Abstract
In this chapter we examine a variety of modeling approaches that have been historically used to understand the sub-cellular and cellular biology of aging. We find that there are a large array of methods from discrete to continuous and from deterministic to stochastic. This chapter is not meant to be a comprehensive coverage of all of the modeling efforts but rather a buffet introduction to what has been done in the field over the last 50–60 years.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Carnes BA, Holden LR, Olshansky SJ, Witten TM, Siegel JS (2006) Mortality partitions and their relevance to research on aging. Biogerontology 7:183–198
Cutler RG (1982) The dysdifferentiative hypothesis of mammalian aging and longevity. In: Giacobini E, Giacobini G, Filogamo G, Vernadakis A (eds) The aging brain. Raven Press, New York
Dalle Pezze P, Nelson G, Otten EG, Korolchuk GI, Kirkwood TBL (2014) Dynamic modelling of pathways to cellular senescence reveals strategies for targeted interventions. PLoS Comput Biol 10(8):e1003728
Denbigh KG (1981) Three concepts of time. Springer, New York
Featherman DL, Peterson T (1986) Markers of aging. Modeling the clocks that time us. Res. Aging 8(3):339–365
Gallant JA, Prothero J (1980) Testing models of error propagation. J Theor Biol 83:561–578
Gillespie CS, Proctor CJ, Boys RJ, Shanley DP, Wilkinson DJ, Kirkwood TBL (2004) A mathematical model of aging in yeast. J Theor Biol 229:189–196
Glass L (1975) Classification of biological networks by their qualitative dynamics. J Theor Biol 54:85–107
Godfrey K (1983) Compartmental models and their application. Academic, New York
Goel NS, Islam S (1980) Error catastrophe in and the evolution of the protein synthesizing machinery. J Theor Biol 68:167–182
Goel NS, Richter-Dyn N (1974) Stochastic models in biology. Academic, New York
Goel NS, Yças M (1975) The error catastrophe hypothesis with reference to aging and the evolution of the protein synthesizing machinery. J Theor Biol 54:245–282
Grűning A, Vinayak A (2011) The accumulation theory of aging. ar**v:1110.2993v2 [q-bio.QM] 17 Oct 2011
Hannon B, Ruth M (1997) Modeling dynamic biological systems. Springer, New York
Hirsch HR (1974) The multistep theory of aging: relation to the forbidden-clone theory. A theoretical article. Mech Aging Dev 3:165–172
Hirsch HR (1977) The dynamics of repetitive asymmetric cell division. Mech Aging Dev 6:319–332
Hirsch HR (1978) The waste-product theory of aging: waste dilution by cell division. Mech Aging Dev 8:51–62
Hirsch HR (1986) The waste-product theory of aging: cell division rate as a function of waste volume. Mech Aging Dev 36:95–107
Hirsch HR, Coomes JA, Witten M (1989) The waste-product theory of aging: transformation to unlimited growth in cell cultures. Exp Geront 24:97–112
Hoffman GW (1974) On the origin of the genetic code and the stability of the translation apparatus. J Mol Biol 86:349–362
Jacquez JJ (1985) Compartmental analysis in biology and medicine. University of Michigan Press, Ann Arbor
Jones DS, Sleeman BD (1983) Differential equations and mathematical biology. George Allen & Unwin Publishers, London
Kirkwood TBL, Holliday R (1975a) Commitment to senescence: a model for the finite and infinite growth of diploid and transformed human fibroblasts. J Theor Biol 53(2):481–496
Kirkwood TBL, Holliday R (1975b) The stability of the translation apparatus. J Mol Biol 97(2):257–265
Kirkwood TBL, Proctor CJ (2003) Somatic mutations and ageing in silico. Mech Aging Dev 124:85–92
Kirkwood TBL, Boys RJ, Gillespie CS, Procter CJ, Shanley DP, Wilkinson DJ (2006) Computer modeling in the study of aging. In: Masoro EJ, Austad SN (eds) Handbook of the biology of aging, 6th edn. Academic, Burlington
Kowald A, Klipp E (2013) Mathematical models of mitochondrial aging and dynamics. Prog Mol Biol Transl Sci 127:63–92
Lebowitz JL, Rubinow SI (1974) A theory for the age-time distribution of a microbial population. J Math Biol 1:17–36
Lumpkin CK, Smith JR (1980) A stochastic model of cellular senescence. Part I: theoretical considerations. J Theor Biol 86:581–590
Marciniak-Czochra A, Stiehl T, Wagner W (2009) Modeling replicative senescence in hematopoietic development. Aging 1(8):723–732
Meerschaert MM (1993) Mathematical modeling. Academic, San Diego
Mehr R, Abel L, Ubezio P, Globerson A, Agur Z (1993) A mathematical model of the effect of aging on bone marrow cells colonizing the thymus. Mech Aging Dev 67:159–172
Murray JD (1989) Mathematical biology. Springer-Verlag
Orgel LE (1963) The maintenance of the accuracy of protein synthesis and its relevance to aging. Proc Natl Acad Sci USA 49:517–521
Orgel LE (1970) The maintenance of the accuracy of protein synthesis and its relevance to aging: a correction. Proc Natl Acad Sci USA 67:1476
Perelson AS, Bell GI (1977) Mathematical models for the evolution of multigene families by unequal crossing over. Nature 265:304–310
Proctor CJ, Sőti C, Boys RJ, Gillespie CS, Shanley DP, Wilkinson DJ, Kirkwood TBL (2005) Modelling the actions of chaperones and their role in aging. Mech Aging Dev 126:119–131
Rubinow S (1968) A maturity-time representation for cell populations. Biophys J 8:1055–1073
Sheldrake AR (1974) The aging, growth, and death of cells. Nature 250:381
Strehler BL (1967) The nature of cellular age changes. Symp Soc Exp Biol 21:149–178
Strehler BL (1986) Genetic instability as the primary cause of human aging. Exp Gerontol 21:283–319
Strehler BL, Freeman, MR (1980) Randomness, redundancy, and repair: roles and relevance to aging. Mech Aging Dev 14:15–38
Strehler B, Gusseck D, Johnson R, Bick, M (1971) Codon-restriction theory of aging and development. J Theor Biol 3:429–474
Stukalin EB, Aifuwa I, Kim JS, Wirtz D, Sun SX (2013) Age-dependent stochastic models for understanding population fluctuations in continuously cultured cells. J R Soc Interface. doi:http://dx.doi.org/10.1098/rsif.2013.0325
Tautu P (1990) Stochastic modeling in biology. World Scientific Press, Singapore
Trucco E (1965a) Mathematical models for cellular systems: the Von Foerster equation – part 1. Bull Math Biophys 27:285–304
Trucco E (1965b) Mathematical models for cellular systems: the Von Foerster equation – part 2. Bull Math Biophys 27:449–471
Trucco E (1965c) On the use of the Von Foerster equation for the solution and generalization of a problem in cellular studies. Bull Math Biophys 27:39–48
Vrobel S (2011) Fractal time. Studies of nonlinear phenomena in life sciences, vol 14. World Scientific Press, Singapore
West GB, Bergman A (2009) Towards a systems biology framework for understanding aging and healthspan. J Gerontol: Biol Sci Med Sci 64A(2):205–208
Wimble C, Witten TM (2014) Modeling aging networks: a systems biology approach – applications. In: Yashin AI, Jazwinski SM (eds) Aging and health. A systems biology perspective. Karger Press, New York
Witten TM (1980) Some mathematics of recombination: evolution of complexity and genotypic modification in somatic cells-a possible model for aging and cancer effects. Mech Aging Dev 13:187–199
Witten M (1984) Time abberation in living organisms: stochastic effects. Math Model 5:97–101
Witten M (1994) Might stochasticity and sampling variation be a possible explanation for variation in clonal population survival curves? Mech Aging Dev 73:223–248
Witten TM (2014) Modeling aging networks: part 1 introduction to the theory. In: Yashin AI, Jazwinski SM (eds) Aging and health a systems biology perspective. Karger Press, New York
Zheng T (1991) A mathematical model of proliferation and aging of cells in culture. J Theor Biol 149:287–315
Acknowledgements
In 1974 I wrote my very first mathematical modeling of aging manuscript. It was my masters dissertation while I was a student at SUNY Buffalo in the Center for Theoretical Biology. That paper was subsequently published in 1980. In the now 41 years since my initial foray into this field, my career has been touched by many individuals who have provided support and guidance to a then young graduate student trying to find her way. I simply cannot acknowledge all of you. Nevertheless, I would like to acknowledge on singular individual and dedicate this paper to him; Professor Bernard Strehler, my mentor and friend. He took an unknown young mathematical physicist who knew nothing about the biology of aging and guided her into a 44 year long career in this fascinating field of Gerontology by supporting many of her research efforts as publications in the journal Mechanisms of Aging and Development. I cannot thank him enough.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this chapter
Cite this chapter
Witten, T.M. (2016). Modeling Cellular Aging: An Introduction – Mathematical and Computational Approaches. In: Rattan, S., Hayflick, L. (eds) Cellular Ageing and Replicative Senescence. Healthy Ageing and Longevity. Springer, Cham. https://doi.org/10.1007/978-3-319-26239-0_8
Download citation
DOI: https://doi.org/10.1007/978-3-319-26239-0_8
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-26237-6
Online ISBN: 978-3-319-26239-0
eBook Packages: Biomedical and Life SciencesBiomedical and Life Sciences (R0)