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Root-Bernstein RS, Bernstein MI. 'Evolutionary poker': an agent-based model of interactome emergence and epistasis tested against Lenski's long-term E. coli experiments. J Physiol 2024; 602:2511-2535. [PMID: 37707489 DOI: 10.1113/jp284421] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2023] [Accepted: 08/23/2023] [Indexed: 09/15/2023] Open
Abstract
A simple agent-based model is presented that produces results matching the experimental data found by Lenski's group for ≤50,000 generations of Escherichia coli bacteria under continuous selective pressure. Although various mathematical models have been devised previously to model the Lenski data, the present model has advantages in terms of overall simplicity and conceptual accessibility. The model also clearly illustrates a number of features of the evolutionary process that are otherwise not obvious, such as the roles of epistasis and historical contingency in adaptation and why evolution is time irreversible ('Dollo's law'). The reason for this irreversibility is that genomes become increasingly integrated or organized, and this organization becomes a novel selective factor itself, against which future generations must compete. Selection for integrated or synergistic networks, systems or sets of mutations or traits, not for individual mutations, confers the main adaptive advantage. The result is a punctuated form of evolution that follows a logarithmic occurrence probability, in which evolution proceeds very quickly when interactomes begin to form but which slows as interactomes become more robust and the difficulty of integrating new mutations increases. Sufficient parameters exist in the game to suggest not only how equilibrium or stasis is reached but also the conditions in which it will be punctuated, the factors governing the rate at which genomic organization occurs and novel traits appear, and how population size, genome size and gene variability affect these.
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Affiliation(s)
| | - Morton I Bernstein
- Department of Physiology, Michigan State University, East Lansing, MI, USA
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2
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Ziermann JM, Boughner JC, Esteve-Altava B, Diogo R. Anatomical comparison across heads, fore- and hindlimbs in mammals using network models. J Anat 2021; 239:12-31. [PMID: 33629373 DOI: 10.1111/joa.13409] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2020] [Revised: 01/29/2021] [Accepted: 02/01/2021] [Indexed: 12/19/2022] Open
Abstract
Animal body parts evolve with variable degrees of integration that nonetheless yield functional adult phenotypes: but, how? The analysis of modularity with Anatomical Network Analysis (AnNA) is used to quantitatively determine phenotypic modules based on the physical connection among anatomical elements, an approach that is valuable to understand developmental and evolutionary constraints. We created anatomical network models of the head, forelimb, and hindlimb of two taxa considered to represent a 'generalized' eutherian (placental: mouse) and metatherian (marsupial: opossum) anatomical configuration and compared them with our species, which has a derived eutherian configuration. In these models, nodes represent anatomical units and links represent their physical connection. Here, we aimed to identify: (1) the commonalities and differences in modularity between species, (2) whether modules present a potential phylogenetic character, and (3) whether modules preferentially reflect either developmental or functional aspects of anatomy, or a mix of both. We predicted differences between networks of metatherian and eutherian mammals that would best be explained by functional constraints, versus by constraints of development and/or phylogeny. The topology of contacts between bones, muscles, and bones + muscles showed that, among all three species, skeletal networks were more similar than musculoskeletal networks. There was no clear indication that humans and mice are more alike when compared to the opossum overall, even though their musculoskeletal and skeletal networks of fore- and hindlimbs are slightly more similar. Differences were greatest among musculoskeletal networks of heads and next of forelimbs, which showed more variation than hindlimbs, supporting previous anatomical studies indicating that in general the configuration of the hindlimbs changes less across evolutionary history. Most observations regarding the anatomical networks seem to be best explained by function, but an exception is the adult opossum ear ossicles. These ear bones might form an independent module because the incus and malleus are involved in forming a functional primary jaw that enables the neonate to attach to the teat, where this newborn will complete its development. Additionally, the human data show a specialized digit 1 module (thumb/big toe) in both limb types, likely the result of functional and evolutionary pressures, as our ape ancestors had highly movable big toes and thumbs.
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Affiliation(s)
- Janine M Ziermann
- Department of Anatomy, Howard University College of Medicine, Washington, DC, USA
| | - Julia C Boughner
- Department of Anatomy, Physiology & Pharmacology, College of Medicine, University of Saskatchewan, Saskatoon, Canada
| | - Borja Esteve-Altava
- Institute of Evolutionary Biology (UPF-CSI), Department of Experimental and Health Sciences, University Pompeu Fabra, Barcelona, Spain
| | - Rui Diogo
- Department of Anatomy, Howard University College of Medicine, Washington, DC, USA
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3
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Bruner E, Esteve-Altava B, Rasskin-Gutman D. A network approach to brain form, cortical topology and human evolution. Brain Struct Funct 2019; 224:2231-2245. [DOI: 10.1007/s00429-019-01900-1] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2019] [Accepted: 05/31/2019] [Indexed: 12/13/2022]
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4
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Schoenrock A, Burnside D, Moteshareie H, Pitre S, Hooshyar M, Green JR, Golshani A, Dehne F, Wong A. Evolution of protein-protein interaction networks in yeast. PLoS One 2017; 12:e0171920. [PMID: 28248977 PMCID: PMC5382968 DOI: 10.1371/journal.pone.0171920] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2016] [Accepted: 01/28/2017] [Indexed: 01/04/2023] Open
Abstract
Interest in the evolution of protein-protein and genetic interaction networks has been rising in recent years, but the lack of large-scale high quality comparative datasets has acted as a barrier. Here, we carried out a comparative analysis of computationally predicted protein-protein interaction (PPI) networks from five closely related yeast species. We used the Protein-protein Interaction Prediction Engine (PIPE), which uses a database of known interactions to make sequence-based PPI predictions, to generate high quality predicted interactomes. Simulated proteomes and corresponding PPI networks were used to provide null expectations for the extent and nature of PPI network evolution. We found strong evidence for conservation of PPIs, with lower than expected levels of change in PPIs for about a quarter of the proteome. Furthermore, we found that changes in predicted PPI networks are poorly predicted by sequence divergence. Our analyses identified a number of functional classes experiencing fewer PPI changes than expected, suggestive of purifying selection on PPIs. Our results demonstrate the added benefit of considering predicted PPI networks when studying the evolution of closely related organisms.
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Affiliation(s)
| | | | | | - Sylvain Pitre
- School of Computer Science, Carleton University, Ottawa, Canada
| | | | - James R. Green
- Department of Systems and Computer Engineering, Carleton University, Ottawa, Canada
| | | | - Frank Dehne
- School of Computer Science, Carleton University, Ottawa, Canada
| | - Alex Wong
- Department of Biology, Carleton University, Ottawa, Canada
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5
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Abstract
Background Cluster heatmaps are commonly used in biology and related fields to reveal hierarchical clusters in data matrices. This visualization technique has high data density and reveal clusters better than unordered heatmaps alone. However, cluster heatmaps have known issues making them both time consuming to use and prone to error. We hypothesize that visualization techniques without the rigid grid constraint of cluster heatmaps will perform better at clustering-related tasks. Results We developed an approach to “unbox” the heatmap values and embed them directly in the hierarchical clustering results, allowing us to use standard hierarchical visualization techniques as alternatives to cluster heatmaps. We then tested our hypothesis by conducting a survey of 45 practitioners to determine how cluster heatmaps are used, prototyping alternatives to cluster heatmaps using pair analytics with a computational biologist, and evaluating those alternatives with hour-long interviews of 5 practitioners and an Amazon Mechanical Turk user study with approximately 200 participants. We found statistically significant performance differences for most clustering-related tasks, and in the number of perceived visual clusters. Visit git.io/vw0t3 for our results. Conclusions The optimal technique varied by task. However, gapmaps were preferred by the interviewed practitioners and outperformed or performed as well as cluster heatmaps for clustering-related tasks. Gapmaps are similar to cluster heatmaps, but relax the heatmap grid constraints by introducing gaps between rows and/or columns that are not closely clustered. Based on these results, we recommend users adopt gapmaps as an alternative to cluster heatmaps.
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Affiliation(s)
- Sophie Engle
- University of San Francisco, San Francisco, 94117, CA, USA.
| | - Sean Whalen
- Gladstone Institutes, San Francisco, 94158, CA, USA
| | - Alark Joshi
- University of San Francisco, San Francisco, 94117, CA, USA
| | - Katherine S Pollard
- Gladstone Institutes, San Francisco, 94158, CA, USA.,Division of Biostatistics, Institute for Human Genetics, and Institute for Computational Health Sciences, University of California, San Francisco, 94158, CA, USA
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6
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Esteve-Altava B, Diogo R, Smith C, Boughner JC, Rasskin-Gutman D. Anatomical networks reveal the musculoskeletal modularity of the human head. Sci Rep 2015; 5:8298. [PMID: 25656958 PMCID: PMC5389032 DOI: 10.1038/srep08298] [Citation(s) in RCA: 53] [Impact Index Per Article: 5.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2014] [Accepted: 01/14/2015] [Indexed: 11/09/2022] Open
Abstract
Mosaic evolution is a key mechanism that promotes robustness and evolvability in living beings. For the human head, to have a modular organization would imply that each phenotypic module could grow and function semi-independently. Delimiting the boundaries of head modules, and even assessing their existence, is essential to understand human evolution. Here we provide the first study of the human head using anatomical network analysis (AnNA), offering the most complete overview of the modularity of the head to date. Our analysis integrates the many biological dependences that tie hard and soft tissues together, arising as a consequence of development, growth, stresses and loads, and motion. We created an anatomical network model of the human head, where nodes represent anatomical units and links represent their physical articulations. The analysis of the human head network uncovers the presence of 10 musculoskeletal modules, deep-rooted in these biological dependences, of developmental and evolutionary significance. In sum, this study uncovers new anatomical and functional modules of the human head using a novel quantitative method that enables a more comprehensive understanding of the evolutionary anatomy of our lineage, including the evolution of facial expression and facial asymmetry.
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Affiliation(s)
- Borja Esteve-Altava
- Theoretical Biology Research Group, Cavanilles Institute of Biodiversity and Evolutionary Biology, University of Valencia, 46071 Valencia, Spain
| | - Rui Diogo
- Department of Anatomy, Howard Univ. College of Medicine, Washington, DC, USA
| | - Christopher Smith
- Department of Anatomy, Howard Univ. College of Medicine, Washington, DC, USA
| | - Julia C Boughner
- Department of Anatomy and Cell Biology, Univ. of Saskatchewan, Saskatoon, SK, Canada
| | - Diego Rasskin-Gutman
- Theoretical Biology Research Group, Cavanilles Institute of Biodiversity and Evolutionary Biology, University of Valencia, 46071 Valencia, Spain
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Kemppainen P, Knight CG, Sarma DK, Hlaing T, Prakash A, Maung Maung YN, Somboon P, Mahanta J, Walton C. Linkage disequilibrium network analysis (LDna) gives a global view of chromosomal inversions, local adaptation and geographic structure. Mol Ecol Resour 2015; 15:1031-45. [PMID: 25573196 PMCID: PMC4681347 DOI: 10.1111/1755-0998.12369] [Citation(s) in RCA: 64] [Impact Index Per Article: 7.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2014] [Revised: 12/15/2014] [Accepted: 12/29/2014] [Indexed: 12/21/2022]
Abstract
Recent advances in sequencing allow population-genomic data to be generated for virtually any species. However, approaches to analyse such data lag behind the ability to generate it, particularly in nonmodel species. Linkage disequilibrium (LD, the nonrandom association of alleles from different loci) is a highly sensitive indicator of many evolutionary phenomena including chromosomal inversions, local adaptation and geographical structure. Here, we present linkage disequilibrium network analysis (LDna), which accesses information on LD shared between multiple loci genomewide. In LD networks, vertices represent loci, and connections between vertices represent the LD between them. We analysed such networks in two test cases: a new restriction-site-associated DNA sequence (RAD-seq) data set for Anopheles baimaii, a Southeast Asian malaria vector; and a well-characterized single nucleotide polymorphism (SNP) data set from 21 three-spined stickleback individuals. In each case, we readily identified five distinct LD network clusters (single-outlier clusters, SOCs), each comprising many loci connected by high LD. In A. baimaii, further population-genetic analyses supported the inference that each SOC corresponds to a large inversion, consistent with previous cytological studies. For sticklebacks, we inferred that each SOC was associated with a distinct evolutionary phenomenon: two chromosomal inversions, local adaptation, population-demographic history and geographic structure. LDna is thus a useful exploratory tool, able to give a global overview of LD associated with diverse evolutionary phenomena and identify loci potentially involved. LDna does not require a linkage map or reference genome, so it is applicable to any population-genomic data set, making it especially valuable for nonmodel species.
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Affiliation(s)
- Petri Kemppainen
- Computational and Evolutionary Biology, Faculty of Life Sciences, University of Manchester, Manchester, UK.,Institute of Vertebrate Biology, Academy of Sciences of the Czech Republic, Brno, Czech Republic
| | - Christopher G Knight
- Computational and Evolutionary Biology, Faculty of Life Sciences, University of Manchester, Manchester, UK
| | - Devojit K Sarma
- Computational and Evolutionary Biology, Faculty of Life Sciences, University of Manchester, Manchester, UK.,Regional Medical Research Centre, NE (ICMR), Dibrugarh, 786 001, India
| | - Thaung Hlaing
- Department of Medical Research (Lower Myanmar), Medical Entomology Research Division, 5 Ziwaka Road, Dagon P.O., Yangon, 11191, Myanmar
| | - Anil Prakash
- Regional Medical Research Centre, NE (ICMR), Dibrugarh, 786 001, India
| | - Yan Naung Maung Maung
- Department of Medical Research (Lower Myanmar), Medical Entomology Research Division, 5 Ziwaka Road, Dagon P.O., Yangon, 11191, Myanmar
| | - Pradya Somboon
- Department of Parasitology, Faculty of Medicine, Chiang Mai University, Chiang Mai, 50200, Thailand
| | - Jagadish Mahanta
- Regional Medical Research Centre, NE (ICMR), Dibrugarh, 786 001, India
| | - Catherine Walton
- Computational and Evolutionary Biology, Faculty of Life Sciences, University of Manchester, Manchester, UK
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8
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A philosophical evaluation of adaptationism as a heuristic strategy. Acta Biotheor 2014; 62:479-98. [PMID: 24992988 DOI: 10.1007/s10441-014-9232-x] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2013] [Accepted: 06/25/2014] [Indexed: 12/27/2022]
Abstract
Adaptationism has prompted many a debate in philosophy of biology but the focus is usually on empirical and explanatory issues rather than methodological adaptationism (MA). Likewise, the context of evolutionary biology has provided the grounding for most discussions of the heuristic role of adaptationism. This paper extends the debate by drawing on case studies from physiology and systems biology to discuss the productive and problematic aspects of adaptationism in functional as well as evolutionary studies at different levels of biological organization. Gould and Lewontin's Spandrels-paper famously criticized adaptationist methodology for implying a risk of generating 'blind spots' with respect to non-selective effects on evolution. Some have claimed that this bias can be accommodated through the testing of evolutionary hypotheses. Although this is an important aspect of overcoming the pitfalls of adaptationism, I argue that the issue of methodological biases is broader than the question of testability. I demonstrate the productivity of adaptationist heuristics but also discuss the deeper problematic aspects associated with the imperialistic tendencies of the strong account of MA.
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9
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10
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Rasskin-Gutman D, Esteve-Altava B. Connecting the Dots: Anatomical Network Analysis in Morphological EvoDevo. ACTA ACUST UNITED AC 2014. [DOI: 10.1007/s13752-014-0175-x] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
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11
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Dutkowski J, Tiuryn J. A probabilistic model of neutral and selective dynamics of protein network evolution. J Comput Biol 2013; 20:631-42. [PMID: 23931333 DOI: 10.1089/cmb.2012.0295] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022] Open
Abstract
Comparative approaches in genomics have long relied on rigorous mathematical models of sequence evolution. Such models provide the basis for formulating and solving well-defined computational problems, in turn yielding key insights into the evolutionary processes acting on the genome. Analogous model-based approaches for analyzing biological networks are still under development. Here we describe a model-based approach for estimating the probability of network rewiring events during evolution. Our method builds on the standard duplication-and-divergence model and incorporates phylogenetic analysis to guide the comparison of protein networks across species. We apply our algorithm to study the evolution of functional modules and unconstrained network regions in seven available eukaryotic interactomes. Based on this analysis we identify a map of co-functioning protein families whose members participate in strongly conserved interactions and form major complexes and pathways in the eukaryotic cell. The proposed approach provides principled means for inferring the probability of network rewiring events, enabling insights into the conservation and divergence of protein interactions and the formation of functional modules in protein networks.
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Affiliation(s)
- Janusz Dutkowski
- Departments of Medicine and Bioengineering, University of California, San Diego, California, USA
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12
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Kitchen JL, Allaby RG. Systems Modeling at Multiple Levels of Regulation: Linking Systems and Genetic Networks to Spatially Explicit Plant Populations. PLANTS (BASEL, SWITZERLAND) 2013; 2:16-49. [PMID: 27137364 PMCID: PMC4844292 DOI: 10.3390/plants2010016] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/01/2012] [Revised: 12/21/2012] [Accepted: 01/16/2013] [Indexed: 11/16/2022]
Abstract
Selection and adaptation of individuals to their underlying environments are highly dynamical processes, encompassing interactions between the individual and its seasonally changing environment, synergistic or antagonistic interactions between individuals and interactions amongst the regulatory genes within the individual. Plants are useful organisms to study within systems modeling because their sedentary nature simplifies interactions between individuals and the environment, and many important plant processes such as germination or flowering are dependent on annual cycles which can be disrupted by climate behavior. Sedentism makes plants relevant candidates for spatially explicit modeling that is tied in with dynamical environments. We propose that in order to fully understand the complexities behind plant adaptation, a system that couples aspects from systems biology with population and landscape genetics is required. A suitable system could be represented by spatially explicit individual-based models where the virtual individuals are located within time-variable heterogeneous environments and contain mutable regulatory gene networks. These networks could directly interact with the environment, and should provide a useful approach to studying plant adaptation.
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Affiliation(s)
- James L Kitchen
- School of Life Sciences, University of Warwick, Coventry, CV4 7AL, UK
| | - Robin G Allaby
- School of Life Sciences, University of Warwick, Coventry, CV4 7AL, UK.
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13
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Esteve-Altava B, Marugán-Lobón J, Botella H, Rasskin-Gutman D. Structural Constraints in the Evolution of the Tetrapod Skull Complexity: Williston’s Law Revisited Using Network Models. Evol Biol 2012. [DOI: 10.1007/s11692-012-9200-9] [Citation(s) in RCA: 50] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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14
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Evolutionary systems biology: historical and philosophical perspectives on an emerging synthesis. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2012; 751:1-28. [PMID: 22821451 DOI: 10.1007/978-1-4614-3567-9_1] [Citation(s) in RCA: 11] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Systems biology (SB) is at least a decade old now and maturing rapidly. A more recent field, evolutionary systems biology (ESB), is in the process of further developing system-level approaches through the expansion of their explanatory and potentially predictive scope. This chapter will outline the varieties of ESB existing today by tracing the diverse roots and fusions that make up this integrative project. My approach is philosophical and historical. As well as examining the recent origins of ESB, I will reflect on its central features and the different clusters of research it comprises. In its broadest interpretation, ESB consists of five overlapping approaches: comparative and correlational ESB; network architecture ESB; network property ESB; population genetics ESB; and finally, standard evolutionary questions answered with SB methods. After outlining each approach with examples, I will examine some strong general claims about ESB, particularly that it can be viewed as the next step toward a fuller modern synthesis of evolutionary biology (EB), and that it is also the way forward for evolutionary and systems medicine. I will conclude with a discussion of whether the emerging field of ESB has the capacity to combine an even broader scope of research aims and efforts than it presently does.
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15
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Linard B, Nguyen NH, Prosdocimi F, Poch O, Thompson JD. EvoluCode: Evolutionary Barcodes as a Unifying Framework for Multilevel Evolutionary Data. Evol Bioinform Online 2011; 8:61-77. [PMID: 22267905 PMCID: PMC3256995 DOI: 10.4137/ebo.s8814] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022] Open
Abstract
Evolutionary systems biology aims to uncover the general trends and principles governing the evolution of biological networks. An essential part of this process is the reconstruction and analysis of the evolutionary histories of these complex, dynamic networks. Unfortunately, the methodologies for representing and exploiting such complex evolutionary histories in large scale studies are currently limited. Here, we propose a new formalism, called EvoluCode (Evolutionary barCode), which allows the integration of different evolutionary parameters (eg, sequence conservation, orthology, synteny …) in a unifying format and facilitates the multilevel analysis and visualization of complex evolutionary histories at the genome scale. The advantages of the approach are demonstrated by constructing barcodes representing the evolution of the complete human proteome. Two large-scale studies are then described: (i) the mapping and visualization of the barcodes on the human chromosomes and (ii) automatic clustering of the barcodes to highlight protein subsets sharing similar evolutionary histories and their functional analysis. The methodologies developed here open the way to the efficient application of other data mining and knowledge extraction techniques in evolutionary systems biology studies. A database containing all EvoluCode data is available at: http://lbgi.igbmc.fr/barcodes.
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Affiliation(s)
- Benjamin Linard
- Laboratoire De Bioinformatique Et Génomique Intégratives, Institut de Génétique et de Biologie Moléculaire et Cellulaire CNRS/INSERM/UDS, Illkirch, France
| | - Ngoc Hoan Nguyen
- Laboratoire De Bioinformatique Et Génomique Intégratives, Institut de Génétique et de Biologie Moléculaire et Cellulaire CNRS/INSERM/UDS, Illkirch, France
| | | | - Olivier Poch
- Laboratoire De Bioinformatique Et Génomique Intégratives, Institut de Génétique et de Biologie Moléculaire et Cellulaire CNRS/INSERM/UDS, Illkirch, France
| | - Julie D. Thompson
- Laboratoire De Bioinformatique Et Génomique Intégratives, Institut de Génétique et de Biologie Moléculaire et Cellulaire CNRS/INSERM/UDS, Illkirch, France
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16
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Citterio L, Lanzani C, Manunta P, Bianchi G. Genetics of primary hypertension: The clinical impact of adducin polymorphisms. Biochim Biophys Acta Mol Basis Dis 2010; 1802:1285-98. [DOI: 10.1016/j.bbadis.2010.03.014] [Citation(s) in RCA: 24] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2010] [Revised: 03/26/2010] [Accepted: 03/30/2010] [Indexed: 01/11/2023]
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17
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Elements of Computational Systems Biology. Eds. H. M. Lodhi & S. Muggleton. Wiley-Blackwell. 2010. 412 pages. ISBN 9780470180938. Price $115 (hardback). Genet Res (Camb) 2010. [DOI: 10.1017/s0016672310000443] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022] Open
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19
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Montañez R, Medina MA, Solé RV, Rodríguez-Caso C. When metabolism meets topology: Reconciling metabolite and reaction networks. Bioessays 2010; 32:246-256. [PMID: 20127701 DOI: 10.1002/bies.200900145] [Citation(s) in RCA: 44] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
The search for a systems-level picture of metabolism as a web of molecular interactions provides a paradigmatic example of how the methods used to characterize a system can bias the interpretation of its functional meaning. Metabolic maps have been analyzed using novel techniques from network theory, revealing some non-trivial, functionally relevant properties. These include a small-world structure and hierarchical modularity. However, as discussed here, some of these properties might actually result from an inappropriate way of defining network interactions. Starting from the so-called bipartite organization of metabolism, where the two meaningful subsets (reactions and metabolites) are considered, most current works use only one of the subsets by means of so-called graph projections. Unfortunately, projected graphs often ignore relevant biological and chemical constraints, thus leading to statistical artifacts. Some of these drawbacks and alternative approaches need to be properly addressed.
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Affiliation(s)
- Raul Montañez
- Departamento de Biología Molecular y Bioquímica, Facultad de Ciencias, Universidad de Málaga, E-29071 Málaga, and CIBER de Enfermedades Raras (CIBERER), Málaga, Spain
| | - Miguel Angel Medina
- Departamento de Biología Molecular y Bioquímica, Facultad de Ciencias, Universidad de Málaga, E-29071 Málaga, and CIBER de Enfermedades Raras (CIBERER), Málaga, Spain
| | - Ricard V Solé
- ICREA-Complex Systems Lab, Universitat Pompeu Fabra. Parc de Recerca Biomèdica de Barcelona. Dr. Aiguader 88, 08003. Barcelona, Spain.,Santa Fe Institute 1399 Hyde Park Road, Santa Fe, NM 87501, USA
| | - Carlos Rodríguez-Caso
- ICREA-Complex Systems Lab, Universitat Pompeu Fabra. Parc de Recerca Biomèdica de Barcelona. Dr. Aiguader 88, 08003. Barcelona, Spain
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