Abstract
Over the last few decades, researchers in various fields have witnessed applications of novel computing models and algorithmic paradigms in many application areas involving biological processes, quantum computing, nanotechnology, social networks and many other such disciplines. Typical characteristics of these application areas include their interdisciplinary nature going beyond previous traditional approaches that were used, and often high-risk high-gain nature of resulting collaborations. Major research challenges on a macroscopic level for such collaborations include forming appropriate interdisciplinary teams, effective communication between researchers in diverse areas, using individual expertise for overall goal of the project, and collaboration with industry if necessary. In addition, one also faces the usual challenge that lies in collaboration between theory and practice, namely sometimes theory follows application, sometimes theory precedes application, and sometimes they walk hand-in-hand. Recent and not so recent developments on analysis of models of computational processes in biology, in the context of gene and protein networks that arise in organism development and cell signalling, have given rise to many types of discrete, continuous and hybrid models, and researchers have studied the inter-relationships, powers and limitations, computational complexity and algorithmic issues as well as biological implications and validations of these models. Such investigations have given rise to fascinating interplay between many diverse research areas such as biology, control theory, discrete mathematics and computer science.
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Albert, R., DasGupta, B., Gitter, A., Gürsoy, G., Hegde, R., Pal, P., Sivanathan, G.S., Sontag, E.: A New Computationally Efficient Measure of Topological Redundancy of Biological and Social Networks. Physical Review E 84(3), 036117 (2011)
DasGupta, B., Vera-Licona, P., Sontag, E.: Reverse Engineering of Molecular Networks from a Common Combinatorial Approach. In: Elloumi, M., Zomaya, A. (eds.) Algorithms in Computational Molecular Biology: Techniques, Approaches and Applications, ch. 40. John Wiley & Sons, Inc. (2011)
Albert, R., DasGupta, B., Sontag, E.: Inference of signal transduction networks from double causal evidence. In: Fenyo, D. (ed.) Methods in Molecular Biology: Topics in Computational Biology, ch. 16, p. 673. Springer Science+Business Media, LLC (2010)
Berman, P., DasGupta, B., Karpinski, M.: Approximating Transitive Reductions for Directed Networks. In: Dehne, F., Gavrilova, M., Sack, J.-R., Tóth, C.D. (eds.) WADS 2009. LNCS, vol. 5664, pp. 74–85. Springer, Heidelberg (2009)
Albert, R., DasGupta, B., Dondi, R., Sontag, E.: Inferring (Biological) Signal Transduction Networks via Transitive Reductions of Directed Graphs. Algorithmica 51(2), 129–159 (2008)
Kachalo, S., Zhang, R., Sontag, E., Albert, R., DasGupta, B.: NET-SYNTHESIS: A software for synthesis, inference and simplification of signal transduction networks. Bioinformatics 24(2), 293–295 (2008)
Berman, P., DasGupta, B., Sontag, E.: Algorithmic Issues in Reverse Engineering of Protein and Gene Networks via the Modular Response Analysis Method. Annals of the New York Academy of Sciences 1115, 132–141 (2007)
Albert, R., DasGupta, B., Dondi, R., Kachalo, S., Sontag, E., Zelikovsky, A., Westbrooks, K.: A Novel Method for Signal Transduction Network Inference from Indirect Experimental Evidence. Journal of Computational Biology 14(7), 927–949 (2007)
DasGupta, B., Enciso, G.A., Sontag, E., Zhang, Y.: Algorithmic and Complexity Results for Decompositions of Biological Networks into Monotone Subsystems. Biosystems 90(1), 161–178 (2007)
Berman, P., DasGupta, B., Sontag, E.: Computational Complexities of Combinatorial Problems With Applications to Reverse Engineering of Biological Networks. In: Wang, F.-Y., Liu, D. (eds.) Advances in Computational Intelligence: Theory and Applications. Series in Intelligent Control and Intelligent Automation, vol. 5, pp. 303–316. World Scientific Publishers (2007)
Berman, P., DasGupta, B., Sontag, E.: Randomized Approximation Algorithms for Set Multicover Problems with Applications to Reverse Engineering of Protein and Gene Networks. Discrete Applied Mathematics 155(6-7), 733–749 (2007)
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DasGupta, B. (2012). Models and Algorithmic Tools for Computational Processes in Cellular Biology: Recent Developments and Future Directions. In: Bleris, L., Măndoiu, I., Schwartz, R., Wang, J. (eds) Bioinformatics Research and Applications. ISBRA 2012. Lecture Notes in Computer Science(), vol 7292. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-30191-9_8
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DOI: https://doi.org/10.1007/978-3-642-30191-9_8
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