Advertisement

Complex Systems, Data and Inference

  • Paola LeccaEmail author
Chapter
  • 47 Downloads
Part of the SpringerBriefs in Statistics book series (BRIEFSSTATIST)

Abstract

The concepts of complexity and networks are recurrent in modern systems biology. They are intimately linked to the very nature of biological processes governed by mathematically complex laws and orchestrated by thousands of interactions among thousands of molecular components. In this chapter, we explain what it means that a system is complex, what are the mathematical tools and the abstract data structures that we can use to describe a complex system, and finally what challenges the scientific community must face today to deduce a mathematical or computational model from observations experimental.

References

  1. 1.
    Ma’ayan A. Complex systems biology. J R Soc Interface. 2017;14(134):20170391.CrossRefGoogle Scholar
  2. 2.
    Galas DJ, Sakhanenko NA, Skupin A, Ignac T. Describing the complexity of systems: multivariable “set complexity” and the information basis of systems biology. J Comput Biol. 2014;21(2):118–40.MathSciNetCrossRefGoogle Scholar
  3. 3.
    Lesne A. Complex networks: from graph theory to biology. Lett Math Phys. 2006;78(3):235–62.MathSciNetCrossRefGoogle Scholar
  4. 4.
    Klamt S, Haus U-U, Theis F. Hypergraphs and cellular networks. PLOS Comput Biol. 2009;5(5):1–6, 05.MathSciNetCrossRefGoogle Scholar
  5. 5.
    Clipart Library. http://clipart-library.com/picture-of-thinking-man.html. Accessed 3 Sept 2018.
  6. 6.
    Rahman A, Poirel CL, Badger DJ, Estep C, Murali TM. Reverse engineering molecular hypergraphs. IEEE/ACM Trans Comput Biol Bioinf. 2013;10(5):1113–24.CrossRefGoogle Scholar
  7. 7.
    Demir E, Cary MP, Paley S, Fukuda K, Lemer C, Vastrik I, Wu G, D’eustachio P, Schaefer C, Luciano J, Schacherer F, Martinez-Flores I, Hu Z, Jimenez-Jacinto V, Joshi-Tope G, Kandasamy K, Lopez-Fuentes AC, Mi H, Pichler E, Rodchenkov I, Splendiani A, Tkachev S, Zucker J, Gopinath G, Rajasimha H, Ramakrishnan R, Shah I, Syed M, Anwar N, Babur Ö, Blinov M, Brauner E, Corwin D, Donaldson S, Gibbons F, Goldberg R, Hornbeck P, Luna A, Murray-Rust P, Neumann E, Ruebenacker O, Samwald M, van Iersel M, Wimalaratne S, Allen K, Braun B, Whirl-Carrillo M, Cheung K-H, Dahlquist K, Finney A, Gillespie M, Glass E, Gong L, Haw R, Honig M, Hubaut O, Kane D, Krupa S, Kutmon M, Leonard J, Marks D, Merberg D, Petri V, Pico A, Ravenscroft D, Ren L, Shah N, Sunshine M, Tang R, Whaley R, Letovksy S, Buetow KH, Rzhetsky A, Schachter V, Sobral BS, Dogrusoz U, McWeeney S, Aladjem M, Birney E, Collado-Vides J, Goto S, Hucka M, Le Novère N, Maltsev N, Pandey A, Thomas P, Wingender E, Karp PD, Sander C, Bader GD. The BioPAX community standard for pathway data sharing. Nat Biotechnol. 2010;28(9):935–42.CrossRefGoogle Scholar
  8. 8.
    Schaefer CF, Anthony K, Krupa S, Buchoff J, Day M, Hannay T, Buetow KH. PID: the pathway interaction database. Nucleic Acids Res. 2008;37(suppl\(\_\)1):D674–9.CrossRefGoogle Scholar
  9. 9.
    Encyclopedia of Mathematics:. www.encyclopediaofmath.org/index.php/Hypergraph. Accessed 3 Sept 2018.
  10. 10.
    The R Project for Statistical Computing. https://www.r-project.org/. Accessed 10 Jan 2019.
  11. 11.
    Temkin ON, Zeigarnik AV, Bonchev D. Chemical reaction networks: a graph-theoretical approach; 1996.Google Scholar
  12. 12.
    Estrada E, Rodríguez-Velázquez2 JA. Complex networks as hypergraphs; 2005. http://cds.cern.ch/record/836579/files/?ln=it.
  13. 13.
    Stenesh J. The citric acid cycle. In: Biochemistry. Springer US; 1998. p. 273–91.Google Scholar
  14. 14.
    KEGG Pathway Database. https://www.genome.jp/kegg/pathway.html. Accessed 03 Feb 2019.
  15. 15.
    MetaCyc. https://metacyc.org/. Accessed 03 Feb 2019.
  16. 16.
    Wikipdia: metabolic pathways. https://en.wikipedia.org/wiki/Metabolic_pathway. Accessed 03 March 2019.
  17. 17.
    Estrada E, Rodríguez-Velázquez JA. Subgraph centrality and clustering in complex hyper-networks. Phys A: Stat Mech Appl. 2006;364:581–94.MathSciNetCrossRefGoogle Scholar
  18. 18.
    Lecca P, Re A, Ihekwaba AE, Mura I, Nguyen T-P. Computational systems biology: inference and modelling. Sawston: Woodhead Publishing; 2016.zbMATHGoogle Scholar
  19. 19.
    Oates CJ, Mukherjee S. Network inference and biological dynamics. Ann Appl Stat. 2012;6(3):1209–35.MathSciNetCrossRefGoogle Scholar
  20. 20.
    Djordjevic D, Yang A, Zadoorian A, Rungrugeecharoen K, Ho JW. How difficult is inference of mammalian causal gene regulatory networks? PLoS ONE. 2014;9(11):e111661.CrossRefGoogle Scholar
  21. 21.
    Davidson EH. Emerging properties of animal gene regulatory networks. Nat. 2010;468:911–920.CrossRefGoogle Scholar
  22. 22.
    Äijö T, Bonneau R. Biophysically motivated regulatory network inference: progress and prospects. Human Heredity. 2016;81(2):62–77.CrossRefGoogle Scholar
  23. 23.
    Ghersi D, Singh M. Disentangling function from topology to infer the network properties of disease genes. BMC Syst Biol. 2013;7(1):5.CrossRefGoogle Scholar
  24. 24.
    Olsen C, Fleming K, Prendergast N, Rubio R, Emmert-Streib F, Bontempi G, Haibe-Kains B, Quackenbush J. Inference and validation of predictive gene networks from biomedical literature and gene expression data. Genomics. 2014;103(5–6):329–36.CrossRefGoogle Scholar
  25. 25.
    Molinelli EJ, Korkut A, Wang W, Miller ML, Gauthier NP, Jing X, Kaushik P, He Q, Mills G, Solit DB, Pratilas CA, Weigt M, Braunstein A, Pagnani A, Zecchina R, Sander C. Perturbation biology: inferring signaling networks in cellular systems. PLoS Comput Biol. 2013;9(12):e1003290.CrossRefGoogle Scholar
  26. 26.
    Vaske CJ, Benz SC, Sanborn JZ, Earl D, Szeto C, Zhu J, Haussler D, Stuart JM. Inference of patient-specific pathway activities from multi-dimensional cancer genomics data using PARADIGM. Bioinformatics. 2010;26(12):i237–45.CrossRefGoogle Scholar
  27. 27.
    Holger F, Özgür S, Dorit A, Christian B, Tim B. Deterministic effects propagation networks for reconstructing protein signaling networks from multiple interventions. BMC Bioinf. 2009;10(1).Google Scholar
  28. 28.
    Hill SM, Heiser LM, Cokelaer T, Unger M, Nesser NK, Carlin DE, Zhang Y, Sokolov A, Paull EO, Wong CK, Graim K, Bivol A, Wang H, Zhu F, Afsari B, Danilova LV, Favorov AV, Lee WS, Taylor D, Hu CW, Long BL, Noren DP, Bisberg AJ, Afsari B, Al-Ouran R, Anton B, Arodz T, Sichani OA, Bagheri N, Berlow N, Bisberg AJ, Bivol A, Bohler A, Bonet J, Bonneau R, Budak G, Bunescu R, Caglar M, Cai B, Cai C, Carlin DE, Carlon A, Chen L, Ciaccio MF, Cokelaer T, Cooper G, Creighton CJ, Daneshmand S-M-H, de la Fuente A, Di Camillo B, Danilova LV, Dutta-Moscato J, Emmett K, Evelo C, Fassia M-KH, Favorov AV, Fertig EJ, Finkle JD, Finotello F, Friend S, Gao X, Gao J, Garcia-Garcia J, Ghosh S, Giaretta A, Graim K, Gray JW, Großeholz R, Guan Y, Guinney J, Hafemeister C, Hahn O, Haider S, Hase T, Heiser LM, Hill SM, Hodgson J, Hoff B, Hsu CH, Hu CW, Hu Y, Huang X, Jalili M, Jiang X, Kacprowski T, Kaderali L, Kang M, Kannan V, Kellen M, Kikuchi K, Kim D-C, Kitano H, Knapp B, Komatsoulis G, Koeppl H, Krämer A, Kursa MB, Kutmon M, Lee WS, Li Y, Liang X, Liu Z, Liu Y, Long BL, Lu S, Lu X, Manfrini M, Matos MRA, Meerzaman D, Mills GB, Min W, Mukherjee S, Müller CL, Neapolitan RE, Nesser NK, Noren DP, Norman T, Oliva B, Opiyo SO, Pal R, Palinkas A, Paull EO, Planas-Iglesias J, Poglayen D, Qutub AA, Saez-Rodriguez J, Sambo F, Sanavia T, Sharifi-Zarchi A, Slawek J, Sokolov A, Song M, Spellman PT, Streck A, Stolovitzky G, Strunz S, Stuart JM, Taylor D, Tegnér J, Thobe K, Toffolo GM, Trifoglio E, Unger M, Wan Q, Wang H, Welch L, Wong CK, Wu JJ, Xue AY, Yamanaka R, Yan C, Zairis S, Zengerling M, Zenil H, Zhang S, Zhang Y, Zhu F, Zi Z, Mills GB, Gray JW, Kellen M, Norman T, Friend S, Qutub AA, Fertig EJ, Guan Y, Song M, Stuart JM, Spellman PT, Koeppl H, Stolovitzky G, Saez-Rodriguez J, Mukherjee S. Inferring causal molecular networks: empirical assessment through a community-based effort. Nat Methods. 2016;13(4):310–8.CrossRefGoogle Scholar
  29. 29.
    Schaffter T, Marbach D, Floreano D. GeneNetWeaver: in silico benchmark generation and performance profiling of network inference methods. Bioinformatics. 2011;27(16):2263–70.CrossRefGoogle Scholar
  30. 30.
    Kishan KC, Li R, Cui F, Yu Q, Haake AR. GNE: a deep learning framework for gene network inference by aggregating biological information. BMC Syst Biol. 2019;13(S2).Google Scholar
  31. 31.
    Frank A, Kirly T, Király Z. On the orientation of graphs and hypergraphs. Discret Appl Math. 2003;131(2):385–400. Submodularity.MathSciNetCrossRefGoogle Scholar

Copyright information

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Faculty of Computer ScienceFree University of Bozen-BolzanoBozen-BolzanoItaly

Personalised recommendations