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Aligning multiple biological sequences such as protein sequences or DNA/RNA sequences is a fundamental task in bioinformatics and sequence analysis. These alignments may contain invaluable information that scientists need to predict the sequences' structures, determine the evolutionary relationships between them, or discover drug-like compounds that can bind to the sequences. Unfortunately, multiple sequence alignment (MSA) is NP-Complete. In addition, the lack of a reliable scoring method makes it very hard to align the sequences reliably and to evaluate the alignment outcomes. In this dissertation, we have designed a new scoring method for use in multiple sequence alignment. Our scoring method encapsulates stereo-chemical properties of sequence residues and their substitution probabilities into a tree-structure scoring scheme. This new technique provides a reliable scoring scheme with low computational complexity. In addition to the new scoring scheme, we have designed an overlapping sequence clustering algorithm to use in our new three multiple sequence alignment algorithms. One of our alignment algorithms uses a dynamic weighted guidance tree to perform multiple sequence alignment in progressive fashion. The use of dynamic weighted tree allows errors in the early alignment stages to be corrected in the subsequence stages. Other two algorithms utilize sequence knowledge-bases and sequence consistency to produce biological meaningful sequence alignments. To improve the speed of the multiple sequence alignment, we have developed a parallel algorithm that can be deployed on reconfigurable computer models. Analytically, our parallel algorithm is the fastest progressive multiple sequence alignment algorithm.

Bioinformatics and computational biology (BCB) is a rapidly developing multidisciplinary field which encompasses a wide range of domains, including genomic sequence alignments. It is a fundamental tool in molecular biology in searching for homology between sequences. Sequence alignments are currently gaining close attention due to their great impact on the quality aspects of life such as facilitating early disease diagnosis, identifying the characteristics of a newly discovered sequence, and drug engineering. With the vast growth of genomic data, searching for a sequence homology over huge databases (often measured in gigabytes) is unable to produce results within a realistic time, hence the need for acceleration. Since the exponential increase of biological databases as a result of the human genome project (HGP), supercomputers and other parallel architectures such as the special purpose Very Large Scale Integration (VLSI) chip, Graphic Processing Unit (GPUs) and Field Programmable Gate Arrays (FPGAs) have become popular acceleration platforms. Nevertheless, there are always trade-off between area, speed, power, cost, development time and reusability when selecting an acceleration platform. FPGAs generally offer more flexibility, higher performance and lower overheads. However, they suffer from a relatively low level programming model as compared with off-the-shelf microprocessors such as standard microprocessors and GPUs. Due to the aforementioned limitations, the need has arisen for optimized FPGA core implementations which are crucial for this technology to become viable in high performance computing (HPC). This research proposes the use of state-of-the-art reprogrammable system-on-chip technology on FPGAs to accelerate three widely-used sequence alignment algorithms; the Smith-Waterman with affine gap penalty algorithm, the profile hidden Markov model (HMM) algorithm and the Basic Local Alignment Search Tool (BLAST) algorithm. The three novel aspects of this research are firstly that the algorithms are designed and implemented in hardware, with each core achieving the highest performance compared to the state-of-the-art. Secondly, an efficient scheduling strategy based on the double buffering technique is adopted into the hardware architectures. Here, when the alignment matrix computation task is overlapped with the PE configuration in a folded systolic array, the overall throughput of the core is significantly increased. This is due to the bound PE configuration time and the parallel PE configuration approach irrespective of the number of PEs in a systolic array. In addition, the use of only two configuration elements in the PE optimizes hardware resources and enables the scalability of PE systolic arrays without relying on restricted onboard memory resources. Finally, a new performance metric is devised, which facilitates the effective comparison of design performance between different FPGA devices and families. The normalized performance indicator (speed-up per area per process technology) takes out advantages of the area and lithography technology of any FPGA resulting in fairer comparisons. The cores have been designed using Verilog HDL and prototyped on the Alpha Data ADM-XRC-5LX card with the Virtex-5 XC5VLX110-3FF1153 FPGA. The implementation results show that the proposed architectures achieved giga cell updates per second (GCUPS) performances of 26.8, 29.5 and 24.2 respectively for the acceleration of the Smith-Waterman with affine gap penalty algorithm, the profile HMM algorithm and the BLAST algorithm. In terms of speed-up improvements, comparisons were made on performance of the designed cores against their corresponding software and the reported FPGA implementations. In the case of comparison with equivalent software execution, acceleration of the optimal alignment algorithm in hardware yielded an average speed-up of 269x as compared to the SSEARCH 35 software. For the profile HMM-based sequence alignment, the designed core achieved speed-up of 103x and 8.3x against the HMMER 2.0 and the latest version of HMMER (version 3.0) respectively. On the other hand, the implementation of the gapped BLAST with the two-hit method in hardware achieved a greater than tenfold speed-up compared to the latest NCBI BLAST software. In terms of comparison against other reported FPGA implementations, the proposed normalized performance indicator was used to evaluate the designed architectures fairly. The results showed that the first architecture achieved more than 50 percent improvement, while acceleration of the profile HMM sequence alignment in hardware gained a normalized speed-up of 1.34. In the case of the gapped BLAST with the two-hit method, the designed core achieved 11x speed-up after taking out advantages of the Virtex-5 FPGA. In addition, further analysis was conducted in terms of cost and power performances; it was noted that, the core achieved 0.46 MCUPS per dollar spent and 958.1 MCUPS per watt. This shows that FPGAs can be an attractive platform for high performance computation with advantages of smaller area footprint as well as represent economic ‘green’ solution compared to the other acceleration platforms. Higher throughput can be achieved by redeploying the cores on newer, bigger and faster FPGAs with minimal design effort.

Orientador: Zanoni Dias / Dissertação (mestrado) - Universidade Estadual de Campinas, Instituto de Computação / Made available in DSpace on 2018-08-16T22:45:38Z (GMT). No. of bitstreams: 1 Souza_MariaAngelicaLopesde_M.pdf: 2988201 bytes, checksum: 0742d490b058c7a3dae6fddd7314aba4 (MD5) Previous issue date: 2010 / Resumo: O alinhamento múltiplo dc sequências é uma tarefa de grande relevância cm Bioin-formática. Através dele é possível estudar eventos evolucionários c restrições estruturais ou funcionais, sejam de sequências de proteína, DNA ou RNA, tornando possível entender a estrutura, função c evolução dos genes que compõem um organismo. O objetivo do alinhamento múltiplo é a melhor representação do cenário dc evolução das sequencias ao longo do tempo, considerando a possibilidade dc ocorrerem diferentes eventos de mutação. Encontrar um alinhamento múltiplo dc sequencias ótimo é um problema NP-Difícil. Desta forma, diversas abordagens têm sido desenvolvidas no intuito de encontrar uma solução heurística que represente da melhor maneira possível o cenário dc evolução real, dentre elas está a abordagem progressiva. O alinhamento progressivo c uma das maneiras mais simples dc se realizar o alinhamento múltiplo, pois utiliza pouco tempo c memória computacional. Ele c realizado cm três etapas principais: determinar a distância entre as sequências que serão alinhadas, construir uma árvore guia a partir das distâncias c finalmente construir o alinhamento múltiplo. Este trabalho foi desenvolvido a partir do estudo de diferentes métodos para realizar cada etapa dc um alinhamento progressivo. Foram construídos 342 alinhadores resultantes da combinação dos métodos estudados. Os parâmetros dc entrada adequados para a maioria dos alinhadores foram determinados por estudos empíricos. Após a definição dos parâmetros adequados para cada tipo dc ahnhador, foram realizados testes com dois subconjuntos de referencia do BAliBASE. Com esses testes observamos que os melhores alinhadores foram aqueles que utilizam o agrupamento dc perfil para gerar o alinhamento múltiplo, com destaque paTa os que utilizam pontuação afim para penalizar buracos. Observamos também, que dentre os alinhadores dc agrupamento por consenso, os que utilizam função logarítmica, para penalizar buracos demonstraram melhores desempenhos / Abstract: The multiple sequence alignment is a relevant task in Bioinf'ormatics. Using this technique is possible to study evolutionary events and also structural or functional restrictions of protein, DNA, or RNA sequences. This study helps the understanding of the structure, function, and evolution of the genes that make up an organism. The multiple sequence alignment tries to achieve the best representation of a sequence evolution scenario, considering different mutation events occurrence. Finding an optimal multiple sequence alignment is a NP-Hard problem. Thus, several approaches have been developed in order to find an heuristic solution that represents the real evolution cenário, such as the progressive approach. The progressive alignment is a simple way to perform the multiple alignment, because its low memcny usage and computational time. It is performed in three main stages: (i) determining the distance between the sequences to be aligned, (ii) constructing a guide tree from the distances and finally (hi) building the multiple alignment guided by the tree. This work studied different methods for performing each step of progressive alignment and 342 aligners were built combining these methods. The input parameters suitable for most aligners were determined by empirical studies. After the parameters definition for each type of aligner, which where tested against two reference subsets of BAliBASE. The test results showed that the best aligners were those using the profile alignment to generate the multiple alignment, especially those using affine gap penalty function. In addition, this work shows that among the aligners of grouping by consensus, those that use the logarithmic gap penalty function presented better performance / Mestrado / Bioinformatica / Mestre em Ciência da Computação

Orientador: Zanoni Dias / Tese (doutorado) - Universidade Estadual de Campinas, Instituto de Computação / Made available in DSpace on 2018-08-22T15:29:14Z (GMT). No. of bitstreams: 1 Almeida_AndreAtanasioMaranhao_D.pdf: 2248939 bytes, checksum: b57ed5328b80a2fc7f36d1509558e756 (MD5) Previous issue date: 2013 / Resumo: Alinhamento de seqüências é, reconhecidamente, uma das tarefas de maior importância em bioinformática. Tal importância origina-se no fato de ser uma operação básica utilizada por diversos outros procedimentos na área, como busca em bases de dados, visualização do efeito da evolução em uma família de proteínas, construção de árvores filogenéticas e identificação de motifs preservados. Seqüências podem ser alinhadas aos pares, problema para o qual já se conhece algoritmo exato com complexidade de tempo O(l2), para seqüências de comprimento l. Pode-se também alinhar simultaneamente três ou mais seqüências, o que é chamado de alinhamento múltiplo de seqüências (MSA, do inglês Multiple Sequence Alignment ). Este, que é empregado em tarefas como detecção de padrões para caracterizar famílias protéicas e predição de estruturas secundárias e terciárias de proteínas, é um problema NP - Difícil. Neste trabalho foram desenvolvidos métodos heurísticos para alinhamento múltiplo de seqüências de proteína. Estudaram-se as principais abordagens e métodos existentes e foi realizada uma série de implementações e avaliações. Em um primeiro momento foram construídos 342 alinhadores múltiplos utilizando a abordagem progressiva. Esta, que é uma abordagem largamente utilizada para construção de MSAs, consiste em três etapas. Na primeira delas é computada a matriz de distâncias. Em seguida, uma árvore guia é gerada com base na matriz e, finalmente, o MSA é construído através de alinhamentos de pares, cuja ordem é definida pela árvore. Os alinhadores desenvolvidos combinam diferentes métodos aplicados a cada uma das etapas. Para a computação das matrizes de distâncias foram desenvolvidos dois métodos, que são capazes também de gerar alinhamentos de pares de seqüências. Um deles constrói o alinhamento com base em alinhamentos locais e o outro utiliza uma função logarítmica para a penalização de gaps. Foram utilizados ainda outros métodos disponíveis numa ferramenta chamada PHYLIP. Para a geração das árvores guias, foram utilizados os métodos clássicos UPGMA e Neighbor Joining. Usaram-se implementações disponíveis em uma ferramenta chamada R. Já para a construção do alinhamento múltiplo, foram implementados os métodos seleção por bloco único e seleção do par mais próximo. Estes, que se destinam a seleção xiii do par de alinhamentos a agrupar no ciclo corrente, são comumente utilizados para tal tarefa. Já para o agrupamento de um par de alinhamentos, foram implementados 12 métodos inspirados em métodos comumente utilizados - alinhamento de consensos e alinhamento de perfis. Foram feitas todas as combinações possíveis entre esses métodos, resultando em 342 alinhadores. Eles foram avaliados quanto à qualidade dos alinhamentos que geram e avaliou-se também o desempenho dos métodos, utilizados em cada etapa. Em seguida foram realizadas avaliações no contexto de alinhamento baseado em consistência. Nesta abordagem, considera-se MSA ótimo aquele que estão de acordo com a maioria dos alinhamentos ótimos para os n(n ? 1)/2 alinhamentos de pares contidos no MSA. Alterações foram realizadas em um alinhador múltiplo conhecido, MUMMALS, que usa a abordagem. As modificações foram feitas no método de contagem k-mer, assim como, em outro momento, substituiu-se a parte inicial do algoritmo. Foram alterados os métodos para computação da matriz de distâncias e para geração da árvore guia por outros que foram bem avaliados nos testes realizados para a abordagem progressiva. No total, foram implementadas e avaliadas 89 variações do algoritmo original do MUMMALS e, apesar do MUMMALS já produzir alinhamentos de alta qualidade, melhoras significativas foram alcançadas. O trabalho foi concluído com a implementação e a avaliação de algoritmos iterativos. Estes se caracterizam pela dependência de outros alinhadores para a produção de alinhamentos iniciais. Ao alinhador iterativo cabe a tarefa de refinar tais alinhamentos através de uma série de ciclos até que haja uma estabilização na qualidade dos alinhamentos. Foram implementados e avaliados dois alinhadores iterativos não estocásticos, assim como um algoritmo genético (GA) voltado para a geração de MSAs. Nesse algoritmo genético, implementado na forma de um ambiente parametrizável para execução de algoritmos genéticos para MSA, chamado ALGAe, foram realizadas diversas experiências que progressivamente elevaram a qualidade dos alinhamentos gerados. No ALGAe foram incluídas outras abordagens para construção de alinhamentos múltiplos, tais como baseada em blocos, em consenso e em modelos. A primeira foi aplicada na geração de indivíduos para a população inicial. Foram implementados alinhadores baseados em blocos usando duas abordagens distintas e, para uma delas, foram implementadas cinco variações. A segunda foi aplicada na definição de um operador de cruzamento, que faz uso da ferramenta M-COFFEE para realizar alinhamentos baseados em consenso a partir de indivíduos da população corrente do GA, e a terceira foi utilizada para definir uma função de aptidão, que utiliza a ferramenta PSIPRED para predição das estruturas secundárias das seqüências. O ALGAe permite a realização de uma grande variedade de novas avaliações / Abstract: Sequence alignment is one the most important tasks of bioinformatics. It is a basic operation used for several procedures in that domain, such as sequence database searches, evolution effect visualization in an entire protein family, phylogenetic trees construction and preserved motifs identification. Sequences can be aligned in pairs and generate a pairwise alignment. Three or more sequences can also be simultaneously aligned and generate a multiple sequence alignment (MSA). MSAs could be used for pattern recognition for protein family characterization and secondary and tertiary protein structure prediction. Let l be the sequence length. The pairwise alignment takes time O(l2) to build an exact alignment. However, multiple sequence alignment is a NP-Hard problem. In this work, heuristic methods were developed for multiple protein sequence alignment. The main approaches and methods applied to the problem were studied and a series of aligners developed and evaluated. In a first moment 342 multiple aligners using the progressive approach were built. That is a largely used approach for MSA construction and is composed by three steps. In the first one a distance matrix is computed. Then, a guide tree is built based on the matrix and finally the MSA is constructed through pairwise alignments. The order to the pairwise alignments is defined by the tree. The developed aligners combine distinct methods applied to each of steps. Then, evaluations in the consistency based alignment context were performed. In that approach, a MSA is optimal when agree with the majority along all possible optimal pairwise alignments. MUMMALS is a known consistency based aligner. It was changed in this evaluation. The k-mer counting method was modified in two distinct ways. The k value and the compressed alphabet were ranged. In another evaluation, the k-mer counting method and guide tree construction method were replaced. In the last stage of the work, iterative algorithms were developed and evaluated. Those methods are characterized by other aligner's dependence. The other aligners generate an initial population and the iterative aligner performs a refinement procedure, which iteratively changes the alignments until the alignments quality are stabilized. Several evaluations were performed. However, a genetic algorithm for MSA construction stood out along this stage. In that aligner were added other approaches for multiple sequence alignment construction, such as block based, consensus based and template based. The first one was applied to initial population generation, the second one was used for a crossover operator creation and the third one defined a fitness function / Doutorado / Ciência da Computação / Doutor em Ciência da Computação

Researchers use multiple sequence alignment algorithms to detect conserved regions in genetic sequences and to identify drug docking sites for drug development. In this dissertation, a novel algorithm is presented for using physicochemical properties to increase the accuracy of multiple sequence alignments. Secondary structures are also incorporated in the evaluation function. Additionally, the location of the secondary structures is assimilated into the function. Multiple properties are combined with weights, determined from prediction accuracies of protein secondary structures using artificial neural networks. A new metric, the PPD Score is developed, that captures the average change in physicochemical properties. Using the physicochemical properties and the secondary structures for multiple sequence alignment results in alignments that are more accurate, biologically relevant and useful for drug development and other medical uses. In addition to a novel multiple sequence alignment algorithm, we also propose a new protein-coding DNA reference alignment database. This database is a collection of multiple sequence alignment data sets derived from tertiary structural alignments. The primary purpose of the database is to benchmark new and existing multiple sequence alignment algorithms with DNA data. The first known comparative study of protein-coding DNA alignment accuracies is also included in this work.

DNA sequence alignments are usually not homogeneous. Mosaic structures may result as a consequence of recombination or rate heterogeneity. Interspecific recombination, in which DNA subsequences are transferred between different (typically viral or bacterial) strains may result in a change of the topology of the underlying phylogenetic tree. Rate heterogeneity corresponds to a change of the nucleotide substitution rate. Various methods for simultaneously detecting recombination and rate heterogeneity in DNA sequence alignments have recently been proposed, based on complex probabilistic models that combine phylogenetic trees with factorial hidden Markov models or multiple changepoint processes. The objective of my thesis is to identify potential shortcomings of these models and explore ways of how to improve them. One shortcoming that I have identified is related to an approximation made in various recently proposed Bayesian models. The Bayesian paradigm requires the solution of an integral over the space of parameters. To render this integration analytically tractable, these models assume that the vectors of branch lengths of the phylogenetic tree are independent among sites. While this approximation reduces the computational complexity considerably, I show that it leads to the systematic prediction of spurious topology changes in the Felsenstein zone, that is, the area in the branch lengths configuration space where maximum parsimony consistently infers the wrong topology due to long-branch attraction. I demonstrate these failures by using two Bayesian hypothesis tests, based on an inter- and an intra-model approach to estimating the marginal likelihood. I then propose a revised model that addresses these shortcomings, and demonstrate its improved performance on a set of synthetic DNA sequence alignments systematically generated around the Felsenstein zone. The core model explored in my thesis is a phylogenetic factorial hidden Markov model (FHMM) for detecting two types of mosaic structures in DNA sequence alignments, related to recombination and rate heterogeneity. The focus of my work is on improving the modelling of the latter aspect. Earlier research efforts by other authors have modelled different degrees of rate heterogeneity with separate hidden states of the FHMM. Their work fails to appreciate the intrinsic difference between two types of rate heterogeneity: long-range regional effects, which are potentially related to differences in the selective pressure, and the short-term periodic patterns within the codons, which merely capture the signature of the genetic code. I have improved these earlier phylogenetic FHMMs in two respects. Firstly, by sampling the rate vector from the posterior distribution with RJMCMC I have made the modelling of regional rate heterogeneity more flexible, and I infer the number of different degrees of divergence directly from the DNA sequence alignment, thereby dispensing with the need to arbitrarily select this quantity in advance. Secondly, I explicitly model within-codon rate heterogeneity via a separate rate modification vector. In this way, the within-codon effect of rate heterogeneity is imposed on the model a priori, which facilitates the learning of the biologically more interesting effect of regional rate heterogeneity a posteriori. I have carried out simulations on synthetic DNA sequence alignments, which have borne out my conjecture. The existing model, which does not explicitly include the within-codon rate variation, has to model both effects with the same modelling mechanism. As expected, it was found to fail to disentangle these two effects. On the contrary, I have found that my new model clearly separates within-codon rate variation from regional rate heterogeneity, resulting in more accurate predictions.

A computational grid is a hardware and software infrastructure that provides dependable, consistent, pervasive, and inexpensive access to high-end computational capabilities. This talk is divided into three parts. Firstly, we give an overview of the main issues in grid computing. Next, we introduce ALiCE (Adaptive and Scalable Internet-based Computing Engine), a platform independent and lightweight grid. ALiCE exploits object-level parallelism using our Object Network Transport Architecture (ONTA). Grid applications are written using ALiCE Object Programming Template that hides the complexities of the underlying grid fabric. Lastly, we present some performance results of ALiCE applications including the geo-rectification of satellite images and the progressive multiple sequence alignments problem. / Singapore-MIT Alliance (SMA)

Protein sequencing has produced overwhelming amount of protein sequences, especially in the last decade. Nevertheless, the majority of the proteins' functional and structural classes are still unknown, and experimental methods currently used to determine these properties are very expensive, laborious and time consuming. Therefore, automated computational methods are urgently required to accurately and reliably predict functional and structural classes of the proteins. Several bioinformatics methods have been developed to determine such properties of the proteins directly from their sequence information. Such methods that involve signal processing methods have recently become popular in the bioinformatics area and been investigated for the analysis of DNA and protein sequences and shown to be useful and generally help better characterise the sequences. However, there are various technical issues that need to be addressed in order to overcome problems associated with the signal processing methods for the analysis of the proteins sequences. Amino acid indices that are used to transform the protein sequences into signals have various applications and can represent diverse features of the protein sequences and amino acids. As the majority of indices have similar features, this project proposes a new set of computationally derived indices that better represent the original group of indices. A study is also carried out that resulted in finding a unique and universal set of best discriminating amino acid indices for the characterisation of allergenic proteins. This analysis extracts features directly from the protein sequences by using Discrete Fourier Transform (DFT) to build a classification model based on Support Vector Machines (SVM) for the allergenic proteins. The proposed predictive model yields a higher and more reliable accuracy than those of the existing methods. A new method is proposed for performing a multiple sequence alignment. For this method, DFT-based method is used to construct a new distance matrix in combination with multiple amino acid indices that were used to encode protein sequences into numerical sequences. Additionally, a new type of substitution matrix is proposed where the physicochemical similarities between any given amino acids is calculated. These similarities were calculated based on the 25 amino acids indices selected, where each one represents a unique biological protein feature. The proposed multiple sequence alignment method yields a better and more reliable alignment than the existing methods. In order to evaluate complex information that is generated as a result of DFT, Complex Informational Spectrum Analysis (CISA) is developed and presented. As the results show, when protein classes present similarities or differences according to the Common Frequency Peak (CFP) in specific amino acid indices, then it is probable that these classes are related to the protein feature that the specific amino acid represents. By using only the absolute spectrum in the analysis of protein sequences using the informational spectrum analysis is proven to be insufficient, as biologically related features can appear individually either in the real or the imaginary spectrum. This is successfully demonstrated over the analysis of influenza neuraminidase protein sequences. Upon identification of a new protein, it is important to single out amino acid responsible for the structural and functional classification of the protein, as well as the amino acids contributing to the protein's specific biological characterisation. In this work, a novel approach is presented to identify and quantify the relationship between individual amino acids and the protein. This is successfully demonstrated over the analysis of influenza neuraminidase protein sequences. Characterisation and identification problem of the Influenza A virus protein sequences is tackled through a Subgroup Discovery (SD) algorithm, which can provide ancillary knowledge to the experts. The main objective of the case study was to derive interpretable knowledge for the influenza A virus problem and to consequently better describe the relationships between subtypes of this virus. Finally, by using DFT-based sequence-driven features a Support Vector Machine (SVM)-based classification model was built and tested, that yields higher predictive accuracy than that of SD. The methods developed and presented in this study yield promising results and can be easily applied to proteomic fields.

Tous les systèmes biologiques sont formés d’atomes et de molécules qui interagissent et dont émergent des propriétés subtiles et complexes. Par ces interactions, les organismes vivants peuvent subvenir à toutes leurs fonctions vitales. Ces propriétés apparaissent dans tous les systèmes biologiques à des niveaux différents, du niveau des molécules et gènes jusqu’aux niveau des cellules et tissus. Ces dernières années, les physiciens se sont impliqués dans la compréhension de ces aspects particulièrement intrigants, en particulier en étudiant les systèmes vivants dans le cadre de la théorie des réseaux, théorie qui offre des outils d’analyse très puissants. Il est possible aujourd’hui d’identifier deux classes d’approches qui sont utilisée pour étudier ces types de systèmes complexes : les méthodes directes de modélisation et les approches inverses d’inférence. Dans cette thèse, mon travail est basé sur les deux types d’approches appliquées à trois niveaux de systèmes biologiques. Dans la première partie de la thèse, je me concentre sur les premières étapes du développement des tissus biologiques des plantes. Je propose un nouveau modèle pour comprendre la dynamique collective des transporteurs de l’hormone auxine et qui permet la croissance non-homogène des tissu dans l’espace et le temps. Dans la deuxième partie de la thèse, j’analyse comment l’évolution contraint la diversité́ de séquence des protéines tout en conservant leur fonction dans différents organismes. En particulier, je propose une nouvelle méthode pour inférer les sites essentiels pour la fonction ou la structure de protéines à partir d’un ensemble de séquences biologiques. Finalement, dans la troisième partie de la thèse, je travaille au niveau cellulaire et étudie les réseaux de signalisation associés à l’auxine. Dans ce contexte, je reformule un modèle préexistant et propose une nouvelle technique qui permet de définir et d’étudier la réponse du système aux signaux externes pour des topologies de réseaux différentes. J’exploite ce cadre théorique pour identifier le rôle fonctionnel de différentes topologies dans ces systèmes. / All biological systems are made of atoms and molecules interacting in a non- trivial manner. Such non-trivial interactions induce complex behaviours allow- ing organisms to fulfill all their vital functions. These features can be found in all biological systems at different levels, from molecules and genes up to cells and tissues. In the past few decades, physicists have been paying much attention to these intriguing aspects by framing them in network approaches for which a number of theoretical methods offer many powerful ways to tackle systemic problems. At least two different ways of approaching these challenges may be considered: direct modeling methods and approaches based on inverse methods. In the context of this thesis, we made use of both methods to study three different problems occurring on three different biological scales. In the first part of the thesis, we mainly deal with the very early stages of tissue development in plants. We propose a model aimed at understanding which features drive the spontaneous collective behaviour in space and time of PINs, the transporters which pump the phytohormone auxin out of cells. In the second part of the thesis, we focus instead on the structural properties of proteins. In particular we ask how conservation of protein function across different organ- isms constrains the evolution of protein sequences and their diversity. Hereby we propose a new method to extract the sequence positions most relevant for protein function. Finally, in the third part, we study intracellular molecular networks that implement auxin signaling in plants. In this context, and using extensions of a previously published model, we examine how network structure affects network function. The comparison of different network topologies provides insights into the role of different modules and of a negative feedback loop in particular. Our introduction of the dynamical response function allows us to characterize the systemic properties of the auxin signaling when external stimuli are applied.