Cette thèse s'intéresse aux liens entre l'évolution de la coopération et la sélection de second ordre. Dans une première partie, nous montrons comment des organismes digitaux adaptent leurs génomes pour encoder les gènes liées à la coopération d'une manière plus contrainte (suppression d'évolvabilité), notamment à l'aide d'opérons et d'overlaps impliquant aussi des gènes essentiels. Dans une deuxième partie, nous testons expérimentalement cette vision des overlaps de gènes comme "contrainte évolutive" grâce à des outils d'algorithmique et de biologie synthétique que nous avons développés. Dans une troisième partie, nous utilisons des simulations par agents pour montrer comment une forme de division du travail peut être interprétée comme un système coopératif à la lumière de la théorie évolutive moderne. Dans une dernière partie, nous montrons que la dispersion spatiale des allèles coopératives obtenue par des phénomènes de "genetic hitchiking" joue un rôle important dans l'évolution de la coopération, quand bien même ce mécanisme de dispersion s'applique aussi à des allèles non coopératives, grâce à la "relatedness" (aux loci codant pour la coopération) crée par l'invasion locale de mutations bénéfiques (à des loci non liés à la coopération) et par l'équilibre complexe entre ces mutations bénéfiques et la robustesse mutationnelle. L'ensemble de ces résultats appelle à une prise en compte plus importante des pressions sélectives de second ordre dans l'étude de l'évolution sociale, et au développement de modèles plus réalistes qui permettraient d'intégrer de telles forces évolutives. Nous insistons également sur l'importance du paysage mutationnel dans l'étude des populations bactériennes, et montrons le potentiel croissant de la biologie synthétique comme outil d'étude de ce paysage et de l'évolution microbienne en général. / In the first part, I show how digital organisms adapt their genomes to encode cooperation-related genes in a more constrained way (evolvability suppression), especially using operons and overlaps also involving essential genes. In the second part, we experimentally test this view of gene overlaps as an evolutionary constraint, using both algorithmic and synthetic biology tools that we have developed. In the third part, I use agent-based simulations to show how a form of division of labour can be interpreted as a cooperative system in the light of modern evolutionary theory. In the final part, I show that the patterns of dispersal of cooperative alleles due to hitchhiking phenomena play an important role in the evolution of cooperation. The last result holds even though the hitchhiking mechanisms also applies to non-cooperative alleles, thanks to the relatedness (at cooperation-related loci) created by the local invasion of beneficial mutations (at loci not related to cooperation). The beneficial mutations form a complex and interesting equilibrium with mutational robustness, which I investigate using in silico evolution. On the whole, these results call for a more careful consideration of the second-order selection pressures in the study of social evolution, and show the necessity for more realistic models allowing to integrate such evolutionary forces. My thesis research specifically highlights the importance of the mutational landscape in the study of microbial populations and shows the increasing potential of synthetic biology as a tool to study such landscape and microbial evolution in general.

This dissertation clarifies the concept of evolvability, the increased capacity of some organisms or systems to support evolution, especially the evolution of life-like complexity. I survey the literature, which is spread over the fields of population genetics, developmental biology, artificial life, and microbial and molecular evolution. Finding that researchers have often used the term vaguely and incompatibly I identify five distinct kinds or senses of evolvability. I also identify five key constituent ideas, which I discuss in the context of organismic evolvability, a sense of evolvability with deep roots in the traditional fields of animal development and macroevolution. In these fields research into evolvability has historically been hampered by an insufficiently detailed knowledge of development. Research in molecular evolution has produced a thorough knowledge of the folding of RNA into secondary structure, which can be regarded as a model of development. This has motivated new approaches to evolvability based on representing development via a single genotype-phenotype mapping function. I build on these approaches to invent new mathematical methods to formalise the traditional ideas. I create an exact model illustrating a classic example of evolvability, the capacity for repeated segmentation and simple modularity. I analyse this with two new formal approaches. First is the genospace algebra, a propositional calculus based on graph theory. It is a formal language for describing genotype-phenotype maps. It provides a system for making calculations, proofs, and diagrams about mutational structures in genotype space, and it is flexible enough to allow description at arbitrary degrees of resolution. Second is a pair of concepts, the genetic leverage and the genetic fulcrum. The leverage provides a crude numerical measure of evolvability, and the fulcrum provides a heuristic for identifying the genomic and developmental causes of evolvability. Besides its specific relevance to diversification and development, evolvability is also crucial to the fundamental question of how evolution produces ordinary biological life. Simulation systems that implement only a conventional textbook model of evolution -– systems possessing only variation, inheritance, and selection –- fail to evolve anything resembling the complexity of the biological world. Research into evolvability is our best bet to illuminate the "missing ingredient" for life-like evolution.

The past decades have seen the state of the art in aerospace system design progress from a scope of simple optimization to one including robustness, with the objective of permitting a single system to perform well even in off-nominal future environments. Integrating flexibility, or the capability to easily modify a system after it has been fielded in response to changing environments, into system design represents a further step forward. One challenge in accomplishing this rests in that the decision-maker must consider not only the present system design decision, but also sequential future design and operation decisions. Despite extensive interest in the topic, the state of the art in designing flexibility into aerospace systems, and particularly space systems, tends to be limited to analyses that are qualitative, deterministic, single-objective, and/or limited to consider a single future time period. To address these gaps, this thesis develops a stochastic, multi-objective, and multi-period framework for integrating flexibility into space system design decisions. Central to the framework are five steps. First, system configuration options are identified and costs of switching from one configuration to another are compiled into a cost transition matrix. Second, probabilities that demand on the system will transition from one mission to another are compiled into a mission demand Markov chain. Third, one performance matrix for each design objective is populated to describe how well the identified system configurations perform in each of the identified mission demand environments. The fourth step employs multi-period decision analysis techniques, including Markov decision processes (MDPs) from the field of operations research, to find efficient paths and policies a decision-maker may follow. The final step examines the implications of these paths and policies for the primary goal of informing initial system selection. Overall, this thesis unifies state-centric concepts of flexibility from economics and engineering literature with sequential decision-making techniques from operations research. The end objective of this thesis' framework and its supporting analytic and computational tools is to enable selection of the next-generation space systems today, tailored to decision-maker budget and performance preferences, that will be best able to adapt and perform in a future of changing environments and requirements. Following extensive theoretical development, the framework and its steps are applied to space system planning problems of (1) DARPA-motivated multiple- or distributed-payload satellite selection and (2) NASA human space exploration architecture selection.

Business organisations today are faced with the complex problem of dealing with evolution in their software information systems. This effectively concerns the accommodation and facilitation of change, in terms of both changing user requirements and changing technological requirements. An approach that uses the software development life-cycle as a vehicle to study the problem of evolution is adopted. This involves the stages of requirements analysis, system specification, design, implementation, and finally operation and maintenance. The problem of evolution is one requiring proactive as well as reactive solutions for any given application domain. Measuring evolvability in conceptual models and the specification of changing requirements are considered. However, even "best designs" are limited in dealing with unanticipated evolution, and require implementation phase paradigms that can facilitate an evolution correctly (semantic integrity), efficiently (minimal disruption of services) and consistently (all affected parts are consistent following the change). These are also discussed / Computing / M. Sc. (Information Systems)

Business organisations today are faced with the complex problem of dealing with evolution in their software information systems. This effectively concerns the accommodation and facilitation of change, in terms of both changing user requirements and changing technological requirements. An approach that uses the software development life-cycle as a vehicle to study the problem of evolution is adopted. This involves the stages of requirements analysis, system specification, design, implementation, and finally operation and maintenance. The problem of evolution is one requiring proactive as well as reactive solutions for any given application domain. Measuring evolvability in conceptual models and the specification of changing requirements are considered. However, even "best designs" are limited in dealing with unanticipated evolution, and require implementation phase paradigms that can facilitate an evolution correctly (semantic integrity), efficiently (minimal disruption of services) and consistently (all affected parts are consistent following the change). These are also discussed / Computing / M. Sc. (Information Systems)