Abstract
There has been much progress in recent years towards building larger and larger computational models for biochemical networks, driven by advances both in high throughput data techniques, and in computational modeling and simulation. Such models are often given as unstructured lists of species and interactions between them, making it very difficult to understand the logicome of the network, i.e. the logical connections describing the activation of its key nodes. The problem we are addressing here is to predict whether these key nodes will get activated at any point during a fixed time interval (even transiently), depending on their initial activation status. We solve the problem in terms of a Boolean network over the key nodes, that we call the logicome of the biochemical network. The main advantage of the logicome is that it allows the modeler to focus on a well-chosen small set of key nodes, while abstracting away from the rest of the model, seen as biochemical implementation details of the model. We validate our results by showing that the interpretation of the obtained logicome is in line with literature-based knowledge of the EGFR signalling pathway.
References
Akutsu, T., Miyano, S., Kuhara, S.: Identification of genetic networks from a small number of gene expression patterns under the Boolean network model. In: Somogyi, R., Kitano, H. (eds.) Pacific Symposium on Biocomputing, vol. 4, pp. 17–28. Citeseer (1999)
Britton, D., Hutcheson, I.R., Knowlden, J.M., Barrow, D., Giles, M., McClelland, R.A., Gee, J.M., Nicholson, R.I.: Bidirectional cross talk between ER\(\alpha \) and EGFR signalling pathways regulates tamoxifen-resistant growth. Breast Cancer Res. Treat. 96(2), 131–146 (2006)
Bruggeman, F.J., Westerhoff, H.V., Hoek, J.B., Kholodenko, B.N.: Modular response analysis of cellular regulatory networks. J. Theor. Biol. 218(4), 507–520 (2002)
Chaves, M., Sontag, E.D., Albert, R.: Methods of robustness analysis for Boolean models of gene control networks. IEEE Proc. Syst. Biol. 153(4), 154–167 (2006)
Davidich, M.I., Bornholdt, S.: Boolean network model predicts cell cycle sequence of fission yeast. PloS ONE 3(2), e1672 (2008)
Glass, L., Kauffman, S.A.: The logical analysis of continuous, non-linear biochemical control networks. J. Theor. Biol. 39(1), 103–129 (1973)
Gong, Y., Zhao, X.: Shc-dependent pathway is redundant but dominant in mapk cascade activation by egf receptors: a modeling inference. FEBS Lett. 554(3), 467–472 (2003)
Gratie, D.-E., Iancu, B., Petre, I.: ODE analysis of biological systems. In: Bernardo, M., de Vink, E., Di Pierro, A., Wiklicky, H. (eds.) SFM 2013. LNCS, vol. 7938, pp. 29–62. Springer, Heidelberg (2013)
Hoops, S., Sahle, S., Gauges, R., Lee, C., Pahle, J., Simus, N., Singhal, M., Xu, L., Mendes, P., Kummer, U.: COPASI - a complex pathway simulator. Bioinformatics 22(24), 3067–3074 (2006)
Hornberg, J.J., Binder, B., Bruggeman, F.J., Schoeberl, B., Heinrich, R., Westerhoff, H.V.: Control of MAPK signalling: from complexity to what really matters. Oncogene 24(36), 5533–5542 (2005)
Hwa, H.R.: A method for generating prime implicants of a Boolean expression. IEEE Trans. Comput. 23(6), 637–641 (1974)
Janes, K.A., Yaffe, M.B.: Data-driven modelling of signal-transduction networks. Nat. Rev. Mol. Cell Biol. 7(11), 820–828 (2006)
Kauffman, S.: Homeostasis and differentiation in random genetic control networks. Nature 224, 177–178 (1969)
Klipp, E., Herwig, R., Kowald, A., Wierling, C., Lehrach, H.: Systems Biology in Practice: Concepts, Implementation and Application. Wiley, Weinheim (2008)
Le Novere, N.: Quantitative and logic modelling of molecular and gene networks. Nat. Rev. Genet. 16(3), 146–158 (2015)
Liang, S., Fuhrman, S., Somogyi, R.: Reveal, a general reverse engineering algorithm for inference of genetic network architectures. In: Bryant, B., Milosavljevic, A., Somogyi, R. (eds.)Pacific Symposium on Biocomputing, vol. 3, pp. 18–29. Citeseer (1998)
Macklin, D.N., Ruggero, N.A., Covert, M.W.: The future of whole-cell modeling. Curr. Opin. Biotechnol. 28, 111–115 (2014)
Martin, S., Zhang, Z., Martino, A., Faulon, J.: Boolean dynamics of genetic regulatory networks inferred from microarray time series data. Bioinformatics 23(7), 866–874 (2007)
Morris, M.K., Saez-Rodriguez, J., Sorger, P.K., Lauffenburger, D.A.: Logic-based models for the analysis of cell signalling networks. Biochemistry 49(15), 3216–3224 (2010)
Oda, K., Matsuoka, Y., Funahashi, A., Kitano, H.: A comprehensive pathway map of epidermal growth factor receptor signaling. Curr. Opin. Biotechnol. 1(1), 1–17 (2005)
Pantel, P., Pennacchiotti, M.: Espresso: Leveraging generic patterns for automatically harvesting semantic relations. In: Carpuat, M., Duh, K. (eds.) Proceedings of the 21st International Conference on Computational Linguistics and the 44th Annual Meeting of the Association for Computational Linguistics, Association for Computational Linguistics, pp. 113–120 (2006)
Pitkänen, E., Jouhten, P., Hou, J., Syed, M.F., Blomberg, P., Kludas, J., Oja, M., Holm, L., Penttilä, M., Rousu, J., Arvas, M.: Comparative genome-scale reconstruction of gapless metabolic networks for present and ancestral species. PLoS Comput. Biol. 10(2), 1–12 (2014)
Rajalingam, K., Schreck, R., Rapp, U.R., Albert, V.: Ras oncogenes and their downstream targets. Biochimica et Biophysica Acta (BBA) - Molecular Cell Research 1773(8), 1177–1195 (2007)
Rajasekharan, S., Raman, T.: Ras and ras mutations in cancer. Cent. Eur. J. Biol. 8(7), 609–624 (2013)
Roskoski, R.: Raf protein-serine/threonine kinases: structure and regulation. Biochem. Biophys. Res. Commun. 399(3), 313–317 (2010)
Saez-Rodriguez, J., Alexopoulos, L.G., Epperlein, J., Samaga, R., Lauffenburger, D.A., Klamt, S., Sorger, P.K.: Discrete logic modelling as a means to link protein signalling networks with functional analysis of mammalian signal transduction. Mol. Syst. Biol. 5(1), 331 (2009)
Saez-Rodriguez, J., Alexopoulos, L.G., Zhang, M., Morris, M.K., Lauffenburger, D.A., Sorger, P.K.: Comparing signaling networks between normal and transformed hepatocytes using discrete logical models. Cancer Res. 71(16), 5400–5411 (2011)
Schoeberl, B., Eichler-Jonsson, C., Gilles, E.D., Müller, G.: Computational modeling of the dynamics of the MAP kinase cascade activated by surface and internalized EGF receptors. Nat. Biotechnol. 20(118), 370–375 (2002)
Sebastian, S., Settleman, J., Reshkin, S.J., Azzariti, A., Bellizzi, A., Paradiso, A.: The complexity of targeting EGFR signalling in cancer: from expression to turnover. Biochimica et Biophysica Acta (BBA)-Reviews on Cancer 1766(1), 120–139 (2006)
Stötzel, C., Röblitz, S., Siebert, H.: Complementing ODE-based system analysis using Boolean networks derived from an Euler-like transformation. PLoS ONE 10(10), e0140954 (2015)
Wang, D.Y., Cardelli, L., Phillips, A., Piterman, N., Fisher, J.: Computational modeling of the EGFR network elucidates control mechanisms regulating signal dynamics. BMC Syst. Biol. 3(1), 1–18 (2009)
Yarden, Y.: The EGFR family and its ligands in human cancer: signalling mechanisms and therapeutic opportunities. Eur. J. Cancer 37(4), 3–8 (2001)
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Panchal, C., Azimi, S., Petre, I. (2016). Generating the Logicome of a Biological Network. In: Botón-Fernández, M., Martín-Vide, C., Santander-Jiménez, S., Vega-Rodríguez, M.A. (eds) Algorithms for Computational Biology. AlCoB 2016. Lecture Notes in Computer Science(), vol 9702. Springer, Cham. https://doi.org/10.1007/978-3-319-38827-4_4
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