MC EMiNEM Maps the Interaction Landscape of the Mediator

The Mediator is a highly conserved, large multiprotein complex that is involved essentially in the regulation of eukaryotic mRNA transcription. It acts as a general transcription factor by integrating regulatory signals from gene-specific activators or repressors to the RNA Polymerase II. The internal network of interactions between Mediator subunits that conveys these signals is largely unknown. Here, we introduce MC EMiNEM, a novel method for the retrieval of functional dependencies between proteins that have pleiotropic effects on mRNA transcription. MC EMiNEM is based on Nested Effects Models (NEMs), a class of probabilistic graphical models that extends the idea of hierarchical clustering. It combines mode-hopping Monte Carlo (MC) sampling with an Expectation-Maximization (EM) algorithm for NEMs to increase sensitivity compared to existing methods. A meta-analysis of four Mediator perturbation studies in Saccharomyces cerevisiae, three of which are unpublished, provides new insight into the Mediator signaling network. In addition to the known modular organization of the Mediator subunits, MC EMiNEM reveals a hierarchical ordering of its internal information flow, which is putatively transmitted through structural changes within the complex. We identify the N-terminus of Med7 as a peripheral entity, entailing only local structural changes upon perturbation, while the C-terminus of Med7 and Med19 appear to play a central role. MC EMiNEM associates Mediator subunits to most directly affected genes, which, in conjunction with gene set enrichment analysis, allows us to construct an interaction map of Mediator subunits and transcription factors

Reconstructing signaling pathways from RNAi data using probabilistic Boolean threshold networks

Motivation: The reconstruction of signaling pathways from gene knockdown data is a novel research field enabled by developments in RNAi screening technology. However, while RNA interference is a powerful technique to identify genes related to a phenotype of interest, their placement in the corresponding pathways remains a challenging problem. Difficulties are aggravated if not all pathway components can be observed after each knockdown, but readouts are only available for a small subset. We are then facing the problem of reconstructing a network from incomplete data. Results: We infer pathway topologies from gene knockdown data using Bayesian networks with probabilistic Boolean threshold functions. To deal with the problem of underdetermined network parameters, we employ a Bayesian learning approach, in which we can integrate arbitrary prior information on the network under consideration. Missing observations are integrated out. We compute the exact likelihood function for smaller networks, and use an approximation to evaluate the likelihood for larger networks. The posterior distribution is evaluated using mode hopping Markov chain Monte Carlo. Distributions over topologies and parameters can then be used to design additional experiments. We evaluate our approach on a small artificial dataset, and present inference results on RNAi data from the Jak/Stat pathway in a human hepatoma cell line. Availability: Software is available on request. Contact

Markov chain Monte Carlo methods for parameter identification in systems biology models

First, I would like to thank Prof. Dr. Achim Tresch for giving me the opportunity to write this thesis and to work on three fascinating projects. I really appreciate all the fruitful discussions, his constant support and the excellent working atmosphere. I would also like to thank Prof. Dr. Patrick Cramer for being my doctoral supervisor. Furthermore, I would like to thank all the other members of my dissertation committee (Prof. Dr. Rainer Spang

Multivariate Models and Algorithms for Systems Biology

Rapid advances in high-throughput data acquisition technologies, such as microarraysand next-generation sequencing, have enabled the scientists to interrogate the expression levels of tens of thousands of genes simultaneously. However, challenges remain in developingeffective computational methods for analyzing data generated from such platforms. In thisdissertation, we address some of these challenges. We divide our work into two parts. Inthe first part, we present a suite of multivariate approaches for a reliable discovery of geneclusters, often interpreted as pathway components, from molecular profiling data with replicated measurements. We translate our goal into learning an optimal correlation structure from replicated complete and incomplete measurements. In the second part, we focus on thereconstruction of signal transduction mechanisms in the signaling pathway components. Wepropose gene set based approaches for inferring the structure of a signaling pathway.First, we present a constrained multivariate Gaussian model, referred to as the informed-case model, for estimating the correlation structure from replicated and complete molecular profiling data. Informed-case model generalizes previously known blind-case modelby accommodating prior knowledge of replication mechanisms. Second, we generalize theblind-case model by designing a two-component mixture model. Our idea is to strike anoptimal balance between a fully constrained correlation structure and an unconstrained one.Third, we develop an Expectation-Maximization algorithm to infer the underlying correlation structure from replicated molecular profiling data with missing (incomplete) measurements.We utilize our correlation estimators for clustering real-world replicated complete and incompletemolecular profiling data sets. The above three components constitute the first partof the dissertation. For the structural inference of signaling pathways, we hypothesize a directed signal pathway structure as an ensemble of overlapping and linear signal transduction events. We then propose two algorithms to reverse engineer the underlying signaling pathway structure using unordered gene sets corresponding to signal transduction events. Throughout we treat gene sets as variables and the associated gene orderings as random.The first algorithm has been developed under the Gibbs sampling framework and the secondalgorithm utilizes the framework of simulated annealing. Finally, we summarize our findingsand discuss possible future directions

Multivariate Models and Algorithms for Systems Biology

Rapid advances in high-throughput data acquisition technologies, such as microarraysand next-generation sequencing, have enabled the scientists to interrogate the expression levels of tens of thousands of genes simultaneously. However, challenges remain in developingeffective computational methods for analyzing data generated from such platforms. In thisdissertation, we address some of these challenges. We divide our work into two parts. Inthe first part, we present a suite of multivariate approaches for a reliable discovery of geneclusters, often interpreted as pathway components, from molecular profiling data with replicated measurements. We translate our goal into learning an optimal correlation structure from replicated complete and incomplete measurements. In the second part, we focus on thereconstruction of signal transduction mechanisms in the signaling pathway components. Wepropose gene set based approaches for inferring the structure of a signaling pathway.First, we present a constrained multivariate Gaussian model, referred to as the informed-case model, for estimating the correlation structure from replicated and complete molecular profiling data. Informed-case model generalizes previously known blind-case modelby accommodating prior knowledge of replication mechanisms. Second, we generalize theblind-case model by designing a two-component mixture model. Our idea is to strike anoptimal balance between a fully constrained correlation structure and an unconstrained one.Third, we develop an Expectation-Maximization algorithm to infer the underlying correlation structure from replicated molecular profiling data with missing (incomplete) measurements.We utilize our correlation estimators for clustering real-world replicated complete and incompletemolecular profiling data sets. The above three components constitute the first partof the dissertation. For the structural inference of signaling pathways, we hypothesize a directed signal pathway structure as an ensemble of overlapping and linear signal transduction events. We then propose two algorithms to reverse engineer the underlying signaling pathway structure using unordered gene sets corresponding to signal transduction events. Throughout we treat gene sets as variables and the associated gene orderings as random.The first algorithm has been developed under the Gibbs sampling framework and the secondalgorithm utilizes the framework of simulated annealing. Finally, we summarize our findingsand discuss possible future directions

Inférence des réseaux de régulation de la synthèse des protéines de réserve du grain de blé tendre (Triticum aestivum L.) en réponse à l'approvisionnement en azote et en soufre: Inférence des réseaux de régulation de la synthèse des protéines de réserve du grain de blé tendre (Triticum aestivum L.) en réponse à l'approvisionnement en azote et en soufre

Grain storage protein content and composition are the main determinants of bread wheat (Triticum aestivum L.) end-use value. Scaling laws governing grain protein composition according to grain nitrogen and sulfur content could be the outcome of a finely tuned regulation network. Although it was demonstrated that the main regulation of grain storage proteins accumulation occurs at the transcriptomic level in cereals, knowledge of the underlying molecular mechanisms is elusive. Moreover, the effects of nitrogen and sulfur on these mechanisms are unknown. The issue of skyrocketing data generation in research projects is addressed by developing high-throughput bioinformatics approaches. Extracting knowledge on from such massive amounts of data is therefore an important challenge. The work presented herein aims at elucidating regulatory networks involved in grain storage protein synthesis and their response to nitrogen and sulfur supply using a rule discovery approach. This approach was extended, implemented in the form of a web-oriented platform dedicated to the inference and analysis of regulatory networks from qualitative and quantitative –omics data. This platform allowed us to define different semantics in a comprehensive framework; each semantic having its own biological meaning, thus providing us with global informative networks. Spatiotemporal specificity of transcription factors expression was observed and particular attention was paid to their relationship with grain storage proteins in the inferred networks. The work initiated here opens up a field of innovative investigation to identify new targets for plant breeding and for an improved end-use value and nutritional quality of wheat in the context of inputs limitation. Further analyses should enhance the understanding of the control of grain protein composition and allow providing wheat adapted to specific uses or deficient in protein fractions responsible for gluten allergenicity and intolerance.La teneur et la composition en protéines de réserve du grain de blé tendre (Triticum aestivum L.) sont les principaux déterminants de sa valeur d’usage et de sa qualité nutritionnelle. La composition en protéines de réserve du grain est déterminée par la teneur en assimilâts azotés et soufrés par grain via des lois d’échelle qui pourraient être les propriétés émergentes de réseaux de régulation. Plusieurs facteurs de transcription intervenant dans cette régulation ont été mis en évidence, mais les voies et mécanismes impliqués sont encore très peu connus. Le constat est identique en ce qui concerne l’impact de la nutrition azotée et soufrée sur ce réseau de régulation. Le développement des outils de génomique fonctionnelle et de bioinformatique permet aujourd’hui d’aborder ces régulations de manière globale via une approche systémique mettant en relation plusieurs niveaux de régulation. L’objectif du travail présenté est d’explorer les réseaux de régulation –omiques impliqués dans le contrôle de l’accumulation des protéines de réserve dans le grain de blé tendre et leur réponse à l’approvisionnement en azote et en soufre. Une approche d’inférence de réseaux basée sur la découverte de règles a été étendue, implémentée sous la forme d’une plateforme web. L’utilisation de cette plateforme a permis de définir des sémantiques multiples afin d’inférer dans un cadre global, des règles possédant différentes significations biologiques. Des facteurs de transcription spécifiques de certains organes et certaines phases de développement ont été mis en évidence et un intérêt particulier a été apporté à leur position dans les réseaux de règles inférés, notamment en relation avec les protéines de réserve. Les travaux initiés dans cette thèse ouvrent un champ d’investigation innovant pour l’identification de nouvelles cibles de sélection variétale pour l’amélioration de la valeur technologique et de la qualité nutritionnelle du blé. Ils devraient ainsi permettre de mieux maîtriser la composition en protéines de réserve et ainsi produire des blés adaptés à des utilisations ciblées ou carencé en certaines fractions protéiques impliquées dans des phénomènes d’allergénicité et d’intolérance du gluten, ce dans un contexte d’agriculture durable et plus économe en intrants