Cell signaling is a list of chemical reactions that are used for intercellular and intracellular communication. Signaling pathways denote these chemical reactions in a systematic manner. Today, many signaling pathways are constructed by several experimental methods. However there are still many communication skills of cells that are needed to be discovered. RNAi system allows us to see the phenotypes when some genes are removed from living cells. By observing these phenotypes, we can build signaling pathways. However it is costly in terms of time and space complexity. Furthermore, there are some interactions RNAi data cannot distinguish that results in many different signaling pathways all of which are consistent with the RNAi data. In this thesis, we combine genetic algorithms with some greedy approaches to find the topologies that fit the Boolean single knock-down RNAi experiments. Our algorithm finds nearly all of the results for small inputs in a few minutes. It can also find a significant number of results for larger inputs. Then we eliminate isomorphic topologies from the output set of this algorithm. This process fairly reduces the number of topologies. Afterwards we offer a simple scheme for suggesting new wet-lab RNAi experiments which is necessary to have a complete approach to find the actual network. Also we describe a new activation and deactivation model for pathways when the activation of the phenotype after RNAi knock-downs are given as weighted variables. We adapt the first genetic algorithm approach to this model for directly finding the most possible network.

Reconstructing the topology of a signaling network by means of RNA interference (RNAi) technology is an underdetermined problem especially when a single gene in the network is knocked down or observed. In addition, the exponential search space limits the existing methods to small signaling networks of size 10-15 genes. In this thesis, we propose integrating RNAi data with a reference physical interaction network. We formulate the problem of signaling network reconstruction as finding the minimum number of edit operations on a given reference network. The edit operations transform the reference network to a network that satisfy the RNAi observations. We show that using a reference network does not simplify the computational complexity of the problem. Therefore, we propose an approach that provides near optimal results and can scale well for reconstructing networks up to hundreds of components. We validate the proposed method on synthetic and real datasets. Comparison with the state of the art on real signaling networks shows that the proposed methodology can scale better and generates biologically significant results.

Doctor of Philosophy / Genetics Interdepartmental Program / James C. Nelson / QTL (quantitative-trait locus) analysis aims to locate and estimate the effects of genes that are responsible for quantitative traits, by means of statistical methods that evaluate the association of genetic variation with trait (phenotypic) variation. Quantitative traits are typically controlled by multiple genes with varying degrees of influence on the phenotype. I describe a new QTL analysis method based on shrinkage and a unifying framework based on the generalized linear model for non-normal data. I develop their extensions to multiple-trait QTL analysis. Expression QTL, or eQTL, analysis is QTL analysis applied to gene expression data to reveal the eQTLs controlling transcript-abundance variation, with the goal of elucidating gene regulatory networks. For exploiting eQTL data, I develop a novel extension of the graphical Gaussian model that produces an undirected graph of a gene regulatory network. To reduce the dimensionality, the extension constructs networks one cluster at a time. However, because Fuzzy-K, the clustering method of choice, relies on subjective visual cutoffs for cluster membership, I develop a bootstrap method to overcome this disadvantage. Finally, I describe QGene, an extensible QTL- and eQTL-analysis software platform written in Java and used for implementation of all analyses.

Les réseaux d’entreprises sont des formes organisationnelles conduisant à un certain nombre d’avantages pour les entreprises qui en sont membres. Pour ces dernières, cette forme organisationnelle présente l’intérêt de ne pas supprimer leur autonomie ni leur indépendance, tout en bénéficiant des avantages liés aux rapprochements inter-organisationnels. De par cette particularité, les réseaux comprennent ainsi deux niveaux organisationnels distincts mais indissociables : le niveau organisationnel des entreprises membres et le niveau organisationnel du réseau. Avant de bénéficier des avantages auxquels conduit l’organisation en réseau, il est nécessaire qu’il soit construit par les organisations qui en sont à l’origine. Dans le temps, la construction du réseau conduit à un certain renforcement de son degré de néguentropie, se traduisant par une complexification sur le long-terme. Cette complexification s’opère tout en conservant l’autonomie et l’indépendance des organisations qui en sont membres. Peu de recherches se sont intéressées à cette question de la construction des réseaux. Cette thèse propose d’y répondre un utilisant l’approche par les oppositions paradoxales (approche par les paradoxes et approche par les dialectiques), qui permet de prendre en considération l’indissociabilité du double niveau organisationnel des réseaux. Par le biais d’une étude de cas appliquée à un réseau d’entreprises dans le secteur horticole de la région angevine, nous cherchons à identifier comment les objectifs de chacun des deux niveaux organisationnels contribuent à la construction du réseau en question et au renforcement de son degré de néguentropie. / Business networks are organizational forms that lead to a number of benefits for business members. For the latter, this organizational form has the advantage of not eliminating their autonomy or their independence, while benefiting from the advantages linked to inter-organizational reconciliations. In this way, the networks thus comprise two distinct but inseparable organizational levels : the organizational level of the member companies and the organizational level of the network. Before benefiting from the advantages of networking, it is necessary that it be built by the organizations that are at the origin of it. In time, the construction of the network leads to a certain strengthening of its degree of negentropy, resulting in a long-term complexification. This complexity takes place while preserving the autonomy and independence of the member organizations. Little attention has been paid to this question of network construction. This thesis proposes to answer it using a paradoxical approach (paradoxical approach and dialectical approach), which makes it possible to take into account the indissociability of the dual organizational level of networks. Through a case study applied to a network of companies in the horticultural sector of the Angevin region, we seek to identify how the objectives of each of the two organizational levels contribute to the construction of the network in question and to the reinforcement of its degree of negentropy.

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Empirical studies show that similar patterns emerge from a large number of different biological systems. For example, the group size distributions of several fish species and house sparrows all follow power law distributions with an exponential truncation. Networks built by ant colonies, slime mold and those are designed by engineers resemble each other in terms of structure and transportation efficiency. Based on the investigation of experimental data, we propose a variety of simple stochastic models to unravel the underlying mechanisms which lead to the collective phenomena in different systems. All the mechanisms employed in these models are rooted in the concept of selective reinforcement. In some systems the reinforcement can build optimal solutions for biological problem solving. This thesis consists of five papers. In the first three papers, I collaborate with biologists to look into group formation in house sparrows  and the movement decisions of damsel fish.  In the last two articles, I look at how shortest paths and networks are  constructed by slime molds and pheromone laying ants, as well as studying  speed-accuracy tradeoffs in slime molds' decision making. The general goal of the study is to better understand how macro level patterns and behaviors emerges from micro level interactions in both spatial and non-spatial biological systems. With the combination of mathematical modeling and experimentation, we are able to reproduce the macro level patterns in the studied biological systems and predict behaviors of the systems using minimum number of parameters.

This thesis is motivated by the need for scalable and reliable methods and technologies that support the construction of network data based on information from text data. Ultimately, the resulting data can be used for answering substantive and graph-theoretical questions about socio-technical networks. One main limitation with constructing network data from text data is that the validation of the resulting network data can be hard to infeasible, e.g. in the cases of covert, historical and large-scale networks. This thesis addresses this problem by identifying the impact of coding choices that must be made when extracting network data from text data on the structure of networks and network analysis results. My findings suggest that conducting reference resolution on text data can alter the identity and weight of 76% of the nodes and 23% of the links, and can cause major changes in the value of commonly used network metrics. Also, performing reference resolution prior to relation extraction leads to the retrieval of completely different sets of key entities in comparison to not applying this pre-processing technique. Based on the outcome of the presented experiments, I recommend strategies for avoiding or mitigating the identified issues in practical applications. When extracting socio-technical networks from texts, the set of relevant node classes might go beyond the classes that are typically supported by tools for named entity extraction. I address this lack of technology by developing an entity extractor that combines an ontology for sociotechnical networks that originates from the social sciences, is theoretically grounded and has been empirically validated in prior work, with a supervised machine learning technique that is based on probabilistic graphical models. This thesis does not stop at showing that the resulting prediction models achieve state of the art accuracy rates, but I also describe the process of integrating these models into an existing and publically available end-user product. As a result, users can apply these models to new text data in a convenient fashion. While a plethora of methods for building network data from information explicitly or implicitly contained in text data exists, there is a lack of research on how the resulting networks compare with respect to their structure and properties. This also applies to networks that can be extracted by using the aforementioned entity extractor as part of the relation extraction process. I address this knowledge gap by comparing the networks extracted by using this process to network data built with three alternative methods: text coding based on thesauri that associate text terms with node classes, the construction of network data from meta-data on texts, such as key words and index terms, and building network data in collaboration with subject matter experts. The outcomes of these comparative analyses suggest that thesauri generated with the entity extractor developed for this thesis need adjustments with respect to particular categories and types of errors. I am providing tools and strategies to assist with these refinements. My results also show that once these changes have been made and in contrast to manually constructed thesauri, the prediction models generalize with acceptable accuracy to other domains (news wire data, scientific writing, emails) and writing styles (formal, casual). The comparisons of networks constructed with different methods show that ground truth data built by subject matter experts are hardly resembled by any automated method that analyzes text bodies, and even less so by exploiting existing meta-data from text corpora. Thus, aiming to reconstruct social networks from text data leads to largely incomplete networks. Synthesizing the findings from this work, I outline which types of information on socio-technical networks are best captured by what network data construction method, and how to best combine these methods in order to gain a more comprehensive view on a network. When both, text data and relational data, are available as a source of information on a network, people have previously integrated these data by enhancing social networks with content nodes that represent salient terms from the text data. I present a methodological advancement to this technique and test its performance on the datasets used for the previously mentioned evaluation studies. By using this approach, multiple types of behavioral data, namely interactions between people as well as their language use, can be taken into account. I conclude that extracting content nodes from groups of structurally equivalent agents can be an appropriate strategy for enabling the comparison of the content that people produce, perceive or disseminate. These equivalence classes can represent a variety of social roles and social positions that network members occupy. At the same time, extracting content nodes from groups of structurally coherent agents can be suitable for enabling the enhancement of social networks with content nodes. The results from applying the latter approach to text data include a comparison of the outcome of topic modeling; an efficient and unsupervised information extraction technique, to the outcomes of alternative methods, including entity extraction based on supervised machine learning. My findings suggest that key entities from meta-data knowledge networks might serve as proper labels for unlabeled topics. Also, unsupervised and supervised learning leads to the retrieval of similar entities as highly likely members of highly likely topics, and key nodes from text-based knowledge networks, respectively. In summary, the contributions made with this thesis help people to collect, manage and analyze rich network data at any scale. This is a precondition for asking substantive and graph-theoretical questions, testing hypotheses, and advancing theories about networks. This thesis uses an interdisciplinary and computationally rigorous approach to work towards this goal; thereby advancing the intersection of network analysis, natural language processing and computing.

This work deals with the modern trends in FTTX, but mainly focuses on FTTH, who represents the connecting of fiber to the homes of the participants. Sum up the basic problems of communication on the optical fibre, followed by comparison with other types of access networks. Another part is the analysis of the problems of construction and a description of the optical network topologies and technologies used in FTTH. In following chapter are characterized active and passive elements forming AON and PON networks. Next part deals with the study of the most common services that can be on the optical access network to operate. They are mainly associated with the TriplePlay services. An integral part of the project is the study of management and supervision optical networks. In the main part is created real model situation where is the requirement for the creation of optical access networks with broadband of TriplePlay, followed by selection of appropriate options and detailed project with a selection of active and passive elements with economic balance.