Дисертації з теми "Evolution on networks"
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Willmann, Stana. "Evolution of genetic networks." [S.l. : s.n.], 2005. http://deposit.ddb.de/cgi-bin/dokserv?idn=973677864.
Повний текст джерелаRizzi, Giacomo. "Genetic Evolution of Neural Networks." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2018. http://amslaurea.unibo.it/16769/.
Повний текст джерелаTrusina, Ala. "Complex Networks : Structure, Function , Evolution." Doctoral thesis, Umeå University, Physics, 2005. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-608.
Повний текст джерелаA complex system is a system for which the statement "the whole is greater than the sum of its parts" holds. A network can be viewed as a backbone of a complex system. Combining the knowledge about the entities constituting the complex system with the properties of the interaction patterns we can get a better understanding of why the whole is greater than the sum. One of the purposes of network studies, is to relate the particular structural and dynamical properties of the network to the function it is designed to perform. In the present work I am briefly presenting some of the advances that have been achieved in the field of the complex networks together with the contributions which I have been involved in.
Landassuri, Moreno Victor Manuel. "Evolution of modular neural networks." Thesis, University of Birmingham, 2012. http://etheses.bham.ac.uk//id/eprint/3243/.
Повний текст джерелаMohan, Madan Babu. "Evolution of transcriptional regulatory networks." Thesis, University of Cambridge, 2004. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.616113.
Повний текст джерелаWhitaker, John William. "On the evolution of metabolic networks." Thesis, University of Leeds, 2009. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.511155.
Повний текст джерелаOpsahl, Tore. "Structure and Evolution of Weighted Networks." Thesis, Queen Mary, University of London, 2009. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.507253.
Повний текст джерелаCooper, Max B. "Evolution of small gene regulatory networks." Thesis, University of Nottingham, 2008. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.495599.
Повний текст джерелаAlotaibi, Sultan. "3GPP Long Term Evolution LTE Scheduling." Thesis, University of North Texas, 2013. https://digital.library.unt.edu/ark:/67531/metadc490046/.
Повний текст джерелаCatanese, Salvatore Amato. "New perspectives in criminal network analysis: multilayer networks, time evolution, and visualization." Doctoral thesis, Università di Catania, 2017. http://hdl.handle.net/10761/3793.
Повний текст джерелаMohammed, Sirajuddin. "PERFORMANCE EVOLUTION OF PEER TO PEER NETWORKS." Thesis, Högskolan Dalarna, Datateknik, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:du-3959.
Повний текст джерелаLight, Sara. "Investigations into the evolution of biological networks." Doctoral thesis, Stockholm : Department of Biochemistry and Biophysics, Stockholm University, 2006. http://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-1004.
Повний текст джерелаDeng, Qichen. "Antenna Optimization in Long-Term Evolution Networks." Thesis, KTH, Optimeringslära och systemteori, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-119147.
Повний текст джерелаWilds, Roy. "Dynamics, function and evolution of regulatory networks." Thesis, McGill University, 2009. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=66809.
Повний текст джерелаBiochemical réglementaire des réseaux sont impliqués dans de nombreux processusessentiels de la vie, tels que les conditions en restant homéostatique, le cycle dedivision cellulaire, et de répondre 'a l'environnement stimuli. Ces réseaux contiennentun grand degré de complexité, qui nous empéche de comprendre comment ils fonctionnent.Deux défis majeurs dans la biologie quantitative sont abordées dans cetteth'ese. Tout d'abord, le probl'eme de l'identification des mod'eles de réglementation 'apartir de données d'observation. Deuxi'emement, révélant les processus par lesquelsles réseaux de régulation évoluent. L'utilisation d'une simple réglementation mod'elequi est basé sur les équations différentielles linéaires par morceaux; les enquêtes surces questions sont mises en oeuvre.Une nouvelle méthode pour déduire des réseaux de réglementation de la connaissancede la dynamique est présenté. Ce résultat est utilisé pour tirer un atlasdes réseaux tr'es robuste cycle de limite dynamiques dans les dimensions 3,4 et 5.En outre, une nouvelle approche théorique est également utilisé pour identifier deuxfamilles de réseaux de régulation: la rétroaction négative de réseaux et séquentieldisinhibition, avec de solides périodiques dynamiques qui existe aussi dans toutes lesdimensions supérieures aussi. Les généralisations de linéaire par lisse morceaux del'esp'ece, appelée continue homologues, sont également considérée. Pour la famillede cyclique, la rétroaction négative de réseaux, il est montré que, dans le homologuecontinue, un cycle de limite existe et il est prévu hyperbolique, il est alors asymptotiquementstable.Réglementaire des réseaux part de nombreuses fonctionnalités 'a travers les différentesesp'eces, ce qui soul'eve la question de savoir comment ils évoluent. Un mod'ele simpleévolutif dans lequel les réseaux sont linéaires par morceaux soumis 'a$
Muthuraman, Sethuraman. "The evolution of modular artificial neural networks." Thesis, Robert Gordon University, 2005. http://hdl.handle.net/10059/284.
Повний текст джерелаVincent, Graham Richard. "The evolution of gauged cosmic string networks." Thesis, University of Sussex, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.390521.
Повний текст джерелаChandalia, Juhi Kiran 1979. "Evolution and statistics of biological regulatory networks." Thesis, Massachusetts Institute of Technology, 2005. http://hdl.handle.net/1721.1/32313.
Повний текст джерелаIncludes bibliographical references (p. 57-58).
In this thesis, I study the process of evolution of the gene regulatory network in Escherichia coli. First, I characterize the portion of the network that has been documented, and then I simulate growth of the network. In this study, I assume that the network evolves by gene duplication and divergence. Initially, the duplicated gene will retain its old interactions. As the gene accumulates mutations, it gains new interactions and may or may not lose the old interactions. I investigate evidence for the duplication-divergence model by looking at the homology and regulatory networks in E. coli and propose a simple duplication-divergence model for growth. The results show that this simple model cannot fully account for the complexity in the real network fragment as measured by conventional metrics.
by Juhi Kiran Chandalia
S.M.
Banos, Thomas Anthony MacCarthy. "Evolution of gene networks in sex determination." Thesis, University College London (University of London), 2005. http://discovery.ucl.ac.uk/1445677/.
Повний текст джерелаSantos, Francisco C. "Topological evolution: from biological to social networks." Doctoral thesis, Universite Libre de Bruxelles, 2007. http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/210702.
Повний текст джерелаAgarwal, Sumeet. "Networks in nature : dynamics, evolution, and modularity." Thesis, University of Oxford, 2012. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.564283.
Повний текст джерелаMortazavi, Ali Rothenberg Ellen V. Wold Barbara J. "Structure and evolution of mammalian gene networks /." Diss., Pasadena, Calif. : California Institute of Technology, 2008. http://resolver.caltech.edu/CaltechETD:etd-05292008-140438.
Повний текст джерелаHuang, Jie. "Growth, evolution and scaling in transport networks." Thesis, University of Leeds, 2015. http://etheses.whiterose.ac.uk/9966/.
Повний текст джерелаSeshadri, Anand. "A Demand Driven Airline and Airport Evolution Study." Diss., Virginia Tech, 2009. http://hdl.handle.net/10919/29526.
Повний текст джерелаPh. D.
Hamdoun, Hassan. "Practical network coding schemes for energy efficient long term evolution radio access networks." Thesis, Swansea University, 2013. https://cronfa.swan.ac.uk/Record/cronfa42828.
Повний текст джерелаShorten, David. "Spectral analysis of neutral evolution." Master's thesis, University of Cape Town, 2017. http://hdl.handle.net/11427/27420.
Повний текст джерелаEdström, Petter. "Overhead Impacts on Long-Term Evolution Radio Networks." Thesis, KTH, Kommunikationssystem, CoS, 2007. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-91991.
Повний текст джерелаSom ett resultat av ständiga ansträngningar att förbättra såväl prestanda som spektrumeffektivitet för mobila system, definierar 3GPPs standardiseringsforum nya krav på arkitektur och funktionalitet. Dessa är avsedda att säkerställa långsiktig utveckling (explicit definierat som konceptet “Long-Term Evolution (LTE)”, samt framtida konkurrenskraft för både 2G och 3G som radioaccess-teknologier. Tidigare diskussioner rörande effektivitet inom LTE har fokuserat på allmänna antaganden vad gäller kontrolldata för signallering och övergripande systemprestanda. Dessa har i sin tur baserats på erfarenheter från existerande mobilsystem. När standardiseringen inom 3GPP mognar uppstår nu ett behov av att undersöka hur olika tjänster inom LTE påverkas, av såväl hur man använder den kontrollinformation som finns tillgänglig, som av basala algoritmer for schemaläggning av resurser. Denna rapport undersöker påverkan från lägre protokoll-lagers kontrollinformation på nerlänken hos olika paket-kopplade tjänster inom ett radioaccessnät för LTE. Resultaten visar att användandet av ROHC (som packar kontrollinformation för protokollen RTP/TCP/IP), är det ensamt viktigaste bidraget till minskad kontrollinformation i relation till informationsbitar för paketstorlekar upp till c:a 1kB. För större paket är vinsten med ROHC dock försumbar. Kontrollinformation för protokoll – inkluderat data avsett för AMR-tal-ramen, RLC/MAC-protokollen, samt CRC – utgör för övrigt en stor del av kontrollinformationen relativt informationsbitar, oavsett paketstorlek och packning av kontrolldata. Tjänster som kräver paketstorlekar på över c:a 1 kB kräver uppskattningsvis samma mängd energi per informationsbit, oavsett andelen kontrollinformation.
Ye, Cheng. "Entropic characterization and time evolution of complex networks." Thesis, University of York, 2016. http://etheses.whiterose.ac.uk/15550/.
Повний текст джерелаRead, Warren James. "Evolution of protein interdependence from pairs to networks." Thesis, University of Reading, 2015. http://centaur.reading.ac.uk/68267/.
Повний текст джерелаYu, Bin. "Emergence and Evolution of Agent-Based Referral Networks." NCSU, 2002. http://www.lib.ncsu.edu/theses/available/etd-20020201-085537.
Повний текст джерелаNumerous studies have shown that interpersonal communication acts as animportant channel for gathering information. But if we wish to rely oninterpersonal communication, we still need to figure out how to determinethe right person to ask. Usually we cannot find the potential expert(s)directly, and we need some assistance from our friends or friends'friends to locate them. The phenomenon of Six Degrees of Separationindicates that it is possible to use some intelligent software agents,who can interpret the links between people and follow only therelevant one, to find the desired experts quickly. A computational model of agent-based referral networks was proposed to assistand simplify the users to find potential experts for a specified topic in aperson-to-person social network, in which each user is assigned a softwareagent, and software agents help automate the process by a series of ``referralchains''. Unlike most previous approaches, our architecture is fullydistributed and includes agents who preserve the privacy and autonomy oftheir users. These agents learn models of each other in terms of expertise(ability to produce correct domain answers), and sociability (ability toproduce accurate referrals). We study this framework experimentally to seethe effects that the different variables have on each other and the efficiencyof the referral networks. Furthermore, a social mechanism of reputation management was proposed tohelp agents (users) avoid interaction with undesirable participants inthe referral networks. The mathematical theory of evidence is used torepresent and propagate the reputation information in a referral network.Our approach adjusts the ratings of agents based on their observations aswell the testimony from others. Moreover, we conducted several experimentsto study the reputation management in different settings. Social mechanismsare even more important when some centralized reputation managementmechanisms, i.e., trusted third parties, are not available. Our specificapproach to reputation management leads to a decentralized society in whichagents help each other weed out undesirable players.
Lavoie, Hugo. "Evolution of the transcriptional regulatory networks of ascomycetes." Thesis, McGill University, 2009. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=66845.
Повний текст джерелаContrairement à la structure des domaines protéiques et aux réseaux métaboliques qui sont difficilement interchangeables, le réseau de régulation transcriptionnelle (TRN) d'un organisme se doit d'être extrêmement plastique et peut être sujet à plusieurs types de mutations influant sur le phénotype. Les levures ascomycètes constituent un groupe phylétique idéal pour évaluer la conservation des facteurs de transcription (TFs) et de leurs sites de liaison sur l'ADN. Dans cette thèse, 1) je décris de nouveaux outils permettant l'analyse fonctionnelle des protéines de C. albicans, 2) Je rapporte le premier exemple d'un changement dans le TRN baptisé substitution de TF qui s'est produit dans l'évolution du régulon des protéines ribosomales (RP) et 3) j'explore les modalités de réorganisation du réseau transcriptionnel dans un système recâblé suite à une substitution de TF. Ces travaux pavent la voie à une étude élargie de l'évolution du TRN chez les ascomycètes. Ils illustrent aussi l'extrême plasticité du TRN ribosomal dans l'évolution des ascomycètes et soulèvent d'intéressantes questions quant à l'importance des changements du réseau régulatoire dans l'évolution des eucaryotes.
Wang, Juan. "Scalability, evolution and topology of networks of agents." Thesis, Imperial College London, 2006. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.430138.
Повний текст джерелаKershaw, Daniel. "Language change and evolution in Online Social Networks." Thesis, Lancaster University, 2018. http://eprints.lancs.ac.uk/129787/.
Повний текст джерелаQian, Jichao M. Eng Massachusetts Institute of Technology. "Structure and evolution of communication networks in organizations." Thesis, Massachusetts Institute of Technology, 2008. http://hdl.handle.net/1721.1/46513.
Повний текст джерелаIncludes bibliographical references (p. 129).
We study three types of communication data--emails, phone calls, and meetings-in a modern IT firm. Using network descriptive statistics, we show how communication networks in an organization differ from random networks and other social networks. We also compare and contrast the three types of communication networks. Using Quadratic Assignment Procedure (QAP), Multiple Regression Quadratic Assignment Procedure (MRQAP) and Exponential Random Graph Models (ERGM), we identify significant factors affecting the size and shape of communication networks. These parameters include organizational structure, homophily, job position, and physical proximity. We record the evolution of the networks and discuss how the factors affecting initial network growth differ from the steady state of the network.Erik Brynjolfsson George and Sandi Schussel Professor of Management Director, Center for Digital Business, MIT Sloan School of Management
by Jichao Qian.
M.Eng.
Leong, Yuen Yoong. "Biopharmaceutical development networks : architecture, dynamic processes and evolution." Thesis, University of Cambridge, 2005. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.615052.
Повний текст джерелаKaza, Siddharth. "Instance, Evolution, and Predictive Modeling of Social Networks." Diss., The University of Arizona, 2008. http://hdl.handle.net/10150/193625.
Повний текст джерелаD'ANTONIO, MATTEO. "EVOLUTION OF PROTEIN INTERACTION NETWORKS THROUGH GENE DUPLICATION." Doctoral thesis, Università degli Studi di Milano, 2011. http://hdl.handle.net/2434/214608.
Повний текст джерелаKönig, Michael David. "Dynamic R&D networks : the efficiency and evolution of interfirm collaboration networks /." Zürich : ETH, 2009. http://e-collection.ethbib.ethz.ch/show?type=diss&nr=18182.
Повний текст джерелаMahmoud, Qusay H. "Evolution of network computing paradigms : applications of mobile agents in wired and wireless networks." Thesis, Middlesex University, 2002. http://eprints.mdx.ac.uk/10745/.
Повний текст джерелаMorrison, Erin Seidler, and Erin Seidler Morrison. "Exploring the Deterministic Landscape of Evolution: An Example with Carotenoid Diversification in Birds." Diss., The University of Arizona, 2017. http://hdl.handle.net/10150/624290.
Повний текст джерелаSchütte, Moritz. "Evolutionary fingerprints in genome-scale networks." Phd thesis, Universität Potsdam, 2011. http://opus.kobv.de/ubp/volltexte/2012/5748/.
Повний текст джерелаDie biologische Zelle ist ein sehr kompliziertes Gebilde. Bei ihrer Betrachtung gilt es, das Zusammenspiel von Tausenden bis Millionen von Genen, Regulatoren, Proteinen oder Molekülen zu beschreiben und zu verstehen. Durch enorme Verbesserungen experimenteller Messgeräte gelingt es mittlerweile allerdings in geringer Zeit enorme Datenmengen zu messen, seien dies z.B. die Entschlüsselung eines Genoms oder die Konzentrationen der Moleküle in einer Zelle. Die Systembiologie nimmt sich dem Problem an, aus diesem Datenmeer ein quantitatives Verständnis für die Gesamtheit der Wechselwirkungen in der Zelle zu entwickeln. Dabei stellt die mathematische Modellierung und computergestützte Analyse ein eminent wichtiges Werkzeug dar, lassen sich doch am Computer in kurzer Zeit eine Vielzahl von Fällen testen und daraus Hypothesen generieren, die experimentell verifiziert werden können. Diese Doktorarbeit beschäftigt sich damit, wie durch mathematische Modellierung Rückschlüsse auf die Evolution und deren Mechanismen geschlossen werden können. Dabei besteht die Arbeit aus zwei Teilen. Zum Einen wurde ein Modell entwickelt, dass die Evolution des Stoffwechsels nachbaut. Der zweite Teil beschäftigt sich mit der Analyse von Genexpressionsdaten, d.h. der Stärke mit der ein bestimmtes Gen in ein Protein umgewandelt, "exprimiert", wird. Der Stoffwechsel bezeichnet die Gesamtheit der chemischen Vorgänge in einem Organismus; zum Einen werden Nahrungsstoffe für den Organismus verwertbar zerlegt, zum Anderen aber auch neue Stoffe aufgebaut. Da für nahezu jede chemische Reaktion ein katalysierendes Enzym benötigt wird, ist davon auszugehen, dass sich der Stoffwechsel parallel zu den Enzymen entwickelt hat. Auf dieser Annahme basiert das entwickelte Modell zur Enzyme-Stoffwechsel-Koevolution. Von einer Anfangsmenge von Enzymen und Molekülen ausgehend, die etwa in einer primitiven Atmosphäre vorgekommen sind, werden sukzessive Enzyme und die nun katalysierbaren Reaktionen hinzugefügt, wodurch die Stoffwechselkapazität anwächst. Die Auswahl eines neuen Enzyms geschieht dabei in Abhängigkeit von der Ähnlichkeit mit bereits vorhandenen und ist so an den evolutionären Vorgang der Mutation angelehnt: je ähnlicher ein neues Enzym zu den vorhandenen ist, desto schneller kann es hinzugefügt werden. Dieser Vorgang wird wiederholt, bis der Stoffwechsel die heutige Form angenommen hat. Interessant ist vor allem der zeitliche Verlauf dieser Evolution, der mittels einer Zeitreihenanalyse untersucht wird. Dabei zeigt sich, dass neue Enzyme gebündelt in Gruppen kurzer Zeitfolge auftreten, gefolgt von Intervallen relativer Stille. Dasselbe Phänomen kennt man von der Evolution neuer Arten, die ebenfalls gebündelt auftreten, und wird Punktualismus genannt. Diese Arbeit liefert somit ein besseres Verständnis dieses Phänomens durch eine Beschreibung auf molekularer Ebene. Im zweiten Projekt werden Genexpressionsdaten von Pflanzen analysiert. Einerseits geschieht dies mit einem eigens entwickelten Cluster-Algorithmus. Hier läßt sich beobachten, dass Gene mit einer ähnlichen Funktion oft auch ein ähnliches Expressionsmuster aufweisen. Das Clustering liefert einige Genkandidaten, deren Funktion bisher unbekannt war, von denen aber nun vermutet werden konnte, dass sie enorm wichtig für das Wachstum der Pflanze sind. Durch Experimente von Pflanzen mit und ohne diese Gene zeigte sich, dass sechs neuen Genen dieses essentielle Erscheinungsbild zugeordnet werden kann. Weiterhin wurden Netzwerke der Genexpressionsdaten einer Pflanze, eines Pilzes und eines Bakteriums untersucht. In diesen Netzwerken werden zwei Gene verbunden, falls sie ein sehr ähnliches Expressionsprofil aufweisen. Nun zeigten diese Netzwerke sehr ähnliche und charakteristische Eigenschaften auf. Im Rahmen dieser Arbeit wurde daher ein weiteres evolutionäres Modell entwickelt, das die Expressionsprofile anhand von Duplikation, Mutation und Selektion beschreibt. Obwohl das Modell auf sehr simplen Eigenschaften beruht, spiegelt es die beobachteten Eigenschaften sehr gut wider, und es läßt sich der Schluss ziehen, dass diese als Resultat der Evolution betrachtet werden können. Die Ergebnisse dieser Arbeiten sind als Doktorarbeit in kumulativer Form bestehend aus vier veröffentlichten Artikeln vereinigt.
Kunegis, Jérôme [Verfasser]. "On the Spectral Evolution of Large Networks / Jérôme Kunegis." Koblenz : Universitätsbibliothek Koblenz, 2011. http://d-nb.info/1017370893/34.
Повний текст джерелаFaraggi, Paul. "Smart Grids: Evolution of the networks' economic steering modes." Thesis, KTH, Energi och klimatstudier, ECS, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-91419.
Повний текст джерелаLaghos, Andrew. "Assessing the evolution of social networks in e-learning." Thesis, City University London, 2007. http://openaccess.city.ac.uk/8504/.
Повний текст джерелаRommel, Jacob. "Topological analysis of the evolution of public transport networks." Thesis, KTH, Systemanalys och ekonomi, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-170641.
Повний текст джерелаPearson, Michael. "Equilibrium and evolution in supply chain and social networks." Thesis, Edinburgh Napier University, 2005. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.421746.
Повний текст джерелаGrochow, Joshua A. "On the structure and evolution of protein interaction networks." Thesis, Massachusetts Institute of Technology, 2006. http://hdl.handle.net/1721.1/42053.
Повний текст джерелаThis electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Includes bibliographical references (p. 107-114).
The study of protein interactions from the networks point of view has yielded new insights into systems biology [Bar03, MA03, RSM+02, WS98]. In particular, "network motifs" become apparent as a useful and systematic tool for describing and exploring networks [BP06, MKFV06, MSOI+02, SOMMA02, SV06]. Finding motifs has involved either exact counting (e.g. [MSOI+02]) or subgraph sampling (e.g. [BP06, KIMA04a, MZW05]). In this thesis we develop an algorithm to count all instances of a particular subgraph, which can be used to query whether a given subgraph is a significant motif. This method can be used to perform exact counting of network motifs faster and with less memory than previous methods, and can also be combined with subgraph sampling to find larger motifs than ever before -- we have found motifs with up to 15 nodes and explored subgraphs up to 20 nodes. Unlike previous methods, this method can also be used to explore motif clustering and can be combined with network alignment techniques [FNS+06, KSK+03]. We also present new methods of estimating parameters for models of biological network growth, and present a new model based on these parameters and underlying binding domains. Finally, we propose an experiment to explore the effect of the whole genome duplication [KBL04] on the protein-protein interaction network of S. cerevisiae, allowing us to distinguish between cases of subfunctionalization and neofunctionalization.
by Joshua A. Grochow.
M.Eng.
Pomeroy, Linda. "The evolution of knowledge transfer boundary networks in healthcare." Thesis, Imperial College London, 2013. http://hdl.handle.net/10044/1/24832.
Повний текст джерелаMinoarivelo, Henintsoa Onivola. "Probabilistic modelling of the evolution of ecological interaction networks." Thesis, Stellenbosch : Stellenbosch University, 2011. http://hdl.handle.net/10019.1/17990.
Повний текст джерелаENGLISH ABSTRACT: In any ecological system, organisms need to interact with each other for their survival. Such interactions form ecological networks which are usually very complex. Nevertheless, they exhibit well de ned patterns; these regularities are often interpreted as products of meaningful ecological processes. As the networks are evolving through time, biological evolution is one of the factors that affects ecological network architecture. In this work, we develop a mathematical model that represents the evolution through time of such ecological interaction networks. The problem is approached by modelling network evolution as a continuous time Markov process, in such a way that the interactions in which a parent species is involved are potentially inherited by its descendant species. This approach allows us to infer ecological parameters and ecological network histories from real-world network data, as well as to simulate ecological networks under our model. While ecologists have long been aware of the in uence of evolutionary processes in shaping ecological networks, we are now able to evaluate the importance of such in uence.
AFRIKAANSE OPSOMMING: In enige ekologiese stelsel benodig organismes wisselwerkings met mekaar ten einde te oorleef. Sulke interaksies vorm ekologiese netwerke wat gewoonlik baie kompleks is maar nogtans goed-gede nieerde patrone vertoon. Hierdie patrone word dikwels geïnterpreteer as die produk van betekenisvolle ekologiese prosesse. Aangesien die netwerke met die verloop van tyd ontwikkel, is biologiese ewolusie een van die faktore wat ekologiese netwerkargitektuur beïnvloed. In hierdie studie ontwikkel ons 'n wiskundige model wat die ewolusie van sulke ekologiese interaksienetwerke voorstel. Die probleem word benader deur netwerkewolusie as 'n kontinue-tyd Markov-proses te modelleer, op so 'n manier dat die interaksies waarin 'n voorouerspesie betrokke is potensieel oorerf kan word deur die afstammelingspesies. Hierdie benadering laat ons toe om ekologiese parameters en ekologiese netwerkgeskiedenisse vanuit regte-wêreld data af te lei, sowel as om ekologiese netwerke onder ons model te simuleer. Alhoewel ekoloë al lank reeds bewus is van die invloed wat ewolusionêre prosesse het op die vorming van ekologiese netwerke, is ons nou in staat om die belangrikheid van hierdie invloed te evalueer.
Morrison, Erin S., and Alexander V. Badyaev. "Structuring evolution: biochemical networks and metabolic diversification in birds." BioMed Central, 2016. http://hdl.handle.net/10150/620926.
Повний текст джерелаFreschi, Luca. "Post-translational modifications regulatory networks : evolution, mechanisms et implications." Doctoral thesis, Université Laval, 2015. http://hdl.handle.net/20.500.11794/25812.
Повний текст джерелаPost-translational modifications (PTMs) are chemical modification of proteins that allow the cell to finely tune its functions as well as to encode and integrate environmental signals. The recent advancements in the experimental and bioinformatic techniques have allowed us to determine the PTM profiles of entire proteomes as well as to identify the molecules that write or erase PTMs to/from each protein. This data have made possible to define cellular PTM regulatory networks. Here, we study the evolution of these networks to get new insights about how they may contribute to increase organismal complexity and diversity and to better understand their molecular mechanisms of functioning. We first address the question of how and to which extent a PTM network can be rewired after a gene duplication event, by studying how the budding yeast phosphoregulatory network was rewired after a whole genome duplication event that occurred 100 million years ago. Our results highlight the role of gene duplication as a key mechanism to innovate and complexify PTM regulatory networks. Then, we address the question of how PTM networks may contribute to organismal diversity by comparing the human and mouse phosphorylation profiles. We find that there are substantial differences in the PTM profiles of these two species that have the potential to explain, at least in part, the phenotypic differences observed between them. Moreover, we find evidence supporting the idea that PTMs can jump to new positions during evolution and still regulate the same biological functions. This phenomenon should be taken into account when comparing the PTM profiles of different species, in order to avoid overestimating the divergence in PTM regulation. Finally, we investigate how multiple and alternative PTMs that affect the same residues interact with each other to control proteins functions. We focus on two of the most studied PTMs, protein phosphorylation and O-GlcNAcylation, that affect serine and threonine residues and we study their potential mechanisms of interactions in human and mouse. Our results support the hypothesis that these two PTMs control multiple biological functions rather than a single one. Globally this work provides new findings that elucidate the evolutionary dynamics, the functional mechanisms and the biological implications of PTMs.