Adaptive Benefits of Storage Strategy and Dual AMPK/TOR Signaling in Metabolic Stress Response

Abstract

Cellular metabolism must ensure that supply of nutrient meets the biosynthetic and bioenergetic needs. Cells have therefore developed sophisticated signaling and regulatory pathways in order to cope with dynamic fluctuations of both resource and demand and to regulate accordingly diverse anabolic and catabolic processes. Intriguingly, these pathways are organized around a relatively small number of regulatory hubs, such as the highly conserved AMPK and TOR kinase families in eukaryotic cells. Here, the global metabolic adaptations upon dynamic environment are investigated using a prototypical model of regulated metabolism. In this model, the optimal enzyme profiles as well as the underlying regulatory architecture are identified by combining perturbation and evolutionary methods. The results reveal the existence of distinct classes of adaptive strategies, which differ in the management of storage reserve depending on the intensity of the stress and in the regulation of ATP-producing reaction depending on the nature of the stress. The regulatory architecture that optimally implements these adaptive features is characterized by a crosstalk between two specialized signaling pathways, which bears close similarities with the sensing and regulatory properties of AMPK and TOR pathways.

Quantitative modeling of pentose phosphate pathway response to oxidative stress reveals a cooperative regulatory strategy

“…This error function is used first with a population-based metaheuristic algorithm called evolution strategy to quickly explore and sample local solutions ( Pfeuty and Thommen, 2016 ). Starting with a pool of parameter vectors of random values (bounded uniform distribution), the algorithm involves three steps: (i) a reproduction step where a parent is randomly selected to be duplicated without applying any fitness criterion at this stage, and to generate offspring; (ii) a mutation step where the parameters of each offspring are modified with a probability through multiplication by a factor , where is a random number of uniform distribution; (iii) a selection step where the nRMSE of the offspring are evaluated, and only the highest-fitness individuals in the pool of parameter sets (parents and offspring) are selected to generate the parents of the next generation.…”

Protein level variability determines phenotypic heterogeneity in proteotoxic stress response

“…Cell-tocell variability can arise from such intracellular biochemical fluctuations and is called nongenetic heterogeneity (NGH) [13]. NGH plays a functional role in surviving unpredictable environmental changes [11,14,15], and it has been identified in anticancer treatment as an inducer of fractional killing [16][17][18]. Accurate clinical models including NGH are still to be built in order to guide diagnosis and treatment of diseases [19].…”

Quantitative modeling of pentose phosphate pathway response to oxidative stress reveals a cooperative regulatory strategy

“…A selected set of allosteric regulatory mechanisms is not only required to explain experimental data, but is also demonstrated to exhibit a complementary range of efficiency and pleiotropic roles associated to the flux control of regulated reactions and concentration control of allosteric effectors. These modes of cooperation could be understand from a metabolic control perspective as second-order effects (Liebermeister, 2013), rather than those based on structural properties (Stelling et al, 2002;Notebaart et al, 2008;Sajitz-Hermstein and Nikoloski, 2013;Nikerel et al, 2012), optimality properties Wessely et al (2011);Chubukov et al (2012); Berkhout et al (2012); Pfeuty and Thommen (2016) or dynamical properties (Krishna et al, 2007;Grimbs et al, 2007;Mulukutla et al, 2015) of metabolic networks.…”

“…(2012); Berkhout et al . (2012); Pfeuty and Thommen (2016) or dynamical properties (Krishna et al ., 2007; Grimbs et al ., 2007; Mulukutla et al ., 2015) of metabolic networks.…”