Test2
Ontology highlight
ABSTRACT:
SUBMITTER: Malgorzata Adamczyk
PROVIDER: 47 | FAIRDOMHub | 2016-05-12
REPOSITORIES: FAIRDOMHub
Similar Datasets
Project description:Cells and tissues are exposed to stress from numerous sources. Senescence is a protective mechanism that prevents malignant tissue changes and constitutes a fundamental mechanism of aging. It can be accompanied by a senescence associated secretory phenotype (SASP) that causes chronic inflammation. We present a Boolean network model-based gene regulatory network of the SASP, incorporating published gene interaction data. The simulation results describe current biological knowledge. The model predicts different in-silico knockouts that prevent key SASP-mediators, IL-6 and IL-8, from getting activated upon DNA damage. The NF-B Essential Modulator (NEMO) was the most promising in-silico knockout candidate and we were able to show its importance in the inhibition of IL-6 and IL-8 following DNA-damage in murine dermal fibroblasts in-vitro. We strengthen the speculated regulator function of the NF-B signaling pathway in the onset and maintenance of the SASP using in-silico and in-vitro approaches. We were able to mechanistically show, that DNA damage mediated SASP triggering of IL-6 and IL-8 is mainly relayed through NF-B, giving access to possible therapy targets for SASP-accompanied diseases.
| 11863 | Cell Collective
Project description:Background Structural analysis of cellular interaction networks contributes to a deeper understanding of network-wide interdependencies, causal relationships, and basic functional capabilities. While the structural analysis of metabolic networks is a well-established field, similar methodologies have been scarcely developed and applied to signaling and regulatory networks. Results We propose formalisms and methods, relying on adapted and partially newly introduced approaches, which facilitate a structural analysis of signaling and regulatory networks with focus on functional aspects. We use two different formalisms to represent and analyze interaction networks: interaction graphs and (logical) interaction hypergraphs. We show that, in interaction graphs, the determination of feedback cycles and of all the signaling paths between any pair of species is equivalent to the computation of elementary modes known from metabolic networks. Knowledge on the set of signaling paths and feedback loops facilitates the computation of intervention strategies and the classification of compounds into activators, inhibitors, ambivalent factors, and non-affecting factors with respect to a certain species. In some cases, qualitative effects induced by perturbations can be unambiguously predicted from the network scheme. Interaction graphs however, are not able to capture AND relationships which do frequently occur in interaction networks. The consequent logical concatenation of all the arcs pointing into a species leads to Boolean networks. For a Boolean representation of cellular interaction networks we propose a formalism based on logical (or signed) interaction hypergraphs, which facilitates in particular a logical steady state analysis (LSSA). LSSA enables studies on the logical processing of signals and the identification of optimal intervention points (targets) in cellular networks. LSSA also reveals network regions whose parametrization and initial states are crucial for the dynamic behavior. We have implemented these methods in our software tool CellNetAnalyzer (successor of FluxAnalyzer) and illustrate their applicability using a logical model of T-Cell receptor signaling providing non-intuitive results regarding feedback loops, essential elements, and (logical) signal processing upon different stimuli. Conclusion The methods and formalisms we propose herein are another step towards the comprehensive functional analysis of cellular interaction networks. Their potential, shown on a realistic T-cell signaling model, makes them a promising tool.
| 2691 | Cell Collective
Project description:The TOL system encoded by plasmid pWW0 of Pseudomonas putida mt-2 is able to sense a large number of both exogenous and endogenous signals as inputs for the genetic and metabolic circuit that determines the biodegradation of m-xylene. However, whether the enormous combinatorial space of inputs is translated into an equally variable response landscape or is processed into very few outcomes remains unclear. To address this question, we set out to define the number of states that can be obtained by a network of a given set of genes under the control of a specified regulatory circuit that is exposed to all possible combinations of inputs. To this end, the TOL network and its regulatory wiring were formalized as a synchronous logic Boolean circuit that had 10 signals (i.e. pathway substrates, temperature, sugars, amino acids, metabolic regimes and global regulators) as possible inputs. The analysis of the attractors of the circuit using a satisfiability (SAT) algorithm revealed that only eight transcriptional states are reached in response to the 1024 possible combinations of stimuli. The experimental validation resulted in a refinement of the model through the addition of a previously unknown interaction that controls the meta catabolic pathway. The full induction of the two xyl operons occurred with only 1.6% of the input combinations, which suggests that the architecture of the network allows the expression of the xyl genes only under a very narrow range of conditions. These data not only explain much of the unusual layout of the TOL circuit but also strengthen the view of the regulatory circuits of environmental bacteria as digital decision-making devices.
| 3491 | Cell Collective