Hearkening to calls from life scientists for aid in interpreting rapidly-growing repositories of data, the fields of bioinformatics and computational systems biology continue to bear increasingly sophisticated methods capable of summarizing and distilling pertinent phenomena captured by high-throughput experiments. Techniques in analysis of genome-wide gene expression (e.g., microarray) data, for example, have moved beyond simply detecting individual genes perturbed in treatment-control experiments to reporting the collective perturbation of biologically-related collections of genes, or "processes". Recent expression analysis methods have focused on improving comprehensibility of results by reporting concise, non-redundant sets of processes by leveraging statistical modeling techniques such as Bayesian networks. Simultaneously, integrating gene expression measurements with gene interaction networks has led to computation of response networks--subgraphs of interaction networks in which genes exhibit strong collective perturbation or co-expression. Methods that integrate process annotations of genes with interaction networks identify high-level connections between biological processes, themselves. To identify context-specific changes in these inter-process connections, however, techniques beyond process-based expression analysis, which reports only perturbed processes and not their relationships, response networks, composed of interactions between genes rather than processes, and existing techniques in process connection detection, which do not incorporate specific biological context, proved necessary. We present two novel methods which take inspiration from the latest techniques in process-based gene expression analysis, computation of response networks, and computation of inter-process connections. We motivate the need for detecting inter-process connections by identifying a collection of processes exhibiting significant differences in collective expression in two liver tissue culture systems widely used in toxicological and pharmaceutical assays. Next, we identify perturbed connections between these processes via a novel method that integrates gene expression, interaction, and annotation data. Finally, we present another novel method that computes non-redundant sets of perturbed inter-process connections, and apply it to several additional liver-related data sets. These applications demonstrate the ability of our methods to capture and report biologically relevant high-level trends. / Ph. D.

The ever-growing size and complexity of molecular network models makes them difficult to construct and understand. Modifying a model that consists of tens of reactions is no easy task. Attempting the same on a model containing hundreds of reactions can seem nearly impossible. We present the JigCell Model Connector, a software tool that supports large-scale molecular network modeling. Our approach to developing large models is to combine together smaller models, making the result easier to comprehend. At the base, the smaller models (called modules) are defined by small collections of reactions. Modules connect together to form larger modules through clearly defined interfaces, called ports. In this work, we enhance the port concept by defining different types of ports. Not all modules connect together the same way, therefore multiple connection options need to exist. / Master of Science

Background Molecular data of histologically classified oligodendrogliomas are available offering the possibility to stratify these human brain tumors into clinically relevant molecular subtypes. Methods Gene copy number, mutation, and expression data of 193 histologically classified oligodendrogliomas from The Cancer Genome Atlas (TCGA) were analyzed by well-established computational approaches (unsupervised clustering, statistical testing, network inference). Results We applied hierarchical clustering to tumor gene copy number profiles and revealed three molecular subgroups within histologically classified oligodendrogliomas. We further screened these subgroups for molecular glioma markers (1p/19q co-deletion, IDH mutation, gain of chromosome 7 and loss of chromosome 10) and found that our subgroups largely resemble known molecular glioma subtypes. We excluded glioblastoma-like tumors (7a10d subgroup) and derived a gene expression signature distinguishing histologically classified oligodendrogliomas with concurrent 1p/19q co-deletion and IDH mutation (1p/19q subgroup) from those with predominant IDH mutation alone (IDHme subgroup). Interestingly, many signature genes were part of signaling pathways involved in the regulation of cell proliferation, differentiation, migration, and cell-cell contacts. We further learned a gene regulatory network associated with the gene expression signature revealing novel putative major regulators with functions in cytoskeleton remodeling (e.g. APBB1IP, VAV1, ARPC1B), apoptosis (CCNL2, CREB3L1), and neural development (e.g. MYTIL, SCRT1, MEF2C) potentially contributing to the manifestation of differences between both subgroups. Moreover, we revealed characteristic expression differences of several HOX and SOX transcription factors suggesting the activity of different glioma stemness programs in both subgroups. Conclusions We show that gene copy number profiles alone are sufficient to derive molecular subgroups of histologically classified oligodendrogliomas that are well-embedded into general glioma classification schemes. Moreover, our revealed novel putative major regulators and characteristic stemness signatures indicate that different developmental programs might be active in these subgroups, providing a basis for future studies.

Background Molecular data of histologically classified oligodendrogliomas are available offering the possibility to stratify these human brain tumors into clinically relevant molecular subtypes. Methods Gene copy number, mutation, and expression data of 193 histologically classified oligodendrogliomas from The Cancer Genome Atlas (TCGA) were analyzed by well-established computational approaches (unsupervised clustering, statistical testing, network inference). Results We applied hierarchical clustering to tumor gene copy number profiles and revealed three molecular subgroups within histologically classified oligodendrogliomas. We further screened these subgroups for molecular glioma markers (1p/19q co-deletion, IDH mutation, gain of chromosome 7 and loss of chromosome 10) and found that our subgroups largely resemble known molecular glioma subtypes. We excluded glioblastoma-like tumors (7a10d subgroup) and derived a gene expression signature distinguishing histologically classified oligodendrogliomas with concurrent 1p/19q co-deletion and IDH mutation (1p/19q subgroup) from those with predominant IDH mutation alone (IDHme subgroup). Interestingly, many signature genes were part of signaling pathways involved in the regulation of cell proliferation, differentiation, migration, and cell-cell contacts. We further learned a gene regulatory network associated with the gene expression signature revealing novel putative major regulators with functions in cytoskeleton remodeling (e.g. APBB1IP, VAV1, ARPC1B), apoptosis (CCNL2, CREB3L1), and neural development (e.g. MYTIL, SCRT1, MEF2C) potentially contributing to the manifestation of differences between both subgroups. Moreover, we revealed characteristic expression differences of several HOX and SOX transcription factors suggesting the activity of different glioma stemness programs in both subgroups. Conclusions We show that gene copy number profiles alone are sufficient to derive molecular subgroups of histologically classified oligodendrogliomas that are well-embedded into general glioma classification schemes. Moreover, our revealed novel putative major regulators and characteristic stemness signatures indicate that different developmental programs might be active in these subgroups, providing a basis for future studies.

This thesis encompasses research on Artificial Intelligence in support of automating scientific discovery in the fields of biology and medicine. At the core of this research is the ongoing development of a general-purpose artificial intelligence framework emulating various facets of human-level intelligence necessary for building cross-domain knowledge that may lead to new insights and discoveries. To learn and build models in a data-driven manner, we develop a general-purpose learning framework called Syntactic Nonparametric Analysis of Complex Systems (SYNACX), which uses tools from Bayesian nonparametric inference to learn the statistical and syntactic properties of biological phenomena from sequence data. We show that the models learned by SYNACX offer performance comparable to that of standard neural network architectures. For complex biological systems or processes consisting of several heterogeneous components with spatio-temporal interdependencies across multiple scales, learning frameworks like SYNACX can become unwieldy due to the the resultant combinatorial complexity. Thus we also investigate ways to robustly reduce data dimensionality by introducing a new data abstraction. In particular, we extend traditional string and graph grammars in a new modeling formalism which we call Simplicial Grammar. This formalism integrates the topological properties of the simplicial complex with the expressive power of stochastic grammars in a computation abstraction with which we can decompose complex system behavior, into a finite set of modular grammar rules which parsimoniously describe the spatial/temporal structure and dynamics of patterns inferred from sequence data.

Our modern information society provides us with a tremendous amount of information. Several issues have surfaced due to the complexity inherent in the handling of the information systems. One of the most important issues is that of providing an architecture and methodology that provide for the development and maintenance of complex, distributed information systems. As the information flow and quantity hinders us from having qualitative information when needed, the architecture must address the reach, richness and value of the information. Network-centric warfare is a problem domain that has been initiated to meet the power of information. To be able to support such continouos sustainability, a robust network infrastructure is critical. A systemic perspective on network-centric environments as well as a technical perspective on network-centric environment shows that, although promising, contemporary implementations having a service-oriented architecture lack support for physical scalability and a cognitive decoupling that would provide for multiple users acting on the same environment. Consequently, a service-oriented layered architecture for communicating entities is presented where these issues are addressed. For verification, a demonstrator is developed upon a service-oriented layered architecture for communicating based on a network-centric warfare scenario.

Mathematical models are important tools in systems biology, since the regulatory networks in biological cells are too complicated to understand by biological experiments alone. Analytical solutions can be derived only for the simplest models and numerical simulations are necessary in most cases to evaluate the models and their properties and to compare them with measured data. This thesis focuses on the mesoscopic simulation level, which captures both, space dependent behavior by diffusion and the inherent stochasticity of cellular systems. Space is partitioned into compartments by a mesh and the number of molecules of each species in each compartment gives the state of the system. We first examine how to compute the jump coefficients for a discrete stochastic jump process on unstructured meshes from a first exit time approach guaranteeing the correct speed of diffusion. Furthermore, we analyze different methods leading to non-negative coefficients by backward analysis and derive a new method, minimizing both the error in the diffusion coefficient and in the particle distribution. The second part of this thesis investigates macromolecular crowding effects. A high percentage of the cytosol and membranes of cells are occupied by molecules. This impedes the diffusive motion and also affects the reaction rates. Most algorithms for cell simulations are either derived for a dilute medium or become computationally very expensive when applied to a crowded environment. Therefore, we develop a multiscale approach, which takes the microscopic positions of the molecules into account, while still allowing for efficient stochastic simulations on the mesoscopic level. Finally, we compare on- and off-lattice models on the microscopic level when applied to a crowded environment.

Diante da constante necessidade de funcionamento ininterrupto dos sistemas computacionais, das mais diversas organizações, é imperativo que existam meios de continuidade dos negócios e recuperação de desastres implantados, testados e prontos para serem invocados. Diante disso, torna-se essencial a existência de uma maneira de avaliar se as informações, os procedimentos e o nível do conhecimento dos colaboradores da organização estão adequados para enfrentar uma ocorrência inesperada e devastadora no ambiente computacional da organização. A presente tese propõe um modelo hierárquico para se representar e avaliar a qualidade dos Projetos de Planos de Continuidade de Negócios (PPCN) aplicados a sistemas computacionais. Este modelo apresenta o mapeamento das principais características que esses planos devem possuir, de acordo com as principais normas relativas ao tema (BS 25999, ABNT NBR ISO/IEC 27001 e ABNT NBR ISO/IEC 27002), as experiências de especialistas da área e dados reais dos usuários dos PPCNs obtidos por meio da utilização de questionários. É proposto neste trabalho, também, um Índice de Qualidade (IQ) para os PPCNs que permite a comparação de um PPCN existente com um PPCN ideal, identificando-se os pontos fracos nele existentes e munindo a organização com informações para a busca de soluções que resultarão na melhoria do PPCN atual. / Given the need for computer systems uninterrupted operation, for the most different organizations, it is imperative that business continuity and disaster recovery plans be already in place, tested and ready to be invoked. Given this, it is essential for there being a way to assess whether the information, procedures and organizations employees knowledge level are adequate to deal with an unexpected and devastating event in the organization\'s computing environment. This thesis proposes a hierarchical model to represent and assess the organizations computer systems Business Continuity Plan Project (BCPP) quality. This model maps the main features these plans should have, in accordance with the main standards related to this area (BS 25999, ISO/IEC 27001 and ISO/IEC 27002), specialists experience and real data from BCPPs users obtained from questionnaires. As a complementary proposal, a BCPP Quality Index (QI) is suggested, which will allow organizations to compare their existing BCPP against an ideal BCPP, identifying the gaps between these plans and providing the organization with information for seeking solutions that will result in the improvement of current BCPP.

Diante da constante necessidade de funcionamento ininterrupto dos sistemas computacionais, das mais diversas organizações, é imperativo que existam meios de continuidade dos negócios e recuperação de desastres implantados, testados e prontos para serem invocados. Diante disso, torna-se essencial a existência de uma maneira de avaliar se as informações, os procedimentos e o nível do conhecimento dos colaboradores da organização estão adequados para enfrentar uma ocorrência inesperada e devastadora no ambiente computacional da organização. A presente tese propõe um modelo hierárquico para se representar e avaliar a qualidade dos Projetos de Planos de Continuidade de Negócios (PPCN) aplicados a sistemas computacionais. Este modelo apresenta o mapeamento das principais características que esses planos devem possuir, de acordo com as principais normas relativas ao tema (BS 25999, ABNT NBR ISO/IEC 27001 e ABNT NBR ISO/IEC 27002), as experiências de especialistas da área e dados reais dos usuários dos PPCNs obtidos por meio da utilização de questionários. É proposto neste trabalho, também, um Índice de Qualidade (IQ) para os PPCNs que permite a comparação de um PPCN existente com um PPCN ideal, identificando-se os pontos fracos nele existentes e munindo a organização com informações para a busca de soluções que resultarão na melhoria do PPCN atual. / Given the need for computer systems uninterrupted operation, for the most different organizations, it is imperative that business continuity and disaster recovery plans be already in place, tested and ready to be invoked. Given this, it is essential for there being a way to assess whether the information, procedures and organizations employees knowledge level are adequate to deal with an unexpected and devastating event in the organization\'s computing environment. This thesis proposes a hierarchical model to represent and assess the organizations computer systems Business Continuity Plan Project (BCPP) quality. This model maps the main features these plans should have, in accordance with the main standards related to this area (BS 25999, ISO/IEC 27001 and ISO/IEC 27002), specialists experience and real data from BCPPs users obtained from questionnaires. As a complementary proposal, a BCPP Quality Index (QI) is suggested, which will allow organizations to compare their existing BCPP against an ideal BCPP, identifying the gaps between these plans and providing the organization with information for seeking solutions that will result in the improvement of current BCPP.

For cell-biological processes, it is the complex interaction of their biochemical components, affected by both stochastic and spatial considerations, that create the overall picture. Formal modeling provides a method to overcome the limits of experimental observation in the wet-lab by moving to the abstract world of the computer. The limits of the abstract world again depend on the expressiveness of the modeling language used to formally describe the system under study. In this thesis, reaction constraints for the pi-calculus are proposed as a language for the stochastic and spatial modeling of cell-biological processes. The goal is to develop a language with sufficient expressive power to model dynamic cell structures, like fusing compartments. To this end, reaction constraints are augmented with two language constructs: priority and a global imperative store, yielding two different modeling languages, including non-deterministic and stochastic semantics. By several modeling examples, e.g. of Euglena's phototaxis, and extensive expressiveness studies, e.g. an encoding of the spatial modeling language BioAmbients, including a prove of its correctness, the usefulness of reaction constraints, priority, and a global imperative store for the modeling of cell-biological processes is shown. Thereby, besides dynamic cell structures, different modeling styles, e.g. individual-based vs. population-based modeling, and different abstraction levels, as e.g. provided by reaction kinetics following the law of Mass action or the Michaelis-Menten theory, are considered.