A cana-de-açúcar (Saccharum spp.) é uma das principais gramíneas cultivadas do mundo e o Brasil é seu maior produtor, ela se tornou uma importante cultura devido às altas taxas de assimilação de carbono permitindo a síntese e acumulação de grandes quantidades de sacarose em seus entrenós. Com isso, faz-se necessário uma melhor compreensão dos mecanismos moleculares que regulam o acúmulo de sacarose nesta planta. Tais mecanismos têm sido estudados em vários níveis, tais como, identificação e localização de genes, identificação de lócus controladores de características quantitativas, transcriptoma, proteôma, caracterização e identificação de metabólitos. Com todos esses estudos é evidente a necessidade de uma abordagem holística para o entendimento global da planta durante o acúmulo de sacarose. Assim este trabalho teve por objetivo integrar dados de metabolômica e proteômica de tecidos da cana-de-açúcar da variedade SP80-3280 durante o desenvolvimento e o acúmulo de sacarose, utilizando a bioinformática para unir esses resultados por meio da análise de correlação canônica regularizada em uma abordagem de biologia sistêmica. Os resultados obtidos indicam diferenças no perfil metabólico e proteico da cana-açúcar durante o desenvolvimento e acúmulo de sacarose. Foram propostas classes de metabólitos que podem estar relacionados com o acúmulo de sacarose na cana-de-açúcar tais como glicerolipídeos, glicerofosfolipídeos, cumarinas e derivados, esteroides e derivados de esteroides e acil graxos. Também foram propostas proteínas que podem estar relacionadas com o acúmulo de sacarose, onde as histonas foram as que mais se destacaram. Nas redes biológicas de correlações também foram observadas correlações entre possíveis metabólitos e proteínas que podem estar correlacionadas com o acúmulo de sacarose na cana-de-açúcar / Sugarcane (Saccharum spp.) is one of the most important cultivated grasses of the world and Brazil is the largest producer, it has become an important crop due to high carbon assimilation rates allowing the synthesis and accumulation of large amounts of sucrose in their internodes. Thus, it is necessary a high understanding of the molecular mechanisms involved in the regulation of sucrose accumulation in this plant. These mechanisms have been studied at various levels, such as gene identification and localization, identification of quantitative trait locus controlling, transcriptome, proteome, characterization and metabolites identification. With all these studies is evident the necessity for a holistic approach to global understanding of the plant during the sucrose accumulation. Thus, this work aims to integrate metabolomics and proteomics data from tissues of sugarcane variety SP80-3280, during plant development and sucrose accumulation, using bioinformatics to link these results by regularized canonical correlation analysis in a systems biology approach. The results indicate differences in the metabolic and protein profile of sugarcane during development and sucrose accumulation. Metabolites classes have been proposed that may be related to sugarcane sucrose accumulation as glycerolipids, glycerophospholipids, coumarins and derivatives, steroids and steroid derivatives and fatty acyl. In addition, some proteins have been proposed that may be related to sucrose accumulation, where the most highlighted were the histones. In the biological correlations networks, have been also observed correlations between possible metabolites and proteins that can be correlated with the accumulation of sucrose in sugarcane

Le travail présenté s'articule autour de l'étude in silico des réseaux biologiques en abordant aussi bien les aspects d'intégration, de formalisation et de modélisation des réseaux et sous-réseaux biologiques. Dans ce contexte, les travaux ont porté dans un premier temps, sur le développement d'un outil d'intégration Cyclone à même d'assurer un accès et une exploitation simplifiés des données présentes dans la base de données BioCyc puis, dans un second temps, sur le développement d'un cadre de modélisation des graphes particulièrement adapté à l'étude de réseaux d'interactions hétérogènes, MIB (pour Modèle d'Interaction Biologique). Enfin, ces développements ont été mis à profit afin d'une part, de caractériser et d'étudier la présence et le mode de connexion de sous-réseaux ou motifs à l'intérieur de réseaux plus vastes et d'autre part, d'étudier et de modéliser la voie métabolique du galactose chez la levure Saccharomyces cerevisiae en tant que boucle de rétroaction impliquant régulation transcriptionnelle et interaction protéine-protéine.

A large number and variety of genome-wide genomics and proteomics datasets are now available for model organisms. Each dataset on its own presents a distinct but noisy view of cellular state. However, collectively, these datasets embody a more comprehensive view of cell function. This motivates the prediction of function for uncharacterized genes by combining multiple datasets, in order to exploit the associations between such genes and genes of known function--all in a query-specific fashion. Commonly, heterogeneous datasets are represented as networks in order to facilitate their combination. Here, I show that it is possible to accurately predict gene function in seconds by combining multiple large-scale networks. This facilitates function prediction on-demand, allowing users to take advantage of the persistent improvement and proliferation of genomics and proteomics datasets and continuously make up-to-date predictions for large genomes such as humans. Our algorithm, GeneMANIA, uses constrained linear regression to combine multiple association networks and uses label propagation to make predictions from the combined network. I introduce extensions that result in improved predictions when the number of labeled examples for training is limited, or when an ontological structure describing a hierarchy of gene function categorization scheme is available. Further, motivated by our empirical observations on predicting node labels for general networks, I propose a new label propagation algorithm that exploits common properties of real-world networks to increase both the speed and accuracy of our predictions.

A large number and variety of genome-wide genomics and proteomics datasets are now available for model organisms. Each dataset on its own presents a distinct but noisy view of cellular state. However, collectively, these datasets embody a more comprehensive view of cell function. This motivates the prediction of function for uncharacterized genes by combining multiple datasets, in order to exploit the associations between such genes and genes of known function--all in a query-specific fashion. Commonly, heterogeneous datasets are represented as networks in order to facilitate their combination. Here, I show that it is possible to accurately predict gene function in seconds by combining multiple large-scale networks. This facilitates function prediction on-demand, allowing users to take advantage of the persistent improvement and proliferation of genomics and proteomics datasets and continuously make up-to-date predictions for large genomes such as humans. Our algorithm, GeneMANIA, uses constrained linear regression to combine multiple association networks and uses label propagation to make predictions from the combined network. I introduce extensions that result in improved predictions when the number of labeled examples for training is limited, or when an ontological structure describing a hierarchy of gene function categorization scheme is available. Further, motivated by our empirical observations on predicting node labels for general networks, I propose a new label propagation algorithm that exploits common properties of real-world networks to increase both the speed and accuracy of our predictions.

It is becoming increasingly important to understand biological networks in order to understand complex diseases, identify novel, safer protein targets for therapies and design efficient drugs. 'Systems biology' has emerged as a discipline to uncover biological networks through genomic data. Computational methods for identifying these networks become immensely important and have been growing in number in parallel to increasing amount of genomic data under the discipline of 'Systems Biology'. In this thesis we introduced novel computational methods for identifying topological and dynamic properties of biological networks. Biological data is available in various forms. Experimental data on the interactions between biological components provides a connectivity map of the system as a network of interactions and time series or steady state experiments on concentrations or activity levels of biological constituents will give a dynamic picture of the web of these interactions. Biological data is scarce usually relative to the number of components in the networks and subject to high levels of noise. The data is available from various resources however it can have missing information and inconsistencies. Hence it is critical to design intelligent computational methods that can incorporate data from different resources while considering noise component. This thesis is organized as follows; Chapter 1 and 2 will introduce the basic concepts for biological network types. Chapter 2 will give a background on biochemical network identification data types and computational approaches for reverse engineering of these networks. Chapter 3 will introduce our novel constrained total least squares approach for recovering network topology and dynamics through noisy measurements. We proved our method to be superior over existing reverse engineering methods. Chapter 4 is an extension of chapter 3 where a Bayesian parameter estimation algorithm is presented that is capable of incorporating noisy time series and prior information for the connectivity of network. The quality of prior information is critical to be able to infer dynamics of the networks. The major drawback of prior connectivity data is the presence of false negatives, missing links. Hence, powerful link prediction methods are necessary to be able to identify missing links. At this junction a novel link prediction method is introduced in Chapter 5. This method is capable of predicting missing links in a connectivity data. An application of this method on protein-protein association data from a literature mining database will be demonstrated. In chapter 6 a further extension into link prediction applications will be given. An interesting application of these methods is the drug adverse effect prediction. Adverse effects are the major reason for the failure of drugs in pharmaceutical industry, therefore it is very important to identify potential toxicity risks in the early drug development process. Motivated by this chapter 6 introduces our computational framework that integrates drug-target, drug-side effect, pathway-target and mouse phenotype-mouse genes data to predict side effects. Chapter 7 will give the significant findings and overall achievements of the thesis. Subsequent steps will be suggested that can follow the work presented here to improve network prediction methods.

Many diseases, such as cardiovascular diseases, cancer and diabetes, originate from several malfunctions in biological systems. The human body is regulated by a wide range of biological systems, composed of biological entities interacting in complex networks, responsible for carrying out specific functions. Some parts of the networks, such as hubs serving as master regulators, are more important for maintaining a function. To find the cause of diseases, where hubs are possible disease regulators, it is critical to know the structure of these biological systems. Such structures can be reverse engineered from high-throughput data with measured levels of biological entities. However, the complexity of biological systems makes inferring their structure a complicated task, demanding the use of computational methods, called network inference methods. Today, many network inference methods have been developed, that predicts the interactions of biological networks, with varying degree of success. In the DREAM5 challenge 35 network inference methods were evaluated on how well interactions in gene regulatory networks (GRNs) were predicted. Herein, in contrast to the DREAM5 challenge, we have evaluated network inference methods’ ability to predict hubs in GRNs. In accordance with the DREAM5 challenge, different methods performed the best on different data sets. Moreover, we discovered that network inference methods were not able to identify hubs from groups of similarly expressed genes. Also, we noticed that hubs in GRNs had a distinct expression in the data, leading to the development of a new method (the PCA method) for the prediction of hubs. Furthermore, the DREAM5 challenge showed that community predictions, combining the predictions from many network inference methods, resulted in more robust predictions of interactions. Herein, the community approach was applied on predicting hubs, with the conclusion that community predictions is the more robust approach. However, we also concluded that it was enough to combine 6-7 network inference methods to achieve robust predictions of hubs.

A cana-de-açúcar (Saccharum spp.) é uma das principais gramíneas cultivadas do mundo e o Brasil é seu maior produtor, ela se tornou uma importante cultura devido às altas taxas de assimilação de carbono permitindo a síntese e acumulação de grandes quantidades de sacarose em seus entrenós. Com isso, faz-se necessário uma melhor compreensão dos mecanismos moleculares que regulam o acúmulo de sacarose nesta planta. Tais mecanismos têm sido estudados em vários níveis, tais como, identificação e localização de genes, identificação de lócus controladores de características quantitativas, transcriptoma, proteôma, caracterização e identificação de metabólitos. Com todos esses estudos é evidente a necessidade de uma abordagem holística para o entendimento global da planta durante o acúmulo de sacarose. Assim este trabalho teve por objetivo integrar dados de metabolômica e proteômica de tecidos da cana-de-açúcar da variedade SP80-3280 durante o desenvolvimento e o acúmulo de sacarose, utilizando a bioinformática para unir esses resultados por meio da análise de correlação canônica regularizada em uma abordagem de biologia sistêmica. Os resultados obtidos indicam diferenças no perfil metabólico e proteico da cana-açúcar durante o desenvolvimento e acúmulo de sacarose. Foram propostas classes de metabólitos que podem estar relacionados com o acúmulo de sacarose na cana-de-açúcar tais como glicerolipídeos, glicerofosfolipídeos, cumarinas e derivados, esteroides e derivados de esteroides e acil graxos. Também foram propostas proteínas que podem estar relacionadas com o acúmulo de sacarose, onde as histonas foram as que mais se destacaram. Nas redes biológicas de correlações também foram observadas correlações entre possíveis metabólitos e proteínas que podem estar correlacionadas com o acúmulo de sacarose na cana-de-açúcar / Sugarcane (Saccharum spp.) is one of the most important cultivated grasses of the world and Brazil is the largest producer, it has become an important crop due to high carbon assimilation rates allowing the synthesis and accumulation of large amounts of sucrose in their internodes. Thus, it is necessary a high understanding of the molecular mechanisms involved in the regulation of sucrose accumulation in this plant. These mechanisms have been studied at various levels, such as gene identification and localization, identification of quantitative trait locus controlling, transcriptome, proteome, characterization and metabolites identification. With all these studies is evident the necessity for a holistic approach to global understanding of the plant during the sucrose accumulation. Thus, this work aims to integrate metabolomics and proteomics data from tissues of sugarcane variety SP80-3280, during plant development and sucrose accumulation, using bioinformatics to link these results by regularized canonical correlation analysis in a systems biology approach. The results indicate differences in the metabolic and protein profile of sugarcane during development and sucrose accumulation. Metabolites classes have been proposed that may be related to sugarcane sucrose accumulation as glycerolipids, glycerophospholipids, coumarins and derivatives, steroids and steroid derivatives and fatty acyl. In addition, some proteins have been proposed that may be related to sucrose accumulation, where the most highlighted were the histones. In the biological correlations networks, have been also observed correlations between possible metabolites and proteins that can be correlated with the accumulation of sucrose in sugarcane

As respostas das plantas ao ambiente são orquestradas por fatores genéticos, bem como sua flexibilidade metabólica, uma vez que essas são sésseis. As respostas das plantas ao ambiente são regidas por fatores genéticos, bem como sua flexibilidade metabólica, uma vez que essas são sésseis. A forma com que os padrões gênicos e metabólicos redundam entre as células, refletem nos diferentes níveis organizacionais (célula, tecido, órgão e até o organismo como um todo). Por isso, para entendermos as respostas das plantas em determinados estágios de desenvolvimento ou condições é importante explorarmos ao máximo os diferentes níveis de regulação. Neste sentido, tem crescido a quantidade de dados biológicos obtidos através de métodos que produzem dados em larga escala, visando um estudo de forma sistêmica. Embora existam várias ferramentas para a integração de dados biológicos, elas estão desenvolvidas para organismos modelos, inviabilizando análises para outros, como a cana-de-açúcar, que possui vários dados biológicos disponíveis, mas com genoma complexo e incompleto. Tendo em vista a importância econômica da cana-de-açúcar e o interesse em entendermos o processo de degradação da parede celular, desenvolvemos a ferramenta SIT (Systems Integration Tool), para integração dos dados disponíveis (transcritoma, proteoma e atividade enzimática). A implementação da ferramenta foi realizada utilizando as linguagens de programação Perl e Java. SIT possui uma interface gráfica, podendo ser executada localmente, a qual possibilita a integração de até seis diferentes conjuntos de dados. A visualização do resultado é obtida na forma de redes complexas, permitindo ao usuário a visualização e edição dinâmica da integração. O uso da SIT permitiu no presente estudo, entre outros, a identificação de elementos chave na degradação da parede celular, presentes nos diferentes conjuntos de dados explorados, apontando portanto, potenciais alvos de estudos experimentais. SIT pode ser aplicada à diferentes conjuntos de dados, a qual poderá auxiliar em estudos futuros em várias áreas do conhecimento. / Plant are sessile organisms, and their responses to environmental stimuli are orchestrated by genetic factors, as well as by their metabolic flexibility. Inside the cell, there are genetic and metabolic patterns responsible for cell redundancy, and that reflects on different organizational levels (cell, tissue, organ, until a whole organism). Thus, to understand plant responses to certain conditions, it is important to understanding different regulatory levels. Recently, there was a large increase in availability of biological data. This happened due to the advance in next-generation sequencing techniques, which now enables more profound system biology studies. Despite the availability of several integration tools for analysis of biological data, these were developed for organism modeling. However, such tools are partially effective for sugarcane, for which there are large amounts of data, but has incomplete genome data. Due to the economic importance of sugarcane and aiming at understanding cell wall degradation process, we develop the software Systems Integration Tool (SIT). The tool integrates available data (transcriptomics, proteomics, and enzymatic activity). The implementation was performed in Perl and Java. SIT has a graphical interface, standalone execution, enabling integration until six layers of data. Integration results are generated as complex networks, allowing the users to visualize and dynamically edit the networks. The present study allowed the identification of key cell wall regulatory elements present on different data sets pointing out to potential targets for experimental validation. SIT can be applied to various data sets being capable of helping future studies in different areas of knowledge.

In this thesis, inference of biological networks from in vivo data generated by perturbation experiments is considered, i.e. deduction of causal interactions that exist among the observed variables. Knowledge of such regulatory influences is essential in biology. A system property–interampatteness–is introduced that explains why the variation in existing gene expression data is concentrated to a few “characteristic modes” or “eigengenes”, and why previously inferred models have a large number of false positive and false negative links. An interampatte system is characterized by strong INTERactions enabling simultaneous AMPlification and ATTEnuation of different signals and we show that perturbation of individual state variables, e.g. genes, typically leads to ill-conditioned data with both characteristic and weak modes. The weak modes are typically dominated by measurement noise due to poor excitation and their existence hampers network reconstruction. The excitation problem is solved by iterative design of correlated multi-gene perturbation experiments that counteract the intrinsic signal attenuation of the system. The next perturbation should be designed such that the expected response practically spans an additional dimension of the state space. The proposed design is numerically demonstrated for the Snf1 signalling pathway in S. cerevisiae. The impact of unperturbed and unobserved latent state variables, that exist in any real biological system, on the inferred network and required set-up of the experiments for network inference is analysed. Their existence implies that a subnetwork of pseudo-direct causal regulatory influences, accounting for all environmental effects, in general is inferred. In principle, the number of latent states and different paths between the nodes of the network can be estimated, but their identity cannot be determined unless they are observed or perturbed directly. Network inference is recognized as a variable/model selection problem and solved by considering all possible models of a specified class that can explain the data at a desired significance level, and by classifying only the links present in all of these models as existing. As shown, these links can be determined without any parameter estimation by reformulating the variable selection problem as a robust rank problem. Solution of the rank problem enable assignment of confidence to individual interactions, without resorting to any approximation or asymptotic results. This is demonstrated by reverse engineering of the synthetic IRMA gene regulatory network from published data. A previously unknown activation of transcription of SWI5 by CBF1 in the IRMA strain of S. cerevisiae is proven to exist, which serves to illustrate that even the accumulated knowledge of well studied genes is incomplete. / Denna avhandling behandlar inferens av biologiskanätverk från in vivo data genererat genom störningsexperiment, d.v.s. bestämning av kausala kopplingar som existerar mellan de observerade variablerna. Kunskap om dessa regulatoriska influenser är väsentlig för biologisk förståelse. En system egenskap—förstärksvagning—introduceras. Denna förklarar varför variationen i existerande genexpressionsdata är koncentrerat till några få ”karakteristiska moder” eller ”egengener” och varför de modeller som konstruerats innan innehåller många falska positiva och falska negativa linkar. Ett system med förstärksvagning karakteriseras av starka kopplingar som möjliggör simultan FÖRSTÄRKning och förSVAGNING av olika signaler. Vi demonstrerar att störning av individuella tillståndsvariabler, t.ex. gener, typiskt leder till illakonditionerat data med både karakteristiska och svaga moder. De svaga moderna domineras typiskt av mätbrus p.g.a. dålig excitering och försvårar rekonstruktion av nätverket. Excitationsproblemet löses med iterativdesign av experiment där korrelerade störningar i multipla gener motverkar systemets inneboende försvagning av signaller. Följande störning bör designas så att det förväntade svaret praktiskt spänner ytterligare en dimension av tillståndsrummet. Den föreslagna designen demonstreras numeriskt för Snf1 signalleringsvägen i S. cerevisiae. Påverkan av ostörda och icke observerade latenta tillståndsvariabler, som existerar i varje verkligt biologiskt system, på konstruerade nätverk och planeringen av experiment för nätverksinferens analyseras. Existens av dessa tillståndsvariabler innebär att delnätverk med pseudo-direkta regulatoriska influenser, som kompenserar för miljöeffekter, generellt bestäms. I princip så kan antalet latenta tillstånd och alternativa vägar mellan noder i nätverket bestämmas, men deras identitet kan ej bestämmas om de inte direkt observeras eller störs. Nätverksinferens behandlas som ett variabel-/modelselektionsproblem och löses genom att undersöka alla modeller inom en vald klass som kan förklara datat på den önskade signifikansnivån, samt klassificera endast linkar som är närvarande i alla dessa modeller som existerande. Dessa linkar kan bestämmas utan estimering av parametrar genom att skriva om variabelselektionsproblemet som ett robustrangproblem. Lösning av rangproblemet möjliggör att statistisk konfidens kan tillskrivas individuella linkar utan approximationer eller asymptotiska betraktningar. Detta demonstreras genom rekonstruktion av det syntetiska IRMA genreglernätverket från publicerat data. En tidigare okänd aktivering av transkription av SWI5 av CBF1 i IRMA stammen av S. cerevisiae bevisas. Detta illustrerar att t.o.m. den ackumulerade kunskapen om välstuderade gener är ofullständig. / <p>QC 20130508</p>

Els mecanismes de regulaci o de les c el lules poden ser modelats per controlar i entendre la biologia cel lular. Diferents nivells d'abstracci o s'utilitzen per descriure els processos biol ogics. En aquest treball s'han utilitzat grafs i equacions diferencials per modelar les interaccions cel lulars tant qualitativament com quantitativa. En aquest treball s'han analitzat dades d'interacci o i activitat de diferents organismes, E. coli i S. cerevisiae: xarxes d'interacci o prote na-prote na, de regulaci o de la transcripci o, i metab oliques, aix com per ls d'expressi o gen omica i prote omica. De la rica varietat de mesures de grafs, una variable important d'aquestes xarxes biol ogiques es la distribuci o de grau, i he aplicat eines d'an alisi estad stica per tal de caracteritzar-la. En tots els casos estudiats les distribucions de grau tenen una forma de cua pesada, per o la majoria d'elles presenten difer encies signi catives respecte un model de llei de pot encia, d'acord amb proves estad stiques. D'altra banda, cap de les xarxes podrien ser assignades de forma inequvoca a cap distribuci o testejada. Pel que fa a un nivell m es microsc opic, hem utilitzat equacions diferencials per estudiar la din amica de models de diversos sistemes bioqu mics. En primer lloc, una eina de programari anomenada ByoDyn ha estat creada des de zero. L'eina permet realitzar simulacions deterministes i estoc astiques, analitzar models mitjan cant estimaci o de par ametres, sensibilitat i an alisi d'identi cabilitat, aix com dissenyar optimament experiments. S'ha creat una interf cie web que ofereix la possibilitat d'interactuar amb el programa d'una manera gr a ca, independentment de la con guraci o de l'usuari, permetent l'execuci o del programa en diferents entorns computacionals. Finalment, hem aplicat un protocol de disseny experimental optim en un model multicel lular de l'embriog enesi en vertebrats. / Regulatory mechanisms of cells can be modelled to control and under- stand cellular biology. Di erent levels of abstraction are used to describe biological processes. In this work we have used graphs and di erential equations to model cellular interactions qualitatively and quantitatively. From di erent organisms, E. coli and S. cerevisiae, we have analysed data available for they complete interaction and activity networks. At the level of interaction, the protein-protein interaction network, the tran- scriptional regulatory networks and the metabolic network have been studied; for the activity, both gene and protein pro les of the whole or- ganism have been examined. From the rich variety of graph measures, one of primer importance is the degree distribution. I have applied sta- tistical analysis tools to such biological networks in order to characterise the degree distribution. In all cases the studied degree distributions have a heavy-tailed shape, but most of them present signi cant di erences from a power-law model according to a statistical test. Moreover, none of the networks could be unequivocally assigned to any of the tested distribution. On the other hand, in a more ne-grained view, I have used di erential equations to model dynamics of biochemical systems. First, a software tool called ByoDyn has been created from scratch incorporating a fairly complete range of analysis methods. Both deterministic and stochas- tic simulations can be performed, models can be analysed by means of parameter estimation, sensitivity, identi ability analysis, and optimal ex- perimental design. Moreover, a web interface has been created that pro- vides with the possibility interact with the program in a graphical man- ner, independent of the user con guration, allowing the execution of the program at di erent computational environments. Finally, we have ap- plied a protocol of optimal experimental design on a multicellular model of embryogenesis.