Rule-based cell systems model of aging using feedback loop motifs mediated by stress responses.
Investigating the complex systems dynamics of the aging process requires integration of a broad range of cellular processes describing damage and functional decline co-existing with adaptive and protective regulatory mechanisms. We evolve an integrated generic cell network to represent the connectivity of key cellular mechanisms structured into positive and negative feedback loop motifs centrally important for aging. The conceptual network is casted into a fuzzy-logic, hybrid-intelligent framework based on interaction rules assembled from a priori knowledge. Based upon a classical homeostatic representation of cellular energy metabolism, we first demonstrate how positive-feedback loops accelerate damage and decline consistent with a vicious cycle. This model is iteratively extended towards an adaptive response model by incorporating protective negative-feedback loop circuits. Time-lapse simulations of the adaptive response model uncover how transcriptional and translational changes, mediated by stress sensors NF-kappaB and mTOR, counteract accumulating damage and dysfunction by modulating mitochondrial respiration, metabolic fluxes, biosynthesis, and autophagy, crucial for cellular survival. The model allows consideration of lifespan optimization scenarios with respect to fitness criteria using a sensitivity analysis. Our work establishes a novel extendable and scalable computational approach capable to connect tractable molecular mechanisms with cellular network dynamics underlying the emerging aging phenotype.
Kriete
Andres
A
School of Biomedical Engineering, Science and Health Systems, Drexel University, Bossone Research Center, Philadelphia, Pennsylvania, United States of America. andres.kriete@drexel.edu
Bosl
William J
WJ
Booker
Glenn
G
D000255:Adenosine Triphosphate / Q000378:metabolism; D000818:Animals; D016922:Cellular Senescence / Q000502:physiology*; D003198:Computer Simulation; D004734:Energy Metabolism; D025461:Feedback, Physiological / Q000502:physiology*; D017143:Fuzzy Logic; D006801:Humans; D047908:Intracellular Signaling Peptides and Proteins / Q000378:metabolism; D008954:Models, Biological; D016328:NF-kappa B / Q000378:metabolism; D017346:Protein Serine-Threonine Kinases / Q000378:metabolism; D017382:Reactive Oxygen Species / Q000378:metabolism; D013312:Stress, Physiological / Q000502:physiology*; D049490:Systems Biology / Q000379:methods*; D058570:TOR Serine-Threonine Kinases
D047908:Intracellular Signaling Peptides and Proteins; D016328:NF-kappa B; D017382:Reactive Oxygen Species; D000255:Adenosine Triphosphate; C546842:MTOR protein, human; D017346:Protein Serine-Threonine Kinases; D058570:TOR Serine-Threonine Kinases