1
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Ghosh S, Baltussen MG, Ivanov NM, Haije R, Jakštaitė M, Zhou T, Huck WTS. Exploring Emergent Properties in Enzymatic Reaction Networks: Design and Control of Dynamic Functional Systems. Chem Rev 2024; 124:2553-2582. [PMID: 38476077 PMCID: PMC10941194 DOI: 10.1021/acs.chemrev.3c00681] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2023] [Revised: 02/13/2024] [Accepted: 02/20/2024] [Indexed: 03/14/2024]
Abstract
The intricate and complex features of enzymatic reaction networks (ERNs) play a key role in the emergence and sustenance of life. Constructing such networks in vitro enables stepwise build up in complexity and introduces the opportunity to control enzymatic activity using physicochemical stimuli. Rational design and modulation of network motifs enable the engineering of artificial systems with emergent functionalities. Such functional systems are useful for a variety of reasons such as creating new-to-nature dynamic materials, producing value-added chemicals, constructing metabolic modules for synthetic cells, and even enabling molecular computation. In this review, we offer insights into the chemical characteristics of ERNs while also delving into their potential applications and associated challenges.
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Affiliation(s)
- Souvik Ghosh
- Institute for Molecules and
Materials, Radboud University, Heyendaalseweg 135, 6525 AJ Nijmegen, The Netherlands
| | - Mathieu G. Baltussen
- Institute for Molecules and
Materials, Radboud University, Heyendaalseweg 135, 6525 AJ Nijmegen, The Netherlands
| | - Nikita M. Ivanov
- Institute for Molecules and
Materials, Radboud University, Heyendaalseweg 135, 6525 AJ Nijmegen, The Netherlands
| | - Rianne Haije
- Institute for Molecules and
Materials, Radboud University, Heyendaalseweg 135, 6525 AJ Nijmegen, The Netherlands
| | - Miglė Jakštaitė
- Institute for Molecules and
Materials, Radboud University, Heyendaalseweg 135, 6525 AJ Nijmegen, The Netherlands
| | - Tao Zhou
- Institute for Molecules and
Materials, Radboud University, Heyendaalseweg 135, 6525 AJ Nijmegen, The Netherlands
| | - Wilhelm T. S. Huck
- Institute for Molecules and
Materials, Radboud University, Heyendaalseweg 135, 6525 AJ Nijmegen, The Netherlands
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2
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Lyutov A, Uygun Y, Hütt MT. Local topological features of robust supply networks. APPLIED NETWORK SCIENCE 2022; 7:33. [PMID: 35615080 PMCID: PMC9122087 DOI: 10.1007/s41109-022-00470-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/17/2021] [Accepted: 05/06/2022] [Indexed: 06/15/2023]
Abstract
UNLABELLED The design of robust supply and distribution systems is one of the fundamental challenges at the interface of network science and logistics. Given the multitude of performance criteria, real-world constraints, and external influences acting upon such a system, even formulating an appropriate research question to address this topic is non-trivial. Here we present an abstraction of a supply and distribution system leading to a minimal model, which only retains stylized facts of the systemic function and, in this way, allows us to investigate the generic properties of robust supply networks. On this level of abstraction, a supply and distribution system is the strategic use of transportation to eliminate mismatches between production patterns (i.e., the amounts of goods produced at each production site of a company) and demand patterns (i.e., the amount of goods consumed at each location). When creating networks based on this paradigm and furthermore requiring the robustness of the system with respect to the loss of transportation routes (edge of the network) we see that robust networks are built from specific sets of subgraphs, while vulnerable networks display a markedly different subgraph composition. Our findings confirm a long-standing hypothesis in the field of network science, namely, that network motifs-statistically over-represented small subgraphs-are informative about the robust functioning of a network. Also, our findings offer a blueprint for enhancing the robustness of real-world supply and distribution systems. SUPPLEMENTARY INFORMATION The online version contains supplementary material available at 10.1007/s41109-022-00470-2.
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Affiliation(s)
- Alexey Lyutov
- Department of Mathematics and Logistics, Jacobs University Bremen, Campus Ring 1, 28759 Bremen, Germany
| | - Yilmaz Uygun
- Department of Mathematics and Logistics, Jacobs University Bremen, Campus Ring 1, 28759 Bremen, Germany
| | - Marc-Thorsten Hütt
- Department of Life Sciences and Chemistry, Jacobs University Bremen, Campus Ring 1, 28759 Bremen, Germany
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3
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The economy of chromosomal distances in bacterial gene regulation. NPJ Syst Biol Appl 2021; 7:49. [PMID: 34911953 PMCID: PMC8674286 DOI: 10.1038/s41540-021-00209-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2021] [Accepted: 11/12/2021] [Indexed: 12/04/2022] Open
Abstract
In the transcriptional regulatory network (TRN) of a bacterium, the nodes are genes and a directed edge represents the action of a transcription factor (TF), encoded by the source gene, on the target gene. It is a condensed representation of a large number of biological observations and facts. Nonrandom features of the network are structural evidence of requirements for a reliable systemic function. For the bacterium Escherichia coli we here investigate the (Euclidean) distances covered by the edges in the TRN when its nodes are embedded in the real space of the circular chromosome. Our work is motivated by 'wiring economy' research in Computational Neuroscience and starts from two contradictory hypotheses: (1) TFs are predominantly employed for long-distance regulation, while local regulation is exerted by chromosomal structure, locally coordinated by the action of structural proteins. Hence long distances should often occur. (2) A large distance between the regulator gene and its target requires a higher expression level of the regulator gene due to longer reaching times and ensuing increased degradation (proteolysis) of the TF and hence will be evolutionarily reduced. Our analysis supports the latter hypothesis.
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4
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Stivala A, Lomi A. Testing biological network motif significance with exponential random graph models. APPLIED NETWORK SCIENCE 2021; 6:91. [PMID: 34841042 PMCID: PMC8608783 DOI: 10.1007/s41109-021-00434-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/09/2021] [Accepted: 11/08/2021] [Indexed: 06/13/2023]
Abstract
UNLABELLED Analysis of the structure of biological networks often uses statistical tests to establish the over-representation of motifs, which are thought to be important building blocks of such networks, related to their biological functions. However, there is disagreement as to the statistical significance of these motifs, and there are potential problems with standard methods for estimating this significance. Exponential random graph models (ERGMs) are a class of statistical model that can overcome some of the shortcomings of commonly used methods for testing the statistical significance of motifs. ERGMs were first introduced into the bioinformatics literature over 10 years ago but have had limited application to biological networks, possibly due to the practical difficulty of estimating model parameters. Advances in estimation algorithms now afford analysis of much larger networks in practical time. We illustrate the application of ERGM to both an undirected protein-protein interaction (PPI) network and directed gene regulatory networks. ERGM models indicate over-representation of triangles in the PPI network, and confirm results from previous research as to over-representation of transitive triangles (feed-forward loop) in an E. coli and a yeast regulatory network. We also confirm, using ERGMs, previous research showing that under-representation of the cyclic triangle (feedback loop) can be explained as a consequence of other topological features. SUPPLEMENTARY INFORMATION The online version contains supplementary material available at 10.1007/s41109-021-00434-y.
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Affiliation(s)
- Alex Stivala
- Institute of Computational Science, Università della Svizzera italiana, Via Giuseppe Buffi 13, 6900 Lugano, Switzerland
| | - Alessandro Lomi
- Institute of Computational Science, Università della Svizzera italiana, Via Giuseppe Buffi 13, 6900 Lugano, Switzerland
- The University of Exeter Business School, Rennes Drive, Exeter, EX4 4PU UK
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5
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Voutsa V, Battaglia D, Bracken LJ, Brovelli A, Costescu J, Díaz Muñoz M, Fath BD, Funk A, Guirro M, Hein T, Kerschner C, Kimmich C, Lima V, Messé A, Parsons AJ, Perez J, Pöppl R, Prell C, Recinos S, Shi Y, Tiwari S, Turnbull L, Wainwright J, Waxenecker H, Hütt MT. Two classes of functional connectivity in dynamical processes in networks. J R Soc Interface 2021; 18:20210486. [PMID: 34665977 PMCID: PMC8526174 DOI: 10.1098/rsif.2021.0486] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2021] [Accepted: 09/13/2021] [Indexed: 12/12/2022] Open
Abstract
The relationship between network structure and dynamics is one of the most extensively investigated problems in the theory of complex systems of recent years. Understanding this relationship is of relevance to a range of disciplines-from neuroscience to geomorphology. A major strategy of investigating this relationship is the quantitative comparison of a representation of network architecture (structural connectivity, SC) with a (network) representation of the dynamics (functional connectivity, FC). Here, we show that one can distinguish two classes of functional connectivity-one based on simultaneous activity (co-activity) of nodes, the other based on sequential activity of nodes. We delineate these two classes in different categories of dynamical processes-excitations, regular and chaotic oscillators-and provide examples for SC/FC correlations of both classes in each of these models. We expand the theoretical view of the SC/FC relationships, with conceptual instances of the SC and the two classes of FC for various application scenarios in geomorphology, ecology, systems biology, neuroscience and socio-ecological systems. Seeing the organisation of dynamical processes in a network either as governed by co-activity or by sequential activity allows us to bring some order in the myriad of observations relating structure and function of complex networks.
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Affiliation(s)
- Venetia Voutsa
- Department of Life Sciences and Chemistry, Jacobs University Bremen, 28759 Bremen, Germany
| | - Demian Battaglia
- Aix-Marseille Université, Inserm, Institut de Neurosciences des Systèmes (UMR 1106), Marseille, France
- University of Strasbourg Institute for Advanced Studies (USIAS), Strasbourg 67083, France
| | | | - Andrea Brovelli
- Aix-Marseille Université, CNRS, Institut de Neurosciences de la Timone (UMR 7289), Marseille, France
| | - Julia Costescu
- Department of Geography, Durham University, Durham DH1 3LE, UK
| | - Mario Díaz Muñoz
- Department of Sustainability, Governance and Methods, Modul University Vienna, 1190 Vienna, Austria
| | - Brian D. Fath
- Department of Biological Sciences, Towson University, Towson, Maryland 21252, USA
- Advancing Systems Analysis Program, International Institute for Applied Systems Analysis, Laxenburg 2361, Austria
- Department of Environmental Studies, Masaryk University, 60200 Brno, Czech Republic
| | - Andrea Funk
- Institute of Hydrobiology and Aquatic Ecosystem Management (IHG), University of Natural Resources and Life Sciences Vienna (BOKU), 1180 Vienna, Austria
- WasserCluster Lunz - Biologische Station GmbH, Dr. Carl Kupelwieser Promenade 5, 3293 Lunz am See, Austria
| | - Mel Guirro
- Department of Geography, Durham University, Durham DH1 3LE, UK
| | - Thomas Hein
- Institute of Hydrobiology and Aquatic Ecosystem Management (IHG), University of Natural Resources and Life Sciences Vienna (BOKU), 1180 Vienna, Austria
- WasserCluster Lunz - Biologische Station GmbH, Dr. Carl Kupelwieser Promenade 5, 3293 Lunz am See, Austria
| | - Christian Kerschner
- Department of Sustainability, Governance and Methods, Modul University Vienna, 1190 Vienna, Austria
- Department of Environmental Studies, Masaryk University, 60200 Brno, Czech Republic
| | - Christian Kimmich
- Department of Environmental Studies, Masaryk University, 60200 Brno, Czech Republic
- Regional Science and Environmental Research, Institute for Advanced Studies, 1080 Vienna, Austria
| | - Vinicius Lima
- Aix-Marseille Université, Inserm, Institut de Neurosciences des Systèmes (UMR 1106), Marseille, France
- Aix-Marseille Université, CNRS, Institut de Neurosciences de la Timone (UMR 7289), Marseille, France
| | - Arnaud Messé
- Department of Computational Neuroscience, University Medical Center Eppendorf, Hamburg University, Germany
| | | | - John Perez
- Department of Geography, Durham University, Durham DH1 3LE, UK
| | - Ronald Pöppl
- Department of Geography and Regional Research, University of Vienna, Universitätsstr. 7, 1010 Vienna, Austria
| | - Christina Prell
- Department of Cultural Geography, University of Groningen, 9747 AD, Groningen, The Netherlands
| | - Sonia Recinos
- Institute of Hydrobiology and Aquatic Ecosystem Management (IHG), University of Natural Resources and Life Sciences Vienna (BOKU), 1180 Vienna, Austria
| | - Yanhua Shi
- Department of Environmental Studies, Masaryk University, 60200 Brno, Czech Republic
| | - Shubham Tiwari
- Department of Geography, Durham University, Durham DH1 3LE, UK
| | - Laura Turnbull
- Department of Geography, Durham University, Durham DH1 3LE, UK
| | - John Wainwright
- Department of Geography, Durham University, Durham DH1 3LE, UK
| | - Harald Waxenecker
- Department of Environmental Studies, Masaryk University, 60200 Brno, Czech Republic
| | - Marc-Thorsten Hütt
- Department of Life Sciences and Chemistry, Jacobs University Bremen, 28759 Bremen, Germany
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6
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Dusad V, Thiel D, Barahona M, Keun HC, Oyarzún DA. Opportunities at the Interface of Network Science and Metabolic Modeling. Front Bioeng Biotechnol 2021; 8:591049. [PMID: 33569373 PMCID: PMC7868444 DOI: 10.3389/fbioe.2020.591049] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2020] [Accepted: 12/22/2020] [Indexed: 12/17/2022] Open
Abstract
Metabolism plays a central role in cell physiology because it provides the molecular machinery for growth. At the genome-scale, metabolism is made up of thousands of reactions interacting with one another. Untangling this complexity is key to understand how cells respond to genetic, environmental, or therapeutic perturbations. Here we discuss the roles of two complementary strategies for the analysis of genome-scale metabolic models: Flux Balance Analysis (FBA) and network science. While FBA estimates metabolic flux on the basis of an optimization principle, network approaches reveal emergent properties of the global metabolic connectivity. We highlight how the integration of both approaches promises to deliver insights on the structure and function of metabolic systems with wide-ranging implications in discovery science, precision medicine and industrial biotechnology.
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Affiliation(s)
- Varshit Dusad
- Department of Life Sciences, Imperial College London, London, United Kingdom
| | - Denise Thiel
- Department of Mathematics, Imperial College London, London, United Kingdom
| | - Mauricio Barahona
- Department of Mathematics, Imperial College London, London, United Kingdom
| | - Hector C. Keun
- Department of Surgery and Cancer, Imperial College London, London, United Kingdom
- Department of Metabolism, Digestion and Reproduction, Imperial College London, London, United Kingdom
| | - Diego A. Oyarzún
- School of Biological Sciences, University of Edinburgh, Edinburgh, United Kingdom
- School of Informatics, University of Edinburgh, Edinburgh, United Kingdom
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7
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Defoort J, Van de Peer Y, Vermeirssen V. Function, dynamics and evolution of network motif modules in integrated gene regulatory networks of worm and plant. Nucleic Acids Res 2019; 46:6480-6503. [PMID: 29873777 PMCID: PMC6061849 DOI: 10.1093/nar/gky468] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2017] [Accepted: 05/14/2018] [Indexed: 12/29/2022] Open
Abstract
Gene regulatory networks (GRNs) consist of different molecular interactions that closely work together to establish proper gene expression in time and space. Especially in higher eukaryotes, many questions remain on how these interactions collectively coordinate gene regulation. We study high quality GRNs consisting of undirected protein–protein, genetic and homologous interactions, and directed protein–DNA, regulatory and miRNA–mRNA interactions in the worm Caenorhabditis elegans and the plant Arabidopsis thaliana. Our data-integration framework integrates interactions in composite network motifs, clusters these in biologically relevant, higher-order topological network motif modules, overlays these with gene expression profiles and discovers novel connections between modules and regulators. Similar modules exist in the integrated GRNs of worm and plant. We show how experimental or computational methodologies underlying a certain data type impact network topology. Through phylogenetic decomposition, we found that proteins of worm and plant tend to functionally interact with proteins of a similar age, while at the regulatory level TFs favor same age, but also older target genes. Despite some influence of the duplication mode difference, we also observe at the motif and module level for both species a preference for age homogeneity for undirected and age heterogeneity for directed interactions. This leads to a model where novel genes are added together to the GRNs in a specific biological functional context, regulated by one or more TFs that also target older genes in the GRNs. Overall, we detected topological, functional and evolutionary properties of GRNs that are potentially universal in all species.
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Affiliation(s)
- Jonas Defoort
- Department of Plant Biotechnology and Bioinformatics, Ghent University, 9052 Ghent, Belgium.,VIB Center for Plant Systems Biology, 9052 Ghent, Belgium.,Bioinformatics Institute Ghent, Ghent University, 9052 Ghent, Belgium
| | - Yves Van de Peer
- Department of Plant Biotechnology and Bioinformatics, Ghent University, 9052 Ghent, Belgium.,VIB Center for Plant Systems Biology, 9052 Ghent, Belgium.,Bioinformatics Institute Ghent, Ghent University, 9052 Ghent, Belgium.,Department of Biochemistry, Genetics and Microbiology, University of Pretoria, Pretoria 0028, South Africa
| | - Vanessa Vermeirssen
- Department of Plant Biotechnology and Bioinformatics, Ghent University, 9052 Ghent, Belgium.,VIB Center for Plant Systems Biology, 9052 Ghent, Belgium.,Bioinformatics Institute Ghent, Ghent University, 9052 Ghent, Belgium
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8
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Abstract
Food webs have been found to exhibit remarkable "motif profiles", patterns in the relative prevalences of all possible three-species subgraphs, and this has been related to ecosystem properties such as stability and robustness. Analysing 46 food webs of various kinds, we find that most food webs fall into one of two distinct motif families. The separation between the families is well predicted by a global measure of hierarchical order in directed networks-trophic coherence. We find that trophic coherence is also a good predictor for the extent of omnivory, defined as the tendency of species to feed on multiple trophic levels. We compare our results to a network assembly model that admits tunable trophic coherence via a single free parameter. The model is able to generate food webs in either of the two families by varying this parameter, and correctly classifies almost all the food webs in our database. This is in contrast with the two most popular food web models, the generalized cascade and niche models, which can only generate food webs within a single motif family. Our findings suggest the importance of trophic coherence in modelling local preying patterns in food webs.
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Affiliation(s)
- Janis Klaise
- Centre for Complexity Science, University of Warwick, Coventry, CV4 7AL, United Kingdom.
| | - Samuel Johnson
- Centre for Complexity Science, University of Warwick, Coventry, CV4 7AL, United Kingdom
- School of Mathematics, University of Birmingham, Birmingham, B15 2TT, United Kingdom
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9
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Kuang J, Cadotte MW, Chen Y, Shu H, Liu J, Chen L, Hua Z, Shu W, Zhou J, Huang L. Conservation of Species- and Trait-Based Modeling Network Interactions in Extremely Acidic Microbial Community Assembly. Front Microbiol 2017; 8:1486. [PMID: 28848508 PMCID: PMC5554326 DOI: 10.3389/fmicb.2017.01486] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2017] [Accepted: 07/24/2017] [Indexed: 11/29/2022] Open
Abstract
Understanding microbial interactions is essential to decipher the mechanisms of community assembly and their effects on ecosystem functioning, however, the conservation of species- and trait-based network interactions along environmental gradient remains largely unknown. Here, by using the network-based analyses with three paralleled data sets derived from 16S rRNA gene pyrosequencing, functional microarray, and predicted metagenome, we test our hypothesis that the network interactions of traits are more conserved than those of taxonomic measures, with significantly lower variation of network characteristics along the environmental gradient in acid mine drainage. The results showed that although the overall network characteristics remained similar, the structural variation was significantly lower at trait levels. The higher conserved individual node topological properties at trait level rather than at species level indicated that the responses of diverse traits remained relatively consistent even though different species played key roles under different environmental conditions. Additionally, the randomization tests revealed that it could not reject the null hypothesis that species-based correlations were random, while the tests suggested that correlation patterns of traits were non-random. Furthermore, relationships between trait-based network characteristics and environmental properties implied that trait-based networks might be more useful in reflecting the variation of ecosystem function. Taken together, our results suggest that deterministic trait-based community assembly results in greater conservation of network interaction, which may ensure ecosystem function across environmental regimes, emphasizing the potential importance of measuring the complexity and conservation of network interaction in evaluating the ecosystem stability and functioning.
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Affiliation(s)
- Jialiang Kuang
- State Key Laboratory of Biocontrol, Guangdong Key Laboratory of Plant Resources and Conservation of Guangdong Higher Education Institutes, College of Ecology and Evolution, Sun Yat-sen UniversityGuangzhou, China.,Department of Microbiology and Plant Biology, Institute for Environmental Genomics, University of OklahomaNorman, OK, United States
| | - Marc W Cadotte
- State Key Laboratory of Biocontrol, Guangdong Key Laboratory of Plant Resources and Conservation of Guangdong Higher Education Institutes, College of Ecology and Evolution, Sun Yat-sen UniversityGuangzhou, China.,Department of Biological Sciences, University of Toronto-ScarboroughToronto, ON, Canada.,Ecology and Evolutionary Biology, University of TorontoToronto, ON, Canada
| | - Yongjian Chen
- State Key Laboratory of Biocontrol, Guangdong Key Laboratory of Plant Resources and Conservation of Guangdong Higher Education Institutes, College of Ecology and Evolution, Sun Yat-sen UniversityGuangzhou, China
| | - Haoyue Shu
- State Key Laboratory of Biocontrol, Guangdong Key Laboratory of Plant Resources and Conservation of Guangdong Higher Education Institutes, College of Ecology and Evolution, Sun Yat-sen UniversityGuangzhou, China
| | - Jun Liu
- State Key Laboratory of Biocontrol, Guangdong Key Laboratory of Plant Resources and Conservation of Guangdong Higher Education Institutes, College of Ecology and Evolution, Sun Yat-sen UniversityGuangzhou, China
| | - Linxing Chen
- State Key Laboratory of Biocontrol, Guangdong Key Laboratory of Plant Resources and Conservation of Guangdong Higher Education Institutes, College of Ecology and Evolution, Sun Yat-sen UniversityGuangzhou, China
| | - Zhengshuang Hua
- State Key Laboratory of Biocontrol, Guangdong Key Laboratory of Plant Resources and Conservation of Guangdong Higher Education Institutes, College of Ecology and Evolution, Sun Yat-sen UniversityGuangzhou, China
| | - Wensheng Shu
- State Key Laboratory of Biocontrol, Guangdong Key Laboratory of Plant Resources and Conservation of Guangdong Higher Education Institutes, College of Ecology and Evolution, Sun Yat-sen UniversityGuangzhou, China
| | - Jizhong Zhou
- Department of Microbiology and Plant Biology, Institute for Environmental Genomics, University of OklahomaNorman, OK, United States.,Earth Sciences Division, Lawrence Berkeley National LaboratoryBerkeley, CA, United States.,State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua UniversityBeijing, China
| | - Linan Huang
- State Key Laboratory of Biocontrol, Guangdong Key Laboratory of Plant Resources and Conservation of Guangdong Higher Education Institutes, College of Ecology and Evolution, Sun Yat-sen UniversityGuangzhou, China
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10
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Beber ME, Sobetzko P, Muskhelishvili G, Hütt MT. Interplay of digital and analog control in time-resolved gene expression profiles. ACTA ACUST UNITED AC 2016. [DOI: 10.1140/epjnbp/s40366-016-0035-7] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
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11
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Beber ME, Muskhelishvili G, Hütt MT. Effect of database drift on network topology and enrichment analyses: a case study for RegulonDB. Database (Oxford) 2016; 2016:baw003. [PMID: 26980514 PMCID: PMC4792529 DOI: 10.1093/database/baw003] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2015] [Revised: 12/12/2015] [Accepted: 01/11/2016] [Indexed: 12/11/2022]
Abstract
RegulonDB is a database storing the biological information behind the transcriptional regulatory network (TRN) of the bacterium Escherichia coli. It is one of the key bioinformatics resources for Systems Biology investigations of bacterial gene regulation. Like most biological databases, the content drifts with time, both due to the accumulation of new information and due to refinements in the underlying biological concepts. Conclusions based on previous database versions may no longer hold. Here, we study the change of some topological properties of the TRN of E. coli, as provided by RegulonDB across 16 versions, as well as a simple index, digital control strength, quantifying the match between gene expression profiles and the transcriptional regulatory networks. While many of network characteristics change dramatically across the different versions, the digital control strength remains rather robust and in tune with previous results for this index. Our study shows that: (i) results derived from network topology should, when possible, be studied across a range of database versions, before detailed biological conclusions are derived, and (ii) resorting to simple indices, when interpreting high-throughput data from a network perspective, may help achieving a robustness of the findings against variation of the underlying biological information. Database URL: www.regulondb.ccg.unam.mx.
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Affiliation(s)
- Moritz E Beber
- Department of Life Sciences and Chemistry, Jacobs University, Campus Ring 1, Bremen 28759, Germany and Bioinformatics Group, Max Planck Institute for Molecular Genetics, Ihnestraße 63-73, Berlin 14195, Germany
| | - Georgi Muskhelishvili
- Department of Life Sciences and Chemistry, Jacobs University, Campus Ring 1, Bremen 28759, Germany and
| | - Marc-Thorsten Hütt
- Department of Life Sciences and Chemistry, Jacobs University, Campus Ring 1, Bremen 28759, Germany and
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12
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Takemoto K. Habitat variability does not generally promote metabolic network modularity in flies and mammals. Biosystems 2015; 139:46-54. [PMID: 26723229 DOI: 10.1016/j.biosystems.2015.12.004] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2015] [Revised: 12/06/2015] [Accepted: 12/09/2015] [Indexed: 11/24/2022]
Abstract
The evolution of species habitat range is an important topic over a wide range of research fields. In higher organisms, habitat range evolution is generally associated with genetic events such as gene duplication. However, the specific factors that determine habitat variability remain unclear at higher levels of biological organization (e.g., biochemical networks). One widely accepted hypothesis developed from both theoretical and empirical analyses is that habitat variability promotes network modularity; however, this relationship has not yet been directly tested in higher organisms. Therefore, I investigated the relationship between habitat variability and metabolic network modularity using compound and enzymatic networks in flies and mammals. Contrary to expectation, there was no clear positive correlation between habitat variability and network modularity. As an exception, the network modularity increased with habitat variability in the enzymatic networks of flies. However, the observed association was likely an artifact, and the frequency of gene duplication appears to be the main factor contributing to network modularity. These findings raise the question of whether or not there is a general mechanism for habitat range expansion at a higher level (i.e., above the gene scale). This study suggests that the currently widely accepted hypothesis for habitat variability should be reconsidered.
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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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13
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Fischer R, Leitão JC, Peixoto TP, Altmann EG. Sampling Motif-Constrained Ensembles of Networks. PHYSICAL REVIEW LETTERS 2015; 115:188701. [PMID: 26565509 DOI: 10.1103/physrevlett.115.188701] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/30/2015] [Indexed: 06/05/2023]
Abstract
The statistical significance of network properties is conditioned on null models which satisfy specified properties but that are otherwise random. Exponential random graph models are a principled theoretical framework to generate such constrained ensembles, but which often fail in practice, either due to model inconsistency or due to the impossibility to sample networks from them. These problems affect the important case of networks with prescribed clustering coefficient or number of small connected subgraphs (motifs). In this Letter we use the Wang-Landau method to obtain a multicanonical sampling that overcomes both these problems. We sample, in polynomial time, networks with arbitrary degree sequences from ensembles with imposed motifs counts. Applying this method to social networks, we investigate the relation between transitivity and homophily, and we quantify the correlation between different types of motifs, finding that single motifs can explain up to 60% of the variation of motif profiles.
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Affiliation(s)
- Rico Fischer
- Max Planck Institute for the Physics of Complex Systems, 01187 Dresden, Germany
| | - Jorge C Leitão
- Max Planck Institute for the Physics of Complex Systems, 01187 Dresden, Germany
| | - Tiago P Peixoto
- Institut für Theoretische Physik, Universität Bremen, Hochschulring 18, 28359 Bremen, Germany
| | - Eduardo G Altmann
- Max Planck Institute for the Physics of Complex Systems, 01187 Dresden, Germany
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14
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Liang C, Luo J, Song D. Network simulation reveals significant contribution of network motifs to the age-dependency of yeast protein-protein interaction networks. MOLECULAR BIOSYSTEMS 2015; 10:2277-88. [PMID: 24964354 DOI: 10.1039/c4mb00230j] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Advances in proteomic technologies combined with sophisticated computing and modeling methods have generated an unprecedented amount of high-throughput data for system-scale analysis. As a result, the study of protein-protein interaction (PPI) networks has garnered much attention in recent years. One of the most fundamental problems in studying PPI networks is to understand how their architecture originated and evolved to their current state. By investigating how proteins of different ages are connected in the yeast PPI networks, one can deduce their expansion procedure in evolution and how the ancient primitive network expanded and evolved. Studies have shown that proteins are often connected to other proteins of a similar age, suggesting a high degree of age preference between interacting proteins. Though several theories have been proposed to explain this phenomenon, none of them considered protein-clusters as a contributing factor. Here we first investigate the age-dependency of the proteins from the perspective of network motifs. Our analysis confirms that proteins of the same age groups tend to form interacting network motifs; furthermore, those proteins within motifs tend to be within protein complexes and the interactions among them largely contribute to the observed age preference in the yeast PPI networks. In light of these results, we describe a new modeling approach, based on "network motifs", whereby topologically connected protein clusters in the network are treated as single evolutionary units. Instead of modeling single proteins, our approach models the connections and evolutionary relationships of multiple related protein clusters or "network motifs" that are collectively integrated into an existing PPI network. Through simulation studies, we found that the "network motif" modeling approach can capture yeast PPI network properties better than if individual proteins were considered to be the simplest evolutionary units. Our approach provides a fresh perspective on modeling the evolution of yeast PPI networks, specifically that PPI networks may have a much higher age-dependency of interaction density than had been previously envisioned.
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Affiliation(s)
- Cheng Liang
- College of Information Science and Engineering, Hunan University, Changsha, Hunan, China.
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15
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Liang C, Li Y, Luo J, Zhang Z. A novel motif-discovery algorithm to identify co-regulatory motifs in large transcription factor and microRNA co-regulatory networks in human. Bioinformatics 2015; 31:2348-55. [DOI: 10.1093/bioinformatics/btv159] [Citation(s) in RCA: 29] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2014] [Accepted: 03/13/2015] [Indexed: 01/23/2023] Open
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16
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Klosik DF, Bornholdt S, Hütt MT. Motif-based success scores in coauthorship networks are highly sensitive to author name disambiguation. PHYSICAL REVIEW. E, STATISTICAL, NONLINEAR, AND SOFT MATTER PHYSICS 2014; 90:032811. [PMID: 25314486 DOI: 10.1103/physreve.90.032811] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/15/2014] [Indexed: 06/04/2023]
Abstract
Following the work of Krumov et al. [Eur. Phys. J. B 84, 535 (2011)] we revisit the question whether the usage of large citation datasets allows for the quantitative assessment of social (by means of coauthorship of publications) influence on the progression of science. Applying a more comprehensive and well-curated dataset containing the publications in the journals of the American Physical Society during the whole 20th century we find that the measure chosen in the original study, a score based on small induced subgraphs, has to be used with caution, since the obtained results are highly sensitive to the exact implementation of the author disambiguation task.
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Affiliation(s)
- David F Klosik
- Institute for Theoretical Physics, University of Bremen, Hochschulring 18, 28359 Bremen, Germany
| | - Stefan Bornholdt
- Institute for Theoretical Physics, University of Bremen, Hochschulring 18, 28359 Bremen, Germany
| | - Marc-Thorsten Hütt
- School of Engineering and Science, Jacobs University Bremen, Campus Ring 1, 28759 Bremen, Germany
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17
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Sandefur CI, Mincheva M, Schnell S. Network representations and methods for the analysis of chemical and biochemical pathways. MOLECULAR BIOSYSTEMS 2014; 9:2189-200. [PMID: 23857078 DOI: 10.1039/c3mb70052f] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Systems biologists increasingly use network representations to investigate biochemical pathways and their dynamic behaviours. In this critical review, we discuss four commonly used network representations of chemical and biochemical pathways. We illustrate how some of these representations reduce network complexity but result in the ambiguous representation of biochemical pathways. We also examine the current theoretical approaches available to investigate the dynamic behaviour of chemical and biochemical networks. Finally, we describe how the critical chemical and biochemical pathways responsible for emergent dynamic behaviour can be identified using network mining and functional mapping approaches.
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Affiliation(s)
- Conner I Sandefur
- Cystic Fibrosis and Pulmonary Diseases Research and Treatment Center and Department of Pharmacology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
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18
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Towards an Understanding of the Relation between Topological Characteristics and Dynamic Behavior in Manufacturing Networks. ACTA ACUST UNITED AC 2014. [DOI: 10.1016/j.procir.2014.05.005] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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19
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Schmidt C, Weiss T, Lehmann T, Witte H, Leistritz L. Extracting labeled topological patterns from samples of networks. PLoS One 2013; 8:e70497. [PMID: 23950945 PMCID: PMC3741309 DOI: 10.1371/journal.pone.0070497] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2013] [Accepted: 06/22/2013] [Indexed: 11/18/2022] Open
Abstract
An advanced graph theoretical approach is introduced that enables a higher level of functional interpretation of samples of directed networks with identical fixed pairwise different vertex labels that are drawn from a particular population. Compared to the analysis of single networks, their investigation promises to yield more detailed information about the represented system. Often patterns of directed edges in sample element networks are too intractable for a direct evaluation and interpretation. The new approach addresses the problem of simplifying topological information and characterizes such a sample of networks by finding its locatable characteristic topological patterns. These patterns, essentially sample-specific network motifs with vertex labeling, might represent the essence of the intricate topological information contained in all sample element networks and provides as well a means of differentiating network samples. Central to the accurateness of this approach is the null model and its properties, which is needed to assign significance to topological patterns. As a proof of principle the proposed approach has been applied to the analysis of networks that represent brain connectivity before and during painful stimulation in patients with major depression and in healthy subjects. The accomplished reduction of topological information enables a cautious functional interpretation of the altered neuronal processing of pain in both groups.
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Affiliation(s)
- Christoph Schmidt
- Bernstein Group for Computational Neuroscience Jena, Institute of Medical Statistics, Computer Sciences and Documentation, Jena University Hospital, Friedrich Schiller University Jena, Jena, Germany.
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20
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Winkler M, Reichardt J. Motifs in triadic random graphs based on Steiner triple systems. PHYSICAL REVIEW. E, STATISTICAL, NONLINEAR, AND SOFT MATTER PHYSICS 2013; 88:022805. [PMID: 24032881 DOI: 10.1103/physreve.88.022805] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/10/2013] [Indexed: 06/02/2023]
Abstract
Conventionally, pairwise relationships between nodes are considered to be the fundamental building blocks of complex networks. However, over the last decade, the overabundance of certain subnetwork patterns, i.e., the so-called motifs, has attracted much attention. It has been hypothesized that these motifs, instead of links, serve as the building blocks of network structures. Although the relation between a network's topology and the general properties of the system, such as its function, its robustness against perturbations, or its efficiency in spreading information, is the central theme of network science, there is still a lack of sound generative models needed for testing the functional role of subgraph motifs. Our work aims to overcome this limitation. We employ the framework of exponential random graph models (ERGMs) to define models based on triadic substructures. The fact that only a small portion of triads can actually be set independently poses a challenge for the formulation of such models. To overcome this obstacle, we use Steiner triple systems (STSs). These are partitions of sets of nodes into pair-disjoint triads, which thus can be specified independently. Combining the concepts of ERGMs and STSs, we suggest generative models capable of generating ensembles of networks with nontrivial triadic Z-score profiles. Further, we discover inevitable correlations between the abundance of triad patterns, which occur solely for statistical reasons and need to be taken into account when discussing the functional implications of motif statistics. Moreover, we calculate the degree distributions of our triadic random graphs analytically.
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Affiliation(s)
- Marco Winkler
- Institute for Theoretical Physics, University of Würzburg, Am Hubland, 97074 Würzburg, Germany
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QuateXelero: an accelerated exact network motif detection algorithm. PLoS One 2013; 8:e68073. [PMID: 23874498 PMCID: PMC3715482 DOI: 10.1371/journal.pone.0068073] [Citation(s) in RCA: 39] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2013] [Accepted: 05/23/2013] [Indexed: 01/31/2023] Open
Abstract
Finding motifs in biological, social, technological, and other types of networks has become a widespread method to gain more knowledge about these networks’ structure and function. However, this task is very computationally demanding, because it is highly associated with the graph isomorphism which is an NP problem (not known to belong to P or NP-complete subsets yet). Accordingly, this research is endeavoring to decrease the need to call NAUTY isomorphism detection method, which is the most time-consuming step in many existing algorithms. The work provides an extremely fast motif detection algorithm called QuateXelero, which has a Quaternary Tree data structure in the heart. The proposed algorithm is based on the well-known ESU (FANMOD) motif detection algorithm. The results of experiments on some standard model networks approve the overal superiority of the proposed algorithm, namely QuateXelero, compared with two of the fastest existing algorithms, G-Tries and Kavosh. QuateXelero is especially fastest in constructing the central data structure of the algorithm from scratch based on the input network.
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22
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Beber ME, Hütt MT. How do production systems in biological cells maintain their function in changing environments? LOGISTICS RESEARCH 2012. [DOI: 10.1007/s12159-012-0090-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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