A Boolean view separates platelet activatory and inhibitory signalling as verified by phosphorylation monitoring including threshold behaviour and integrin modulation.

Platelets are critical for haemostasis and blood clotting. However, since under normal circumstances blood should flow without clotting, its function is regulated via a complex interplay of activating and inhibiting signal transduction pathways. Understanding this network is crucial for treatment of cardiovascular and bleeding diseases. Detailed protein interaction and phosphorylation data are explored to establish a simplified Boolean model of the central platelet cascades. We implemented the model by means of CellNetAnalyzer and showed how different signalling events coalesce into a fully activated system state. Furthermore, we examined the networks' inherent threshold behaviour using the semi-quantitative modelling software SQUAD. Finally, predictions are verified monitoring phosphorylations which mark different activation phases as modelled. The model can also be applied to simulate different pharmacological conditions as they modify node activity (aspirin, clopidogrel, milrinon, iloprost, combination) and is available for further studies. It agrees well with observations. Activatory pathways are diversified to cope with complex environmental conditions. Platelet activation needs several activation steps to integrate over different network subsets, as they are formed by the interplay of activating kinases, calcium mobilization, and the inhibiting cAMP-PKA system. System stability analysis shows two phases: a sub-threshold behaviour, characterized by integration over different activatory and inhibitory conditions, and a beyond threshold phase, represented by competition and shutting down of counter-regulatory pathways. The integrin network and Akt-protein are critical for stable effector response. Dynamic threshold-analysis reveals a dependency of the relative activating input strength necessary to irreversibly engage the system from the absolute inhibitory signal strength.

Integrated systems view on networking by hormones in Arabidopsis immunity reveals multiple crosstalk for cytokinin.

Phytohormones signal and combine to maintain the physiological equilibrium in the plant. Pathogens enhance host susceptibility by modulating the hormonal balance of the plant cell. Unlike other plant hormones, the detailed role of cytokinin in plant immunity remains to be fully elucidated. Here, extensive data mining, including of pathogenicity factors, host regulatory proteins, enzymes of hormone biosynthesis, and signaling components, established an integrated signaling network of 105 nodes and 163 edges. Dynamic modeling and system analysis identified multiple cytokinin-mediated regulatory interactions in plant disease networks. This includes specific synergism between cytokinin and salicylic acid pathways and previously undiscovered aspects of antagonism between cytokinin and auxin in plant immunity. Predicted interactions and hormonal effects on plant immunity are confirmed in subsequent experiments with Pseudomonas syringae pv tomato DC3000 and Arabidopsis thaliana. Our dynamic simulation is instrumental in predicting system effects of individual components in complex hormone disease networks and synergism or antagonism between pathways.

Integration of Boolean models exemplified on hepatocyte signal transduction.

The number of mathematical models for biological pathways is rapidly growing. In particular, Boolean modelling proved to be suited to describe large cellular signalling networks. Systems biology is at the threshold to holistic understanding of comprehensive networks. In order to reach this goal, connection and integration of existing models of parts of cellular networks into more comprehensive network models is necessary. We discuss model combination approaches for Boolean models. Boolean modelling is qualitative rather than quantitative and does not require detailed kinetic information. We show that these models are useful precursors for large-scale quantitative models and that they are comparatively easy to combine. We propose modelling standards for Boolean models as a prerequisite for smooth model integration. Using these standards, we demonstrate the coupling of two logical models on two different examples concerning cellular interactions in the liver. In the first example, we show the integration of two Boolean models of two cell types in order to describe their interaction. In the second example, we demonstrate the combination of two models describing different parts of the network of a single cell type. Combination of partial models into comprehensive network models will take systems biology to the next level of understanding. The combination of logical models facilitated by modelling standards is a valuable example for the next step towards this goal.

Modeling system states in liver cells: survival, apoptosis and their modifications in response to viral infection.

BACKGROUND: The decision pro- or contra apoptosis is complex, involves a number of different inputs, and is central for the homeostasis of an individual cell as well as for the maintenance and regeneration of the complete organism. RESULTS: This study centers on Fas ligand (FasL)-mediated apoptosis, and a complex and internally strongly linked network is assembled around the central FasL-mediated apoptosis cascade. Different bioinformatical techniques are employed and different crosstalk possibilities including the integrin pathway are considered. This network is translated into a Boolean network (74 nodes, 108 edges). System stability is dynamically sampled and investigated using the software SQUAD. Testing a number of alternative crosstalk possibilities and networks we find that there are four stable system states, two states comprising cell survival and two states describing apoptosis by the intrinsic and the extrinsic pathways, respectively. The model is validated by comparing it to experimental data from kinetics of cytochrome c release and caspase activation in wildtype and Bid knockout cells grown on different substrates. Pathophysiological modifications such as input from cytomegalovirus proteins M36 and M45 again produces output behavior that well agrees with experimental data. CONCLUSION: A network model for apoptosis and crosstalk in hepatocytes shows four different system states and reproduces a number of different conditions around apoptosis including effects of different growth substrates and viral infections. It produces semi-quantitative predictions on the activity of individual nodes, agreeing with experimental data. The model (SBML format) and all data are available for further predictions and development.

Distinguishing species.

Given two organisms, how can one distinguish whether they belong to the same species or not? This might be straightforward for two divergent organisms, but can be extremely difficult and laborious for closely related ones. A molecular marker giving a clear distinction would therefore be of immense benefit. The internal transcribed spacer 2 (ITS2) has been widely used for low-level phylogenetic analyses. Case studies revealed that a compensatory base change (CBC) in the helix II or helix III ITS2 secondary structure between two organisms correlated with sexual incompatibility. We analyzed more than 1300 closely related species to test whether this correlation is generally applicable. In 93%, where a CBC was found between organisms classified within the same genus, they belong to different species. Thus, a CBC in an ITS2 sequence-structure alignment is a sufficient condition to distinguish even closely related species.