Evolutionary genomics and ecoligical interactions of biofilm adaptation

How diversity evolves and persists in biofilms is essential for understanding much of microbial life, including the uncertain dynamics of chronic infections. We developed a novel biofilm model enabling long-term selection for daily adherence to and dispersal from a plastic bead in a test tube. Focusing on a pathogen of the cystic fibrosis (CF) lung, Burkholderia cenocepacia, we sequenced clones and metagenomes to unravel the mutations and evolutionary forces responsible for adaptation and diversification of a single biofilm community during 1050 generations of selection. The mutational patterns revealed recurrent evolution of biofilm specialists from generalist types and multiple adaptive alleles at relatively few loci. Fitness assays also demonstrated strong interference competition among contending mutants. Metagenomes from five other independently evolved biofilm lineages revealed extraordinary mutational parallelism that outlined common routes of adaptation; these mutations in turn were surprisingly well represented among mutations that evolved in CF isolates of both Burkholderia and Pseudomonas. These convergent pathways included altered metabolism of cyclic di-GMP, polysaccharide production, TCA cycle enzymes, global transcription, and iron scavenging. Evolution in chronic infections may therefore be driven by selection for both biofilm formation and dispersal, which lends hope that experimental evolution may illuminate the ecology and selective dynamics of chronic infections and improve treatment strategies

Modelling evolvability in genetic programming

We develop a tree-based genetic programming system, capable of modelling evolvability during evolution through artificial neural networks (ANN) and exploiting those networks to increase the generational fitness of the system. This thesis is empirically focused; we study the effects of evolvability selection under varying conditions to demonstrate the effectiveness of evolvability selection. Evolvability is the capacity of an individual to improve its future fitness. In genetic programming (GP), we typically measure how well a program performs a given task at its current capacity only. We improve upon GP by directly selecting for evolvability. We construct a system, Sample-Evolvability Genetic Programming (SEGP), that estimates the true evolvability of a program by conducting a limited number of evolvability samples. Evolvability is sampled by conducting a number of genetic operations upon a program and comparing the fitnesses of resulting programs with the original. SEGP is able to achieve an increase in fitness at a cost of increased computational complexity. We then construct a system which improves upon SEGP, Model-Evolvability Genetic Programming (MEGP), that models the true evolvability of a program by training an ANN to predict its evolvability. MEGP reduces the computational cost of sampling evolvability while maintaining the fitness gains. MEGP is empirically shown to improve generational fitness for a streaming domain, in exchange for an upfront increase in computational time

Skull diversity and evolution in miniaturized amphibians, genus Brachycephalus (Anura: Brachycephalidae)

Miniaturized amphibians of the genus Brachycephalus are phenotypically diverse. The species of Brachycephalus have bufoniform or leptodactyliform baupläne and any of three skeletal states: nonhyperossified, hyperossified without dorsal shield, and hyperossified with dorsal shield. We integrate high-resolution microcomputed tomography, geometric morphometrics, and an estimate of molecular phylogenetic relationships to investigate skull diversity in shape and size-shape space in selected species of Brachycephalus. Skull diversity amongst species of Brachycephalus can be partitioned into shape and size-shape space according to the four conditions of skeletal states-baupläne, namely, nonhyperossified leptodactyliform, nonhyperossified bufoniform, hyperossified bufoniform without dorsal shield, and hyperossified bufoniform with dorsal shield. Skull diversity in shape and size-shape space in nonhyperossified leptodactyliform species of Brachycephalus is markedly larger, when compared to skull diversity in species of the three other conditions of skeletal states-baupläne. Variation in skull shape scales with size across Brachycephalus and, therefore, can be explained by allometry. Skull diversity, baupläne, and skeletal states covary to a large extent with monophyletic lineages of Brachycephalus, as revealed by a mitochondrial DNA species tree. Nonhyperossified bufoniform species and hyperossified bufoniform species with or without dorsal shield are monophyletic lineages, as inferred from a mitochondrial DNA species tree. Nonhyperossified leptodactyliform species of Brachycephalus do not share, however, a most recent common ancestor. The nonhyperossified leptodactyliform species of Brachycephalus, due to their marked skull diversity and lack of monophyly, emerge as evolutionarily complex. Therefore, further sampling of the nonhyperossified leptodactyliform condition of skeletal states-baupläne will be necessary to further understand the evolutionary history of Brachycephalus.Fil: dos Reis, Sérgio F.. Universidade Estadual de Campinas. Instituto de Biología; BrasilFil: Clemente Carvalho, Rute B.G.. Queens University; CanadáFil: dos Santos, Caio M. S. F. F.. Universidade Federal do Rio de Janeiro; BrasilFil: Lopes, Ricardo T.. Universidade Federal do Rio de Janeiro; BrasilFil: Von Zuben, Fernando J.. Universidade Estadual de Campinas; BrasilFil: Laborda, Prianda R.. Universidade Estadual de Campinas. Instituto de Biología; BrasilFil: Perez, Sergio Ivan. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata; Argentina. Universidad Nacional de La Plata. Facultad de Ciencias Naturales y Museo. Departamento Científico de Antropología; Argentin

On the evolution of genotype-phenotype mapping: exploring viability in the Avida articial life system

The seminal architecture of machine self-reproduction originally formulated by John von Neumann underpins the mechanism of self-reproduction equipped with genotype and phenotype. In this thesis, initially, a hand-designed prototype von Neumann style selfreproducer as an ancestor is described within the context of the artificial life system Avida. The behaviour of the prototype self-reproducer is studied in search of evolvable genotype-phenotype mapping that may potentially give rise to evolvable complexity. A finding of immediate degeneration of the prototype into a self-copying mode of reproduction requires further systematic analysis of mutational pathways. Through demarcating a feasible and plausible characterisation and classification of strains, the notion of viability is revisited, which ends up being defined as quantitative potential for exponential population growth. Based on this, a framework of analysis of mutants' evolutionary potential is proposed, and, subsequently, the implementation of an enhanced version of the standard Avida analysis tool for viability analysis as well as the application of it to the prototype self-reproducer strain are demonstrated. Initial results from a one-step single-point-mutation space of the prototype, and further, from a multi-step mutation space, are presented. In the particular case of the analysis of the prototype, the majority of mutants unsurprisingly turn out to be simply infertile, without viability; whereas mutants that prove to be viable are a minority. Nevertheless, by and large, it is pointed out that distinguishing reproduction modes algorithmically is still an open question, much less finer-grained distinction of von Neumann style self-reproducers. Including this issue, speciifc limitations of the enhanced analysis are discussed for future investigation in this direction

A complex systems approach to education in Switzerland

The insights gained from the study of complex systems in biological, social, and engineered systems enables us not only to observe and understand, but also to actively design systems which will be capable of successfully coping with complex and dynamically changing situations. The methods and mindset required for this approach have been applied to educational systems with their diverse levels of scale and complexity. Based on the general case made by Yaneer Bar-Yam, this paper applies the complex systems approach to the educational system in Switzerland. It confirms that the complex systems approach is valid. Indeed, many recommendations made for the general case have already been implemented in the Swiss education system. To address existing problems and difficulties, further steps are recommended. This paper contributes to the further establishment complex systems approach by shedding light on an area which concerns us all, which is a frequent topic of discussion and dispute among politicians and the public, where billions of dollars have been spent without achieving the desired results, and where it is difficult to directly derive consequences from actions taken. The analysis of the education system's different levels, their complexity and scale will clarify how such a dynamic system should be approached, and how it can be guided towards the desired performance

Exploring design principles of cellular information processing

As a summary, this work attempts to explore and uncovered design principles of certain dynamics of cellular networks by combining evolution in silico with rule-based modelling approach. Biological systems exhibit complex dynamics, due to the complex interactions in the intra- and inter- cellular biochemical reaction networks. For instance, signalling networks are composed of many enzymes and scaffolding proteins which have combinatorial interactions. These complex systems often generate response dynamics that are essential for correct decision-makings in cells. Especially, these complex interactions are results of long term of evolutionary process. With such evolutionary complexity, systems biologists aim to decipher the structure and dynamics of signalling and regulatory networks underpinning cellular responses; synthetic biologists can use this insight to alter existing networks or engineer de novo ones. Both tasks will benefit from an understanding of which structural and dynamic features of networks can emerge from evolutionary processes, through which intermediary steps these arise, and whether they embody general design principles. As natural evolution at the level of network dynamics is difficult to study, in silico evolution of network models can provide important insights. However, current tools used for in silico evolution of network dynamics are limited to ad hoc computer simulations and models. In my PhD study, with collaborators I construct the BioJazz, an extendable, user-friendly tool for simulating the evolution of dynamic biochemical networks. Unlike previous tools for in silico evolution, BioJazz allows for evolution of cellular networks with theoretically unbounded complexity by combining rule-based modelling with an encoding of networks that is akin to a genome. BioJazz can be used to implement biologically realistic selective pressures, and allows exploration of the space of network architectures and dynamics that implement prescribed physiological functions. It is provided as an open-source tool to facilitate its further development and use. I use this tool to explore the possible biochemical designs for signalling networks displaying ultrasensitive and adaptive response dynamics. By running evolutionary simulations mimicking different biochemical scenarios, we find that enzyme sequestration emerges as a key biochemical mechanism for both dynamics. Detailed analysis of these evolved networks revealed that enzyme sequestration enables both ultrasensitive and adaptive response dynamics. I verified this proposition by designing a generic model of a signalling cycle, featuring two enzymes and a sequestering (scaffold) protein. This simple system is capable of displaying both ultrasensitive and adaptive response dynamics, even more interestingly modulating the system switching between two response dynamics through perturbing the scaffold protein. These results show that enzyme sequestration can be exploited by evolution so to generate diverse response dynamics in signalling networks. From evolutionary simulations towards ultrasensitivity, bistable dynamics emerged as an alternative solution. On one hand, inspired by such results I used the fitness function as an objective function combined with different constraints to design and optimise bistable signalling networks with completely new structure and mechanism. Studying designed bistable signalling network explicates how such bistable network can be experimentally implemented. On the other hand, from studying the evolved bistable networks allosteric enzymes catalysing futile cycles appear to be a new mechanism of bistability in signalling networks. Furthermore, one of the smallest bistable signalling motifs is derived. This motif is composed of one kinase protein with two distinct conformational states and one substrate subject to phosphorylation by the kinase and auto-dephosphorylation reactions. The sufficient and necessary condition on parameters, with which the signalling motif displays bistable response dynamics, is analytically defined. By expanding the systems with more kinases, unlimited multistability emerges with potentials of implementing complex logic gates and cell state transitions. Further exploring the discovered and natural signalling networks implies shared design patterns. Motivated by searching structural boundaries between monostationary and multistationary networks, I performed algorithmic searching of multistationary signalling networks intending to find the sufficient structural conditions for multistationarity in signalling networks

Mechanistic bases of metal tolerance: linking phenotype to genotype

Ecotoxicology is currently undergoing a revolution as the result of new technological advances in molecular biology, capable of finely resolving metabolic mechanisms associated with exposure. These high-throughput analyses can detail the evolutionary and ecological implications of exposure in non-model organisms, such as the earthworm, Lumbricus rubellus. This terrestrial sentinel has been observed across former mine sites that are highly contaminated with arsenic and have been found to mitigate toxicity at soil concentrations that cause mortality in unadapted individuals. This is indicative of the adaptive capacity of natural populations recently exposed to persistent and strong selection pressure. However, mechanisms surrounding adaptation to arsenic in L. rubellus have yet to be characterised, and so the effects of exposure are broadly reported with the aim of distinguishing resistance from phenotypic plasticity in natural populations. Unadapted earthworms were initially used to derive basal phenotypic variation associated with arsenic exposure. Variation in life-history parameters was observed among adult and juvenile L. rubellus, establishing relative sensitivity and population-level inferences. A systems biology approach was employed to describe molecular mechanisms associated with arsenic metabolism, encompassing transcriptomic and metabolomic analyses, underpinned by arsenic speciation. Insight into the genetic bases of arsenic resistance, which enable persistence of L. rubellus at highly contaminated sites, was sought. Recombinant inbred lineages derived from adapted populations, were cultivated and their phenotypes relative to arsenic exposure determined. Phylogeographic analyses were used to interrogate genetic variation among populations inhabiting former mine sites as well as proximal control sites. A mitochondrial marker defined cryptic species across the UK, but did not establish soil chemical profiles relative to clade occurrence. RADseq better resolved genetic variation at these sites, determining that soil geochemistry is strongly associated with genetic variation. Furthermore, genomic markers inferred genetic erosion, found to selectively reduce variation at sites relative to a single clade

Fibronectin domain engineering

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Chemical Engineering, 2009.Vita. Cataloged from PDF version of thesis.Includes bibliographical references.Molecular recognition reagents are a critical component of targeted therapeutics, in vivo and in vitro diagnostics, and biotechnology applications such as purification, detection, and crystallization. Antibodies have served as the gold standard binding molecule because of their high affinity and specificity and, historically, because of their ability to be generated by immunization. However, antibodies suffer from several shortcomings that hinder their production and reduce their efficacy in a breadth of applications. The tenth type III domain of human fibronectin provides a small, stable, single-domain, cysteine-free protein scaffold upon which molecular recognition capability can be engineered. In the current work, we provide substantial improvements in each phase of protein engineering through directed evolution and develop a complete platform for engineering high affinity binders based on the fibronectin domain. Synthetic combinatorial library design is substantially enhanced through extension of diversity to include three peptide loops with inclusion of loop length diversity. The efficiency of sequence space search is improved by library focusing with tailored diversity for structural bias and binding capacity. Evolution of lead clones was substantially improved through development of recursive dual mutagenesis in which each fibronectin gene is subtly mutated or the binding loops are aggressively mutated and shuffled. This engineering platform enables robust generation of high affinity binders to a multitude of targets. Moreover, the development of this technology is directly applicable to other protein engineering campaigns and advances the scientific understanding of molecular recognition. Binders were engineered to tumor targets carcinoembryonic antigen, CD276, and epidermal growth factor receptor as well as biotechnology targets human serum albumin and goat, mouse, and rabbit immunoglobulin G. Binders have demonstrated utility in affinity purification, laboratory detection, and cellular labeling and delivery. Of particular interest, a panel of domains was engineered that bind multiple epitopes of epidermal growth factor receptor. Select non-competitive heterobivalent combinations of binders effectively downregulate receptor in a non-agonistic manner in multiple cell types. These agents inhibit proliferation and migration and provide a novel potential cancer therapy.by Benjamin Joseph Hackel.Ph.D

Deep molecular phylogeny of the Pterygota

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