Test2
Ontology highlight
ABSTRACT:
SUBMITTER: Malgorzata Adamczyk
PROVIDER: 47 | FAIRDOMHub | 2016-05-12
REPOSITORIES: FAIRDOMHub
Similar Datasets
Project description:There is a vast amount of molecular information regarding the differentiation of T lymphocytes, in particular regarding in vitro experimental treatments that modify their differentiation process. This publicly available information was used to infer the regulatory network that controls the differentiation of T lymphocytes into CD4+ and CD8+ cells. Hereby we present a network that consists of 50 nodes and 97 regulatory interactions, representing the main signaling circuits established among molecules and molecular complexes regulating the differentiation of T cells. The network was converted into a continuous dynamical system in the form of a set of coupled ordinary differential equations, and its dynamical behavior was studied. With the aid of numerical methods, nine fixed point attractors were found for the dynamical system. These attractors correspond to the activation patterns observed experimentally for the following cell types: CD4?CD8?, CD4+CD8+, CD4+ naive, Th1, Th2, Th17, Treg, CD8+ naive, and CTL. Furthermore, the model is able to describe the differentiation process from the precursor CD4?CD8? to any of the effector types due to a specific series of extracellular signals.
| 2215 | 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:Due to the large number of diseases associated to a malfunction of the hematopoietic system, there is an interest in knowing the molecular mechanisms controlling the differentiation of blood cell lineages. However, the structure and dynamical properties of the underlying regulatory network controlling this process is not well understood. This manuscript presents a regulatory network of 81 nodes, representing several types of molecules that regulate each other during the process of lymphopoiesis. The regulatory interactions were inferred mostly from published experimental data. However, 15 out of 159 regulatory interactions are predictions arising from the present study. The network is modelled as a continuous dynamical system, in the form of a set of differential equations. The dynamical behaviour of the model describes the differentiation process from the common lymphocyte precursor (CLP) to several mature B and T cell types; namely, plasma cell (PC), cytotoxic T lymphocyte (CTL), T helper 1 (Th1), Th2, Th17, and T regulatory (Treg) cells. The model qualitatively recapitulates key cellular differentiation events, being able to represent the directional and branched nature of lymphopoiesis, going from a multipotent progenitor to fully differentiated cell types.
| 8080 | Cell Collective