A systems biology analysis of adrenergically stimulated adiponectin exocytosis in white adipocytes

Abstract

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A comprehensive mechanistic model of adipocyte signaling with layers of confidence

“…To be able to compare the combined core model to the ingoing individual models, we divided the data in the same way as done in the previous works. In practice, this meant that we used the data for the β3-adrenergic receptor agonist CL 316243 and ATP stimulated adiponectin release [14], and the data for the phosphorylation of HSL in response to stimulation with insulin and isoprenaline [15] for model validation, and thus removed this validation data from the set of training data. After estimating the model parameters using the training data, we found the best agreement to be acceptable, supported by a χ 2 -test ( < χ 2 ( p = 0.05, dgf = 582) = 639.23).…”

“…We first established a core model of adipocyte signaling (Fig. 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.…”

“…For methods that divide the problem into sub-problems and adding a single interaction or molecule at a time, the establishment of a core model is key. Within adipocyte signaling, there are several different models for different aspects of the adipocyte, such as glucose uptake, lipolysis, and adiponectin secretion [10][11][12][13][14][15]. The adipocytes of an expanding adipose tissue are central to understand the mechanisms behind type 2 diabetes, and several of the mentioned models are developed using not only data from adipocytes from non-diabetics, but also from type 2 diabetic patients [10][11][12]15].…”

A systems biology analysis of lipolysis and fatty acid release from adipocytes in vitro and from adipose tissue in vivo

“…uncertainty was estimated in the same way as in [27], by maximizing/minimizing the simulation in all experimental data points while requiring the cost to not exceed the χ 2 -threshold.…”

“…The uncertainty of both the parameters and the model simulations for estimation, validation, and predictions, were gathered as proposed in [39] and implemented in [27]. In short, we estimate the model uncertainty by subdividing the problem into multiple optimization problems, with one problem per model property (p) of interest.…”