Motivation: Novel techniques are required to analyze computational models of intracellular processes as they increase steadily in size and complexity. The theory of chemical organizations has recently been introduced as such a technique that links the topology of biochemical reaction network models to their dynamical repertoire. The network is decomposed into algebraically closed and self-maintaining subnetworks called organizations. They form a hierarchy representing all feasible system states including all steady states. Results: We present three algorithms to compute the hierarchy of organizations for network models provided in SBML format. Two of them compute the complete organization hierarchy, while the third one uses heuristics to obtain a subset of all organizations for large models. While the constructive approach computes the hierarchy starting from the smallest organization in a bottom-up fashion, the flux-based approach employs self-maintaining flux distributions to determine organizations. A runtime comparison on 16 different network models of natural systems showed that none of the two exhaustive algorithms is superior in all cases. Studying a ‘genome-scale’ network model with 762 species and 1193 reactions, we demonstrate how the organization hierarchy helps to uncover the model structure and allows to evaluate the model's quality, for example by detecting components and subsystems of the model whose maintenance is not explained by the model. Availability: All data and a Java implementation that plugs into the Systems Biology Workbench is available from http://www.minet.uni-jena.de/csb/prj/ot/tools. Contact:dittrich@minet.uni-jena.de Supplementary Information: Supplementary data are available at Bioinformatics online.

Suburothelial myofibroblasts (sMF) are located underneath the urothelium in close proximity to afferent nerves. They express purinergic receptors and show calcium transients in response to ATP. Therefore they are supposed to be involved in afferent signaling of the bladder fullness. Since ATP concentration is likely to be very low during the initial filling phase, we hypothesized that sMF Ca²⁺ activity is affected even at very low ATP concentrations. We investigated ATP induced modulation of spontaneous activity, intracellular calcium response and purinergic signaling in cultured sMF. Myofibroblast cultures, established from cystectomies, were challenged by exogenous ATP in presence or absence of purinergic antagonist. Fura-2 calcium imaging was used to monitor ATP (10⁻¹⁶ to 10⁻⁴ mol/l) induced alterations of calcium activity. Purinergic receptors (P2X1, P2X2, P2X3) were analysed by confocal immunofluorescence. We found spontaneous calcium activity in 55.18%±1.65 of the sMF (N = 48 experiments). ATP significantly increased calcium activity even at 10⁻¹⁶ mol/l. The calcium transients were partially attenuated by subtype selective antagonist (TNP-ATP, 1 µM; A-317491, 1 µM), and were mimicked by the P2X1, P2X3 selective agonist α,β-methylene ATP. The expression of purinergic receptor subtypes in sMF was confirmed by immunofluorescence. Our experiments demonstrate for the first time that ATP can modulate spontaneous activity and induce intracellular Ca²⁺ response in cultured sMF at very low concentrations, most likely involving P2X receptors. These findings support the notion that sMF are able to register bladder fullness very sensitively, which predestines them for the modulation of the afferent bladder signaling in normal and pathological conditions.

Background: Due to the growing amount of biological knowledge that is incorporated into metabolic network models, their analysis has become more and more challenging. Here, we examine the capabilities of the recently introduced chemical organization theory (OT) to ease this task. Considering only network stoichiometry, the theory allows the prediction of all potentially persistent species sets and therewith rigorously relates the structure of a network to its potential dynamics. By this, the phenotypes implied by a metabolic network can be predicted without the need for explicit knowledge of the detailed reaction kinetics.Results: We propose an approach to deal with regulation – and especially inhibitory interactions – in chemical organization theory. One advantage of this approach is that the metabolic network and its regulation are represented in an integrated way as one reaction network. To demonstrate the feasibility of this approach we examine a model by Covert and Palsson (J Biol Chem, 277(31), 2002) of the central metabolism ofE. colithat incorporates the regulation of all involved genes. Our method correctly predicts the known growth phenotypes on 16 different substrates. Without specific assumptions, organization theory correctly predicts the lethality of knockout experiments in 101 out of 116 cases. Taking into account the same model specific assumptions as in the regulatory flux balance analysis (rFBA) by Covert and Palsson, the same performance is achieved (106 correctly predicted cases). Two model specific assumptions had to be considered: first, we have to assume that secreted molecules do not influence the regulatory system, and second, that metabolites with increasing concentrations indicate a lethal state.Conclusion: The introduced approach to model a metabolic network and its regulation in an integrated way as one reaction network makes organization analysis a universal technique to study the potential behavior of biological network models. Applying multiple methods like OT and rFBA is shown to be valuable to uncover critical assumptions and helps to improve model coherence.

Influenza A virus is recognized today as one of the most challenging viruses that threatens both human and animal health worldwide. Understanding the control mechanisms of influenza infection and dynamics is crucial and could result in effective future treatment strategies. Many kinetic models based on differential equations have been developed in recent decades to capture viral dynamics within a host. These models differ in their complexity in terms of number of species elements and number of reactions. Here, we present a new approach to understanding the overall structure of twelve influenza A virus infection models and their relationship to each other. To this end, we apply chemical organization theory to obtain a hierarchical decomposition of the models into chemical organizations. The decomposition is based on the model structure (reaction rules) but is independent of kinetic details such as rate constants. We found different types of model structures ranging from two to eight organizations. Furthermore, the model’s organizations imply a partial order among models entailing a hierarchy of model, revealing a high model diversity with respect to their long-term behavior. Our methods and results can be helpful in model development and model integration, also beyond the influenza area.