Indian Journal of Pathology and Microbiology
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Year : 2011  |  Volume : 54  |  Issue : 2  |  Page : 435-436
Butterfly effect and cancer

Department of Cytology, PGIMER, Chandigarh, India

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Date of Web Publication27-May-2011

How to cite this article:
Dey P. Butterfly effect and cancer. Indian J Pathol Microbiol 2011;54:435-6

How to cite this URL:
Dey P. Butterfly effect and cancer. Indian J Pathol Microbiol [serial online] 2011 [cited 2020 Jul 12];54:435-6. Available from: http://www.ijpmonline.org/text.asp?2011/54/2/435/81626


Butterfly effect is a phase, which means that a small initial change in a system may have tremendous effect on the whole system, such as the flapping of the wings of a butterfly in the Himalayan mountains may develop a tornado in New York City. Lorenz [1] first discovered the butterfly effect in weather forecasting. He noted that a very minute change in one of the variables (instead of putting a value as 0.506127, he put 0.506) in computer model for weather forecasting affected the whole system tremendously. [1] The butterfly effect has been well appreciated in weather forecasting. However, in medical science, its potentiality has not been explored till now.

In a linear system the input produces a linear or log-linear response, which is predictable, such as raising the temperature increases the length of an iron rod or increases the volume of gas. However, the biological system follows a nonlinear dynamics. [2] The change of one factor may not affect the other factor/s proportionately, and therefore the cause and effect relationship may not be evident in a nonlinear dynamic system. As an example, in case of neoplasm the initial growth of the tumor cell is exponential but in course of time the behavior of a malignant tumor becomes complex and it will be difficult to predict it by simple linear mathematics. Scientists following Newton's laws of physics believe that the future of the universe is predictable if the exact locations of the celestial structures are defined. The biological system also follows the laws of physics. Therefore, biologists also believe that the exact modeling of a cell or biological system is possible and also the prediction of different physiological and pathological phenomena is also possible by the help of non linear dynamics such as deterministic chaos. A repeated iteration of the initial system may produce a model of the whole organ. [3] Fractal model of cancer growth has been generated by various scientists. [4] Different cell models and biological systems have been generated by researchers based on enormous data and findings. [5],[6],[7] However, it is very important to remember the butterfly effect in the area of deterministic chaos. Chaotic systems are highly responsive to the initial conditions and a negligible initial input or apparently minor variation in the measurement of the basal state of the system may produce an unexpected output [Figure 1]. Therefore, in spite of our best efforts, we may never predict a biological system or behavior of malignancy with certainty.
Figure 1: Butterfly effect in cancer

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The nucleus is the central area where the genetic or epigenetic changes occur initially. [8] During carcinogenesis, out of the millions of cells in our body, a single or handful of cells develop genetic changes. This minor change apparently is negligible in relation to the host cell. However, the cascades of functional changes happen with serious consequences. In course of time, this butterfly effect affects the whole system enormously and the system ultimately collapses. [9]

It is now the correct time to take a re-look at our medical science in the light of newer mathematics. An interdisciplinary approach is mandatory to solve the various problems of medical science, particularly cancer.

   References Top

1.Lorenz E N. Deterministic non periodic flow. J Atmos Sci 1963;20:130-41.  Back to cited text no. 1
2.Dey P. Fractal geometry: Basic principles and applications in pathology. Anal Quant Cytol Histol 2005;27:284-90.  Back to cited text no. 2
3.Rew DA. Modelling in surgical oncology - part III: Massive data sets and complex systems . Eur J Surg Oncol 2000;26:805-9.  Back to cited text no. 3
4.Baish JW, Gazit Y, Berk DA, Nozue M, Baxter LT, Jain RK. Role of tumor vascular architecture in nutrient and drug delivery: an invasion percolation based network model. Microvasc Res 1996;51:327-46.   Back to cited text no. 4
5.Dey P. Cell modeling and simulation: Fantasy to fact. Bull Med Educ Res 2010; 42:170-76.  Back to cited text no. 5
6.Endy D, Roger B. Modelling cellular behaviour. Nature 2001;409:391-5.  Back to cited text no. 6
7.Tomita M, Hashimoto K, Takahashi K, Shimizu S, Matsuzaki Y. E-cell: Software environment for whole cell simulation. Bioinformatics 1999;15:72-84.  Back to cited text no. 7
8.Dey P. Chromatin remodeling, cancer and chemotherapy. Curr Med Chem 2006;13:2909-19.  Back to cited text no. 8
9.Joshi A, Cao D. TGF-beta signaling, tumor microenvironment and tumor progression: The butterfly effect. Front Biosci 2010;15:180-94.  Back to cited text no. 9

Correspondence Address:
Pranab Dey
Department of Cytology, Post Graduate Institute of Medical Education and Research , Chandigarh
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Source of Support: None, Conflict of Interest: None

DOI: 10.4103/0377-4929.81626

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