Complex systems are those which exhibit one or more of the following inter-related behaviours: 1. Nonlinear behaviour: the component parts do not act in linear ways, that is the superposition of the actions of the parts is not the output of the system. 2. Emergent behaviour: the output of the system may be inexpressible in terms of the rules or equations of the component parts. 3. Self-organisation: order appears from the chaotic interactions of individuals and the rules they obey. 4. Layers of description: in which a rule may apply at some higher levels of description but not at lower layers. 5. Adaptation: in which the environment becomes encoded in the rules governing the structure and/or behaviour of the parts (in this case strictly agents) that undergo selection in which those that are by some measure better become more numerous than those that are not as “fit”. A single cell is a complex system: we cannot explain all of its behaviour as simply the sum of its parts. Similarly, DNA structures, social networks, cancers, the brain, and living beings are intricate complex systems. This thesis tackles all of these topics from a complex systems approach. I have skirted some of the philosophical issues of complex systems and mainly focussed on appropriate tools to analyse these systems, addressing important questions such as: • What is the best way to extract information from DNA? • How can we model and analyse mutations in DNA? • Can we determine the likely spread of both viruses and ideas in social networks? • How can we model the growth of cancer? • How can we model and analyse interactions between genes in such living systems as the fruit fly, cancers, and humans? • Can complex systems techniques give us some insight into the human brain? / http://proxy.library.adelaide.edu.au/login?url= http://library.adelaide.edu.au/cgi-bin/Pwebrecon.cgi?BBID=1290759 / Thesis (Ph.D.)-- School of Electrical and Electronic Engineering, 2007

Complex systems are those which exhibit one or more of the following inter-related behaviours: 1. Nonlinear behaviour: the component parts do not act in linear ways, that is the superposition of the actions of the parts is not the output of the system. 2. Emergent behaviour: the output of the system may be inexpressible in terms of the rules or equations of the component parts. 3. Self-organisation: order appears from the chaotic interactions of individuals and the rules they obey. 4. Layers of description: in which a rule may apply at some higher levels of description but not at lower layers. 5. Adaptation: in which the environment becomes encoded in the rules governing the structure and/or behaviour of the parts (in this case strictly agents) that undergo selection in which those that are by some measure better become more numerous than those that are not as “fit”. A single cell is a complex system: we cannot explain all of its behaviour as simply the sum of its parts. Similarly, DNA structures, social networks, cancers, the brain, and living beings are intricate complex systems. This thesis tackles all of these topics from a complex systems approach. I have skirted some of the philosophical issues of complex systems and mainly focussed on appropriate tools to analyse these systems, addressing important questions such as: • What is the best way to extract information from DNA? • How can we model and analyse mutations in DNA? • Can we determine the likely spread of both viruses and ideas in social networks? • How can we model the growth of cancer? • How can we model and analyse interactions between genes in such living systems as the fruit fly, cancers, and humans? • Can complex systems techniques give us some insight into the human brain? / http://proxy.library.adelaide.edu.au/login?url= http://library.adelaide.edu.au/cgi-bin/Pwebrecon.cgi?BBID=1290759 / Thesis (Ph.D.)-- School of Electrical and Electronic Engineering, 2007

Um problema importante na área de Biologia Sistêmica é o de inferência de redes de regulação gênica. Os avanços científicos e tecnológicos nos permitem analisar a expressão gênica de milhares de genes simultaneamente. Por \"expressão gênica\'\', estamos nos referindo ao nível de mRNA dentro de uma célula. Devido a esta grande quantidade de dados, métodos matemáticos, estatísticos e computacionais têm sido desenvolvidos com o objetivo de elucidar os mecanismos de regulação gênica presentes nos organismos vivos. Para isso, modelos matemáticos de redes de regulação gênica têm sido propostos, assim como algoritmos para inferir estas redes. Neste trabalho, focamos nestes dois aspectos: modelagem e inferência. Com relação à modelagem, estudamos modelos existentes para o ciclo celular da levedura (Saccharomyces cerevisiae). Após este estudo, propomos um modelo baseado em redes Booleanas probabilísticas sensíveis ao contexto, e em seguida, um aprimoramento deste modelo, utilizando cadeias de Markov não homogêneas. Mostramos os resultados, comparando os nossos modelos com os modelos estudados. Com relação à inferência, propomos um novo algoritmo utilizando o paradigma de crescimento de semente de genes. Neste contexto, uma semente é um pequeno subconjunto de genes de interesse. Nosso algoritmo é baseado em dois passos: passo de crescimento de semente e passo de amostragem. No primeiro passo, o algoritmo adiciona outros genes à esta semente, seguindo algum critério. No segundo, o algoritmo realiza uma amostragem de redes, definindo como saída um conjunto de redes potencialmente interessantes. Aplicamos o algoritmo em dados artificiais e dados biológicos de células HeLa, mostrando resultados satisfatórios. / A key problem in Systems Biology is the inference of gene regulatory networks. The scientific and technological advancement allow us to analyze the gene expression of thousands of genes, simultaneously. By \"gene expression\'\' we refer to the mRNA concentration level inside a cell. Due to this large amount of data, mathematical, statistical and computational methods have been developed in order to elucidate the gene regulatory mechanisms that take part of every living organism. To this end, mathematical models of gene regulatory networks have been proposed, along with algorithms to infer these networks. In this work, we focus in two aspects: modeling and inference. Regarding the modeling, we studied existing models for the yeast (Saccharomyces cerevisiae) cell cycle. After that, we proposed a model based on context sensitive probabilistic Boolean networks, and then, an improvement of this model, using nonhomogeneous Markov chain. We show the results, comparing our models against the studied models. Regarding the inference, we proposed a new algorithm using the seed growing paradigm. In this context, a seed is a small subset of genes. Our algorithm is based in two main steps: seed growing step and sampling step. In the first step, the algorithm adds genes into the seed, according to some criterion. In the second step, the algorithm performs a sampling process on the space of networks, defining as its output a set of potentially interesting networks. We applied the algorithm on artificial and biological HeLa cells data, showing satisfactory results.

I extended an artificial life platform called GReaNs (the name stands for Gene Regulatory evolving artificial Networks) to explore the evolutionary abilities of biologically inspired Spiking Neural Network (SNN) model. The encoding of SNNs in GReaNs was inspired by the encoding of gene regulatory networks. As proof-of-principle, I used GReaNs to evolve SNNs to obtain a network with an output neuron which generates a predefined spike train in response to a specific input. Temporal pattern recognition was one of the main tasks during my studies. It is widely believed that nervous systems of biological organisms use temporal patterns of inputs to encode information. The learning technique used for temporal pattern recognition is not clear yet. I studied the ability to evolve spiking networks with different numbers of interneurons in the absence and the presence of noise to recognize predefined temporal patterns of inputs. Results showed, that in the presence of noise, it was possible to evolve successful networks. However, the networks with only one interneuron were not robust to noise. The foraging behaviour of many small animals depends mainly on their olfactory system. I explored whether it was possible to evolve SNNs able to control an agent to find food particles on 2-dimensional maps. Using ring rate encoding to encode the sensory information in the olfactory input neurons, I managed to obtain SNNs able to control an agent that could detect the position of the food particles and move toward it. Furthermore, I did unsuccessful attempts to use GReaNs to evolve an SNN able to control an agent able to collect sound sources from one type out of several sound types. Each sound type is represented as a pattern of different frequencies. In order to use the computational power of neuromorphic hardware, I integrated GReaNs with the SpiNNaker hardware system. Only the simulation part was carried out using SpiNNaker, but the rest steps of the genetic algorithm were done with GReaNs.

Cells must regulate their metabolism in order to grow, adapt to changes in nutrient availability and maintain homeostasis. Flux, or the turnover of metabolites, through the metabolic network can be regulated at the allosteric and transcriptional levels. While study of allosteric regulation is limited to biochemical examination of individual proteins, transcriptional control of metabolism can be explored at a systems level. We endeavored to elucidate transcriptional mechanisms of metabolic flux regulation in the model organism Caenorhabditis elegans (C. elegans). We also worked to create a visual tool to explore metabolic pathways that will support future efforts in the research of metabolic gene regulation. C. elegans is a small, free-living nematode that feeds on bacteria and experiences a high level of diversity in nutrient level and composition. Previously, we identified a mechanism by which the essential cofactor, vitamin B12, regulates the expression of genes involved in the degradation of propionate, referred to as B12‑mechanism‑I. This mechanism functions to prevent the toxic accumulation of propionate and requires the TFs NHR-10 and NHR-68. Using genetic screens as well as transcriptomic and metabolomic approaches, we discover a second mechanism by which vitamin B12 regulates metabolic gene expression: B12-mechanism-II. Unlike B12-mechanism-I, B12-mechanism-II is independent of propionate, requires the transcription factor NHR-114 and functions to maintain the metabolic activity of the Methionine/S-adenosylmethionine cycle in a tightly regulated regime. We also present WormPaths, an online resource that allows visualization of C. elegans metabolic pathways and enables metabolic pathway enrichment of user-uploaded transcriptomic data.

Maintaining cellular homeostasis is a complex task, which involves monitoring energy states and essential nutrients, regulating metabolic fluxes to accommodate energy and biomass needs, and preventing buildup of potentially toxic metabolic intermediates and byproducts. Measures aimed at maintaining a healthy cellular economy inherently depend on the composition of nutrients available to the organism through its diet. We sought to delineate links between dietary composition, metabolic gene regulation, and physiological responses in the model organism C. elegans. As a soil-dwelling bacterivore, C. elegans encounters diverse bacterial diets. Compared to a diet of E. coli OP50, a diet of Comamonas aquatica accelerates C. elegans developmental rate, alters egg-laying dynamics and shortens lifespan. These physiological responses are accompanied by gene expression changes. Taking advantage of this natural, genetically tractable predator-prey system, we performed genetic screens i) in C. elegans to identify regulators of diet-responsive genes, and ii) in E. coli and Comamonas to determine dietary factors driving transcriptional responses in C. elegans. We identified a C. elegans transcriptional program that regulates metabolic genes in response to vitamin B12 content in the bacterial diet. Interestingly, several B12- repressed metabolic genes of unknown function are highly activated when B12- dependent propionyl-CoA breakdown is impaired, and inactivation of these genes renders animals sensitive to propionate-induced toxicity. We provide genetic and metabolomic evidence in support of the hypothesis that these genes form a parallel, B12-independent, β-oxidation-like propionate breakdown shunt in C. elegans, similar to the pathway utilized by organisms like yeast and plants that do not use vitamin B12.

No description available.

Os avanços na pesquisa em biologia molecular e bioquímica permitiram o desenvolvimento de técnicas capazes de extrair informações moleculares de milhares de genes simultaneamente, como DNA Microarrays, SAGE e, mais recentemente RNA-Seq, gerando um volume massivo de dados biológicos. O mapeamento dos níveis de transcrição dos genes em larga escala é motivado pela proposição de que o estado funcional de um organismo é amplamente determinado pela expressão de seus genes. No entanto, o grande desafio enfrentado é o pequeno número de amostras (experimentos) com enorme dimensionalidade (genes). Dessa forma, se faz necessário o desenvolvimento de novas técnicas computacionais e estatísticas que reduzam o erro de estimação intrínseco cometido na presença de um pequeno número de amostras com enorme dimensionalidade. Neste contexto, um foco importante de pesquisa é a modelagem e identificação de redes de regulação gênica (GRNs) a partir desses dados de expressão. O objetivo central nesta pesquisa é inferir como os genes estão regulados, trazendo conhecimento sobre as interações moleculares e atividades metabólicas de um organismo. Tal conhecimento é fundamental para muitas aplicações, tais como o tratamento de doenças, estratégias de intervenção terapêutica e criação de novas drogas, bem como para o planejamento de novos experimentos. Nessa direção, este trabalho apresenta algumas contribuições: (1) software de seleção de características; (2) nova abordagem para a geração de Redes Gênicas Artificiais (AGNs); (3) função critério baseada na entropia de Tsallis; (4) estratégias alternativas de busca para a inferência de GRNs: SFFS-MR e SFFS-BA; (5) investigação biológica das redes gênicas envolvidas na biossíntese de tiamina, usando a Arabidopsis thaliana como planta modelo. O software de seleção de características consiste de um ambiente de código livre, gráfico e multiplataforma para problemas de bioinformática, que disponibiliza alguns algoritmos de seleção de características, funções critério e ferramentas de visualização gráfica. Em particular, implementa um método de inferência de GRNs baseado em seleção de características. Embora existam vários métodos propostos na literatura para a modelagem e identificação de GRNs, ainda há um problema muito importante em aberto: como validar as redes identificadas por esses métodos computacionais? Este trabalho apresenta uma nova abordagem para validação de tais algoritmos, considerando três aspectos principais: (a) Modelo para geração de Redes Gênicas Artificiais (AGNs), baseada em modelos teóricos de redes complexas, os quais são usados para simular perfis temporais de expressão gênica; (b) Método computacional para identificação de redes gênicas a partir de dados temporais de expressão; e (c) Validação das redes identificadas por meio do modelo AGN. O desenvolvimento do modelo AGN permitiu a análise e investigação das características de métodos de inferência de GRNs, levando ao desenvolvimento de um estudo comparativo entre quatro métodos disponíveis na literatura. A avaliação dos métodos de inferência levou ao desenvolvimento de novas metodologias para essa tarefa: (a) uma função critério, baseada na entropia de Tsallis, com objetivo de inferir os inter-relacionamentos gênicos com maior precisão; (b) uma estratégia alternativa de busca para a inferência de GRNs, chamada SFFS-MR, a qual tenta explorar uma característica local das interdependências regulatórias dos genes, conhecida como predição intrinsecamente multivariada; e (c) uma estratégia de busca, interativa e flutuante, que baseia-se na topologia de redes scale-free, como uma característica global das GRNs, considerada como uma informação a priori, com objetivo de oferecer um método mais adequado para essa classe de problemas e, com isso, obter resultados com maior precisão. Também é objetivo deste trabalho aplicar a metodologia desenvolvida em dados biológicos, em particular na identificação de GRNs relacionadas a funções específicas de Arabidopsis thaliana. Os resultados experimentais, obtidos a partir da aplicação das metodologias propostas, mostraram que os respectivos ganhos de desempenho foram significativos e adequados para os problemas a que foram propostos. / Thanks to recent advances in molecular biology and biochemistry, allied to an ever increasing amount of experimental data, the functional state of thousands of genes can now be extracted simultaneously by using methods such as DNA microarrays, SAGE, and more recently RNA-Seq, generating a massive volume of biological data. The mapping of gene transcription levels at large scale is motivated by the proposition that information of the functional state of an organism is broadly determined by its gene expression. However, the main limitation faced is the small number of samples (experiments) with huge dimensionalities (genes). Thus, it is necessary to develop new computational and statistics techniques to reduce the inherent estimation error committed in the presence of a small number of samples with large dimensionality. In this context, particularly important related investigations are the modeling and identification of gene regulatory networks from expression data sets. The main objective of this research is to infer how genes are regulated, bringing knowledge about the molecular interactions and metabolic activities of an organism. Such a knowledge is fundamental for many applications, such as disease treatment, therapeutic intervention strategies and drugs design, as well as for planning high-throughput new experiments. In this direction, this work presents some contributions: (1) feature selection software; (2) new approach for the generation of artificial gene networks (AGN); (3) criterion function based on Tsallis entropy; (4) alternative search strategies for GRNs inference: SFFS-MR and SFFS-BA; (5) biological investigation of GRNs involved in the thiamine biosynthesis by adopting the Arabidopsis thaliana as a model plant. The feature selection software is an open-source multiplataform graphical environment for bioinformatics problems, which supports many feature selection algorithms, criterion functions and graphic visualization tools. In particular, a feature selection method for GRNs inference is also implemented in the software. Although there are several methods proposed in the literature for the modeling and identification of GRNs, an important open problem regards: how to validate such methods and its results? This work presents a new approach for validation of such algorithms by considering three main aspects: (a) Artificial Gene Networks (AGNs) model generation through theoretical models of complex networks, which is used to simulate temporal expression data; (b) computational method for GRNs identification from temporal expression data; and (c) Validation of the identified AGN-based network through comparison with the original network. Through the development of the AGN model was possible the analysis and investigation of the characteristics of GRNs inference methods, leading to the development of a comparative study of four inference methods available in literature. The evaluation of inference methods led to the development of new methodologies for this task: (a) a new criterion function based on Tsallis entropy, in order to infer the genetic inter-relationships with better precision; (b) an alternative search strategy for the GRNs inference, called SFFS-MR, which tries to exploit a local property of the regulatory gene interdependencies, which is known as intrinsically multivariate prediction; and (c) a search strategy, interactive and floating, which is based on scale-free network topology, as a global property of the GRNs, which is considered as a priori information, in order to provide a more appropriate method for this class of problems and thereby achieve results with better precision. It is also an objective of this work, to apply the developed methodology in biological data, particularly in identifying GRNs related to specific functions of the Arabidopsis thaliana. The experimental results, obtained from the application of the proposed methodologies, indicate that the respective performances of each methodology were significant and adequate to the problems that have been proposed.

O câncer configura uma das maiores causas de mortalidade no mundo, caracterizando-se como uma doença complexa orquestrada por alterações genômicas e epigenômicas capazes de alterar a expressão gênica e a identidade celular. Nova evidência obtida por meio de um estudo genômico em larga escala e cujos dados encontram-se disponíveis no banco público do TCGA sugere que um em cada dez pacientes portadores de câncer pode ser classificado com maior eficácia tendo como base a taxonomia molecular quando comparada à histologia. Dessa maneira, nós hipotetizamos que o estabelecimento de mapas genômicos exibindo a localização de sítios de ligação de fatores de transcrição combinada à identificação de regiões diferencialmente metiladas e perfis alterados de expressão gênica possa nos auxiliar a caracterizar e explorar, ao nível molecular, fenótipos associados ao câncer. Avanços tecnológicos e bancos de dados públicos a exemplo do The Cancer Genome Atlas (TCGA), The Encyclopedia of DNA Elements (ENCODE) e o NIH Roadmap Epigenomics Mapping Consortium (Roadmap) têm proporcionado um recurso inestimável para interrogar o (epi)genoma de linhagens de células tumorais em cultura, bem como de tecidos normais e tumorais em alta resolução. Todavia, a informação biológica encontra-se armazenada em diferentes formatos e não há ferramentas computacionais para integrar esses dados, evidenciando um cenário atual que requer, com urgência, o desenvolvimento de ferramentas de bioinformática e softwares capazes de direcionar a solução deste obstáculo. Nesse contexto, o objetivo principal deste estudo consiste em implementar o desenvolvimento de ferramentas de bioinformática, na linguagem de programação R que, ao final do estudo, será submetido à comunidade científica do projeto Bioconductor sob a licença de código aberto GNU GPL versão 3. Além disso, ajudaremos nossos colaboradores com o aperfeiçoamento do ELMER, um pacote R/Bioconductor que identifica elementos reguladores usando dados de expressão gênica, de metilação do DNA e análise de motivo. Nossa expectativa é que essas ferramentas possam automatizar com acurácia a pesquisa, o download e a análise dos dados (epi)genômicos que se encontram atualmente disponíveis nas bases de dados públicas dos consórcios internacionais TCGA, ENCODE e Roadmap, além de integrá-los facilmente aos dados genômicos e epigenômicos gerados por pesquisadores por meio de experimentos em larga escala. Além disso, realizaremos também o processamento e a análise manual dos dados que serão automatizados pelas ferramentas, visando validar sua capacidade em descobrir assinaturas epigenômicas que possam redefinir subtipos de câncer. Por xi fim, as usaremos para investigar as diferenças moleculares entre dois subgrupos de gliomas recentemente descobertos por nosso laboratório. / Cancer, which is one of the major causes of mortality worldwide, is a complex disease orchestrated by aberrant genomic and epigenomic changes that can modify gene regulatory circuits and cellular identity. Emerging evidence obtained through high-throughput genomic data deposited within the public TCGA international consortium suggests that one in ten cancer patients would be more accurately classified by molecular taxonomy versus histology. Therefore, we have hypothesized that the establishment of genome-wide maps of the de novo DNA binding motifs localization coupled with differentially methylated regions and gene expression changes might help to characterize and exploit cancer phenotypes at the molecular level. Technological advances and public databases like The Cancer Genome Atlas (TCGA), The Encyclopedia of DNA Elements (ENCODE), and The NIH Roadmap Epigenomics Mapping Consortium (roadmap) have provided unprecedented opportunities to interrogate the epigenome of cultured cancer cell lines as well as normal and tumor tissues with high resolution. Markedly however, biological information is stored in different formats and there is no current tool to integrate the data, highlighting an urgent need to develop bioinformatic tools and/or computational softwares to overcome this challenge. In this context, the main purpose of this study is the development of bioinformatics tools in R programming language that will be submitted to the larger open-source Bioconductor community project under the GNU GPL3 (General Public License version 3). Also, we will help our collaborators improve of the R/Bioconductor ELMER package that identifies regulatory enhancers using gene expression, DNA methylation data and motif analysis. Our expectation is that these tools can effectively automate search, retrieve, and analyze the vast (epi)genomic data currently available from TCGA, ENCODE, and Roadmap, and integrate genomics and epigenomics features with researchers own high-throughput data. Furthermore, we will also navigate through these data manually in order to validate the capacity of these tools in discovering epigenomic signatures able to redefine subtypes of cancer. Finally, we will use them to investigate the molecular differences between two subgroups of gliomas, one of the most aggressive primary brain cancer, recently discovered by our laboratory.

As Escherichia coli produtoras da toxina Shiga (STEC) são importantes patógenos humanos, causando desde diarréias até a síndrome hemolítica urêmica (SHU). Há diversos sorotipos associados a SHU, tais como O157:H7 e O113:H21. No Brasil o sorotipo O113:H21 ainda não aparece associado a SHU, embora seja frequentemente isolado de carcaças e fezes bovinas. Nosso grupo já investigou comparativamente as redes de coexpressão gênica (RCG) de STEC EH41 (associado à SHU) e Ec472/01 (isolado de fezes bovinas). A análise comparativa do perfil transcricional de EH41 e Ec472/01 revelou que somente EH41 expressa um conjunto de genes que inclui o regulador transcricional dicA. A maioria destes genes está situada em um único módulo transcricional e podem estar associados a fatores de virulência. Assim, este trabalho centrou-se numa abordagem de biologia de sistemas, integrando análises genômica e fenotípica da resposta de enterócitos Caco-2 à EH41 e Ec472/01. A análise genômica baseou-se no estudo temporal de RCG para compreender os mecanismos moleculares envolvidos na patogenicidade desses dois isolados. As alterações fenotípicas ocorridas nas células Caco-2 ao longo da exposição a cada um dos isolados de STEC foram visualizadas através de MEV. A análise genômica mostrou que o mecanismo molecular da resposta de Caco-2 durante a interação com EH41 ou Ec472/01 é claramente distinto. Nas redes do grupo Caco-2/EH41 as alterações topológicas incluíram a perda do status scale free e a sua recuperação, com o estabelecimento de uma nova hierarquia de genes na rede. Esses resultados se enquadram no modelo de redes para transição saúde-doença: a nova rede representa a resposta adaptativa da célula ao patógeno, o que não significa um retorno à normalidade. Já no grupo Caco-2/Ec472 as redes, após a perda do status scale free, não recuperam esse status até o final do período estudado, o que sugere um estado de transição mais prolongado para reorganização da hierarquia da rede. Mais ainda, através da caracterização dos módulos transcricionais, foi possível compreender dinamicamente os mecanismos moleculares envolvidos na resposta diferencial de Caco-2 aos dois isolados aqui estudados. STEC EH41 induz rapidamente a resposta inflamatória e apoptótica a partir da primeira hora de interação enterócito-bactéria. Por outro lado, células Caco-2 em contato com Ec472/01 ativam, a partir de uma hora, a fagocitose e, a partir da segunda hora, expressam moduladores da homeostase imune. A análise fenotípica das células Caco-2 mostrou, de forma nítida, uma maior destruição dos microvilos dos enterócitos em contato com EH41 do que com Ec472/01. Integrando os resultados genômicos e fenotípicos pode-se concluir que EH41 induz em Caco-2 - em comparação com Ec472/01 - maiores e mais rápidas alterações na expressão gênica global, além de uma resposta inflamatória e apoptótica excessiva, levando assim a alterações morfológicas mais pronunciadas nas células Caco-2. Em seu conjunto, esses resultados contribuem para uma melhor compreensão dos mecanismos moleculares envolvidos na patogenicidade das STECs associadas à SHU. Assim, as perspectivas de desenvolvimento deste trabalho deverão incluir a investigação de fatores de virulência e vias moleculares envolvidas na indução das respostas imunes que podem conduzir à SHU / Shiga toxin-producing Escherichia coli (STEC) O113:H21 strains are associated with human diarrhea and some of these strains may cause hemolytic uremic syndrome (HUS). In Brazil O113:H21 strains are commonly found in cattle but, so far, were not isolated from HUS patients. Previously, our group conducted comparative gene co-expression network (GCN) analyses of two O113:H21 STEC strains: EH41, isolated from a HUS patient in Australia, and Ec472/01, isolated from bovine feces in Brazil. Differential transcriptome profiles for EH41 and Ec472/01 revealed a gene set exclusively expressed in EH41, which includes the dicA putative virulence factor regulator. GCN analysis showed that this set of genes constitutes an EH41 specific transcriptional module which may be associated to virulence factors. Therefore, in the present work a system biology approach was conducted to investigate the differential Caco-2 response - genomic and phenotypic - to EH41 (Caco-2/EH41) or to Ec472/01 (Caco- 2/Ec472) along enterocyte-bacteria interaction. The genomic analysis was based on temporal GCN data in order to gain a better understanding on the molecular mechanisms underlying the capacity to cause HUS. The phenotypic alterations in Caco-2 during enterocyte-bacteria interaction were assessed by scanning electronic microscopy (SEM). The genomic analysis showed that the molecular mechanism of Caco-2 response to EH41 or to Ec472/01 during enterocyte-bacteria interaction is clearly different. The GCN topological analyses for Caco-2/EH41 group revealed loss of the scale-free status after one hour of interaction, persistence of this condition along the second hour and establishment of a new gene hierarchy thereafter. These events resemble the network mechanism of health-disease transition. The new established network represents an adaptive cell response to the pathogen and not the return to a \"normal\" state. Conversely, the networks for Caco-2/Ec472 group showed a slow and progressive loss of the scale-free status without its restoration at the end of the time interval here studied. Through transcriptional module characterization it was possible to reveal the dynamic of the molecular mechanism involved in the Caco-2 differential responses to the STEC isolates. EH41 induces a rapid inflammatory and apoptotic response just after the first hour of enterocyte-bacteria interaction. Instead, the Caco-2 response to Ec472/01 is characterized by phagocytosis activation at the first hour, followed by the expression of immune response modulators after the second hour. SEM phenotypic analysis of Caco-2 cells along enterocyte-bacteria interaction showed more intense microvilli destruction in cells exposed to EH41, when compared to cells exposed to Ec472/01. The integration of genomic and phenotypic data allowed us to conclude that EH41, comparatively to Ec472/01, induces greater and precocious global gene expression alterations in Caco-2, what is related to excessive inflammatory and apoptotic responses. These responses are associated with the pronounced morphological alterations observed by SEM in Caco-2 cells exposed to EH41. Altogether, these results contribute for a better understanding of the molecular mechanism involved in STEC pathogenicity associated to HUS. Therefore, the future perspectives for the development of the present work should include the investigation of virulence factors and molecular pathways involved in the induction of immune responses leading to HUS