The capacity of pluripotent embryonic stem cells to differentiate into any cell type in the body makes them invaluable in the field of regenerative medicine. However, because of the complexity of both the core pluripotency network and the process of cell fate computation it is not yet possible to control the fate of stem cells. We present a theoretical model of stem cell fate computation that is based on Halley and Winkler's Branching Process Theory (BPT) and on Greaves et al.'s agent-based computer simulation derived from that theoretical model. BPT abstracts the complex production and action of a Transcription Factor (TF) into a single critical branching process that may dissipate, maintain, or become supercritical. Here we take the single TF model and extend it to multiple interacting TFs, and build an agent-based simulation of multiple TFs to investigate the dynamics of such coupled systems. We have developed the simulation and the theoretical model together, in an iterative manner, with the aim of obtaining a deeper understanding of stem cell fate computation, in order to influence experimental efforts, which may in turn influence the outcome of cellular differentiation. The model used is an example of self-organization and could be more widely applicable to the modelling of other complex systems. The simulation based on this model, though currently limited in scope in terms of the biology it represents, supports the utility of the Halley and Winkler branching process model in describing the behaviour of stem cell gene regulatory networks. Our simulation demonstrates three key features: (i) the existence of a critical value of the branching process parameter, dependent on the details of the cistrome in question; (ii) the ability of an active cistrome to "ignite" an otherwise fully dissipated cistrome, and drive it to criticality; (iii) how coupling cistromes together can reduce their critical branching parameter values needed to drive them to criticality.

SUMMARY: The DICS database is a dynamic web repository of computationally predicted functional modules from the human protein-protein interaction network. It provides references to the CORUM, DrugBank, KEGG and Reactome pathway databases. DICS can be accessed for retrieving sets of overlapping modules and protein complexes that are significantly enriched in a gene list, thereby providing valuable information about the functional context. AVAILABILITY: Supplementary information on datasets and methods is available on the web server http://mips.gsf.de/proj/dics.

MOTIVATION: Modern systems biology aims at understanding how the different molecular components of a biological cell interact. Often, cellular functions are performed by complexes consisting of many different proteins. The composition of these complexes may change according to the cellular environment, and one protein may be involved in several different processes. The automatic discovery of functional complexes from protein interaction data is challenging. While previous approaches use approximations to extract dense modules, our approach exactly solves the problem of dense module enumeration. Furthermore, constraints from additional information sources such as gene expression and phenotype data can be integrated, so we can systematically mine for dense modules with interesting profiles. RESULTS: Given a weighted protein interaction network, our method discovers all protein sets that satisfy a user-defined minimum density threshold. We employ a reverse search strategy, which allows us to exploit the density criterion in an efficient way. Our experiments show that the novel approach is feasible and produces biologically meaningful results. In comparative validation studies using yeast data, the method achieved the best overall prediction performance with respect to confirmed complexes. Moreover, by enhancing the yeast network with phenotypic and phylogenetic profiles and the human network with tissue-specific expression data, we identified condition-dependent complex variants. AVAILABILITY: A C++ implementation of the algorithm is available at http://www.kyb.tuebingen.mpg.de/~georgii/dme.html. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

We analysed human interaction data from MINT, Intact, HPRD, and DIP in the context of tissue-specific gene expression data in human provided by Su et al . [ 3 ]. We discretized the expression information into binary states (expressed versus not expressed) and searched for densely connected modules where all proteins are expressed in at least 3 tissues and all proteins are not expressed in at least 10 tissues. To deal with the fact that protein interaction data contain a high number of false positives, we computed reliability scores for each experimental source. Similarly to the work by Jansen et al . [ 4 ], we used for that purpose a gold standard set of known interactions as well as a gold standard set of false interactions and calculated the likelihood ratio, which was used to assign edge weights to the interaction graph. The density of a module is defined as the sum of the edge weights inside the module divided by the maximal possible weight sum for a module of that size. Setting the minimum density threshold to 35% and removing modules that are totally contained in other modules, we obtained a set of 949 differentially expressed modules. They were ranked in descending order according to the average weight per node (see [ 5 ]), so larger and denser modules appear first. On the one hand, we discovered known complexes and modules that link strongly cooperating complexes like MCM and ORC. On the other hand, we found extensions of known complexes that confirm hypothetical functional annotation in Uniprot as well as modules which are not contained in the manually curated set of known complexes, but share the same functional annotation. Finally, some modules are candidates for further biological investigation, containing proteins with unknown functional relationships. We developed a general method for exhaustive dense module extraction from networks. Remarkably, it allows to determine exact P-values for the predicted modules without having to rely on any network model and can easily integrate information from different heterogeneous data sources.

Abstract Motivation: Molecular Surface Patches (MSPs) of proteins are responsible for selective interactions between internal parts of one protein molecule or between protein and other molecules. The prediction of the neighbours of a distinct Secondary Structural Element (SSE) would be an important step for protein structure prediction. Results: Based on a computational analysis of complementary molecular patches of SSEs, feed-forward Neural Networks (NNs) are trained on a large set of helices for predicting the neighbours of given MSPs. Accuracy of prediction is 96% if only two types of neighbours: solvent or ‘protein’ are considered, yet drops to 81% for three types of neighbours: (1) solvent, (2) helix/strand or (3) coil. Implications of the method for the prediction of protein structure and subunit interaction are discussed. As a special test case, the structurally equivalent helices of monomeric myoglobin and the homologous subunits of tetrameric haemoglobin are compared. Availability: Programs are available on request from the authors. Contact: [email protected]; [email protected] * To whom correspondence should be addressed. 2 Present address: EMBL Qutstation EBI, Hinxton, Cambridge CB10 ISD, UK.

MOTIVATION: How critical is the sequence order information in predicting protein secondary structure segments? We tried to get a rough insight on it from a theoretical approach using both a prediction algorithm and structural fragments from Protein Databank (PDB). RESULTS: Using reverse protein sequences and PDB structural fragments, we theoretically estimated the significance of the order for protein secondary structure and prediction. On average: (1) 79% of protein sequence segments resulted in the same prediction in both normal and reverse directions, which indicated a relatively high conservation of secondary structure propensity in the reverse direction; (2) the reversed sequence prediction alone performed less accurately than the normal forward sequence prediction, but comparably high (2% difference); (3) the commonly predicted regions showed a slightly higher prediction accuracy (4%) than the normal sequences prediction; and (4) structural fragments which have counterparts in reverse direction in the same protein showed a comparable degree of secondary structure conservation (73% identity with reversed structures on average for pentamers). CONTACT: [email protected]; [email protected]; [email protected]; [email protected]