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SteatoNet: the first integrated human metabolic model with multi-layered regulation to investigate liver-associated pathologies.

Naik A, Rozman D, Belič A - PLoS Comput. Biol. (2014)

Bottom Line: 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.Cholesterol metabolism and its transcription regulators are highlighted as novel steatosis factors.SteatoNet thus serves as an intuitive in silico platform to identify systemic changes associated with complex hepatic metabolic disorders.

View Article: PubMed Central - PubMed

Affiliation: Faculty of Computer Sciences and Informatics, University of Ljubljana, Ljubljana, Slovenia; Faculty of Electrical Engineering, University of Ljubljana, Ljubljana, Slovenia.

ABSTRACT
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 in silico 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 in silico platform to identify systemic changes associated with complex hepatic metabolic disorders.

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Validation of SteatoNet.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 (FAB), 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 (CPT1A).
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pcbi-1003993-g004: Validation of SteatoNet.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 (FAB), 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 (CPT1A).

Mentions: To simulate fasting, the glucose influx into the network was reduced by 10-fold compared to the initial steady state. As illustrated in Fig. 4a and Table 2, the reduced glucose influx results in downregulation of serum insulin and glucose, hepatic glycogen stores, lipogenic enzymes (ACC1 and fatty acid synthase), sterol-regulatory element binding protein 1c (SREBP1c) and increased levels of serum glucagon, serum fatty acids, gluconeogenic enzyme PEPCK, the β-oxidation enzyme, carnitine acyl transferase-1 (CPT-1) and urea cycle enzymes. This corresponds accurately to the expected changes in the fasted state.


SteatoNet: the first integrated human metabolic model with multi-layered regulation to investigate liver-associated pathologies.

Naik A, Rozman D, Belič A - PLoS Comput. Biol. (2014)

Validation of SteatoNet.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 (FAB), 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 (CPT1A).
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4263370&req=5

pcbi-1003993-g004: Validation of SteatoNet.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 (FAB), 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 (CPT1A).
Mentions: To simulate fasting, the glucose influx into the network was reduced by 10-fold compared to the initial steady state. As illustrated in Fig. 4a and Table 2, the reduced glucose influx results in downregulation of serum insulin and glucose, hepatic glycogen stores, lipogenic enzymes (ACC1 and fatty acid synthase), sterol-regulatory element binding protein 1c (SREBP1c) and increased levels of serum glucagon, serum fatty acids, gluconeogenic enzyme PEPCK, the β-oxidation enzyme, carnitine acyl transferase-1 (CPT-1) and urea cycle enzymes. This corresponds accurately to the expected changes in the fasted state.

Bottom Line: 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.Cholesterol metabolism and its transcription regulators are highlighted as novel steatosis factors.SteatoNet thus serves as an intuitive in silico platform to identify systemic changes associated with complex hepatic metabolic disorders.

View Article: PubMed Central - PubMed

Affiliation: Faculty of Computer Sciences and Informatics, University of Ljubljana, Ljubljana, Slovenia; Faculty of Electrical Engineering, University of Ljubljana, Ljubljana, Slovenia.

ABSTRACT
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 in silico 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 in silico platform to identify systemic changes associated with complex hepatic metabolic disorders.

Show MeSH
Related in: MedlinePlus