sequence comparison approaches are, in general, preserved at the level of expression and can sometimes interact into larger co-regulated networks, such as the protein translation process. Differences and similarities in the expression between cytoplasmic-mitochondrial and interspecies translation machineries complement evolutionary information from sequence similarity. CONCLUSIONS Combination of several microarray experiments is a powerful tool for the identification of upstream regulatory motifs of yeast genes involved in protein synthesis. Comparison of these yeast co-regulated genes against the archaeal and bacterial operons indicates that the components of the protein translation process are conserved across organisms at the expression level with minor specific adaptations.
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Affiliation(s)
- Jose L Jiménez
- Computational Genome Analysis Laboratory, Cancer Research UK, 44 Lincoln's Inn Fields, London WC2A 3PX, UK.
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Abstract
To understand complex biological systems requires the integration of experimental and computational research -- in other words a systems biology approach. Computational biology, through pragmatic modelling and theoretical exploration, provides a powerful foundation from which to address critical scientific questions head-on. The reviews in this Insight cover many different aspects of this energetic field, although all, in one way or another, illuminate the functioning of modular circuits, including their robustness, design and manipulation. Computational systems biology addresses questions fundamental to our understanding of life, yet progress here will lead to practical innovations in medicine, drug discovery and engineering.
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Affiliation(s)
- Hiroaki Kitano
- Sony Computer Science Laboratories, Inc., Shinagwa, Tokyo, Japan.
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Affiliation(s)
- Barry K Lavine
- Department of Chemistry, Clarkson University, Potsdam, New York 13699, USA
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Wolf YI, Karev G, Koonin EV. Scale-free networks in biology: new insights into the fundamentals of evolution? Bioessays 2002; 24:105-9. [PMID: 11835273 DOI: 10.1002/bies.10059] [Citation(s) in RCA: 74] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Scale-free network models describe many natural and social phenomena. In particular, networks of interacting components of a living cell were shown to possess scale-free properties. A recent study((1)) compares the system-level properties of metabolic and information networks in 43 archaeal, bacterial and eukaryal species and claims that the scale-free organization of these networks is more conserved during evolution than their content.
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Affiliation(s)
- Yuri I Wolf
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD 20894, USA
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Abstract
Extremophiles thrive in ice, boiling water, acid, the water core of nuclear reactors, salt crystals, and toxic waste and in a range of other extreme habitats that were previously thought to be inhospitable for life. Extremophiles include representatives of all three domains (Bacteria, Archaea, and Eucarya); however, the majority are microorganisms, and a high proportion of these are Archaea. Knowledge of extremophile habitats is expanding the number and types of extraterrestrial locations that may be targeted for exploration. In addition, contemporary biological studies are being fueled by the increasing availability of genome sequences and associated functional studies of extremophiles. This is leading to the identification of new biomarkers, an accurate assessment of cellular evolution, insight into the ability of microorganisms to survive in meteorites and during periods of global extinction, and knowledge of how to process and examine environmental samples to detect viable life forms. This paper evaluates extremophiles and extreme environments in the context of astrobiology and the search for extraterrestrial life.
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Affiliation(s)
- Ricardo Cavicchioli
- School of Biotechnology and Biomolecular Sciences, University of New South Wales, Sydney, New South Wales, Australia.
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