Abstract
Qualitative modeling approach is widely used to study the behavior of Biological Regulatory Networks. The approach uses directed graphs also called as , to represent system dynamics. As the number of genes increase, the complexity of stategraph increases exponentially. The identification of important trajectories and isolation of more probable dynamics from less significant ones constitutes an important problem in qualitative modeling of biological networks. In this work, we implement a parallel approach for identification of important dynamics in qualitative models. Our implementation uses the concept of . For parallelization, we used a Java based library MPJ Express to implement our approach. We evaluate the performance of our implementation on well known case study of bacteriophage lambda. We demonstrate the effectiveness of our implementation by selecting important trajectories and correlating with experimental data.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Ahmad, J., Bernot, G., Comet, J.P., Lime, D., Roux, O.: Hybrid modelling and dynamical analysis of gene regulatory networks with delays. Complexus 3(4), 231–251 (2006). https://doi.org/10.1159/000110010
Ahmad, J., Niazi, U., Mansoor, S., Siddique, U., Bibby, J.: Formal modeling and analysis of the MAL-associated biological regulatory network: insight into cerebral malaria 7(3) (2012)
Aslam, B., Ahmad, J., Ali, A., Zafar Paracha, R., Tareen, S.H.K., Niazi, U., Saeed, T.: On the modelling and analysis of the regulatory network of dengue virus pathogenesis and clearance. Comput. Biol. Chem. 53, 277–291 (2014). http://linkinghub.elsevier.com/retrieve/pii/S1476927114001261
Atkinson, D.E.: Biological feedback control at the molecular level. Science 150(3698), 851–857 (1965)
Baker, M., Carpenter, B., Shafi, A.: MPJ express: towards thread safe Java HPC. In: 2006 IEEE International Conference on Cluster Computing, pp. 1–10. IEEE (2006)
Barabasi, A.L., Oltvai, Z.N.: Network biology: understanding the cell’s functional organization. Nat. Rev. Genet. 5(2), 101–113 (2004)
Bernot, G., Cassez, F., Comet, J.P., Delaplace, F., Müller, C., Roux, O.: Semantics of biological regulatory networks. Electron. Notes Theor. Comput. Sci. 180(3), 3–14 (2007)
Bernot, G., Comet, J.P., Richard, A., Guespin, J.: Application of formal methods to biological regulatory networks: extending thomas asynchronous logical approach with temporal logic. J. Theor. Biol. 229(3), 339–347 (2004)
De Jong, H.: Modeling and simulation of genetic regulatory systems: a literature review. J. Comput. Biol. 9(1), 67–103 (2002)
Juncker, B., Schreiber, F.: Analysis of Biological Networks. Wiley, Hoboken (2008)
Karlebach, G., Shamir, R.: Modelling and analysis of gene regulatory networks. Nat. Rev. Mol. Cell Biol. 9(10), 770–780 (2008)
Khalis, Z., Comet, J.P., Richard, A., Bernot, G.: The smbionet method for discovering models of gene regulatory networks. Genes Genomes Genomics 3(1), 15–22 (2009)
Naldi, A., Berenguier, D., Fauré, A., Lopez, F., Thieffry, D., Chaouiya, C.: Logical modelling of regulatory networks with GINsim 2.3. Biosystems 97(2), 134–139 (2009)
Richard, A., Comet, J.P., Bernot, G.: Formal methods for modeling biological regulatory networks. Mod. Formal Methods Appl. 5, 83–122 (2006)
Richard, A., Comet, J.-P., Bernot, G.: Formal methods for modeling biological regulatory networks. In: Gabbar, H.A. (ed.) Modern Formal Methods and Applications, pp. 83–122. Springer, Dordrecht (2006). https://doi.org/10.1007/1-4020-4223-X_5
Saeed, M.T., Ahmad, J., Kanwal, S., Holowatyj, A.N., Sheikh, I.A., Paracha, R.Z., Shafi, A., Siddiqa, A., Bibi, Z., Khan, M., et al.: Formal modeling and analysis of the hexosamine biosynthetic pathway: role of O-linked N-acetylglucosamine transferase in oncogenesis and cancer progression. PeerJ 4, e2348 (2016)
Saeed, T., Ahmad, J.: A parallel approach for accelerated parameter identification of gene regulatory networks
Tareen, S.H.K., Ahmad, J., Roux, O.: Parametric linear hybrid automata for complex environmental systems modeling. Front. Environ. Sci. 3, 47 (2015)
Thieffry, D., Thomas, R.: Dynamical behaviour of biological regulatory networks–II. Immunity control in bacteriophage lambda. Bull. Math. Biol. 57(2), 277–297 (1995)
Thomas, R.: Boolean formalization of genetic control circuits. J. Theor. Biol. 42(3), 563–585 (1973)
Thomas, R.: Logical analysis of systems comprising feedback loops. J. Theor. Biol. 73(4), 631–656 (1978)
Thomas, R.: Regulatory networks seen as asynchronous automata: a logical description. J. Theor. Biol. 153(1), 1–23 (1991)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG, part of Springer Nature
About this paper
Cite this paper
Saeed, M.T., Ahmad, J., Ali, A. (2018). On the Use of Betweenness Centrality for Selection of Plausible Trajectories in Qualitative Biological Regulatory Networks. In: Rojas, I., Ortuño, F. (eds) Bioinformatics and Biomedical Engineering. IWBBIO 2018. Lecture Notes in Computer Science(), vol 10813. Springer, Cham. https://doi.org/10.1007/978-3-319-78723-7_47
Download citation
DOI: https://doi.org/10.1007/978-3-319-78723-7_47
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-78722-0
Online ISBN: 978-3-319-78723-7
eBook Packages: Computer ScienceComputer Science (R0)