What makes the study of aging particularly challenging is the wide spectrum of phenotypical changes that can be observed during its progression. While initial attention was paid to damage accumulation, dysfunction, and failure, it is now realized that aging, and associated diseases including dementias, are influenced by a multitude of interacting factors. Proximal mechanisms beyond passive accumulation of damage include regulatory mechanisms, stress responses, changes in networks, as well as genetic and stochastic effects. The application of computational systems biology in aging, which is in line with other attempts to overcome the study of isolated or compartmentalized mechanisms, has made initial progress allowing us to simulate partial aspects of the aging dynamics and to make new hypotheses about how these aging mechanism shape disease progression. Here we provide examples for analysis of networks, regulatory mechanisms, and spatiotemporal effects in the study of proximal mechanisms of aging and Parkinson’s Disease. In addition, we introduce complexity theories that may contribute to explain the ultimate causes of aging with an evolutionary view.
This comprehensively revised second edition of Computational Systems Biology discusses the experimental and theoretical foundations of the function of biological systems at the molecular, cellular or organismal level over temporal and spatial scales, as systems biology advances to provide clinical solutions to complex medical problems. In particular the work focuses on the engineering of biological systems and network modeling. Logical information flow aids understanding of basic building blocks of life through disease phenotypes Evolved principles gives insight into underlying organizational principles of biological organizations, and systems processes, governing functions such as adaptation or response patterns Coverage of technical tools and systems helps researchers to understand and resolve specific systems biology problems using advanced computation Multi-scale modeling on disparate scales aids researchers understanding of dependencies and constraints of spatio-temporal relationships fundamental to biological organization and function.
1.1 Overview The precise knowledge of the three-dimensional (3-D) assembly of biological structures is still in its origin. As an example, a widely accepted concept and common belief of the structure of the airway network oflung is that of a regular, dichotomous branching pattern, also known as the trumpet model. This model, first introduced by Weibel in 1963, is often used in clinical and physiological applications. However, if this concept of dichotomy is used to model lung, a shape is obtained that is quite different from a real lung. As a matter of fact, many previous quantitative morphological and stereological investigations of lung did not concentrate on the spatial aspect of lung morphology but delivered data in a more statistical fashion. Accordingly, the functional behavior predicted by such a model becomes questionable and indeed, the morphometrically predicted lung capacity exceeds the physiological required capacity by a factor of 1.3 up to a factor of2. This problem has also been termed a paradox, as discussed by Weibel in 1983. In the rare cases where descriptive models of the mammalian bronchial tree exist, monopodial in small mammals, dichotomous in larger ones, the understanding of the historical and/or functional reasons for size-related changes in the general design is not explainable. This investigation is trying to overcome this gap by computer modeling and functional simulation.
1.1 Overview The precise knowledge of the three-dimensional (3-D) assembly of biological structures is still in its origin. As an example, a widely accepted concept and common belief of the structure of the airway network oflung is that of a regular, dichotomous branching pattern, also known as the trumpet model. This model, first introduced by Weibel in 1963, is often used in clinical and physiological applications. However, if this concept of dichotomy is used to model lung, a shape is obtained that is quite different from a real lung. As a matter of fact, many previous quantitative morphological and stereological investigations of lung did not concentrate on the spatial aspect of lung morphology but delivered data in a more statistical fashion. Accordingly, the functional behavior predicted by such a model becomes questionable and indeed, the morphometrically predicted lung capacity exceeds the physiological required capacity by a factor of 1.3 up to a factor of2. This problem has also been termed a paradox, as discussed by Weibel in 1983. In the rare cases where descriptive models of the mammalian bronchial tree exist, monopodial in small mammals, dichotomous in larger ones, the understanding of the historical and/or functional reasons for size-related changes in the general design is not explainable. This investigation is trying to overcome this gap by computer modeling and functional simulation.
What makes the study of aging particularly challenging is the wide spectrum of phenotypical changes that can be observed during its progression. While initial attention was paid to damage accumulation, dysfunction, and failure, it is now realized that aging, and associated diseases including dementias, are influenced by a multitude of interacting factors. Proximal mechanisms beyond passive accumulation of damage include regulatory mechanisms, stress responses, changes in networks, as well as genetic and stochastic effects. The application of computational systems biology in aging, which is in line with other attempts to overcome the study of isolated or compartmentalized mechanisms, has made initial progress allowing us to simulate partial aspects of the aging dynamics and to make new hypotheses about how these aging mechanism shape disease progression. Here we provide examples for analysis of networks, regulatory mechanisms, and spatiotemporal effects in the study of proximal mechanisms of aging and Parkinson’s Disease. In addition, we introduce complexity theories that may contribute to explain the ultimate causes of aging with an evolutionary view.
Thank you for visiting our website. Would you like to provide feedback on how we could improve your experience?
This site does not use any third party cookies with one exception — it uses cookies from Google to deliver its services and to analyze traffic.Learn More.