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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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Related in: MedlinePlus

Hepatic triglyceride sensitivity network.The represented pathways highlight high impact branches that significantly influence hepatic triglyceride concentration. The branch-points in the red boxes indicate high impact branches with low tolerance to flux alterations. Pathway regulators (blue text) that are influenced by alterations in flux distribution are labelled at the corresponding high impact branch-points. AdipoR- Adiponectin Receptor, AMPK- Adenosine Monophosphate- activated Kinase, BHB- beta-Hydroxybutyrate, ChREBP- Carbohydrate Response Element Binding Protein, CM- Chylomicron, DAG- Diacylglycerol, DHAP- Dihydroxyacetone phosphate, FA- Fatty Acid pool, F6P- Fructose-6-phosphate, F16BP- Fructose-1,6-bisphosphate, FXR- Farnesoid×Receptor, G3P- Glycerol-3-Phosphate, G6P- Glucose-6-Phosphate, HDL- High Density Lipoprotein, HMG CoA- 3-Hydroxy 3-Methylglutaryl CoA, LD- Lipid Droplet, LDL- Low Density Lipoprotein, LPA- Lysophosphatidic Acid, LXR- Liver×Receptor, MAG- Monoacylglycerol, PGC1A- Peroxisome Proliferator- Activated Receptor Gamma Coactivator 1 alpha, PPARA/G- Peroxisome Proliferator- Activated Receptor Alpha/Gamma, SFA CoA- Saturated Fatty Acyl CoA, SREBP- Sterol Regulatory Element Binding Protein, TG- Triglyceride, TLR4- Toll-like Receptor 4, TNFA- Tumour Necrosis Factor Alpha, USFA CoA- Unsaturated Fatty Acyl CoA, VLDL- Very-Low Density Lipoprotein.
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pcbi-1003993-g006: Hepatic triglyceride sensitivity network.The represented pathways highlight high impact branches that significantly influence hepatic triglyceride concentration. The branch-points in the red boxes indicate high impact branches with low tolerance to flux alterations. Pathway regulators (blue text) that are influenced by alterations in flux distribution are labelled at the corresponding high impact branch-points. AdipoR- Adiponectin Receptor, AMPK- Adenosine Monophosphate- activated Kinase, BHB- beta-Hydroxybutyrate, ChREBP- Carbohydrate Response Element Binding Protein, CM- Chylomicron, DAG- Diacylglycerol, DHAP- Dihydroxyacetone phosphate, FA- Fatty Acid pool, F6P- Fructose-6-phosphate, F16BP- Fructose-1,6-bisphosphate, FXR- Farnesoid×Receptor, G3P- Glycerol-3-Phosphate, G6P- Glucose-6-Phosphate, HDL- High Density Lipoprotein, HMG CoA- 3-Hydroxy 3-Methylglutaryl CoA, LD- Lipid Droplet, LDL- Low Density Lipoprotein, LPA- Lysophosphatidic Acid, LXR- Liver×Receptor, MAG- Monoacylglycerol, PGC1A- Peroxisome Proliferator- Activated Receptor Gamma Coactivator 1 alpha, PPARA/G- Peroxisome Proliferator- Activated Receptor Alpha/Gamma, SFA CoA- Saturated Fatty Acyl CoA, SREBP- Sterol Regulatory Element Binding Protein, TG- Triglyceride, TLR4- Toll-like Receptor 4, TNFA- Tumour Necrosis Factor Alpha, USFA CoA- Unsaturated Fatty Acyl CoA, VLDL- Very-Low Density Lipoprotein.

Mentions: The branch-points were classified according to their concentration control coefficients with respect to hepatic triglycerides as ‘high’ (>1), ‘moderate’ (0.1≤≤0.99) or ‘low’ (<0.1) impact. A high value (high impact) of a branch indicates that the metabolic flux distribution at this point in the network significantly influences the concentration of hepatic triglycerides. Additionally, several branch-points displayed low tolerance to flux changes i.e. incurred instability beyond a limited range of metabolic influx and were sub-classified as low tolerance branch-points. Table 3 lists branch-points that have a high impact on hepatic triglyceride concentration but low tolerance. We also determined the sensitivity of regulatory factors to alterations in flux distributions. Fig. 6 illustrates high impact branch-points that significantly influence hepatic triglyceride concentration, and their associated regulatory factors.


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)

Hepatic triglyceride sensitivity network.The represented pathways highlight high impact branches that significantly influence hepatic triglyceride concentration. The branch-points in the red boxes indicate high impact branches with low tolerance to flux alterations. Pathway regulators (blue text) that are influenced by alterations in flux distribution are labelled at the corresponding high impact branch-points. AdipoR- Adiponectin Receptor, AMPK- Adenosine Monophosphate- activated Kinase, BHB- beta-Hydroxybutyrate, ChREBP- Carbohydrate Response Element Binding Protein, CM- Chylomicron, DAG- Diacylglycerol, DHAP- Dihydroxyacetone phosphate, FA- Fatty Acid pool, F6P- Fructose-6-phosphate, F16BP- Fructose-1,6-bisphosphate, FXR- Farnesoid×Receptor, G3P- Glycerol-3-Phosphate, G6P- Glucose-6-Phosphate, HDL- High Density Lipoprotein, HMG CoA- 3-Hydroxy 3-Methylglutaryl CoA, LD- Lipid Droplet, LDL- Low Density Lipoprotein, LPA- Lysophosphatidic Acid, LXR- Liver×Receptor, MAG- Monoacylglycerol, PGC1A- Peroxisome Proliferator- Activated Receptor Gamma Coactivator 1 alpha, PPARA/G- Peroxisome Proliferator- Activated Receptor Alpha/Gamma, SFA CoA- Saturated Fatty Acyl CoA, SREBP- Sterol Regulatory Element Binding Protein, TG- Triglyceride, TLR4- Toll-like Receptor 4, TNFA- Tumour Necrosis Factor Alpha, USFA CoA- Unsaturated Fatty Acyl CoA, VLDL- Very-Low Density Lipoprotein.
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Related In: Results  -  Collection

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pcbi-1003993-g006: Hepatic triglyceride sensitivity network.The represented pathways highlight high impact branches that significantly influence hepatic triglyceride concentration. The branch-points in the red boxes indicate high impact branches with low tolerance to flux alterations. Pathway regulators (blue text) that are influenced by alterations in flux distribution are labelled at the corresponding high impact branch-points. AdipoR- Adiponectin Receptor, AMPK- Adenosine Monophosphate- activated Kinase, BHB- beta-Hydroxybutyrate, ChREBP- Carbohydrate Response Element Binding Protein, CM- Chylomicron, DAG- Diacylglycerol, DHAP- Dihydroxyacetone phosphate, FA- Fatty Acid pool, F6P- Fructose-6-phosphate, F16BP- Fructose-1,6-bisphosphate, FXR- Farnesoid×Receptor, G3P- Glycerol-3-Phosphate, G6P- Glucose-6-Phosphate, HDL- High Density Lipoprotein, HMG CoA- 3-Hydroxy 3-Methylglutaryl CoA, LD- Lipid Droplet, LDL- Low Density Lipoprotein, LPA- Lysophosphatidic Acid, LXR- Liver×Receptor, MAG- Monoacylglycerol, PGC1A- Peroxisome Proliferator- Activated Receptor Gamma Coactivator 1 alpha, PPARA/G- Peroxisome Proliferator- Activated Receptor Alpha/Gamma, SFA CoA- Saturated Fatty Acyl CoA, SREBP- Sterol Regulatory Element Binding Protein, TG- Triglyceride, TLR4- Toll-like Receptor 4, TNFA- Tumour Necrosis Factor Alpha, USFA CoA- Unsaturated Fatty Acyl CoA, VLDL- Very-Low Density Lipoprotein.
Mentions: The branch-points were classified according to their concentration control coefficients with respect to hepatic triglycerides as ‘high’ (>1), ‘moderate’ (0.1≤≤0.99) or ‘low’ (<0.1) impact. A high value (high impact) of a branch indicates that the metabolic flux distribution at this point in the network significantly influences the concentration of hepatic triglycerides. Additionally, several branch-points displayed low tolerance to flux changes i.e. incurred instability beyond a limited range of metabolic influx and were sub-classified as low tolerance branch-points. Table 3 lists branch-points that have a high impact on hepatic triglyceride concentration but low tolerance. We also determined the sensitivity of regulatory factors to alterations in flux distributions. Fig. 6 illustrates high impact branch-points that significantly influence hepatic triglyceride concentration, and their associated regulatory factors.

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