Bioinformatics and Machine Learning for Cancer Biology

Cancer is a leading cause of death worldwide, claiming millions of lives each year. Cancer biology is an essential research field to understand how cancer develops, evolves, and responds to therapy. By taking advantage of a series of “omics” technologies (e.g., genomics, transcriptomics, and epigenomics), computational methods in bioinformatics and machine learning can help scientists and researchers to decipher the complexity of cancer heterogeneity, tumorigenesis, and anticancer drug discovery. Particularly, bioinformatics enables the systematic interrogation and analysis of cancer from various perspectives, including genetics, epigenetics, signaling networks, cellular behavior, clinical manifestation, and epidemiology. Moreover, thanks to the influx of next-generation sequencing (NGS) data in the postgenomic era and multiple landmark cancer-focused projects, such as The Cancer Genome Atlas (TCGA) and Clinical Proteomic Tumor Analysis Consortium (CPTAC), machine learning has a uniquely advantageous role in boosting data-driven cancer research and unraveling novel methods for the prognosis, prediction, and treatment of cancer

Toward General Principles for Resilience Engineering

Maintaining the performance of infrastructure‐dependent systems in the face of surprises and unknowable risks is a grand challenge. Addressing this issue requires a better understanding of enabling conditions or principles that promote system resilience in a universal way. In this study, a set of such principles is interpreted as a group of interrelated conditions or organizational qualities that, taken together, engender system resilience. The field of resilience engineering identifies basic system or organizational qualities (e.g., abilities for learning) that are associated with enhanced general resilience and has packaged them into a set of principles that should be fostered. However, supporting conditions that give rise to such first‐order system qualities remain elusive in the field. An integrative understanding of how such conditions co‐occur and fit together to bring about resilience, therefore, has been less clear. This article contributes to addressing this gap by identifying a potentially more comprehensive set of principles for building general resilience in infrastructure‐dependent systems. In approaching this aim, we organize scattered notions from across the literature. To reflect the partly self‐organizing nature of infrastructure‐dependent systems, we compare and synthesize two lines of research on resilience: resilience engineering and social‐ecological system resilience. Although some of the principles discussed within the two fields overlap, there are some nuanced differences. By comparing and synthesizing the knowledge developed in them, we recommend an updated set of resilience‐enhancing principles for infrastructure‐dependent systems. In addition to proposing an expanded list of principles, we illustrate how these principles can co‐occur and their interdependencies.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/156462/2/risa13494_am.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/156462/1/risa13494.pd

Tackling complexity in biological systems: Multi-scale approaches to tuberculosis infection

Tuberculosis is an ancient disease responsible for more than a million deaths per year worldwide, whose complex infection cycle involves dynamical processes that take place at different spatial and temporal scales, from single pathogenic cells to entire hosts' populations. In this thesis we study TB disease at different levels of description from the perspective of complex systems sciences. On the one hand, we use complex networks theory for the analysis of cell interactomes of the causative agent of the disease: the bacillus Mycobacterium tuberculosis. Here, we analyze the gene regulatory network of the bacterium, as well as its network of protein interactions and the way in which it is transformed as a consequence of gene expression adaptation to disparate environments. On the other hand, at the level of human societies, we develop new models for the description of TB spreading on complex populations. First, we develop mathematical models aimed at addressing, from a conceptual perspective, the interplay between complexity of hosts' populations and certain dynamical traits characteristic of TB spreading, like long latency periods and syndemic associations with other diseases. On the other hand, we develop a novel data-driven model for TB spreading with the objective of providing faithful impact evaluations for novel TB vaccines of different types

Linear and nonlinear approaches to unravel dynamics and connectivity in neuronal cultures

[eng] In the present thesis, we propose to explore neuronal circuits at the mesoscale, an approach in which one monitors small populations of few thousand neurons and concentrates in the emergence of collective behavior. In our case, we carried out such an exploration both experimentally and numerically, and by adopting an analysis perspective centered on time series analysis and dynamical systems. Experimentally, we used neuronal cultures and prepared more than 200 of them, which were monitored using fluorescence calcium imaging. By adjusting the experimental conditions, we could set two basic arrangements of neurons, namely homogeneous and aggregated. In the experiments, we carried out two major explorations, namely development and disintegration. In the former we investigated changes in network behavior as it matured; in the latter we applied a drug that reduced neuronal interconnectivity. All the subsequent analyses and modeling along the thesis are based on these experimental data. Numerically, the thesis comprised two aspects. The first one was oriented towards a simulation of neuronal connectivity and dynamics. The second one was oriented towards the development of linear and nonlinear analysis tools to unravel dynamic and connectivity aspects of the measured experimental networks. For the first aspect, we developed a sophisticated software package to simulate single neuronal dynamics using a quadratic integrate–and–fire model with adaptation and depression. This model was plug into a synthetic graph in which the nodes of the network are neurons, and the edges connections. The graph was created using spatial embedding and realistic biology. We carried out hundreds of simulations in which we tuned the density of neurons, their spatial arrangement and the characteristics of the fluorescence signal. As a key result, we observed that homogeneous networks required a substantial number of neurons to fire and exhibit collective dynamics, and that the presence of aggregation significantly reduced the number of required neurons. For the second aspect, data analysis, we analyzed experiments and simulations to tackle three major aspects: network dynamics reconstruction using linear descriptions, dynamics reconstruction using nonlinear descriptors, and the assessment of neuronal connectivity from solely activity data. For the linear study, we analyzed all experiments using the power spectrum density (PSD), and observed that it was sufficiently good to describe the development of the network or its disintegration. PSD also allowed us to distinguish between healthy and unhealthy networks, and revealed dynamical heterogeneities across the network. For the nonlinear study, we used techniques in the context of recurrence plots. We first characterized the embedding dimension m and the time delay δ for each experiment, built the respective recurrence plots, and extracted key information of the dynamics of the system through different descriptors. Experimental results were contrasted with numerical simulations. After analyzing about 400 time series, we concluded that the degree of dynamical complexity in neuronal cultures changes both during development and disintegration. We also observed that the healthier the culture, the higher its dynamic complexity. Finally, for the reconstruction study, we first used numerical simulations to determine the best measure of ‘statistical interdependence’ among any two neurons, and took Generalized Transfer Entropy. We then analyzed the experimental data. We concluded that young cultures have a weak connectivity that increases along maturation. Aggregation increases average connectivity, and more interesting, also the assortativity, i.e. the tendency of highly connected nodes to connect with other highly connected node. In turn, this assortativity may delineates important aspects of the dynamics of the network. Overall, the results show that spatial arrangement and neuronal dynamics are able to shape a very rich repertoire of dynamical states of varying complexity.[cat] L’habilitat dels teixits neuronals de processar i transmetre informació de forma eficient depèn de les propietats dinàmiques intrínseques de les neurones i de la connectivitat entre elles. La present tesi proposa explorar diferents tècniques experimentals i de simulació per analitzar la dinàmica i connectivitat de xarxes neuronals corticals de rata embrionària. Experimentalment, la gravació de l’activitat espontània d’una població de neurones en cultiu, mitjançant una càmera ràpida i tècniques de fluorescència, possibilita el seguiment de forma controlada de l’activitat individual de cada neurona, així com la modificació de la seva connectivitat. En conjunt, aquestes eines permeten estudiar el comportament col.lectiu emergent de la població neuronal. Amb l’objectiu de simular els patrons observats en el laboratori, hem implementat un model mètric aleatori de creixement neuronal per simular la xarxa física de connexions entre neurones, i un model quadràtic d’integració i dispar amb adaptació i depressió per modelar l’ampli espectre de dinàmiques neuronals amb un cost computacional reduït. Hem caracteritzat la dinàmica global i individual de les neurones i l’hem correlacionat amb la seva estructura subjacent mitjançant tècniques lineals i no–lineals de series temporals. L’anàlisi espectral ens ha possibilitat la descripció del desenvolupament i els canvis en connectivitat en els cultius, així com la diferenciació entre cultius sans dels patològics. La reconstrucció de la dinàmica subjacent mitjançant mètodes d’incrustació i l’ús de gràfics de recurrència ens ha permès detectar diferents transicions dinàmiques amb el corresponent guany o pèrdua de la complexitat i riquesa dinàmica del cultiu durant els diferents estudis experimentals. Finalment, a fi de reconstruir la connectivitat interna hem testejat, mitjançant simulacions, diferents quantificadors per mesurar la dependència estadística entre neurona i neurona, seleccionant finalment el mètode de transferència d’entropia gereralitzada. Seguidament, hem procedit a caracteritzar les xarxes amb diferents paràmetres. Malgrat presentar certs tres de xarxes tipus ‘petit món’, els nostres cultius mostren una distribució de grau ‘exponencial’ o ‘esbiaixada’ per, respectivament, cultius joves i madurs. Addicionalment, hem observat que les xarxes homogènies presenten la propietat de disassortativitat, mentre que xarxes amb un creixent nivell d’agregació espaial presenten assortativitat. Aquesta propietat impacta fortament en la transmissió, resistència i sincronització de la xarxa

Strategy evolution on dynamic networks

Models of strategy evolution on static networks help us understand how population structure can promote the spread of traits like cooperation. One key mechanism is the formation of altruistic spatial clusters, where neighbors of a cooperative individual are likely to reciprocate, which protects prosocial traits from exploitation. But most real-world interactions are ephemeral and subject to exogenous restructuring, so that social networks change over time. Strategic behavior on dynamic networks is difficult to study, and much less is known about the resulting evolutionary dynamics. Here, we provide an analytical treatment of cooperation on dynamic networks, allowing for arbitrary spatial and temporal heterogeneity. We show that transitions among a large class of network structures can favor the spread of cooperation, even if each individual social network would inhibit cooperation when static. Furthermore, we show that spatial heterogeneity tends to inhibit cooperation, whereas temporal heterogeneity tends to promote it. Dynamic networks can have profound effects on the evolution of prosocial traits, even when individuals have no agency over network structures.Comment: 45 pages; final versio

Computation in Complex Networks

Complex networks are one of the most challenging research focuses of disciplines, including physics, mathematics, biology, medicine, engineering, and computer science, among others. The interest in complex networks is increasingly growing, due to their ability to model several daily life systems, such as technology networks, the Internet, and communication, chemical, neural, social, political and financial networks. The Special Issue “Computation in Complex Networks" of Entropy offers a multidisciplinary view on how some complex systems behave, providing a collection of original and high-quality papers within the research fields of: • Community detection • Complex network modelling • Complex network analysis • Node classification • Information spreading and control • Network robustness • Social networks • Network medicin

Role of horizontally transferred copper resistance genes in Staphylococcus aureus and Listeria monocytogenes

Bacteria have evolved mechanisms which enable them to control intracellular concentrations of metals. In the case of transition metals, such as copper, iron and zinc, bacteria must ensure enough is available as a cofactor for enzymes whilst at the same time preventing the accumulation of excess concentrations, which can be toxic. Interestingly, metal homeostasis and resistance systems have been found to play important roles in virulence. This review will discuss the copper homeostasis and resistance systems in Staphylococcus aureus and Listeria monocytogenes and the implications that acquisition of additional copper resistance genes may have in these pathogens

Evolutionary dynamics of populations with genotype-phenotype map

In this thesis we develop a multi-scale model of the evolutionary dynamics of a population of cells, which accounts for the mapping between genotype and phenotype as determined by a model of the gene regulatory network. We study topological properties of genotype-phenotype networks obtained from the multi-scale model. Moreover, we study the problem of evolutionary escape and survival taking into account a genotype-phenotype map. An outstanding feature of populations with genotype-phenotype map is that selective pressures are determined by the phenotype, rather than genotypes. Our multi-scale model generates the evolution of a genotype-phenotype network represented by a pseudo-bipartite graph, that allows formulate a topological definition of the concepts of robustness and evolvability. We further study the problem of evolutionary escape for cell populations with genotype-phenotype map, based on a multi-type branching process. We present a comparative analysis between genotype-phenotype networks obtained from the multi-scale model and networks constructed assuming that the genotype space is a regular hypercube. We compare the effects on the probability of escape and the escape rate associated to the evolutionary dynamics between both classes of graphs. We further the study of evolutionary escape by analysing the long term survival conditioned to escape. Traditional approaches to the study of escape assume that the reproduction number of the escape genotype approaches infinity, and, therefore, survival is a surrogate of escape. Here, we analyse the process of survival upon escape by taking advantage of the fact that the natural setting of the escape problem endows the system with a separation of time scales: an initial, fast-decaying regime where escape actually occurs, is followed by a much slower dynamics within the (neutral network of) the escape phenotype. The probability of survival is analysed in terms of topological features of the neutral network of the escape phenotype.En aquesta tesi es desenvolupa un model multi-escala de la dinàmica evolutiva d'una població de cèl·lules, tenint en compte la correspondència entre el genotip i el fenotip determinat per un model de la xarxa de regulació genètica. Estudiem les propietats topològiques de les xarxes genotip-fenotip obtingudes a partir del model multi-escala. D'altra banda, s'estudia el problema de la fugida evolutiva i la supervivència, tenint en compte una aplicació entre genotip i fenotip. Una característica destacable de les poblacions amb aplicació genotip-fenotip és que les pressions selectives actuen sobre els fenotips, en lloc dels genotips. El nostre model multi-escala genera l'evolució d'una xarxa genotip-fenotip representada per un graf pseudo-bipartit, el qual permet formular una definició topològica dels conceptes de robustesa y capacitat evolutiva. A més a més, estudiem el problema de fugida evolutiva de poblacions de cèl¿lules amb una aplicació genotip-fenotip, basat en en un procés de ramificació multi-tipus. Presentem un anàlisi comparatiu entre les xarxes de genotip-fenotip obtingudes a partir del model multi-escala i les xarxes construïdes assumint un espai de genotips de tipus hipercub regular. Comparem els efectes de la probabilitat de fugida i la freqüència d'escapament associades a la dinàmica evolutiva entre ambdues classes de grafs. Anem més enllà de l'estudi de fugida evolutiva mitjançant l'anàlisi de la supervivència a llarg plaç condicionat a fugir. Els enfocaments tradicionals per a l'estudi de la fugida o escapament suposen una taxa de reproducció en el genotip de fugida propera a infinit. Per tant, la supervivència és equivalent a la fugida. Aquí analitzem el procés de supervivència suposant fugida aprofitant el fet que l'entorn natural del problema de fugida dota al sistema amb una separació d'escales de temps: un règim inicial, de temps ràpid, on la fugida realment es produeix; seguit d'una dinàmica molt més lenta dins de la (xarxa neutra del) fenotip de fugida. La probabilitat de supervivència s'analitza en termes de les característiques topològiques de la xarxa neutra del fenotip de fugidaPostprint (published version

On The Application Of Computational Modeling To Complex Food Systems Issues

Transdisciplinary food systems research aims to merge insights from multiple fields, often revealing confounding, complex interactions. Computational modeling offers a means to discover patterns and formulate novel solutions to such systems-level problems. The best models serve as hubs—or boundary objects—which ground and unify a collaborative, iterative, and transdisciplinary process of stakeholder engagement. This dissertation demonstrates the application of agent-based modeling, network analytics, and evolutionary computational optimization to the pressing food systems problem areas of livestock epidemiology and global food security. It is comprised of a methodological introduction, an executive summary, three journal-article formatted chapters, and an overarching discussion section. Chapter One employs an agent-based computer model (RUSH-PNBM v.1.1) developed to study the potential impact of the trend toward increased producer specialization on resilience to catastrophic epidemics within livestock production chains. In each run, an infection is introduced and may spread according to probabilities associated with the various modes of contact between hog producer, feed mill, and slaughter plant agents. Experimental data reveal that more-specialized systems are vulnerable to outbreaks at lower spatial densities, have more abrupt percolation transitions, and are characterized by less-predictable outcomes; suggesting that reworking network structures may represent a viable means to increase biosecurity. Chapter Two uses a calibrated, spatially-explicit version of RUSH-PNBM (v.1.2) to model the hog production chains within three U.S. states. Key metrics are calculated after each run, some of which pertain to overall network structures, while others describe each actor’s positionality within the network. A genetic programming algorithm is then employed to search for mathematical relationships between multiple individual indicators that effectively predict each node’s vulnerability. This “meta-metric” approach could be applied to aid livestock epidemiologists in the targeting of biosecurity interventions and may also be useful to study a wide range of complex network phenomena. Chapter Three focuses on food insecurity resulting from the projected gap between global food supply and demand over the coming decades. While no single solution has been identified, scholars suggest that investments into multiple interventions may stack together to solve the problem. However, formulating an effective plan of action requires knowledge about the level of change resulting from a given investment into each wedge, the time before that effect unfolds, the expected baseline change, and the maximum possible level of change. This chapter details an evolutionary-computational algorithm to optimize investment schedules according to the twin goals of maximizing global food security and minimizing cost. Future work will involve parameterizing the model through an expert informant advisory process to develop the existing framework into a practicable food policy decision-support tool

Complement aberrations and autoantibodies to complement proteins in relation to disease mechanisms.

The complement system, a part of the innate immune system with several links to the adaptive immune system, plays an important role in the pathogenesis of many diseases. The purpose of this thesis was to document and clarify some of these mechanisms. The thesis is based on four papers (I-IV). (I and II) Autoantibodies to the C3 cleaving enzyme complex of the alternative pathway, C3 nephritic factors (C3 NeF), cause partial C3 deficiency and are associated with increased susceptibility to bacterial infections. By analysis of samples from 20 patients with C3 NeF, it was confirmed that C3 NeF are of at least 2 types; one with fluid phase (C3 NeF type I) and one with solid phase (C3 NeF type II) activity. Different types of C3 NeF were associated with different serum complement profiles and symptoms. Only C3 NeF type II were found to be associated with circulating autoantibodies to the collagenous region of C1q (aC1qCLR). Defense against Neisseria meningitidis in 26 patients with low C3 concentrations due to C3 NeF was investigated. In patients and control children, homozygosity for the IgG1 and IgG3 IGHG alleles G1M*f and G3M*b was found to be associated with higher serum bactericidal activity (SBA) than was heterozygosity. IGHG alleles correlated to IgG subclass binding to live meningococci, while IgG subclass binding did not correlate to SBA. Thus, the mechanism of influence from IGHG genes on SBA is unclear. IGHG variants are important for immune defense against meningococci in states of deficient complement function. This relationship should be examined further. (III) Serum levels of mannan-binding lectin (MBL) and antibodies to proteins from a potentially nephritogenic Streptococcus pyogenes strain (serotype M1 strain AP1) were investigated in 73 patients with acute poststreptococcal glomerulonephritis (AGN). Antibody responses to the serotype M1-related antigens M1, protein H and streptococcal inhibitor of complement were increased in patients compared to controls. The presence of MBL deficient individuals (serum concentration <0.1 mg/L) among AGN patients showed that the lectin pathway is not required in the pathogenesis of AGN. (IV) Autoimmunity has been implied to participate in the pathogenesis of idiopathic sudden hearing loss (ISHL). Autoantibodies were analysed in sera from 92 patients with ISHL. The most frequently occurring antibody was aC1qCLR, detected in 12 patients (13 %), all with normal serum concentrations of C1q. In ISHL, aC1qCLR probably represents cross-reactivity between C1q and inner ear protein(s)