Knee joint
Ontology highlight
ABSTRACT:
SUBMITTER: 
David Nickerson
PROVIDER: a40 | Physiome Model Repository |
REPOSITORIES: PMR
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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.
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Project description:Co-infections alter the host immune response but how the systemic and local processes at the site of infection interact is still unclear. The majority of studies on co-infections concentrate on one of the infecting species, an immune function or group of cells and often focus on the initial phase of the infection. Here, we used a combination of experiments and mathematical modelling to investigate the network of immune responses against single and co-infections with the respiratory bacterium Bordetella bronchiseptica and the gastrointestinal helminth Trichostrongylus retortaeformis. Our goal was to identify representative mediators and functions that could capture the essence of the host immune response as a whole, and to assess how their relative contribution dynamically changed over time and between single and co-infected individuals. Network-based discrete dynamic models of single infections were built using current knowledge of bacterial and helminth immunology; the two single infection models were combined into a co-infection model that was then verified by our empirical findings. Simulations showed that a T helper cell mediated antibody and neutrophil response led to phagocytosis and clearance of B. bronchiseptica from the lungs. This was consistent in single and co-infection with no significant delay induced by the helminth. In contrast, T. retortaeformis intensity decreased faster when co-infected with the bacterium. Simulations suggested that the robust recruitment of neutrophils in the co-infection, added to the activation of IgG and eosinophil driven reduction of larvae, which also played an important role in single infection, contributed to this fast clearance. Perturbation analysis of the models, through the knockout of individual nodes (immune cells), identified the cells critical to parasite persistence and clearance both in single and co-infections. Our integrated approach captured the within-host immuno-dynamics of bacteria-helminth infection and identified key components that can be crucial for explaining individual variability between single and co-infections in natural populations.
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Project description:The lactose operon in Escherichia coli was the first known gene regulatory network, and it is frequently used as a prototype for new modeling paradigms. Historically, many of these modeling frameworks use differential equations. More recently, Stigler and Veliz-Cuba proposed a Boolean network model that captures the bistability of the system and all of the biological steady states. In this paper, we model the wellknown arabinose operon in E. coli with a Boolean network. This has several complex features not found in the lac operon, such as a protein that is both an activator and repressor, a DNA looping mechanism for gene repression, and the lack of inducer exclusion by glucose. For 11 out of 12 choices of initial conditions, we use computational algebra and Sage to verify that the state space contains a single fixed point that correctly matches the biology. The final initial condition, medium levels of arabinose and no glucose, successfully predicts the systems bistability. Finally, we compare the state space under synchronous and asynchronous update, and see that the former has several artificial cycles that go away under a general asynchronous update.
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