# Unified approach to growth and aging in biological, technical and biotechnical systems

- Paolo Castorina
^{2, 3}Email author and - Philippe Blanchard
^{1}

**1**:7

https://doi.org/10.1186/2193-1801-1-7

© Castorina and Blanchard; licensee Springer. 2012

**Received: **31 March 2012

**Accepted: **9 July 2012

**Published: **9 July 2012

## Abstract

Complex systems, in many different scientific sectors, show coarse-grain properties with simple growth laws with respect to fundamental microscopic algorithms. We propose a classification scheme of growth laws which includes human aging, tumor (and/or tissue) growth, logistic and generalized logistic growth and the aging of technical devices. The proposed classification permits to evaluate the aging/failure of combined new bio-technical “manufactured products”, where part of the system evolves in time according to biological-mortality laws and part according to technical device behaviors. Moreover it suggests a direct relation between the mortality leveling-off for humans and technical devices and the observed small cure probability for large tumors.

## Keywords

## Background

Complex systems with millions of interacting elementary parts are often considered computationally irreducible Wolfram ([1984]); Wolfram ([2002]) which means that the only way to decide about their evolution is to let them evolve in time.

On the other hand, there is an impressive number of experimental verifications, in many different scientific sectors, that coarse-grain properties of systems, with simple laws with respect to fundamental microscopic alghoritms, emerge at different levels of magnification providing important tools for explaining and predicting new phenomena.

In this respect, a priori unrelated systems show similar emergent properties and if an unexpected effect is found experimentally in a field, a similar effect, “mutatis mutandis”, should also be sought in similar experiments in other fields. Therefore a useful tool to greatly facilitate the cross fertilization among different fields of research is a general classification of growth laws Castorina et al. ([2006]).

A very important example is the Gompertz law (GL) Gompertz ([1825]) which applies to human mortality tables (i.e. aging) and tumor growth Steel ([1977]); Wheldon ([1988]); Norton ([1988]).

*f*(

*t*), which describes the time evolution of some macroscopic quantity, and by the specific rate,

*α*, defined as (1/

*f*)(

*df*/

*dt*)=

*α*(

*t*). In the GL

*α*has an exponential dependence on time:

where *a* and *b* are constants. In aging *f* (*t*) indicates the survival probability; while with regards to tumor growth it corresponds to the number of cells *N*(*t*) (depending on the specific case *a* and *b* can be positive or negative).

with *n*>1, called Weibull law (WL) Barlow and Proschan ([1975]); Rigdon and Basu ([2000]). The analogy with the biological systems is intriguing (for clarity, as necessary, one defines the specific rate *α*_{
h
}(*t*) for the human mortality, *α*_{
f
}(*t*) for the technical systems and *α*_{
c
}(*t*) for tumor growth) and deeper than the similarity between eq. (1) and eq. (2).

Indeed, many independent analyses of experimental data on humans and animals suggest that at advanced ages (more than 85-90 years for humans) there is a deceleration in mortality Gavrilov and Gavrilova ([1991]); Vaupel et al. ([1998]); Olshansky ([1998]): in the large range 20 - 85 years for humans the mortality rate is well described by the Gompertz law and then there is a late-life mortality (although a definite conclusion has yet to be reached Gavrilov and Gavrilova ([2011])). A similar trend is observed for technical devices Economos ([1979]), confirming the analogy between biological and technical systems.

The understanding of aging and of late-life mortality is still an open problem and many interesting models have been proposed to explain the similar behavior in metabolic systems and in technical devices Gavrilov and Gavrilova ([2001]). Moreover, a unifying language for the description of performance of metabolic and technical production and distribution has been recently suggested Becker et al. ([2011]) to implement the idea that the robustness of metabolic systems with respect to enviromental changes could represent a useful model for technical systems.

In this letter, rather than focusing on specific models, we shall address the generalization of the classification scheme of growth laws to include human aging, tumor (and/or tissue) growth, logistic and generalized logistic growth and the aging of technical devices. We shall consider two applications of the proposed approach: a) a method to evaluate the aging/failure of combined new bio-technical “manufactured product”, where part of the system evolves in time according to biological-mortality laws and part is a technical device; b) an interpretation of the “tumor size effect”, i.e. the small cure probability for large tumor Stanley et al. ([1977]); Bentzen and Thomas ([1996]); Huchet et al. ([2003]), in analogy with the late-life mortality in aging.

## Results

*f*)(

*df*/

*dt*)=

*α*(

*t*) is obtained by considering the power expansion in

*α*of the function ( see ref. Castorina et al. ([2006]) for details)

which for *b*_{0}=0 and *b*_{
i
}=0 for *i*>1 gives a time independent specific rate *α*_{0} and therefore an exponential growth; for *b*_{0}≠0 and *b*_{
i
}=0 for *i*>1 describes a linear time dependent specific rate and again an exponential growth; at the first order in *α*, for *b*_{0}=0, *b*_{1}≠0 and *b*_{
i
}=0 for *i*>1, reproduces an exponential time behavior of the specific growth and therefore the GL; the second order term , *O*(*α*^{2}), for *b*_{0}=0, *b*_{1}*b*_{2}≠0 and *b*_{
i
}=0 for *i*>2 generates the logistic and generalized logistic growth.

*α*on the function

*f*(

*t*), can be easily derived by the temporal behaviour of the specific rate. For the GL for a growing number of cells,

*N*(

*t*), one has the well known logarithmic non linearity,

where *a*,*b*,*c*,*γ* are constants and the carrying capacity,${N}_{\infty}$ ,corresponds to *α*=0.

*n*positive integer, corresponds to terms O(

*α*

^{(n−1)/n}) in the expansion of

*Φ*(

*α*) and therefore for a general classification scheme of the specific growth/aging/failure rate of biological and technical systems one has to consider:

Note that: a) 0<(*n*−1)/*n*<1 and the nth term in the power series in *α*^{(n−1)/n}tends for large *n* to *α*, i.e. to the Gompertz law; b) the term *b*_{0}≠0, i.e. the exponential growth, has been neglected because one considers the GL, the generalized logistic or more complex growth laws for the biological systems (there is no problem to include this term in the expansion) ; 3) the first sum in the expansion has fractional powers that recall a Puiseux expansion.

As a by-product of the proposed classification scheme one can easily evaluate the aging/failure of combined new bio-technical “manufactured products” by taking explicitely into account the mutual “interference” between the aging behavior of the biological part and the failure of the technical one. The “interference” effect strongly depends on the typical time scales in the coefficients *c*_{
n
} and *b*_{
n
} in the previous expansion: if the life-time of the technical device is much larger than the life-time of the biological part ( or viceversa) there is essentially no effect Muller et al. ([1988]).

*O*(

*α*), and the behavior of technical part is described by a single term

*O*(

^{α n−1/n}),i.e.

*b*

_{1}, i.e.

*τ*=

*b*

_{1}

*t*,$\stackrel{\u0304}{\alpha}=\alpha /{b}_{1}$ and${\stackrel{\u0304}{c}}_{n}={c}_{n}{b}_{1}^{-1-1/n}$, after simple calculations the time dependence of the specific rate is given by:

*n*at fixed${\stackrel{\u0304}{c}}_{n}$ and for various values of${\stackrel{\u0304}{c}}_{n}$ at fixed

*n*.

The next step is to include the term${b}_{2}{\alpha}^{2}$ in the expansion of *Φ*(*α*) (*b*_{2} is dimensionless) which corresponds to a generalized logistic evolution. As we shall see this term is crucial in understanding the late-life mortality effect.

Therefore the general expansion of *Φ*(*α*) in eq. (6) can describe the aging/failure of any biological and technical system including the leveling-off at late mortality which is obtained by taking into account the term *O*(*α*^{2}) in *Φ*(*α*), i.e. by the transition from the GL or WL to a logistic type law Horiuchi and Wilmoth ([1998]).

The proposed unification scheme suggests a practical method to understand growth patterns. Given a set of data on some growth process, the first step of the analysis is a fit in power of *α* of the derivative of the specific growth rate, i.e. of the function *Φ*(*α*). Therefore : a) if the best fit is linear, the growth is a Gompertzian one; b) if the best fit is quadratic, look at the sign of the coefficients of the expansion. For *b*_{1}>0 and *b*_{2}<0 the growth is logistic (or generalized logistic) corresponding to a competitive dynamics; c) if the best fit indicates a fractional power the growth follows the WL. Of course, it is always possible to obtain a better agreement with data by increasing the number of coefficients. However, should increasing the number of parameters indicate only a marginal improvement in the description of data one concludes that the added terms in the expansion are irrelevant.

## Discussion and conclusions

Let us now consider the cross-fertilization among different sectors.

As previously discussed, there is a deceleration of mortality in aging at late time which is described as a “transition” from a Gompertz law to a generalized logistic behavior. On the other hand, tumors evolve in time according to the GL. The obvious indications is to verify if a phenomenon corresponding to the deceleration of mortality, i.e. a transition from the GL to a power law, exists for cancer growth at a later time. As we shall see, this aspect has strong consequences on the therapy.

For tumor growth the *b*_{1}*α* term gives the GL in eq. (4) and the introduction of the *O*(*α*^{2}) term corresponds to the power law non-linear feedback in eq. (5). Therefore one has to investigate if at late-life of a tumor growth there is such a modification in the dependence of the specific growth rate on the cell number *N*(*t*). Since direct informations “in vivo” are almost impossible, the question has to be addressed in an indirect way by considering radiotherapy.

The radiotherapic tumor treatment consists in series of radiation doses at fixed time intervals. However tumors start to re-grow in the interval between two treatments : the re-growth during radiotherapy is therefore an important clinical parameter Kim and Tannock ([2005]) and the probability of treatment benefit critically depends on the tumor re-growth pattern.

The so called “tumor size effect” is a reduction of radiotherapeutic results for large tumors ( which , presumably, has grown since long time). The dependence of the surviving fraction on the tumor volume was already observed by Stanley et al. in 1977 in lung tumors Stanley et al. ([1977]) and re-emphasized by Bentzen et al. and Huchet et al. in Bentzen and Thomas ([1996]); Huchet et al. ([2003]).

The effect of re-growth rate on radiotherapy has been quantitatively investigated in ref. Castorina et al. ([2007]) and the results clearly indicate that to understand the tumor size effect the re-growth rate for large tumor has to follow a power law Guiot et al. ([2003]) rather than the GL.

From this point of view the “tumor size effect” is a phenomenon which indicates that in late -time tumor growth there is a change from a GL specific rate to a power law behavior, corresponding to the deceleration in mortality at advanced age.

One should conclude that such a common feature in aging and in failure in biological and/or technical systems should be considered as a “bifurcation” or a “phase transition” in the specific growth rate at large time from GL or WL to a logistic or generalized logistic behavior.

In closing, the general expansion of *Φ*(*α*) in eq. (6) can describe the growth/aging/failure of biological and technical systems and the transition to a different (“phase”) specific growth rate at late-life could be a common feature of those systems independently on the microscopic dynamics.

## Declarations

### Acknowledgements

It is a pleasure to thank H. Satz for helpful comments and discussions. PC thanks for the hospitality the Department of Physics of Bielefeld University.

## Authors’ Affiliations

## References

- Barlow RE, Proschan F:
*Statistical theory of reliability and testing. Probability models.*New York: Wiley; 1975.Google Scholar - Becker T,
*et al*.:**Flow control by periodic devices: a unifying language for the description of traffic, production and metabolic systems.***J Stat Mech*2011, P05004.Google Scholar - Bentzen SM, Thomas HD:
**Tumor volume and local control probability: clinical data and radiobiological interpretations.***Int Radiat Oncol Biol Phys*1996,**36:**247-251.View ArticleGoogle Scholar - Castorina P, Deisboeck TS, Gabriele P, Guiot C:
**Growth Laws in Cancer: Implications for Radiotherapy.***Rad Res*2007,**169:**349.View ArticleGoogle Scholar - Castorina P, Delsanto PP, Guiot C:
**Classification Scheme for Phenomenological Universalities in Growth Problems in Physics and Other Sciences.***Phys Rev Lett*2006,**188701:**96.Google Scholar - Economos AC:
**A non-gompertzian paradigm for mortality kinetics of metazoan animals and failure kinetics of manufactured products.***AGE*1979,**2:**74-76. 10.1007/BF02432250View ArticleGoogle Scholar - Gavrilov LA, Gavrilova NS:
*The Biology of life span: a quantitative approach*. N.Y: Harwood Academic Publisher; 1991.Google Scholar - Gavrilov LA, Gavrilova NS:
**The reliability theory of aging and longevity.***J Theor Biol*2001,**213**(4):527-545. 10.1006/jtbi.2001.2430View ArticleGoogle Scholar - Gavrilova NS, Gavrilov LA:
**Mortality measurement and modeling beyond age 100, Symposium Orlando, Living to 100 monograph (published on line),.***The Society of Actuaries, 2011*2011.Google Scholar - Gompertz B:
**On the nature of the function expressive of the law of human mortality and a new mode of determining life contingencies.***Phil Trans R Soc*1825,**115:**513. 10.1098/rstl.1825.0026View ArticleGoogle Scholar - Guiot C, Degiorgis PG, Delsanto PP, Gabriele P, Deisboeck TS:
**Does tumor growth follow a ’universal law.***J Theor Biol*2003,**225:**147-151. 10.1016/S0022-5193(03)00221-2View ArticleGoogle Scholar - Horiuchi S, Wilmoth JR:
**Deceleration in tha age pattern of mortality at older ages.***Demography*1998,**35:**391. 10.2307/3004009View ArticleGoogle Scholar - Huchet A, Candry H, Belkaceni Y:
**L’effet volume en radiotherapie. Premiere parie: effect volume et tumeur.***Cancer Radiotherapie*2003,**7:**79-89. 10.1016/S1278-3218(02)00002-1View ArticleGoogle Scholar - Kim JJ, Tannock IF:
**Repopultaion of cancer cells during therapy: an important cause of treatment failure.***Nat Rev Cancer*2005,**5:**516-525. 10.1038/nrc1650View ArticleGoogle Scholar - Muller CH,
*et al*.:**The survival rate of patient with pace-maker is essentially the same of the normal population.***Eur Heart J*1988,**9:**1003.Google Scholar - Norton LA:
**Gompertzian model of human breast cancer growth.***Cancer Res*1988,**48:**7067-7071.Google Scholar - Olshansky SJ:
**On the biodemography of aging: a review essay.***Population and Development Review*1998,**24:**381-393. 10.2307/2807981View ArticleGoogle Scholar - Rigdon SE, Basu AP:
*Statistical methods for the reliability of repairable systems*. New York: Wiley; 2000.Google Scholar - Stanley JA, Shipley WU, Steel GG:
**Influence of tumor size on the hypoxic fraction and therapeutic sensitivity of Lewis lung tumour.***Br J Cancer*1977,**36:**105-13. 10.1038/bjc.1977.160View ArticleGoogle Scholar - Steel GG:
*Growth kinetics of tumours*. Oxford: Clarendon Press; 1977.Google Scholar - Vaupel JW, Carey JR, Christensen K, Johnson T, Yashin AI, Holm NV, Iachine IA, Kannisto V, Khazaeli AA, Liedo P, Longo VD, Zeng Y, Manton K, Curtsinger JW:
**Biodemographic trajectories of longevity.***Science*1998,**280:**855-860. 10.1126/science.280.5365.855View ArticleGoogle Scholar - Wheldon TE:
*Mathematical models in cancer research*. Bristol: Adam Hilger Publisher; 1988.Google Scholar - Wolfram S:
**Nature.**1984,**311:**419-424.Google Scholar - Wolfram S:
*A New Kind of Science*. Wolfram media, Champaign, USA; 2002.Google Scholar

## Copyright

This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.