226
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Computationally Modeling Lipid Metabolism and Aging: A Mini-review

      review-article

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          One of the greatest challenges in biology is to improve the understanding of the mechanisms which underpin aging and how these affect health. The need to better understand aging is amplified by demographic changes, which have caused a gradual increase in the global population of older people. Aging western populations have resulted in a rise in the prevalence of age-related pathologies. Of these diseases, cardiovascular disease is the most common underlying condition in older people. The dysregulation of lipid metabolism due to aging impinges significantly on cardiovascular health. However, the multifaceted nature of lipid metabolism and the complexities of its interaction with aging make it challenging to understand by conventional means. To address this challenge computational modeling, a key component of the systems biology paradigm is being used to study the dynamics of lipid metabolism. This mini-review briefly outlines the key regulators of lipid metabolism, their dysregulation, and how computational modeling is being used to gain an increased insight into this system.

          Related collections

          Most cited references122

          • Record: found
          • Abstract: found
          • Article: not found

          Why do we age?

          The evolutionary theory of ageing explains why ageing occurs, giving valuable insight into the mechanisms underlying the complex cellular and molecular changes that contribute to senescence. Such understanding also helps to clarify how the genome shapes the ageing process, thereby aiding the study of the genetic factors that influence longevity and age-associated diseases.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            The Systems Biology Graphical Notation.

            Circuit diagrams and Unified Modeling Language diagrams are just two examples of standard visual languages that help accelerate work by promoting regularity, removing ambiguity and enabling software tool support for communication of complex information. Ironically, despite having one of the highest ratios of graphical to textual information, biology still lacks standard graphical notations. The recent deluge of biological knowledge makes addressing this deficit a pressing concern. Toward this goal, we present the Systems Biology Graphical Notation (SBGN), a visual language developed by a community of biochemists, modelers and computer scientists. SBGN consists of three complementary languages: process diagram, entity relationship diagram and activity flow diagram. Together they enable scientists to represent networks of biochemical interactions in a standard, unambiguous way. We believe that SBGN will foster efficient and accurate representation, visualization, storage, exchange and reuse of information on all kinds of biological knowledge, from gene regulation, to metabolism, to cellular signaling.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              A new approach to decoding life: systems biology.

              Systems biology studies biological systems by systematically perturbing them (biologically, genetically, or chemically); monitoring the gene, protein, and informational pathway responses; integrating these data; and ultimately, formulating mathematical models that describe the structure of the system and its response to individual perturbations. The emergence of systems biology is described, as are several examples of specific systems approaches.
                Bookmark

                Author and article information

                Contributors
                Journal
                Comput Struct Biotechnol J
                Comput Struct Biotechnol J
                Computational and Structural Biotechnology Journal
                Research Network of Computational and Structural Biotechnology
                2001-0370
                15 November 2014
                2015
                15 November 2014
                : 13
                : 38-46
                Affiliations
                [a ]Faculty of Science and Engineering, Department of Chemical Engineering, Thornton Science Park, University of Chester, UK
                [b ]Faculty of Health and Social Care, Edge Hill University, Ormskirk, Lancashire, UK
                Author notes
                [* ]Corresponding author. Tel.: + 44 151 2913789; fax: + 44 151 2913414. m.mcauley@ 123456chester.ac.uk
                Article
                S2001-0370(14)00047-6
                10.1016/j.csbj.2014.11.006
                4348429
                25750699
                cf19df96-69bc-4609-bc40-59c8d58d13c6
                © 2014 Mc Auley and Mooney. Published by Elsevier B.V. on behalf of the Research Network of Computational and Structural Biotechnology.

                This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/3.0/).

                History
                Categories
                Mini Review

                aging,lipid metabolism,computational modeling,deterministic model,stochastic model,parameter inference

                Comments

                Comment on this article