Summary of SteatoNet validation conditions.

<p>Summary of SteatoNet validation conditions.</p

Pathway branch-points with high and low flux range tolerance.

<p>Pathway branch-points with high and low flux range tolerance.</p

of branch-points in SteatoNet.

<p>Range of of a) activation of saturated (SFA) and unsaturated (USFA) fatty acids in adipose, b) desaturation of SFA to USFA in adipose, c) breakdown of chylomicron into chylomicron remnants, d) reverse cholesterol transport, e) LDL distribution to adipose and peripheral tissues, f) fructose-6-phosphate synthesis from glucose-6-phosphate, g) glucose transport to adipose, h) hepatic release of glucose into blood, i) β-hydroxybutyrate (BHB) synthesis from 3-hydroxy 3-methylglutaryl coenzyme A (HMG CoA), j) acetoacetate transport to blood, and k) uptake of ketone bodies (KB) by adipose.</p

Summary of SteatoNet modelling workflow.

<p>Summary of SteatoNet modelling workflow.</p

Dynamics of enzymatic reaction according to the Michaelis-Menten kinetic formalism.

<p><i>S</i>, <i>E</i>, <i>C</i> and <i>P</i> denote the concentrations of the Substrate, Enzyme, substrate-enzyme Complex and Product respectively, <i>k_C</i> and <i>k_P</i> denote the rate constants of complex formation and product formation respectively, <i>k_CR</i> and <i>k_PR</i> the reverse reaction rate constants of complex dissociation into the enzyme and substrate and product reversibility to complex, respectively. <i>φ_I</i> corresponds to the substrate influx, <i>φ_O</i> to the product efflux, <i>φ_EI</i> to the influx of enzyme, <i>φ_EO</i> to the degradation of enzyme and <i>f</i> denotes the distribution of the total metabolic substrate flux into alternative pathways.</p

Peroxisome proliferator-activated receptor alpha (PPARα) activation in high fat diet- induced steatosis.

<p>Panels a–c illustrate the simulation of different variables in a background of hepatic steatosis induced by a high-fat diet (increased triglyceride and cholesterol influx, from time = 0) and subsequent treatment with a PPARα agonist (from time = 3×10⁵). a) Simulation of hepatic triglyceride (TG_L), plasma high-density lipoprotein (HDL_B) and serum fatty acids (FA_B); b) Simulation of hepatic cholesterol (Cholesterol_L), plasma very low-density lipoprotein (VLDL), carnitine palmitoyltransferase 1 (CPT1) and active proliferator-activated receptor alpha (aPPARα); c) Simulation of low-density lipoprotein (LDL_B).</p

SteatoNet metabolic network.

<p>The key metabolic pathways and their regulation by hormones, adipokines and transcriptional and post-translational regulatory factors are represented in the hepatic, adipose, macrophage, peripheral tissue and pancreatic compartments with inter-tissue connectivity <i>via</i> the blood. The SteatoNet consists of 194 reactions with 159 metabolites, 224 enzymes and 31 non-enzymatic regulatory proteins.</p

Model structure statistics.

<p>Model structure statistics.</p

Validation of SteatoNet.

<p>a) Simulation of fasting condition, b) Simulation of stearoyl CoA desaturase (SCD) knockout. The lipogenic diet was simulated until time 1×10⁵ and the high fat diet was simulated between time 1×10⁵ and 2×10⁵. c) Simulation of adiponectin overexpression. Serum fatty acids (FA_B), phosphoenolpyruvate carboxykinase (PEPCK), acetyl CoA carboxylase 1 (ACC1), fatty acid synthase (FAS), sterol regulatory element-binding protein-1c (SREBP-1c), carnitine palmitoyltrasnferase-1 (CPT1), glycerol-3-phophate acyltransferase (GPAT), hepatic triglycerides (TG), tumour necrosis factor alpha (TNFA), adipose carnitine palmitoyltransferase 1 (CPT1_A).</p

SteatoNet: The First Integrated Human Metabolic Model with Multi-layered Regulation to Investigate Liver-Associated Pathologies

<div><p>Current state-of-the-art mathematical models to investigate complex biological processes, in particular liver-associated pathologies, have limited expansiveness, flexibility, representation of integrated regulation and rely on the availability of detailed kinetic data. We generated the SteatoNet, a multi-pathway, multi-tissue model and <i>in silico</i> platform to investigate hepatic metabolism and its associated deregulations. SteatoNet is based on object-oriented modelling, an approach most commonly applied in automotive and process industries, whereby individual objects correspond to functional entities. Objects were compiled to feature two novel hepatic modelling aspects: the interaction of hepatic metabolic pathways with extra-hepatic tissues and the inclusion of transcriptional and post-transcriptional regulation. SteatoNet identification at normalised steady state circumvents the need for constraining kinetic parameters. Validation and identification of flux disturbances that have been proven experimentally in liver patients and animal models highlights the ability of SteatoNet to effectively describe biological behaviour. SteatoNet identifies crucial pathway branches (transport of glucose, lipids and ketone bodies) where changes in flux distribution drive the healthy liver towards hepatic steatosis, the primary stage of non-alcoholic fatty liver disease. Cholesterol metabolism and its transcription regulators are highlighted as novel steatosis factors. SteatoNet thus serves as an intuitive <i>in silico</i> platform to identify systemic changes associated with complex hepatic metabolic disorders.</p></div