A systems biology analysis of lipolysis and fatty acid release from adipocytesin vitroand from adipose tissuein vivo

Abstract

Lipolysis and the release of fatty acids to supply energy to other organs, such as between meals, during exercise, and starvation, are fundamental functions of the adipose tissue. The intracellular lipolytic pathway in adipocytes is activated by adrenaline and noradrenaline, and inhibited by insulin. Circulating fatty acids are elevated in type 2 diabetic individuals. The mechanisms behind this elevation are not fully known, and to increase the knowledge a link between the systemic circulation and intracellular lipolysis is key. However, data on lipolysis and knowledge from in vitro systems have not been linked to corresponding in vivo data and knowledge in vivo. Here, we use mathematical modelling to provide such a link. We examine mechanisms of insulin action by combining in vivo and in vitro data into an integrated mathematical model that can explain all data. Furthermore, the model can describe independent data not used for training the model. We show the usefulness of the model by simulating new and more challenging experimental setups in silico, e.g. the extracellular concentration of fatty acids during an insulin clamp, and the difference in such simulations between individuals with and without type 2 diabetes. Our work provides a new platform for model-based analysis of adipose tissue lipolysis, under both non-diabetic and type 2 diabetic conditions.

A comprehensive mechanistic model of adipocyte signaling with layers of confidence

“…2), based on our earlier work [11, 14, 15]. The included earlier works are the following: 1) a lipolysis model [15] that describes the release of fatty acids and glycerol in response to α- and β-adrenergic receptor agonists and inhibitors, as well as the anti-lipolytic effect of insulin. The lipolysis model includes intracellular signaling intermediaries and data from both non-diabetic and diabetic patients.…”

“…The three separate models [11, 14, 15] were connected in two steps. Firstly, we connected the lipolysis model [15] with the glucose uptake model [11]. This was done by replacing the Ins 1 effect and the protein kinase B (PKB) equations from the lipolysis model with the insulin receptor and PKB from the glucose uptake model, and by having cAMP from the lipolysis model activate the mammalian target of rapamycin complex 2 (mTORC2) in the glucose uptake model.…”

Dynamical models for metabolomics data integration

“…Dynamical metabolic models have been applied in various contexts. Recurrently pursued objectives are 1) understanding dynamical processes [15,16], for example, through comparison of competing models [17]; 2) inferring control mechanisms and rate-limiting steps [13,18]; and 3) leveraging those to push some system of interest in specific directions, for example, for strain optimization or drug target identification) [14,19,20].…”

“…Given sufficiently informative data, model parameters can be inferred, for example, via optimization- [13][14][15][16][17][18][19][20][21]23] or sampling-based [22] approaches. However, this is computationally costly as, for most cases, it involves thousands to millions of numerical ODE simulations.…”

“…Recent applications of dynamical models of metabolism include examples of identifiability analysis [17,50], sampling-based assessment of parameter and prediction uncertainties [13], as well as other approaches uncertainty analyses [15].…”