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2019 | OriginalPaper | Buchkapitel

A Robustness Analysis of Dynamic Boolean Models of Cellular Circuits

verfasst von : Ariel Bruner, Roded Sharan

Erschienen in: Bioinformatics Research and Applications

Verlag: Springer International Publishing

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Abstract

With ever growing amounts of omics data, the next challenge in biological research is the interpretation of these data to gain mechanistic insights about cellular function. Dynamic models of cellular circuits that capture the activity levels of proteins and other molecules over time offer great expressive power by allowing the simulation of the effects of specific internal or external perturbations on the workings of the cell. However, the study of such models is at its infancy and no large scale analysis of the robustness of real models to changing conditions has been conducted to date. Here we provide a computational framework to study the robustness of such models using a combination of stochastic simulations and integer linear programming techniques. We apply our framework to a large collection of cellular circuits and benchmark the results against randomized models. We find that the steady states of real circuits tend to be more robust in multiple aspects compared to their randomized counterparts.

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Vorheriges Kapitel Detecting Illicit Drug Ads in Google+ Using Machine Learning
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1
The source code used for the analysis can be found at github.​com/​arielbro/​attractor_​learning, commit hash 83474950c9fc3aa61277d5535a142aad90ff7eed.
 
Literatur
1.
Chatr-Aryamontri, A., et al.: The BioGRID interaction database: 2015 update. Nucleic Acids Res. 43(Database issue), D470–D478 (2015)CrossRef
2.
Silberberg, Y., Kupiec, M., Sharan, R.: A method for predicting protein-protein interaction types. PLoS ONE 9, e90904 (2014)CrossRef
3.
Ideker, T., et al.: Discovering regulatory and signalling circuits in molecular interaction networks. Bioinformatics 18(1), s233–s240 (2002)CrossRef
4.
Vandin, F., Upfal, E., Raphael, B.: Algorithms for detecting significantly mutated pathways in cancer. J. Comput. Biol. 18(3), 507–522 (2011)MathSciNetCrossRef
5.
Cowen, L., et al.: Network propagation: a universal amplifier of genetic associations. Nat. Rev. Genet. 18(9), 551–562 (2017)CrossRef
6.
Shachar, R., et al.: A systems-level approach to mapping the telomere length maintenance gene circuitry. Mol. Syst. Biol. 4, 172 (2008)CrossRef
7.
Yeger-Lotem, E., et al.: Bridging high-throughput genetic and transcriptional data reveals cellular responses to alpha-synuclein toxicity. Nat. Genet. 41(3), 316–323 (2009)CrossRef
8.
Huang, S., Fraenkel, E.: Integrating proteomic and transcriptional and and interactome data reveals hidden components of signaling and regulatory networks. Sci. Sig. 2(81), ra40 (2009)
9.
Yosef, N., et al.: Toward accurate reconstruction of functional protein networks. Mol. Syst. Biol. 5, 248 (2009)CrossRef
10.
Dittrich, M., et al.: Identifying functional modules in protein-protein interaction networks: an integrated exact approach. Bioinformatics 24(13), i223–i231 (2008)CrossRef
11.
Said, M., et al.: Global network analysis of phenotypic effects: protein networks and toxicity modulation in Saccharomyces cerevisiae. Proc. Natl. Acad. Sci. USA 101(52), 18006–18011 (2004)CrossRef
12.
Jonsson, P., Bates, P.: Global topological features of cancer proteins in the human interactome. Bioinformatics 22(18), 2291–2297 (2006)CrossRef
13.
Novere, N.L.: Quantitative and logic modelling of molecular and gene networks. Nat. Rev. Genet. 16(3), 146–158 (2015)CrossRef
14.
Morris, M., et al.: Logic-based models for the analysis of cell signaling networks. Biochemistry 49(15), 3216–3224 (2010)CrossRef
15.
Kauffman, S.: Metabolic stability and epigenesis in randomly constructed genetic nets. J. Theor. Biol. 22(3), 437–467 (1969)MathSciNetCrossRef
16.
Samaga, R., et al.: The logic of EGFR/ErbB signaling: theoretical properties and analysis of high-throughput data. PLoS Comput. Biol. 5(8), e1000438 (2009)CrossRef
17.
Oda, K., et al.: A comprehensive pathway map of epidermal growth factor receptor signaling. Mol. Syst. Biol. 1, 2005.0010 (2005)CrossRef
18.
Grieco, L., et al.: Integrative modelling of the influence of MAPK network on cancer cell fate decision. PLoS Comput. Biol. 9(10), e1003286 (2013)CrossRef
19.
Saez-Rodriguez, J., et al.: Discrete logic modelling as a means to link protein signalling networks with functional analysis of mammalian signal transduction. Mol. Syst. Biol. 5, 331 (2009)CrossRef
20.
Mitsos, A., et al.: Identifying drug effects via pathway alterations using an integer linear programming optimization formulation on phosphoproteomic data. PLoS Comput. Biol. 5(12), e1000591 (2009)CrossRef
21.
Moignard, V., et al.: Decoding the regulatory network of early blood development from single-cell gene expression measurements. Nat. Biotechnol. 33(3), 269–276 (2015)CrossRef
22.
Chaouiya, C., Naldi, A., Thieffry, D.: Logical modelling of gene regulatory networks with GINsim. Methods Mol. Biol. 804, 463–479 (2012)CrossRef
23.
Qiu, Y., et al.: On control of singleton attractors in multiple Boolean networks: integer programming-based method. BMC Syst. Biol. 8(Suppl. 1), S7 (2014)CrossRef
24.
Dubrova, E., Teslenko, M.: A SAT-based algorithm for finding attractors in synchronous Boolean networks. IEEE/ACM Trans. Comput. Biol. Bioinform. 8(5), 1393–1399 (2011)CrossRef
25.
Morris, M., et al.: Training signaling pathway maps to biochemical data with constrained fuzzy logic: quantitative analysis of liver cell responses to inflammatory stimuli. PLoS Comput. Biol. 7(3), e1001099 (2011)MathSciNetCrossRef
26.
Huard, J., et al.: An integrative model links multiple inputs and signaling pathways to the onset of DNA synthesis in hepatocytes. FEBS J. 279(18), 3290–3313 (2012)CrossRef
27.
Dasika, M., Burgard, A., Maranas, C.: A computational framework for the topological analysis and targeted disruption of signal transduction networks. Biophys J. 91(1), 382–398 (2006)CrossRef
28.
Vardi, L., Ruppin, E., Sharan, R.: A linearized constraint-based approach for modeling signaling networks. J. Comput. Biol. 19(2), 232–240 (2012)CrossRef
29.
Gat-Viks, I., et al.: A probabilistic methodology for integrating knowledge and experiments on biological networks. J. Comput. Biol. 13(2), 165–181 (2006)MathSciNetCrossRef
30.
Alon, U., et al.: Robustness in bacterial chemotaxis. Nature 387, 913–917 (1997)CrossRef
31.
Li, F., et al.: The yeast cellcycle network is robustly designed. Proc. Natl. Acad. Sci. USA 101, 4781–4786 (2004)CrossRef
32.
Aldana, M., Cluzel, P.: A natural class of robust networks. Proc. Natl. Acad. Sci. USA 100, 8710–8714 (2003)CrossRef
33.
Fretter, C., Szejka, A., Drossel, B.: Perturbation propagation in random and evolved Boolean networks. New J. Phys. 11, 033005:1–033005:13 (2009)CrossRef
34.
Sevim, V., Rikvold, P.: Chaotic gene regulatory networks can be robust against mutations and noise. J. Theor. Biol. 253, 323–332 (2008)MathSciNetCrossRef
35.
Peixoto, T.: Redundancy and error resilience in Boolean networks. Phys. Rev. Lett. 104, 048701 (2010)CrossRef
36.
Klemm, K., Bornholdt, S.: Topology of biological networks and reliability of information processing. Proc. Natl. Acad. Sci. USA 102, 18414–18419 (2005)CrossRef
37.
38.
Daniels, B., et al.: Criticality distinguishes the ensemble of biological regulatory networks. Phys. Rev. Lett. 121, 138102 (2018)CrossRef
39.
40.
Ghanbarnejad, F., Klemm, K.: Impact of individual nodes in Boolean network dynamics. EPL 99, 58006 (2012)CrossRef
41.
Helikar, T., et al.: The cell collective: toward an open and collaborative approach to systems biology. BMC Syst. Biol. 6, 96 (2012)CrossRef
Metadaten
Titel
A Robustness Analysis of Dynamic Boolean Models of Cellular Circuits
verfasst von
Ariel Bruner
Roded Sharan
Copyright-Jahr
2019
Buch
Bioinformatics Research and Applications

Print ISBN: 978-3-030-20241-5

Electronic ISBN: 978-3-030-20242-2

Copyright-Jahr: 2019

DOI
https://doi.org/10.1007/978-3-030-20242-2_16