Skip to main content
SpringerLink
Log in

Complex Systems, Data and Inference

  • Chapter
  • First Online:
Identifiability and Regression Analysis of Biological Systems Models

Part of the book series: SpringerBriefs in Statistics ((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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    The clipart objects of “Thinking man” are taken from the free images databases publicly available at free Clipart Library [5].

References

  1. Ma’ayan A. Complex systems biology. J R Soc Interface. 2017;14(134):20170391.

    Article  Google Scholar 

  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.

    Article  MathSciNet  Google Scholar 

  3. Lesne A. Complex networks: from graph theory to biology. Lett Math Phys. 2006;78(3):235–62.

    Article  MathSciNet  Google Scholar 

  4. Klamt S, Haus U-U, Theis F. Hypergraphs and cellular networks. PLOS Comput Biol. 2009;5(5):1–6, 05.

    Article  MathSciNet  Google Scholar 

  5. Clipart Library. http://clipart-library.com/picture-of-thinking-man.html. Accessed 3 Sept 2018.

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  9. Encyclopedia of Mathematics:. www.encyclopediaofmath.org/index.php/Hypergraph. Accessed 3 Sept 2018.

  10. The R Project for Statistical Computing. https://www.r-project.org/. Accessed 10 Jan 2019.

  11. Temkin ON, Zeigarnik AV, Bonchev D. Chemical reaction networks: a graph-theoretical approach; 1996.

    Google Scholar 

  12. Estrada E, Rodríguez-Velázquez2 JA. Complex networks as hypergraphs; 2005. http://cds.cern.ch/record/836579/files/?ln=it.

  13. Stenesh J. The citric acid cycle. In: Biochemistry. Springer US; 1998. p. 273–91.

    Google Scholar 

  14. KEGG Pathway Database. https://www.genome.jp/kegg/pathway.html. Accessed 03 Feb 2019.

  15. MetaCyc. https://metacyc.org/. Accessed 03 Feb 2019.

  16. Wikipdia: metabolic pathways. https://en.wikipedia.org/wiki/Metabolic_pathway. Accessed 03 March 2019.

  17. Estrada E, Rodríguez-Velázquez JA. Subgraph centrality and clustering in complex hyper-networks. Phys A: Stat Mech Appl. 2006;364:581–94.

    Article  MathSciNet  Google Scholar 

  18. Lecca P, Re A, Ihekwaba AE, Mura I, Nguyen T-P. Computational systems biology: inference and modelling. Sawston: Woodhead Publishing; 2016.

    MATH  Google Scholar 

  19. Oates CJ, Mukherjee S. Network inference and biological dynamics. Ann Appl Stat. 2012;6(3):1209–35.

    Article  MathSciNet  Google Scholar 

  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.

    Article  Google Scholar 

  21. Davidson EH. Emerging properties of animal gene regulatory networks. Nat. 2010;468:911–920.

    Article  Google Scholar 

  22. Äijö T, Bonneau R. Biophysically motivated regulatory network inference: progress and prospects. Human Heredity. 2016;81(2):62–77.

    Article  Google Scholar 

  23. Ghersi D, Singh M. Disentangling function from topology to infer the network properties of disease genes. BMC Syst Biol. 2013;7(1):5.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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. 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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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. Frank A, Kirly T, Király Z. On the orientation of graphs and hypergraphs. Discret Appl Math. 2003;131(2):385–400. Submodularity.

    Article  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Faculty of Computer Science, Free University of Bozen-Bolzano, Bozen-Bolzano, Italy

    Paola Lecca

Authors
  1. Paola Lecca
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Paola Lecca .

Rights and permissions

Reprints and permissions

Copyright information

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

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Lecca, P. (2020). Complex Systems, Data and Inference. In: Identifiability and Regression Analysis of Biological Systems Models. SpringerBriefs in Statistics. Springer, Cham. https://doi.org/10.1007/978-3-030-41255-5_1

Download citation

Publish with us

Policies and ethics

Search

Navigation