Normal, healthy, physiological processes are regulated by a complex interplay of numerous, neuroendocrinal signaling pathways. Although there are many intermediary signaling events, the fundamental purpose of most signaling pathways is the transfer of regulatory information to and from the central nervous system (CNS). Functional CNS decline precedes the metabolic, reproductive, and cognitive disorders associated with aging. For example, recent advances in brain imaging technology have demonstrated that the structural changes eventually causing Alzeheimer’s Disease (AD), actually take place, long before the first symptoms are observed. Given the central role of the CNS in age-related pathologies, the dynamics of CNS function should, clearly, also be the central focus of anti-aging research. Preventing or slowing age-related changes in the CNS has the potential to maintain healthy physiologic function, as we grow older.
Nonlinear Dynamics and the Loss of Complexity Theory of Aging and Disease
In the past, simple reductionist approaches to the study of physiology have been very productive. However, given the inherent complexity of the dynamics of most physiological processes, including neurological function, future advances in the study of these processes will require the use of nonlinear, whole-systems approaches. (“Nonlinear” means that output is not proportional to input, but varies in more complex, often unexpected, ways.) In Neuroscience, the macro phenomena of cognition, emotion, motor activity, etc. are dependent upon the emergent, collective actions of billions of neurons, each of which is a nonlinear element. In general, physiology is the result of interactions of multiple feedback loops of nonlinear systems. The process of aging can be understood as a reduction in the complexity of these feedback and control systems. See: Loss of ‘Complexity’ and Aging.
Normal, healthy physiological processes function on the edge of “chaos”, which is to say, near a critical point. This allows for greater degree of resilient vitality, and resistance to disruption than systems of a simple periodic, or stochastic nature. The nonlinear dynamics of neural systems facilitate functional adaptation to changing environmental conditions. In contrast, the aging phenotype is characterized by a reduced ability to adapt to stress and trauma reflective of a reduction in complexity of underlying regulatory mechanisms. Such a reduction in complexity may reflect the loss of a component, or the disruption of feedback coupling between components. For example, normal secretion patterns of glucocorticoids, sex steroids, and GH result from shrinkage of the hippocampus, loss of neurons, and declining neurogenesis. The number of dopaminergic neurons also declines with age, accompanied by a corresponding reduction in nigrostriatal signaling, which, in turn, produces such age-related disorders as Parkinson’s disease. See: Dopaminergic Neuronal Loss.
Such changes result in reduced complexity of the signaling dynamics of neural networks, resulting in a reduced adaptive capacity. The increased vulnerability of the aging brain to anoxia and ischemia, is one example of reduced neural adaptive capacity, which appears to be the result of compromised ribonomic ability to selectively translate stress-induced mRNA. See: Towards a dynamical network view of brain ischemia.
Clearly, many age-related disorders are the direct result of alterations in regulatory pathways of the CNS. A loss of neurological complexity as measured in EEGs has been found to characterize patients in a vegetative state. See: Complexity loss in physiological time series of patients in a vegetative state.
Nonlinear methods have been applied to the modeling of circadian rhythms. See: Modeling biological complexity. Many physiological processes depend upon circadian rhythms, which are regulated by a complex network of signaling and feedback mechanisms. Disruption of normal circadian rhythm can have profound health consequences, and is implicated in many diseases of the aging including heart disease, obesity, metabolic syndrome, psychiatric/neurological disorders, and even cancer. Many regulatory factors diminish with age, such as the age-related decrease in melatonin signaling, which results in a loss of regulatory complexity leading to circadian dysfunction.
“Recent advances in molecular biology, neurobiology, genetics, and imaging have demonstrated important insights about the nature of neurological diseases. However, a comprehensive understanding of their pathogenesis is still lacking. Although reductionism has been successful in enumerating and characterizing the components of most living organisms, it has failed to generate knowledge on how these components interact in complex arrangements to allow and sustain two of the most fundamental properties of the organism as a whole: its fitness, also termed its robustness, and its capacity to evolve. Systems biology complements the classic reductionist approaches in the biomedical sciences by enabling integration of available molecular, physiological, and clinical information in the context of a quantitative framework typically used by engineers. Systems biology employs tools developed in physics and mathematics such as nonlinear dynamics, control theory, and modeling of dynamic systems. The main goal of a systems approach to biology is to solve questions related to the complexity of living systems such as the brain, which cannot be reconciled solely with the currently available tools of molecular biology and genomics. As an example of the utility of this systems biological approach, network-based analyses of genes involved in hereditary ataxias have demonstrated a set of pathways related to RNA splicing, a novel pathogenic mechanism for these diseases. Network-based analysis is also challenging the current nosology of neurological diseases. This new knowledge will contribute to the development of patient-specific therapeutic approaches, bringing the paradigm of personalized medicine one step closer to reality.”
The loss of complexity theory of aging represents a fundamental paradigm shift away from the classical assumptions of normal physiologic homeostasis. By directly challenging the classical assumptions of health and disease, it also points the way to a new class of therapeutic approaches based on whole-system targets, as opposed to treatments based on individual components, in isolation. It is my belief that such large-scale approaches will be necessary to effectively treat aging.
Nonlinear methods have a distinguished history of application in statistical physics. However, their application to physiology is a recent development, often requiring interdisciplinary approaches, since biologists aren’t historically trained in such methods. Despite their limited use, nonlinear methods have already yielded remarkable successes in our understanding of very disparate physiological processes. In addition to the examples previously mentioned, it turns out that the apparently simple, periodic process of a regular heartbeat is actually a complex, multifactorial process on the edge of chaos. See: Nonlinear dynamics of cardiovascular aging. , Chaotic Signatures of Heart Rate Variability. Pathologies of irregular heartbeat, in contrast, are the result of a reduced complexity resulting from a loss of regulatory feedback control mechanisms. Measures of heartbeat chaos have even proven to be the best predictor of mortality in heart patients. See: Heart rate chaos as a mortality predictor in mild to moderate heart failure.
Nonlinear approaches have had success in modeling the complex dynamics of stem cell populations, a process with direct implications for aging. See: Modelling Perspectives on Aging.
Future advances in genetics and epigenetics will undoubtedly rely very heavily upon advanced computational methods. With the mapping of the human genome, many single-gene, Mendelian, diseases have been identified. In fact, I believe it is safe to say that nearly all such disease have already been identified. (There are surely more, however, many extremely rare Mendelian conditions will likely never be identified, simply due to the small number of people affected by them.) See: Rare Genetic Disorders.
However, most diseases do not result from a single gene, but from the complex interaction of many genes (and epigenetic factors), which individually may have little or no correlation with the pathological condition. In order to identify and better understand such multifactorial relationships, Multifactor Dimensionality Reduction (MDR) methods are now being used. For more information on modeling complex genetic interactions, please see the following references. A future discussion will focus, in greater detail, on the role of neuroendocrinal signaling pathways in aging and longevity.
- hormone replacement therapy says:
13. May 2011 at 08:13
Aging is factor which gives ignition to so many diseases and health problems. I know its impossioble to stop this aging process………does it possible to make the aging process to its slow pace? nice post thanks for sharing it
- Victor says:
14. May 2011 at 03:11
Preventing or slowing age-related decline in the CNS, and related signaling pathways, has the potential to slow, or even reverse aging processes. Many encouraging examples of the reversal of age-related decline in physiologic systems, by restoring proper signaling, can be given. Unfortunately, our present understanding of the molecular effects of various pathways is inadequate.