Skip to main content
SpringerLink
Log in

Using Thermodynamic Functions as an Organizing Principle in Cancer Biology

  • Chapter
  • First Online:
Theoretical and Applied Aspects of Systems Biology

Part of the book series: Computational Biology ((COBO,volume 27))

  • 1110 Accesses

Abstract

One of the most powerful concepts in physics, introduced by Boltzmann, is the idea of entropy. All closed physical systems tend to a state of maximum entropy, which is a very penetrating observation that is yet to be contradicted by experimental evidence. A close examination of entropy and information from the microscopic to the macroscopic level is bound to provide deep insights into how living cells evolve from normal to malignant phenotype respecting the laws of physics. In this short review, we elaborate on the hypothesis that concepts borrowed from statistical thermodynamics, such as entropy and Gibbs free energy, can provide very powerful quantitative measures when applied to cancer research. We discuss how, on all length scales of biological organization hierarchy from cell to tissue and organ representation, cancer progression can be correlated with these thermodynamic measures. We illustrate how this can inform us about grade and stage of cancer and suggest a possible choice of optimal combination therapy. Significant diagnostic, prognostic, and therapeutic implications of these new organizing principles are presented.

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 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.00
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 109.99
Price excludes VAT (USA)
  • Durable hardcover 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

References

  1. Atkins PW. Physical chemistry, 3rd. Oxford: Oxford University Press; 1986.

    Google Scholar 

  2. Berretta R, Moscato P. Cancer biomarker discovery: the entropic hallmark. PLoS One. 2010;5(8):e12262.

    Article  Google Scholar 

  3. Breitkreutz D, Hlatky L, Rietman E, Tuszynski JA. Molecular signaling network complexity is correlated with cancer patient survivability. Proc Natl Acad Sci. 2012;109(23):9209–12.

    Article  Google Scholar 

  4. Breitkreutz BJ, Stark C, Tyers M. The GRID: the general repository for interaction datasets. Genome Biol. 2003;4(3):R23.

    Article  Google Scholar 

  5. Benzekry S, Tuszynski JA, Rietman EA, Klement GL. Design principles for cancer therapy guided by changes in complexity of protein-protein interaction networks. Biol Direct. 2015;10(1):32.

    Article  Google Scholar 

  6. Bollobás B. Graduate texts in mathematics. Modern graph theory. Springer: Berlin; 1998.

    Book  Google Scholar 

  7. Bollobás B, Fulton W, Katok A, Kirwan F, Sarnak P. Cambridge studies in advanced mathematics. In: Random graphs (vol. 73). G. Tourlakis (ed.), Cambridge: Cambridge University Press; 2001.

    Google Scholar 

  8. Carels N, Tilli T, Tuszynski JA. A computational strategy to select optimized protein targets for drug development toward the control of cancer diseases. PLoS One. 2015a;10(1):e0115054.

    Article  Google Scholar 

  9. Carels N, Tilli TM, Tuszynski JA. Optimization of combination chemotherapy based on the calculation of network entropy for protein-protein interactions in breast cancer cell lines. EPJ Nonlinear Biomed Phys. 2015b;3(1):6.

    Article  Google Scholar 

  10. Castro MA, Onsten TT, de Almeida RM, Moreira JC. Profiling cytogenetic diversity with entropy-based karyotypic analysis. J Theor Biol. 2005;234(4):487–95.

    Article  Google Scholar 

  11. Caticha A. Information and entropy. In: AIP conference proceedings (vol. 954, no. 1). AIP; 2007. p. 11–22.

    Google Scholar 

  12. Caticha A. Relative entropy and inductive inference. In: AIP conference proceedings (707(1)). AIP; 2004. p. 75–96.

    Google Scholar 

  13. Chang DT, Oyang YJ, Lin JH. MEDock: a web server for efficient prediction of ligand binding sites based on a novel optimization algorithm. Nucleic Acids Res. 2005;33(suppl_2):W233–8.

    Article  Google Scholar 

  14. Davies PC, Demetrius L, Tuszynski JA. Cancer as a dynamical phase transition. Theor Biol Med Model. 2011;8(1):30.

    Article  Google Scholar 

  15. Davies PC, Rieper E, Tuszynski JA. Self-organization and entropy reduction in a living cell. Biosystems. 2013;111(1):1–10.

    Article  Google Scholar 

  16. Dehmer M, Mowshowitz A. A history of graph entropy measures. Inf Sci. 2011;181(1):57–78.

    Article  MathSciNet  Google Scholar 

  17. Dover Y. A short account of a connection of power laws to the information entropy. Physica A: Stat Mech Appl. 2004;334(3):591–9.

    Article  MathSciNet  Google Scholar 

  18. Freije WA, Castro-Vargas FE, Fang Z, Horvath S, Cloughesy T, Liau LM, Mischel PS, Nelson SF. Gene expression profiling of gliomas strongly predicts survival. Cancer Res. 2004;64(18):6503–10.

    Article  Google Scholar 

  19. Friesen DE, Baracos VE, Tuszynski JA. Modeling the energetic cost of cancer as a result of altered energy metabolism: implications for cachexia. Theor Biol Med Model. 2015;12(1):17.

    Article  Google Scholar 

  20. Fuhrman S, Cunningham MJ, Wen X, Zweiger G, Seilhamer JJ, Somogyi R. The application of Shannon entropy in the identification of putative drug targets. Biosystems. 2000;55(1):5–14.

    Article  Google Scholar 

  21. Garlaschelli D, Ruzzenenti F, Basosi R. Complex networks and symmetry I: a review. Symmetry. 2010;2(3):1683–709.

    Article  MathSciNet  Google Scholar 

  22. Greenbaum D, Colangelo C, Williams K, Gerstein M. Comparing protein abundance and mRNA expression levels on a genomic scale. Genome Biol. 2003;4(9):117.

    Article  Google Scholar 

  23. Hanahan D, Weinberg RA. The hallmarks of cancer. Cell. 2000;100(1):57–70.

    Article  Google Scholar 

  24. Hanahan D, Weinberg RA. Hallmarks of cancer: the next generation. Cell. 2011;144(5):646–74.

    Article  Google Scholar 

  25. Hinow P, Rietman EA, Omar SI, Tuszynski JA. Algebraic and topological indices of molecular pathway networks in human cancers. Math Biosci Eng. 2015;12(6):1289–1302.

    Google Scholar 

  26. Holland AJ, Cleveland DW. Boveri revisited: chromosomal instability, aneuploidy and tumorigenesis. Nat Rev Mol Cell Biol. 2009;10(7):478–87.

    Article  Google Scholar 

  27. Jaynes ET. Information theory and statistical mechanics. Phys Rev. 1957a;106(4):620.

    Article  MathSciNet  Google Scholar 

  28. Jaynes ET. Information theory and statistical mechanics. II. Phys Rev. 1957b;108(2):171.

    Article  MathSciNet  Google Scholar 

  29. Katebi H, Sakallah KA, Markov IL. Graph symmetry detection and canonical labeling: differences and synergies. arXiv preprint arXiv:1208.6271. 2012.

    Google Scholar 

  30. Kim MS, Pinto SM, Getnet D, Nirujogi RS, Manda SS, Chaerkady R, Madugundu AK, Kelkar DS, Isserlin R, Jain S, Thomas JK. A draft map of the human proteome. Nature. 2014;509(7502):575–81.

    Article  Google Scholar 

  31. Lapointe J, Li C, Higgins JP, Van De Rijn M, Bair E, Montgomery K, Ferrari M, Egevad L, Rayford W, Bergerheim U, Ekman P. Gene expression profiling identifies clinically relevant subtypes of prostate cancer. Proc Natl Acad Sci U S A. 2004;101(3):811–6.

    Article  Google Scholar 

  32. Liberles A. Introduction to theoretical organic chemistry. New York: McMillan; 1968.

    Google Scholar 

  33. Liu R, Li M, Liu ZP, Wu J, Chen L, Aihara K. Identifying critical transitions and their leading biomolecular networks in complex diseases. Sci Rep. 2012;2:813.

    Article  Google Scholar 

  34. Loeb LA, Springgate CF, Battula N. Errors in DNA replication as a basis of malignant changes. Cancer Res. 1974;34(9):2311–21.

    Google Scholar 

  35. Loeb LA, Loeb KR, Anderson JP. Multiple mutations and cancer. Proc Natl Acad Sci. 2003;100(3):776–81.

    Article  Google Scholar 

  36. Maskill H. The physical basis of organic chemistry. Oxford: Oxford University Press; 1985.

    Google Scholar 

  37. Metze K, Adam RL, Kayser G, Kayser K. Pathophysiology of cancer and the entropy concept. Model Based Reason Sci Technol. 2010;114:199–206.

    Article  Google Scholar 

  38. McQuarrie DA. Statistical thermodynamics. New York: Harper and Row; 1973.

    Google Scholar 

  39. Mowshowitz A, Dehmer M. Entropy and the complexity of graphs revisited. Entropy. 2012;14(3):559–70.

    Article  MathSciNet  Google Scholar 

  40. Paliouras M, Zaman N, Lumbroso R, Kapogeorgakis L, Beitel LK, Wang E, Trifiro M. Dynamic rewiring of the androgen receptor protein interaction network correlates with prostate cancer clinical outcomes. Integr Biol. 2011;3(10):1020–32.

    Article  Google Scholar 

  41. Rashevsky N. Life, information theory, and topology. Bull Math Biol. 1955;17(3):229–35.

    MathSciNet  Google Scholar 

  42. Rietman E, Bloemendal A, Platig J, Tuszynski J, Klement GL. Gibbs free energy of protein-protein interactions reflects tumor stage. BioRxiv. 2015, January 1:022491.

    Google Scholar 

  43. Rietman EA, Friesen DE, Hahnfeldt P, Gatenby R, Hlatky L, Tuszynski JA. An integrated multidisciplinary model describing initiation of cancer and the Warburg hypothesis. Theor Biol Med Model. 2013;10(1):39.

    Article  Google Scholar 

  44. Rietman EA, Platig J, Tuszynski JA, Klement GL. Thermodynamic measures of cancer: Gibbs free energy and entropy of protein–protein interactions. J Biol Phys. 2016;42(3):339–50.

    Article  Google Scholar 

  45. Stark C, Breitkreutz BJ, Reguly T, Boucher L, Breitkreutz A, Tyers M. BioGRID: a general repository for interaction datasets. Nucleic Acids Res. 2006;34(suppl_1):D535–9.

    Article  Google Scholar 

  46. Schrodinger E. What is life? And, mind and matter, by Erwin Schrodinger. London: Cambridge University Press; 1967.

    Google Scholar 

  47. Shannon CE. A mathematical theory of communication. ACM SIGMOBILE Mob Comput Commun Rev. 2001;5(1):3–55.

    Article  MathSciNet  Google Scholar 

  48. Suvà ML, Rheinbay E, Gillespie SM, Patel AP, Wakimoto H, Rabkin SD, Riggi N, Chi AS, Cahill DP, Nahed BV, Curry WT. Reconstructing and reprogramming the tumor-propagating potential of glioblastoma stem-like cells. Cell. 2014;157(3):580–94.

    Article  Google Scholar 

  49. Tomasetti C, Vogelstein B. Variation in cancer risk among tissues can be explained by the number of stem cell divisions. Science. 2015;347(6217):78–81.

    Article  Google Scholar 

  50. Tomlins SA, Mehra R, Rhodes DR, Cao X, Wang L, Dhanasekaran SM, Kalyana-Sundaram S, Wei JT, Rubin MA, Pienta KJ, Shah RB. Integrative molecular concept modeling of prostate cancer progression. Nat Genet. 2007;39(1):41–51.

    Article  Google Scholar 

  51. Tseng CY, Tuszynski JA. Using entropy leads to a better understanding of biological systems. Entropy. 2010;12(12):2450–69.

    Article  Google Scholar 

  52. Tseng CY, Tuszynski J. A unified approach to computational drug discovery. Drug Discov Today. 2015;20(11):1328–36.

    Article  Google Scholar 

  53. Vander Heiden MG, Locasale JW, Swanson KD, Sharfi H, Heffron GJ, Amador-Noguez D, Christofk HR, Wagner G, Rabinowitz JD, Asara JM, Cantley LC. Evidence for an alternative glycolytic pathway in rapidly proliferating cells. Science. 2010;329(5998):1492–9.

    Article  Google Scholar 

  54. Warburg O. On the origin of cancer. Science. 1956;123(3191):309–14.

    Article  Google Scholar 

  55. Wilhelm M, Schlegl J, Hahne H, Gholami AM, Lieberenz M, Savitski MM, Ziegler E, Butzmann L, Gessulat S, Marx H, Mathieson T. Mass-spectrometry-based draft of the human proteome. Nature. 2014;509(7502):582–7.

    Article  Google Scholar 

  56. Wurmbach E, Chen YB, Khitrov G, Zhang W, Roayaie S, Schwartz M, Fiel I, Thung S, Mazzaferro V, Bruix J, Bottinger E. Genome-wide molecular profiles of HCV-induced dysplasia and hepatocellular carcinoma. Hepatology. 2007;45(4):938–47.

    Article  Google Scholar 

  57. Weinan E, Lu J, Yao Y. The landscape of complex networks. arXiv preprint arXiv:1204.6376. 2012.

    Google Scholar 

  58. Yang K, Bai H, Ouyang Q, Lai L, Tang C. Finding multiple target optimal intervention in disease-related molecular network. Mol Syst Biol. 2008;4(1):228.

    Google Scholar 

  59. Zhang X, Cruz FD, Terry M, Remotti F, Matushansky I. Terminal differentiation and loss of tumorigenicity of human cancers via pluripotency-based reprogramming. Oncogene. 2013;32(18):2249–60.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Computer Science Department, University of Massachusetts, Amherst, MA, USA

    Edward Rietman

  2. Department of Oncology, University of Alberta, Edmonton, AB, Canada

    Jack A. Tuszynski

  3. Department of Physics, University of Alberta, Edmonton, AB, Canada

    Jack A. Tuszynski

  4. Department of Mechanical and Aerospace Engineering, Politecnico di Torino, Turin, Italy

    Jack A. Tuszynski

Authors
  1. Edward Rietman
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jack A. Tuszynski
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jack A. Tuszynski .

Editor information

Editors and Affiliations

  1. Graduate Program in Computational Biology, Oswaldo Cruz Foundation, Rio de Janeiro, Rio de Janeiro, Brazil

    Fabricio Alves Barbosa da Silva

  2. Graduate Program in Computational Biology, Oswaldo Cruz Foundation, Rio de Janeiro, Rio de Janeiro, Brazil

    Nicolas Carels

  3. Graduate Program in Computational Biology, Oswaldo Cruz Foundation, Rio de Janeiro, Rio de Janeiro, Brazil

    Floriano Paes Silva Junior

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer International Publishing AG, part of Springer Nature

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Rietman, E., Tuszynski, J.A. (2018). Using Thermodynamic Functions as an Organizing Principle in Cancer Biology. In: Alves Barbosa da Silva, F., Carels, N., Paes Silva Junior, F. (eds) Theoretical and Applied Aspects of Systems Biology. Computational Biology, vol 27. Springer, Cham. https://doi.org/10.1007/978-3-319-74974-7_8

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-74974-7_8

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-74973-0

  • Online ISBN: 978-3-319-74974-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation