Computational Modeling of the Fast Brassinosteroid Response in the Plasma Membrane of Arabidopsis thaliana: From Molecules to Organ

It is the aim of this thesis to analyze the initiation and regulation of the fast brassinosteroid response pathway Arabidopsis thaliana and its role for cell elongation in an integrative fashion using mathematical modeling. Brassinosteroids are plant steroid hormones that mediate various physiological and developmental processes. One of these processes is cell elongation, which is the major mechanism for organ growth in plants. As sessile organisms, plants have to rely on growth to open up new resources. However, growth is an energy consuming process that has to be tightly regulated. Therefore, it is necessary to understand the activation and regulation of the fast brassinosteroid response. The computational modeling and analysis of the fast brassinosteroid signaling focused on several different aspects. Because of the importance of compartmentalization in biological systems, I first studied the different modeling approaches to describe multi-compartment processes in models consisting of ordinary differential equations and how these modeling approaches react to changes in cell morphology. This analysis shows that including the membrane as interaction area can be crucial to proper modeling behavior depending on the modeled system. Second,I used molecular modeling to clarify the interactions between receptor, co-receptor and a negative regulator of the fast brassinosteroid response. Here, the simulated complexes show that the negative regulator acts by blocking the catalytic domain of the co-receptor, which is then unable to participate in propagating the signal. Third, I used a dynamic model consisting of ordinary differential equations to simulate the fast brassinosteroid response on a cellular scale. The parameters of this model were fitted to dose-response data of the membrane potential change. Furthermore, this model includes the BR-induced increase in cell wall volume. I validated this model with respect to the behavior in the meristematic root zone and the behavior in a deletion of a negative regulator. Based on the model behavior and the quantification of model species, we hypothesize that H+-ATPase levels in the different root zones determine the response to brassinosteroid stimulation in the fast response pathway. Finally, I expanded the ordinary differential equation model for the fast brassinosteroid response to include the process of cell elongation. This model can describe the experi- mentally observed elongation behavior of an epidermis cell from the meristematic zone to the final cell length in the maturation zone. I combined this model with an agent-based representation of the root. This model provides an integrative view on cell elongation. While this multi-scale model is currently limited to one cell type and a maximal cell length of 25µm, this shows that it is a valid approach to modeling root elongation

Inferring causal molecular networks: empirical assessment through a community-based effort

Inferring molecular networks is a central challenge in computational biology. However, it has remained unclear whether causal, rather than merely correlational, relationships can be effectively inferred in complex biological settings. Here we describe the HPN-DREAM network inference challenge that focused on learning causal influences in signaling networks. We used phosphoprotein data from cancer cell lines as well as in silico data from a nonlinear dynamical model. Using the phosphoprotein data, we scored more than 2,000 networks submitted by challenge participants. The networks spanned 32 biological contexts and were scored in terms of causal validity with respect to unseen interventional data. A number of approaches were effective and incorporating known biology was generally advantageous. Additional sub-challenges considered time-course prediction and visualization. Our results constitute the most comprehensive assessment of causal network inference in a mammalian setting carried out to date and suggest that learning causal relationships may be feasible in complex settings such as disease states. Furthermore, our scoring approach provides a practical way to empirically assess the causal validity of inferred molecular networks

Computational modeling of cambium activity provides a regulatory framework for simulating radial plant growth

Precise organization of growing structures is a fundamental process in developmental biology. In plants, radial growth is mediated by the cambium, a stem cell niche continuously producing wood (xylem) and bast (phloem) in a strictly bidirectional manner. While this process contributes large parts to terrestrial biomass, cambium dynamics eludes direct experimental access due to obstacles in live-cell imaging. Here, we present a cell-based computational model visualizing cambium activity and integrating the function of central cambium regulators. Performing iterative comparisons of plant and model anatomies, we conclude that the receptor-like kinase PXY and its ligand CLE41 are part of a minimal framework sufficient for instructing tissue organization. By integrating tissue-specific cell wall stiffness values, we moreover probe the influence of physical constraints on tissue geometry. Our model highlights the role of intercellular communication within the cambium and shows that a limited number of factors are sufficient to create radial growth by bidirectional tissue production

All Driven by Energy Demand? Integrative Comparison of Metabolism of Enterococcus faecalis Wildtype and a Glutamine Synthase Mutant

Lactic acid bacteria (LAB) play a significant role in biotechnology, e.g., food industry and also in human health. Many LAB genera have developed a multidrug resistance in the past few years, causing a serious problem in controlling hospital germs worldwide. Enterococcus faecalis accounts for a large part of the human infections caused by LABs. Therefore, studying its adaptive metabolism under various environmental conditions is particularly important to promote the development of new therapeutic approaches. In this study, we investigated the effect of glutamine auxotrophy (DglnA mutant) on metabolic and proteomic adaptations of E. faecalis in response to a changing pH in its environment. Changing pH values are part of the organism’s natural environment in the human body and play a role in the food industry. We compared the results with those of the wildtype. Using a genome-scale metabolic model constrained by metabolic and proteomic data, our integrative method allows us to understand the bigger picture of the adaptation strategies of this bacterium. The study showed that energy demand is the decisive factor in adapting to a new environmental pH. The energy demand of the mutant was higher at all conditions. It has been reported that DglnA mutants of bacteria are energetically less effective. With the aid of our data and model we are able to explain this phenomenon as a consequence of a failure to regulate glutamine uptake and the costs for the import of glutamine and the export of ammonium. Methodologically, it became apparent that taking into account the nonspecificity of amino acid transporters is important for reproducing metabolic changes with genome-scale models because it affects energy balance. IMPORTANCE The integration of new pH-dependent experimental data on metabolic uptake and release fluxes, as well as of proteome data with a genome-scale computational model of a glutamine synthetase mutant of E. faecalis is used and compared with those of the wildtype to understand why glutamine auxotrophy results in a less efficient metabolism and how—in comparison with the wildtype—the glutamine synthetase knockout impacts metabolic adjustments during acidification or simply exposure to lower pH. We show that forced glutamine auxotrophy causes more energy demand and that this is likely due to a disregulated glutamine uptake. Proteome changes during acidification observed for the mutant resemble those of the wildtype with the exception of glycolysis-related genes, as the mutant is already energetically stressed at a higher pH and the respective proteome changes were in effect.ISSN:2165-049

On Non-Isomorphic NP Complete Sets

In this note we show that if the satisfiability of Boolean formulas of low Kolmogorov complexity can be determined in polynomial-time then there exist NP complete sets that are not polynomial-time isomorphic. Keywords: NP complete sets, isomorphism, Kolmogorov complexity

Inferring causal molecular networks: empirical assessment through a community-based effort

It remains unclear whether causal, rather than merely correlational, relationships in molecular networks can be inferred in complex biological settings. Here we describe the HPN-DREAM network inference challenge, which focused on learning causal influences in signaling networks. We used phosphoprotein data from cancer cell lines as well as in silico data from a nonlinear dynamical model. Using the phosphoprotein data, we scored more than 2,000 networks submitted by challenge participants. The networks spanned 32 biological contexts and were scored in terms of causal validity with respect to unseen interventional data. A number of approaches were effective, and incorporating known biology was generally advantageous. Additional sub-challenges considered time-course prediction and visualization. Our results suggest that learning causal relationships may be feasible in complex settings such as disease states. Furthermore, our scoring approach provides a practical way to empirically assess inferred molecular networks in a causal sense