Network 'small-world-ness': a quantitative method for determining canonical network equivalence

Background: Many technological, biological, social, and information networks fall into the broad class of 'small-world' networks: they have tightly interconnected clusters of nodes, and a shortest mean path length that is similar to a matched random graph (same number of nodes and edges). This semi-quantitative definition leads to a categorical distinction ('small/not-small') rather than a quantitative, continuous grading of networks, and can lead to uncertainty about a network's small-world status. Moreover, systems described by small-world networks are often studied using an equivalent canonical network model-the Watts-Strogatz (WS) model. However, the process of establishing an equivalent WS model is imprecise and there is a pressing need to discover ways in which this equivalence may be quantified. Methodology/Principal Findings: We defined a precise measure of 'small-world-ness' S based on the trade off between high local clustering and short path length. A network is now deemed a 'small-world' if S. 1-an assertion which may be tested statistically. We then examined the behavior of S on a large data-set of real-world systems. We found that all these systems were linked by a linear relationship between their S values and the network size n. Moreover, we show a method for assigning a unique Watts-Strogatz (WS) model to any real-world network, and show analytically that the WS models associated with our sample of networks also show linearity between S and n. Linearity between S and n is not, however, inevitable, and neither is S maximal for an arbitrary network of given size. Linearity may, however, be explained by a common limiting growth process. Conclusions/Significance: We have shown how the notion of a small-world network may be quantified. Several key properties of the metric are described and the use of WS canonical models is placed on a more secure footing

Cross-Platform Microarray Data Normalisation for Regulatory Network Inference

Background Inferring Gene Regulatory Networks (GRNs) from time course microarray data suffers from the dimensionality problem created by the short length of available time series compared to the large number of genes in the network. To overcome this, data integration from diverse sources is mandatory. Microarray data from different sources and platforms are publicly available, but integration is not straightforward, due to platform and experimental differences. Methods We analyse here different normalisation approaches for microarray data integration, in the context of reverse engineering of GRN quantitative models. We introduce two preprocessing approaches based on existing normalisation techniques and provide a comprehensive comparison of normalised datasets. Conclusions Results identify a method based on a combination of Loess normalisation and iterative K-means as best for time series normalisation for this problem

On Propagation of Excitation Waves in Moving Media: The FitzHugh-Nagumo Model

BACKGROUND: Existence of flows and convection is an essential and integral feature of many excitable media with wave propagation modes, such as blood coagulation or bioreactors. METHODS/RESULTS: Here, propagation of two-dimensional waves is studied in parabolic channel flow of excitable medium of the FitzHugh-Nagumo type. Even if the stream velocity is hundreds of times higher that the wave velocity in motionless medium (), steady propagation of an excitation wave is eventually established. At high stream velocities, the wave does not span the channel from wall to wall, forming isolated excited regions, which we called "restrictons". They are especially easy to observe when the model parameters are close to critical ones, at which waves disappear in still medium. In the subcritical region of parameters, a sufficiently fast stream can result in the survival of excitation moving, as a rule, in the form of "restrictons". For downstream excitation waves, the axial portion of the channel is the most important one in determining their behavior. For upstream waves, the most important region of the channel is the near-wall boundary layers. The roles of transversal diffusion, and of approximate similarity with respect to stream velocity are discussed. CONCLUSIONS: These findings clarify mechanisms of wave propagation and survival in flow

Accounting for Redundancy when Integrating Gene Interaction Databases

During the last years gene interaction networks are increasingly being used for the assessment and interpretation of biological measurements. Knowledge of the interaction partners of an unknown protein allows scientists to understand the complex relationships between genetic products, helps to reveal unknown biological functions and pathways, and get a more detailed picture of an organism's complexity. Being able to measure all protein interactions under all relevant conditions is virtually impossible. Hence, computational methods integrating different datasets for predicting gene interactions are needed. However, when integrating different sources one has to account for the fact that some parts of the information may be redundant, which may lead to an overestimation of the true likelihood of an interaction. Our method integrates information derived from three different databases (Bioverse, HiMAP and STRING) for predicting human gene interactions. A Bayesian approach was implemented in order to integrate the different data sources on a common quantitative scale. An important assumption of the Bayesian integration is independence of the input data (features). Our study shows that the conditional dependency cannot be ignored when combining gene interaction databases that rely on partially overlapping input data. In addition, we show how the correlation structure between the databases can be detected and we propose a linear model to correct for this bias. Benchmarking the results against two independent reference data sets shows that the integrated model outperforms the individual datasets. Our method provides an intuitive strategy for weighting the different features while accounting for their conditional dependencies

A computational analysis of the dynamic roles of talin, Dok1, and PIPKI for integrin activation

Integrin signaling regulates cell migration and plays a pivotal role in developmental processes and cancer metastasis. Integrin signaling has been studied extensively and much data is available on pathway components and interactions. Yet the data is fragmented and an integrated model is missing. We use a rule-based modeling approach to integrate available data and test biological hypotheses regarding the role of talin, Dok1 and PIPKI in integrin activation. The detailed biochemical characterization of integrin signaling provides us with measured values for most of the kinetics parameters. However, measurements are not fully accurate and the cellular concentrations of signaling proteins are largely unknown and expected to vary substantially across different cellular conditions. By sampling model behaviors over the physiologically realistic parameter range we find that the model exhibits only two different qualitative behaviours and these depend mainly on the relative protein concentrations, which offers a powerful point of control to the cell. Our study highlights the necessity to characterize model behavior not for a single parameter optimum, but to identify parameter sets that characterize different signaling modes

Reverse Engineering of the Spindle Assembly Checkpoint

The Spindle Assembly Checkpoint (SAC) is an intracellular mechanism that ensures proper chromosome segregation. By inhibiting Cdc20, a co-factor of the Anaphase Promoting Complex (APC), the checkpoint arrests the cell cycle until all chromosomes are properly attached to the mitotic spindle. Inhibition of Cdc20 is mediated by a conserved network of interacting proteins. The individual functions of these proteins are well characterized, but understanding of their integrated function is still rudimentary. We here describe our attempts to reverse-engineer the SAC network based on gene deletion phenotypes. We begun by formulating a general model of the SAC which enables us to predict the rate of chromosomal missegregation for any putative set of interactions between the SAC proteins. Next the missegregation rates of seven yeast strains are measured in response to the deletion of one or two checkpoint proteins. Finally, we searched for the set of interactions that correctly predicted the observed missegregation rates of all deletion mutants. Remarkably, although based on only seven phenotypes, the consistent network we obtained successfully reproduces many of the known properties of the SAC. Further insights provided by our analysis are discussed

A meta-analysis reveals the commonalities and differences in Arabidopsis thaliana response to different viral pathogens

Understanding the mechanisms by which plants trigger host defenses in response to viruses has been a challenging problem owing to the multiplicity of factors and complexity of interactions involved. The advent of genomic techniques, however, has opened the possibility to grasp a global picture of the interaction. Here, we used Arabidopsis thaliana to identify and compare genes that are differentially regulated upon infection with seven distinct (+)ssRNA and one ssDNA plant viruses. In the first approach, we established lists of genes differentially affected by each virus and compared their involvement in biological functions and metabolic processes. We found that phylogenetically related viruses significantly alter the expression of similar genes and that viruses naturally infecting Brassicaceae display a greater overlap in the plant response. In the second approach, virus-regulated genes were contextualized using models of transcriptional and protein-protein interaction networks of A. thaliana. Our results confirm that host cells undergo significant reprogramming of their transcriptome during infection, which is possibly a central requirement for the mounting of host defenses. We uncovered a general mode of action in which perturbations preferentially affect genes that are highly connected, central and organized in modules. © 2012 Rodrigo et al.This work was supported by the Spanish Ministerio de Ciencia e Innovacion (MICINN) grants BFU2009-06993 (S. F. E.) and BIO2006-13107 (C. L.) and by Generalitat Valenciana grant PROMETEO2010/016 (S. F. E.). G. R. is supported by a graduate fellowship from the Generalitat Valenciana (BFPI2007-160) and J.C. by a contract from MICINN grant TIN2006-12860. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.Rodrigo Tarrega, G.; Carrera Montesinos, J.; Ruiz-Ferrer, V.; Del Toro, F.; Llave, C.; Voinnet, O.; Elena Fito, SF. (2012). A meta-analysis reveals the commonalities and differences in Arabidopsis thaliana response to different viral pathogens. PLoS ONE. 7(7):40526-40526. https://doi.org/10.1371/journal.pone.0040526S405264052677Peng, X., Chan, E. Y., Li, Y., Diamond, D. L., Korth, M. J., & Katze, M. G. (2009). Virus–host interactions: from systems biology to translational research. Current Opinion in Microbiology, 12(4), 432-438. doi:10.1016/j.mib.2009.06.003Dodds, P. N., & Rathjen, J. P. (2010). Plant immunity: towards an integrated view of plant–pathogen interactions. Nature Reviews Genetics, 11(8), 539-548. doi:10.1038/nrg2812Maule, A., Leh, V., & Lederer, C. (2002). The dialogue between viruses and hosts in compatible interactions. Current Opinion in Plant Biology, 5(4), 279-284. doi:10.1016/s1369-5266(02)00272-8Whitham, S. A., Quan, S., Chang, H.-S., Cooper, B., Estes, B., Zhu, T., … Hou, Y.-M. (2003). Diverse RNA viruses elicit the expression of common sets of genes in susceptibleArabidopsis thalianaplants. The Plant Journal, 33(2), 271-283. doi:10.1046/j.1365-313x.2003.01625.xBailer, S., & Haas, J. (2009). Connecting viral with cellular interactomes. Current Opinion in Microbiology, 12(4), 453-459. doi:10.1016/j.mib.2009.06.004Whitham, S. A., Yang, C., & Goodin, M. M. (2006). Global Impact: Elucidating Plant Responses to Viral Infection. Molecular Plant-Microbe Interactions, 19(11), 1207-1215. doi:10.1094/mpmi-19-1207MacPherson, J. I., Dickerson, J. E., Pinney, J. W., & Robertson, D. L. (2010). Patterns of HIV-1 Protein Interaction Identify Perturbed Host-Cellular Subsystems. PLoS Computational Biology, 6(7), e1000863. doi:10.1371/journal.pcbi.1000863Jenner, R. G., & Young, R. A. (2005). Insights into host responses against pathogens from transcriptional profiling. Nature Reviews Microbiology, 3(4), 281-294. doi:10.1038/nrmicro1126Andeweg, A. C., Haagmans, B. L., & Osterhaus, A. D. (2008). Virogenomics: the virus–host interaction revisited. Current Opinion in Microbiology, 11(5), 461-466. doi:10.1016/j.mib.2008.09.010Elena, S. F., Carrera, J., & Rodrigo, G. (2011). A systems biology approach to the evolution of plant–virus interactions. Current Opinion in Plant Biology, 14(4), 372-377. doi:10.1016/j.pbi.2011.03.013Tan, S.-L., Ganji, G., Paeper, B., Proll, S., & Katze, M. G. (2007). Systems biology and the host response to viral infection. Nature Biotechnology, 25(12), 1383-1389. doi:10.1038/nbt1207-1383De la Fuente, A. (2010). From ‘differential expression’ to ‘differential networking’ – identification of dysfunctional regulatory networks in diseases. Trends in Genetics, 26(7), 326-333. doi:10.1016/j.tig.2010.05.001Albert, R. (2005). Scale-free networks in cell biology. Journal of Cell Science, 118(21), 4947-4957. doi:10.1242/jcs.02714Yu, H., Braun, P., Yildirim, M. A., Lemmens, I., Venkatesan, K., Sahalie, J., … Vidal, M. (2008). High-Quality Binary Protein Interaction Map of the Yeast Interactome Network. Science, 322(5898), 104-110. doi:10.1126/science.1158684Barabási, A.-L., & Oltvai, Z. N. (2004). Network biology: understanding the cell’s functional organization. Nature Reviews Genetics, 5(2), 101-113. doi:10.1038/nrg1272Albert, R., Jeong, H., & Barabási, A.-L. (2000). Error and attack tolerance of complex networks. Nature, 406(6794), 378-382. doi:10.1038/35019019Mukhtar, M. S., Carvunis, A.-R., Dreze, M., Epple, P., Steinbrenner, J., … Moore, J. (2011). Independently Evolved Virulence Effectors Converge onto Hubs in a Plant Immune System Network. Science, 333(6042), 596-601. doi:10.1126/science.1203659Calderwood, M. A., Venkatesan, K., Xing, L., Chase, M. R., Vazquez, A., Holthaus, A. M., … Johannsen, E. (2007). Epstein-Barr virus and virus human protein interaction maps. Proceedings of the National Academy of Sciences, 104(18), 7606-7611. doi:10.1073/pnas.0702332104De Chassey, B., Navratil, V., Tafforeau, L., Hiet, M. S., Aublin‐Gex, A., Agaugué, S., … Lotteau, V. (2008). Hepatitis C virus infection protein network. Molecular Systems Biology, 4(1), 230. doi:10.1038/msb.2008.66Shapira, S. D., Gat-Viks, I., Shum, B. O. V., Dricot, A., de Grace, M. M., Wu, L., … Hacohen, N. (2009). A Physical and Regulatory Map of Host-Influenza Interactions Reveals Pathways in H1N1 Infection. Cell, 139(7), 1255-1267. doi:10.1016/j.cell.2009.12.018Dyer, M. D., Murali, T. M., & Sobral, B. W. (2008). The Landscape of Human Proteins Interacting with Viruses and Other Pathogens. PLoS Pathogens, 4(2), e32. doi:10.1371/journal.ppat.0040032Golem, S., & Culver, J. N. (2003). Tobacco mosaic virusInduced Alterations in the Gene Expression Profile ofArabidopsis thaliana. Molecular Plant-Microbe Interactions, 16(8), 681-688. doi:10.1094/mpmi.2003.16.8.681Espinoza, C., Medina, C., Somerville, S., & Arce-Johnson, P. (2007). Senescence-associated genes induced during compatible viral interactions with grapevine and Arabidopsis. Journal of Experimental Botany, 58(12), 3197-3212. doi:10.1093/jxb/erm165Yang, C., Guo, R., Jie, F., Nettleton, D., Peng, J., Carr, T., … Whitham, S. A. (2007). Spatial Analysis ofArabidopsis thalianaGene Expression in Response toTurnip mosaic virusInfection. Molecular Plant-Microbe Interactions, 20(4), 358-370. doi:10.1094/mpmi-20-4-0358Agudelo-Romero, P., Carbonell, P., de la Iglesia, F., Carrera, J., Rodrigo, G., Jaramillo, A., … Elena, S. F. (2008). Changes in the gene expression profile of Arabidopsis thaliana after infection with Tobacco etch virus. Virology Journal, 5(1), 92. doi:10.1186/1743-422x-5-92Agudelo-Romero, P., Carbonell, P., Perez-Amador, M. A., & Elena, S. F. (2008). Virus Adaptation by Manipulation of Host’s Gene Expression. PLoS ONE, 3(6), e2397. doi:10.1371/journal.pone.0002397Ascencio-Ibáñez, J. T., Sozzani, R., Lee, T.-J., Chu, T.-M., Wolfinger, R. D., Cella, R., & Hanley-Bowdoin, L. (2008). Global Analysis of Arabidopsis Gene Expression Uncovers a Complex Array of Changes Impacting Pathogen Response and Cell Cycle during Geminivirus Infection. Plant Physiology, 148(1), 436-454. doi:10.1104/pp.108.121038Babu, M., Griffiths, J. S., Huang, T.-S., & Wang, A. (2008). Altered gene expression changes in Arabidopsis leaf tissues and protoplasts in response to Plum pox virus infection. BMC Genomics, 9(1), 325. doi:10.1186/1471-2164-9-325De Vienne, D. M., Giraud, T., & Martin, O. C. (2007). A congruence index for testing topological similarity between trees. Bioinformatics, 23(23), 3119-3124. doi:10.1093/bioinformatics/btm500Wise, R. P., Moscou, M. J., Bogdanove, A. J., & Whitham, S. A. (2007). Transcript Profiling in Host–Pathogen Interactions. Annual Review of Phytopathology, 45(1), 329-369. doi:10.1146/annurev.phyto.45.011107.143944Handford, M. G., & Carr, J. P. (2007). A defect in carbohydrate metabolism ameliorates symptom severity in virus-infected Arabidopsis thaliana. Journal of General Virology, 88(1), 337-341. doi:10.1099/vir.0.82376-0Hou, B., Lim, E.-K., Higgins, G. S., & Bowles, D. J. (2004). N-Glucosylation of Cytokinins by Glycosyltransferases ofArabidopsis thaliana. Journal of Biological Chemistry, 279(46), 47822-47832. doi:10.1074/jbc.m409569200Schwender, J., Goffman, F., Ohlrogge, J. B., & Shachar-Hill, Y. (2004). Rubisco without the Calvin cycle improves the carbon efficiency of developing green seeds. Nature, 432(7018), 779-782. doi:10.1038/nature03145Pagán, I., Alonso-Blanco, C., & García-Arenal, F. (2008). Host Responses in Life-History Traits and Tolerance to Virus Infection in Arabidopsis thaliana. PLoS Pathogens, 4(8), e1000124. doi:10.1371/journal.ppat.1000124Carrera, J., Rodrigo, G., Jaramillo, A., & Elena, S. F. (2009). Reverse-engineering the Arabidopsis thaliana transcriptional network under changing environmental conditions. Genome Biology, 10(9), R96. doi:10.1186/gb-2009-10-9-r96Geisler-Lee, J., O’Toole, N., Ammar, R., Provart, N. J., Millar, A. H., & Geisler, M. (2007). A Predicted Interactome for Arabidopsis. Plant Physiology, 145(2), 317-329. doi:10.1104/pp.107.103465Ma, S., Gong, Q., & Bohnert, H. J. (2007). An Arabidopsis gene network based on the graphical Gaussian model. Genome Research, 17(11), 1614-1625. doi:10.1101/gr.6911207Yamada, T., & Bork, P. (2009). Evolution of biomolecular networks — lessons from metabolic and protein interactions. Nature Reviews Molecular Cell Biology, 10(11), 791-803. doi:10.1038/nrm2787Humphries, M. D., & Gurney, K. (2008). Network ‘Small-World-Ness’: A Quantitative Method for Determining Canonical Network Equivalence. PLoS ONE, 3(4), e0002051. doi:10.1371/journal.pone.0002051Stumpf, M. P. H., & Ingram, P. J. (2005). Probability models for degree distributions of protein interaction networks. Europhysics Letters (EPL), 71(1), 152-158. doi:10.1209/epl/i2004-10531-8Khanin, R., & Wit, E. (2006). How Scale-Free Are Biological Networks. Journal of Computational Biology, 13(3), 810-818. doi:10.1089/cmb.2006.13.810Daudin, J.-J., Picard, F., & Robin, S. (2007). A mixture model for random graphs. Statistics and Computing, 18(2), 173-183. doi:10.1007/s11222-007-9046-7Uetz, P. (2006). Herpesviral Protein Networks and Their Interaction with the Human Proteome. Science, 311(5758), 239-242. doi:10.1126/science.1116804Choi, I.-R., Stenger, D. C., & French, R. (2000). Multiple Interactions among Proteins Encoded by the Mite-Transmitted Wheat Streak Mosaic Tritimovirus. Virology, 267(2), 185-198. doi:10.1006/viro.1999.0117Guo, D., Saarma, M., Rajamäki, M.-L., & Valkonen, J. P. T. (2001). Towards a protein interaction map of potyviruses: protein interaction matrixes of two potyviruses based on the yeast two-hybrid system. Journal of General Virology, 82(4), 935-939. doi:10.1099/0022-1317-82-4-935Lin, L., Shi, Y., Luo, Z., Lu, Y., Zheng, H., Yan, F., … Wu, Y. (2009). Protein–protein interactions in two potyviruses using the yeast two-hybrid system. Virus Research, 142(1-2), 36-40. doi:10.1016/j.virusres.2009.01.006Shen, W., Wang, M., Yan, P., Gao, L., & Zhou, P. (2010). Protein interaction matrix of Papaya ringspot virus type P based on a yeast two-hybrid system. Acta Virologica, 54(1), 49-54. doi:10.4149/av_2010_01_49Redner, S. (2008). Teasing out the missing links. Nature, 453(7191), 47-48. doi:10.1038/453047aIrizarry, R. A. (2003). Exploration, normalization, and summaries of high density oligonucleotide array probe level data. Biostatistics, 4(2), 249-264. doi:10.1093/biostatistics/4.2.249Smyth, G. K. (2004). Linear Models and Empirical Bayes Methods for Assessing Differential Expression in Microarray Experiments. Statistical Applications in Genetics and Molecular Biology, 3(1), 1-25. doi:10.2202/1544-6115.1027Allemeersch, J., Durinck, S., Vanderhaeghen, R., Alard, P., Maes, R., Seeuws, K., … Kuiper, M. T. R. (2005). Benchmarking the CATMA Microarray. A Novel Tool forArabidopsis Transcriptome Analysis. Plant Physiology, 137(2), 588-601. doi:10.1104/pp.104.051300Cleveland, W. S. (1979). Robust Locally Weighted Regression and Smoothing Scatterplots. Journal of the American Statistical Association, 74(368), 829-836. doi:10.1080/01621459.1979.10481038Tarraga, J., Medina, I., Carbonell, J., Huerta-Cepas, J., Minguez, P., Alloza, E., … Dopazo, J. (2008). GEPAS, a web-based tool for microarray data analysis and interpretation. Nucleic Acids Research, 36(Web Server), W308-W314. doi:10.1093/nar/gkn303Al-Shahrour, F., Minguez, P., Vaquerizas, J. M., Conde, L., & Dopazo, J. (2005). BABELOMICS: a suite of web tools for functional annotation and analysis of groups of genes in high-throughput experiments. Nucleic Acids Research, 33(Web Server), W460-W464. doi:10.1093/nar/gki456Al-Shahrour, F., Minguez, P., Tárraga, J., Medina, I., Alloza, E., Montaner, D., & Dopazo, J. (2007). FatiGO +: a functional profiling tool for genomic data. Integration of functional annotation, regulatory motifs and interaction data with microarray experiments. Nucleic Acids Research, 35(suppl_2), W91-W96. doi:10.1093/nar/gkm260Mueller, L. A., Zhang, P., & Rhee, S. Y. (2003). AraCyc: A Biochemical Pathway Database for Arabidopsis. Plant Physiology, 132(2), 453-460. doi:10.1104/pp.102.017236Navratil, V., de Chassey, B., Combe, C., & Lotteau, V. (2011). When the human viral infectome and diseasome networks collide: towards a systems biology platform for the aetiology of human diseases. BMC Systems Biology, 5(1), 13. doi:10.1186/1752-0509-5-13Shannon, C. E. (1948). A Mathematical Theory of Communication. Bell System Technical Journal, 27(3), 379-423. doi:10.1002/j.1538-7305.1948.tb01338.

Gene-Disease Network Analysis Reveals Functional Modules in Mendelian, Complex and Environmental Diseases

Scientists have been trying to understand the molecular mechanisms of diseases to design preventive and therapeutic strategies for a long time. For some diseases, it has become evident that it is not enough to obtain a catalogue of the disease-related genes but to uncover how disruptions of molecular networks in the cell give rise to disease phenotypes. Moreover, with the unprecedented wealth of information available, even obtaining such catalogue is extremely difficult. We developed a comprehensive gene-disease association database by integrating associations from several sources that cover different biomedical aspects of diseases. In particular, we focus on the current knowledge of human genetic diseases including mendelian, complex and environmental diseases. To assess the concept of modularity of human diseases, we performed a systematic study of the emergent properties of human gene-disease networks by means of network topology and functional annotation analysis. The results indicate a highly shared genetic origin of human diseases and show that for most diseases, including mendelian, complex and environmental diseases, functional modules exist. Moreover, a core set of biological pathways is found to be associated with most human diseases. We obtained similar results when studying clusters of diseases, suggesting that related diseases might arise due to dysfunction of common biological processes in the cell. For the first time, we include mendelian, complex and environmental diseases in an integrated gene-disease association database and show that the concept of modularity applies for all of them. We furthermore provide a functional analysis of disease-related modules providing important new biological insights, which might not be discovered when considering each of the gene-disease association repositories independently. Hence, we present a suitable framework for the study of how genetic and environmental factors, such as drugs, contribute to diseases. The gene-disease networks used in this study and part of the analysis are available at http://ibi.imim.es/DisGeNET/DisGeNETweb.html#Download

Regulation of early steps of GPVI signal transduction by phosphatases: a systems biology approach

We present a data-driven mathematical model of a key initiating step in platelet activation, a central process in the prevention of bleeding following Injury. In vascular disease, this process is activated inappropriately and causes thrombosis, heart attacks and stroke. The collagen receptor GPVI is the primary trigger for platelet activation at sites of injury. Understanding the complex molecular mechanisms initiated by this receptor is important for development of more effective antithrombotic medicines. In this work we developed a series of nonlinear ordinary differential equation models that are direct representations of biological hypotheses surrounding the initial steps in GPVI-stimulated signal transduction. At each stage model simulations were compared to our own quantitative, high-temporal experimental data that guides further experimental design, data collection and model refinement. Much is known about the linear forward reactions within platelet signalling pathways but knowledge of the roles of putative reverse reactions are poorly understood. An initial model, that includes a simple constitutively active phosphatase, was unable to explain experimental data. Model revisions, incorporating a complex pathway of interactions (and specifically the phosphatase TULA-2), provided a good description of the experimental data both based on observations of phosphorylation in samples from one donor and in those of a wider population. Our model was used to investigate the levels of proteins involved in regulating the pathway and the effect of low GPVI levels that have been associated with disease. Results indicate a clear separation in healthy and GPVI deficient states in respect of the signalling cascade dynamics associated with Syk tyrosine phosphorylation and activation. Our approach reveals the central importance of this negative feedback pathway that results in the temporal regulation of a specific class of protein tyrosine phosphatases in controlling the rate, and therefore extent, of GPVI-stimulated platelet activation

Evidence for Transcript Networks Composed of Chimeric RNAs in Human Cells

The classic organization of a gene structure has followed the Jacob and Monod bacterial gene model proposed more than 50 years ago. Since then, empirical determinations of the complexity of the transcriptomes found in yeast to human has blurred the definition and physical boundaries of genes. Using multiple analysis approaches we have characterized individual gene boundaries mapping on human chromosomes 21 and 22. Analyses of the locations of the 5′ and 3′ transcriptional termini of 492 protein coding genes revealed that for 85% of these genes the boundaries extend beyond the current annotated termini, most often connecting with exons of transcripts from other well annotated genes. The biological and evolutionary importance of these chimeric transcripts is underscored by (1) the non-random interconnections of genes involved, (2) the greater phylogenetic depth of the genes involved in many chimeric interactions, (3) the coordination of the expression of connected genes and (4) the close in vivo and three dimensional proximity of the genomic regions being transcribed and contributing to parts of the chimeric RNAs. The non-random nature of the connection of the genes involved suggest that chimeric transcripts should not be studied in isolation, but together, as an RNA network