@phdthesis{Karl2016,
  author    = {Karl, Stefan},
  title     = {Control Centrality in Non-Linear Biological Networks},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-150838},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2016},
  abstract  = {Biological systems such as cells or whole organisms are governed by complex regulatory networks of transcription factors, hormones and other regulators which determine the behavior of the system depending on internal and external stimuli. In mathematical models of these networks, genes are represented by interacting "nodes" whose "value" represents the activity of the gene. Control processes in these regulatory networks are challenging to elucidate and quantify. Previous control centrality metrics, which aim to mathematically capture the ability of individual nodes to control biological systems, have been found to suffer from problems regarding biological plausibility. This thesis presents a new approach to control centrality in biological networks. Three types of network control are distinguished: Total control centrality quantifies the impact of gene mutations and identifies potential pharmacological targets such as genes involved in oncogenesis (e.g. zinc finger protein GLI2 or bone morphogenetic proteins in chondrocytes). Dynamic control centrality describes relaying functions as observed in signaling cascades (e.g control in mouse colon stem cells). Value control centrality measures the direct influence of the value of the node on the network (e.g. Indian hedgehog as an essential regulator of proliferation in chondrocytes). Well-defined network manipulations define all three centralities not only for nodes, but also for the interactions between them, enabling detailed insights into network pathways. The calculation of the new metrics is made possible by substantial computational improvements in the simulation algorithms for several widely used mathematical modeling paradigms for genetic regulatory networks, which are implemented in the regulatory network simulation framework Jimena created for this thesis. Applying the new metrics to biological networks and artificial random networks shows how these mathematical concepts correspond to experimentally verified gene functions and signaling pathways in immunity and cell differentiation. In contrast to controversial previous results even from the Barab{\´a}si group, all results indicate that the ability to control biological networks resides in only few driver nodes characterized by a high number of connections to the rest of the network. Autoregulatory loops strongly increase the controllability of the network, i.e. its ability to control itself, and biological networks are characterized by high controllability in conjunction with high robustness against mutations, a combination that can be achieved best in sparsely connected networks with densities (i.e. connections to nodes ratios) around 2.0 - 3.0. The new concepts are thus considerably narrowing the gap between network science and biology and can be used in various areas such as system modeling, plausibility trials and system analyses. Medical applications discussed in this thesis include the search for oncogenes and pharmacological targets, as well their functional characterization.},
  subject      = {Bioinformatik},
  language  = {en}
}
@phdthesis{HagmanngebKischkies2016,
  author    = {Hagmann [geb. Kischkies], Laura Violetta},
  title     = {Stringent response regulation and its impact on ex vivo survival in the commensal pathogen \(Neisseria\) \(meningitidis\)},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-144352},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2016},
  abstract  = {Neisseria meningitidis is a commensal bacterium which sometimes causes serious disease in humans. Recent studies in numerous human pathogenic bacteria have shown that the stringent response contributes to bacterial virulence. Therefore, this study analyzed the regulation of the stringent response in meningococci and in particular of RelA as well as its contribution to ex vivo fitness in a strain- and condition- dependent manner by using the carriage strain α522 and the hyperinvasive strain MC58 in different in vitro and ex vivo conditions. Growth experiments revealed that both wild-type strains were almost indistinguishable in their ex vivo phenotypes. However, quantitative real time PCR (qRT-PCR) found differences in the gene expression of relA between both strains. Furthermore, in contrast to the MC58 RelA mutant strain α522 deficient in RelA was unable to survive in human whole blood, although both strains showed the same ex vivo phenotypes in saliva and cerebrospinal fluid. Moreover, strain α522 was depended on a short non-coding AT-rich repeat element (ATRrelA) in the promoter region of relA to survive in human blood. Furthermore, cell culture experiments with human epithelial cells revealed that in both strains the deletion of relA resulted in a significantly decreased invasion rate while not significantly affecting adhesion. In order to better understand the conditional lethality of the relA deletion, computational and experimental analyses were carried out to unravel differences in amino acid biosynthetic pathways between both strains. Whereas strain MC58 is able to synthesize all 20 amino acids, strain α522 has an auxotrophy for cysteine and glutamine. In addition, the in vitro growth experiments found that RelA is required for growth in the absence of external amino acids in both strains. Furthermore, the mutant strain MC58 harboring an ATRrelA in its relA promoter region showed improved growth in minimal medium supplemented with L-cysteine and/or L-glutamine compared to the wild-type strain. Contrary, in strain α522 no differences between the wild-type and the ATRrelA deletion mutant were observed. Together this indicates that ATRrelA interferes with the complex regulatory interplay between the stringent response pathway and L-cysteine as well as L-glutamine metabolism. It further suggests that meningococcal virulence is linked to relA in a strain- and condition- depended manner. In conclusion, this work highlighted the role of the stringent response and of non-coding regulatory elements for bacterial virulence and indicates that virulence might be related to the way how meningococci accomplish growth within the host environments.},
  subject      = {Neisseria meningitidis},
  language  = {en}
}