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Integrative Approaches to Aging Biological Systems
J. theor. Biol. (2001) 213, 505}507 doi:10.1006/jtbi.2001.2439, available online at http://www.idealibrary.com on
PREFACE Integrative Approaches to Aging Biological Systems Biology has entered what many are calling the &&post-genome era''. This epithet chronicles the complete determination, or nearly so, of the nucleotide sequence of the human genome. Signi"cantly, it is often understood to denote a change in career for those DNA sequencers who have not embarked on an analysis of population di!erences by searching for single-nucleotide polymorphisms. The new career embraced by those individuals and others involves the use of the information that the assembled DNA sequence contains, as well as its derivatives. Such terms as functional genomics, proteomics, and metabolomics have been coined to encompass this e!ort. Bioinformatics is rapidly gaining a position as a discipline that manages and analyses the information developed by these &&omics''. Yet, very frequently the result of these e!orts amounts simply to a more sophisticated and rapid acquisition of data coupled to their presentation in a comprehensible fashion. The promise of a qualitatively new approach to the understanding of biological systems has not been realized, although in some cases we are getting close (Holter et al., 2001; Idekar et al., 2001). Biological systems are complicated and complex. The latter attribute #ows from the interactions that occur among the components of these systems, which renders their dynamics nonlinear. As a consequence, biological systems have emergent properties, or, in other words, the whole is greater than the sum of its parts. Chance events can have extraordinary consequences as they perturb such systems. Biological aging research has rather recently entered the arena of &&big biology''. Like other areas of biology, aging has bene"ted dramatically from the application of genetics to its subject. 0022}5193/01/240505#03 $35.00/0
Aging research takes into account both the role of the genome, as well as the in#uence of the environment. In its complexity, however, aging seems to surpass areas such as development. This may be due to the impact of chance on aging and the lack of &&purpose'' that it displays. The reductionist approach has brought us safely into the post-genome era. It has reached its ultimate expression in molecular biology, so much so that virtually every discipline in biology seeks to attach the adjective molecular to its name. Reductionism will continue to play a decisive role in answering questions related to how organisms function. In particular, it will facilitate the formulation of mechanisms and the development of interventions. It is not, however, very good at answering the &&why'' questions. This has been the domain of evolutionary biology, which describes the broad forces that shape biological systems. Interestingly, its concepts have at times been very useful in helping to explain how organisms function, as well. Selection has been found to be the principle by which the immune system operates, an example of natural selection on an abbreviated time-scale. An understanding of biological aging may require a combination of some of the features of the &&how'' and the &&why'' approaches. Aging is not a programmed process. It plays itself out over a lifetime in a diverse manner among individuals. Aging may best be viewed in the context of the global properties of a biological system and thus it takes us into the domain of integrative biology (Jazwinski, 1996). Owing to its complexity, aging provides the ultimate proving ground for new analytical methodologies and new means of conceptualization of biological problems. 2001 Academic Press
506
PREFACE
Reductionism seeks cause and e!ect among individual components of a biological system. An integrative approach asks questions about how a system functions without searching out the ultimate cause and e!ect relationships among its components, because of the realization that in an interactive system such relationships cannot be meaningfully isolated in the analysis. Cause and e!ect can switch places, depending on the vantage from which such a system is observed. The goal then becomes to understand the operational principles of the system and its responses to external forces. The integrative approach is in essence a top}down approach, in which the hierarchical structure of the biological system is preserved. This contrasts with the bottom}up approach, in which the contributions of individual components to the operation of the system are quanti"ed based on what is already known of their interactions. This special issue of ¹he Journal of ¹heoretical Biology gathers together a set of original articles that demonstrate ways in which the biological aging process can be productively addressed at the integrative level. The article by Keshet et al. (Modeling perspectives on aging: Can mathematics help us stay young) introduces the mathematical modeling approach to the uninitiated biological aging researcher. Both &&simple'' linear models that account for age structure of populations and nonlinear models that give rise to interesting consequences are discussed. This is followed by the article of Gavrilov and Gavrilov (The reliability theory of aging and longevity), which constructs a model of an aging system composed of redundant but irreplaceable elements and examines the mortality of a population of such systems in the context of existing mortality data. Olofsson et al. (An application of a general branching process in the study of the genetics of aging) use general branching process theory to construct survival functions. These are applied to existing familial longevity data thus showing a strong genetic component to exceptional longevity without recourse to comparisons with control populations. Mangel (Complex adaptive systems, aging and longevity) approaches aging from an ecologic perspective. He takes a nonlinear, stochastic approach to understand
mortality trajectories and phenotypic e!ects such as the slowing of aging by caloric restriction. Sozou and Kirkwood (A stochastic model of cell replicative senescence based on telomere shortening, oxidative stress, and somatic mutations in nuclear and mitochondrial DNA) shift the emphasis from the aging organism to the aging cell. They describe a stochastic network model of cell senescence, in which various forms of molecular damage contribute to the aging process. This line of investigation is continued in the article by Luciani et al. (A stochastic model for CD8> T cell dynamics in human immunosenescence: implications for survival and longevity). The rami"cations of the immunosenescence model are explored at both the cellular and demographic level. In the "nal article, Jazwinski and Wawryn (Pro"les of random change during aging contain hidden information about longevity and the aging process) describe a method to model and to extract novel information from a complicated, stochastic aging process. Each of the articles featured here takes a quantitative approach. The expectations generated by the models are tested by comparison with extant datasets. In some cases, computer simulations are used for this purpose. In vivo studies are also used to support the plausibility of some of the proposed models. The brief survey of mathematical methods and models in aging research presented here is not exhaustive. It is designed to provide a grasp of the possibilities available to researchers studying aging. It is also intended to convince theoretical biologists that aging is a fertile area for research. Most of all, it is meant to facilitate a dialog between the theoretically and experimentally inclined across disciplines to further an integrative approach to aging. Such an integrative approach will require not only new models but also as importantly, new ways of gathering and analysing data to test them. This is the challenge that this special issue sets before the reader.
S. MICHAL JAZWINSKI Guest Editor
PREFACE
REFERENCES HOLTER, N. S., MARITAN, A., CIEPLAK, M., FEDEROFF, N. V. & BANAVAR, J. R. (2001). Dynamic modeling of gene expression data. Proc. Natl Acad. Sci. ;.S.A. 98, 1693}1698. IDEKER, T., THORSSON, V., RANISH, J. A., CHRISTMAS, R., BUHLER, J., ENG, J. K., BUMGARNER, R., GOODLETT,
507
D. R., AEBERSOLD, R. & HOOD, L. (2001). Integrated genomic and proteomic analyses of a systematically perturbed metabolic network. Science 292, 929}934. JAZWINSKI, S. M. (1996). Longevity, genes, and aging. Science 273, 54}59.
PREFACE Integrative Approaches to Aging Biological Systems Biology has entered what many are calling the &&post-genome era''. This epithet chronicles the complete determination, or nearly so, of the nucleotide sequence of the human genome. Signi"cantly, it is often understood to denote a change in career for those DNA sequencers who have not embarked on an analysis of population di!erences by searching for single-nucleotide polymorphisms. The new career embraced by those individuals and others involves the use of the information that the assembled DNA sequence contains, as well as its derivatives. Such terms as functional genomics, proteomics, and metabolomics have been coined to encompass this e!ort. Bioinformatics is rapidly gaining a position as a discipline that manages and analyses the information developed by these &&omics''. Yet, very frequently the result of these e!orts amounts simply to a more sophisticated and rapid acquisition of data coupled to their presentation in a comprehensible fashion. The promise of a qualitatively new approach to the understanding of biological systems has not been realized, although in some cases we are getting close (Holter et al., 2001; Idekar et al., 2001). Biological systems are complicated and complex. The latter attribute #ows from the interactions that occur among the components of these systems, which renders their dynamics nonlinear. As a consequence, biological systems have emergent properties, or, in other words, the whole is greater than the sum of its parts. Chance events can have extraordinary consequences as they perturb such systems. Biological aging research has rather recently entered the arena of &&big biology''. Like other areas of biology, aging has bene"ted dramatically from the application of genetics to its subject. 0022}5193/01/240505#03 $35.00/0
Aging research takes into account both the role of the genome, as well as the in#uence of the environment. In its complexity, however, aging seems to surpass areas such as development. This may be due to the impact of chance on aging and the lack of &&purpose'' that it displays. The reductionist approach has brought us safely into the post-genome era. It has reached its ultimate expression in molecular biology, so much so that virtually every discipline in biology seeks to attach the adjective molecular to its name. Reductionism will continue to play a decisive role in answering questions related to how organisms function. In particular, it will facilitate the formulation of mechanisms and the development of interventions. It is not, however, very good at answering the &&why'' questions. This has been the domain of evolutionary biology, which describes the broad forces that shape biological systems. Interestingly, its concepts have at times been very useful in helping to explain how organisms function, as well. Selection has been found to be the principle by which the immune system operates, an example of natural selection on an abbreviated time-scale. An understanding of biological aging may require a combination of some of the features of the &&how'' and the &&why'' approaches. Aging is not a programmed process. It plays itself out over a lifetime in a diverse manner among individuals. Aging may best be viewed in the context of the global properties of a biological system and thus it takes us into the domain of integrative biology (Jazwinski, 1996). Owing to its complexity, aging provides the ultimate proving ground for new analytical methodologies and new means of conceptualization of biological problems. 2001 Academic Press
506
PREFACE
Reductionism seeks cause and e!ect among individual components of a biological system. An integrative approach asks questions about how a system functions without searching out the ultimate cause and e!ect relationships among its components, because of the realization that in an interactive system such relationships cannot be meaningfully isolated in the analysis. Cause and e!ect can switch places, depending on the vantage from which such a system is observed. The goal then becomes to understand the operational principles of the system and its responses to external forces. The integrative approach is in essence a top}down approach, in which the hierarchical structure of the biological system is preserved. This contrasts with the bottom}up approach, in which the contributions of individual components to the operation of the system are quanti"ed based on what is already known of their interactions. This special issue of ¹he Journal of ¹heoretical Biology gathers together a set of original articles that demonstrate ways in which the biological aging process can be productively addressed at the integrative level. The article by Keshet et al. (Modeling perspectives on aging: Can mathematics help us stay young) introduces the mathematical modeling approach to the uninitiated biological aging researcher. Both &&simple'' linear models that account for age structure of populations and nonlinear models that give rise to interesting consequences are discussed. This is followed by the article of Gavrilov and Gavrilov (The reliability theory of aging and longevity), which constructs a model of an aging system composed of redundant but irreplaceable elements and examines the mortality of a population of such systems in the context of existing mortality data. Olofsson et al. (An application of a general branching process in the study of the genetics of aging) use general branching process theory to construct survival functions. These are applied to existing familial longevity data thus showing a strong genetic component to exceptional longevity without recourse to comparisons with control populations. Mangel (Complex adaptive systems, aging and longevity) approaches aging from an ecologic perspective. He takes a nonlinear, stochastic approach to understand
mortality trajectories and phenotypic e!ects such as the slowing of aging by caloric restriction. Sozou and Kirkwood (A stochastic model of cell replicative senescence based on telomere shortening, oxidative stress, and somatic mutations in nuclear and mitochondrial DNA) shift the emphasis from the aging organism to the aging cell. They describe a stochastic network model of cell senescence, in which various forms of molecular damage contribute to the aging process. This line of investigation is continued in the article by Luciani et al. (A stochastic model for CD8> T cell dynamics in human immunosenescence: implications for survival and longevity). The rami"cations of the immunosenescence model are explored at both the cellular and demographic level. In the "nal article, Jazwinski and Wawryn (Pro"les of random change during aging contain hidden information about longevity and the aging process) describe a method to model and to extract novel information from a complicated, stochastic aging process. Each of the articles featured here takes a quantitative approach. The expectations generated by the models are tested by comparison with extant datasets. In some cases, computer simulations are used for this purpose. In vivo studies are also used to support the plausibility of some of the proposed models. The brief survey of mathematical methods and models in aging research presented here is not exhaustive. It is designed to provide a grasp of the possibilities available to researchers studying aging. It is also intended to convince theoretical biologists that aging is a fertile area for research. Most of all, it is meant to facilitate a dialog between the theoretically and experimentally inclined across disciplines to further an integrative approach to aging. Such an integrative approach will require not only new models but also as importantly, new ways of gathering and analysing data to test them. This is the challenge that this special issue sets before the reader.
S. MICHAL JAZWINSKI Guest Editor
PREFACE
REFERENCES HOLTER, N. S., MARITAN, A., CIEPLAK, M., FEDEROFF, N. V. & BANAVAR, J. R. (2001). Dynamic modeling of gene expression data. Proc. Natl Acad. Sci. ;.S.A. 98, 1693}1698. IDEKER, T., THORSSON, V., RANISH, J. A., CHRISTMAS, R., BUHLER, J., ENG, J. K., BUMGARNER, R., GOODLETT,
507
D. R., AEBERSOLD, R. & HOOD, L. (2001). Integrated genomic and proteomic analyses of a systematically perturbed metabolic network. Science 292, 929}934. JAZWINSKI, S. M. (1996). Longevity, genes, and aging. Science 273, 54}59.