完全可換クイバーの区間近似とその応用

京都大学新制・課程博士博士(理学)甲第25087号理博第4994号京都大学大学院理学研究科数学・数理解析専攻(主査)教授 平岡 裕章, 教授 COLLINSBenoit Vincent Pierre, 教授 坂上 貴之学位規則第4条第1項該当Doctor of ScienceKyoto UniversityDFA

LIPIcs, Volume 258, SoCG 2023, Complete Volume

LIPIcs, Volume 258, SoCG 2023, Complete Volum

Network Analysis and Modeling in Systems Biology

This thesis is dedicated to the study and comprehension of biological networks at the molecular level. The objectives were to analyse their topology, integrate it in a genotype-phenotype analysis, develop richer mathematical descriptions for them, study their community structure and compare different methodologies for estimating their internal fluxes. The work presented in this document moves around three main axes. The first one is the biological. Which organisms were studied in this thesis? They range from the simplest biological agents, the viruses, in this case the Potyvirus genus to prokariotes such as Escherichia coli and complex eukariotes (Arabidopsis thaliana, Nicotiana benthamiana). The second axis refers to which biological networks were studied. Those are protein-protein interaction (PPIN) and metabolic networks (MN). The final axis relates to the mathematical and modelling tools used to generate knowledge from those networks. These tools can be classify in three main branches: graph theory, constraint-based modelling and multivariate statistics. The document is structured in six parts. The first part states the justification for the thesis, exposes a general thesis roadmap and enumerates its main contributions. In the second part important literature is reviewed, summarized and integrated. From the birth and development of Systems Biology to one of its most popular branches: biological network analysis. Particular focus is put on PPIN and MN and their structure, representations and features. Finally a general overview of the mathematical tools used is presented. The third, fourth and fifth parts represent the central work of this thesis. They deal respectively with genotypephenotype interaction and classical network analysis, constraint-based modelling methods comparison and modelling metabolic networks and community structure. Finally, in the sixth part the main conclusions of the thesis are summarized and enumerated. This thesis highlights the vital importance of studying biological entities as systems and how powerful and promising this integrated analysis is. Particularly, network analysis becomes a fundamental avenue of research to gain insight into those biological systems and to extract, integrate and display this new information. It generates knowledge from just data.Esta tesis está dedicada al estudio y comprensión de redes biológicas a nivel molecular. Los objetivos fueron analizar su topología, integrar esta en un análisis de genotipo-fenotipo, desarrollar descripciones matemáticas más completas para ellas, estudiar su estructura de comunidades y comparar diferentes metodologías para estimar sus flujos internos. El trabajo presentado en este documento gira entorno a tres ejes principales. El primero es el biológico. ¿Qué organismos han sido estudiados en esta tesis? Estos van desde los agentes biológicos mas simples, los virus, en este caso el género Potyvirus, hasta procariotas como Escherichia coli y eucariotas complejos (Arabidopsis thaliana, Nicotiana benthamiana). El segundo eje hace referencia a las redes biológicas estudiadas, que fueron redes de interacción de proteínas (PPIN) y redes metabólicas (MN). El eje final es el de las herramientas matemáticas y de modelización empleadas para interrogar esas redes. Estas herramientas pueden clasificarse en tres grandes grupos: teoría de grafos, modelización basada en restricciones y estadística multivariante. Este documento está estructurado en seis partes. La primera expone la justificación para la tesis, muestra un mapa visual de la misma y enumera sus contribuciones principales. En la segunda parte, la bibliografía relevante es revisada y resumida. Desde el nacimiento y desarrollo de la Biología de Sistemas hasta una de sus ramas más populares: el análisis de redes biomoleculares. Especial interés es puesto en PPIN y MN: su estructura, representación y características. Finalmente, un resumen general de las herramientas matemáticas usadas es presentado. Los capítulos tercero, cuarto y quinto representan el cuerpo central de esta tesis. Estos tratan respectivamente sobre la interacción de genotipo-fenotipo y análisis topolólogico clásico de redes, modelos basados en restricciones y modelización de redes metabólicas y su estructura de comunidades. Finalmente, en la sexta parte las principales conclusiones de la tesis son resumidas y expuestas. Esta tesis pone énfasis en la vital importancia de estudiar los fenómenos biológicos como sistemas y en la potencia y prometedor futuro de este análisis integrativo. En concreto el análisis de redes supone un camino de investigación fundamental para obtener conocimiento sobre estos sistemas biológicos y para extraer y mostrar información sobre los mismos. Este análisis genera conocimiento partiendo únicamente desde datos.Aquesta tesi està dedicada a l'estudi i comprensió de xarxes biològiques a nivell molecular. Els objectius van ser analitzar la seva topologia, integrar aquesta en una anàlisi de genotip-fenotip, desenvolupar descripcions matemàtiques més completes per a elles, estudiar la seva estructura de comunitats o modularitat i comparar diferents metodologies per estimar els fluxos interns. El treball presentat en aquest document gira entorn de tres eixos principals. El primer és el biològic. ¿Què organismes han estat estudiats en aquesta tesi? Aquests van des dels agents biològics mes simples, els virus, en aquest cas el gènere Potyvirus, fins procariotes com Escherichia coli i eucariotes complexos (Arabidopsis thaliana, Nicotiana benthamiana). El segon eix fa referència a les xarxes biològiques estudiades, que van ser les xarxes d'interacció de proteïnes (PPIN) i les xarxes metabòliques (MN). L'eix final és el de les eines matemàtiques i de modelització emprades per interrogar aquestes xarxes. Aquestes eines poden classificarse en tres grans grups: teoria de grafs, modelització basada en restriccions i estadística multivariant. Aquest document està estructurat en sis parts. La primera exposa la justificació per a la tesi, mostra un mapa visual de la mateixa i enumera les seves contribucions principals. A la segona part, la bibliografia rellevant és revisada i resumida. Des del naixement i desenvolupament de la Biologia de Sistemes fins a una de les seves branques més populars: l'anàlisi de xarxes moleculars. Especial interès és posat en PPIN i MN: la seva estructura, representació i característiques. Finalment, un resum general de les eines matemàtiques utilitzades és presentat. Els capítols tercer, quart i cinquè representen el cos central d'aquesta tesi. Aquests tracten respectivament sobre la interacció de genotip-fenotip i anàlisi topolólogico clàssic de xarxes, models basats en restriccions i modelització de xarxes metabòliques i la seva estructura de comunitats. Finalment, en la sisena part les principals conclusions de la tesi són resumides i exposades. Aquesta tesi posa èmfasi en la vital importància d'estudiar els fenòmens biològics com sistemes i en la potència i prometedor futur d'aquesta anàlisi integratiu. En concret l'anàlisi de xarxes suposa un camí d'investigació fonamental per obtenir coneixement sobre aquests sistemes biològics i per extreure i mostrar informació sobre els mateixos. Aquest anàlisi genera coneixement partint únicament des de dades.Bosque Chacón, G. (2017). Network Analysis and Modeling in Systems Biology [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/79082TESI

LIPIcs, Volume 248, ISAAC 2022, Complete Volume

LIPIcs, Volume 248, ISAAC 2022, Complete Volum

The institution and the network

This research explores how National Research Centres in Higher Education systems can offer dynamic views of ways network-like organisations emerge and self-organise in institutional environments. My thesis considers the interplay between institutional and more network-like forms of organising by exploring the Australian Research Council’s (ARC) Centres of Excellence (CoE) Programme as a complex system of science. I provide a foundational review of the changing relationship between Higher Education Institutions (HEIs) and Research Centres to highlight the perception that today’s science endeavours form part of a larger global research ecosystem. The thesis applies these perspectives to propose that CoEs can be conceptually viewed as a ‘Janus object’ in this complex space - that is, that CoEs occupy and can take views across both institutional and network-like environments. The research integrates three studies to provide this more detailed ‘view from the CoE.’ The first study mobilises two bodies of literature to provide a foundation for the research approach. The first, from neo-institutional theory, considers how the CoE, as an informal organisation, might relate to the HEI through a form of ‘collective rationality.’ The second, from the field of network science, explores how network-like organisations can emerge with different properties of robustness and information exchange. I also respond to calls from empirical studies of research systems to consider how the ‘self-organisation’ of science might offer wider value to inform an understanding of complex systems of organising. The second study explored how research professionals engaged in Research Centres interact within the HEI environment. This informed the third study which details a qualitative, exploratory study of the Australian CoE Programme. Contributions from 22 Research and Professional Leads, which covered three cohorts of the Programme funded in 2011, 2014 and 2017 also represented an overview of ‘life spans’ of the CoE. CoE participants identified characteristics of emergence consistent with organisation in complex systems. Firstly, shared narratives from a high proportion of participants note a paradoxical environment of ‘odd encounters’, rather than formal interactions, with the HEI. Narratives also revealed highly effective forms of co-leadership roles between Research and Professional Leads which align closely with descriptions of ‘authority’ in network science. This suggests effective CoE leadership is via people acting as shared information exchange hubs. The contributions also allow a view of the CoEs through their lifespan in relation to the HEI. From these I develop a set of ‘network narratives’ which demonstrate the pluriform nature of CoEs as an example of emergence. The narratives also reveal CoEs have potential to become highly autonomous, but return value as an important intermediary between the ‘highly localised’ institutional research environment and the global research system. A strong volunteered narrative on gender and diversity policy also demonstrates an unexpected case of network-like ‘percolation.’ This paradoxical finding suggests policy formed within the CoE may be adopted by the institution which may in turn allow the institution to co-evolve. This suggests a potential for true, if less tangible, ecosystem effects as a result of the CoE Programme. In integrating findings across the three studies I contribute to theory by proposing a new open architecture for institutional theory in response to long standing work by Scott (2004; 2008). This aims to realign network considerations inherent within neo-institutional theory with more recent phenomenological findings in network science. In illustrating examples through network narratives, I also extend the work by Watts (2004) to close the gap in the vocabulary between network science and institutional theory in ways that can support studies which explore institutional perspectives of network-like forms in complex systems