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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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Dynamics of enzymatic reaction according to the Michaelis-Menten kinetic formalism.S, E, C and P denote the concentrations of the Substrate, Enzyme, substrate-enzyme Complex and Product respectively, kC and kP denote the rate constants of complex formation and product formation respectively, kCR and kPR the reverse reaction rate constants of complex dissociation into the enzyme and substrate and product reversibility to complex, respectively. φI corresponds to the substrate influx, φO to the product efflux, φEI to the influx of enzyme, φEO to the degradation of enzyme and f denotes the distribution of the total metabolic substrate flux into alternative pathways.
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pcbi-1003993-g003: Dynamics of enzymatic reaction according to the Michaelis-Menten kinetic formalism.S, E, C and P denote the concentrations of the Substrate, Enzyme, substrate-enzyme Complex and Product respectively, kC and kP denote the rate constants of complex formation and product formation respectively, kCR and kPR the reverse reaction rate constants of complex dissociation into the enzyme and substrate and product reversibility to complex, respectively. φI corresponds to the substrate influx, φO to the product efflux, φEI to the influx of enzyme, φEO to the degradation of enzyme and f denotes the distribution of the total metabolic substrate flux into alternative pathways.

Mentions: A systems biology library of objects corresponding to biological entities was utilised to compile the SteatoNet (Steatosis Network) in a systematic workflow (Fig. 1) to form a closed multi-pathway metabolic network (Fig. 2). The dynamics of each reaction in the SteatoNet (Fig. 3) is described by a set of differential algebraic equations (DAEs). The novelty in the approach utilized to generate SteatoNet is the definition of model parameters as a mathematical formalism based on reaction reversibility r, the distribution of the metabolic influx f into alternative pathways, the total influx φI and the ratio between bound and free enzyme, w. This methodology transforms classical Michaelis-Menten kinetic parameters into a notation that differentiates static (r, f) and kinetic parameters (w, φI). The model notation used in SteatoNet reduces the number of model parameters that must be derived from data or prior knowledge. In the presented model, 1046 or 25% of the total model parameters that describe the network must be manually set, the rest are calculated from the steady-state relations. Table 1 summarizes the model structure statistics.


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)

Dynamics of enzymatic reaction according to the Michaelis-Menten kinetic formalism.S, E, C and P denote the concentrations of the Substrate, Enzyme, substrate-enzyme Complex and Product respectively, kC and kP denote the rate constants of complex formation and product formation respectively, kCR and kPR the reverse reaction rate constants of complex dissociation into the enzyme and substrate and product reversibility to complex, respectively. φI corresponds to the substrate influx, φO to the product efflux, φEI to the influx of enzyme, φEO to the degradation of enzyme and f denotes the distribution of the total metabolic substrate flux into alternative pathways.
© Copyright Policy
Related In: Results  -  Collection

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

pcbi-1003993-g003: Dynamics of enzymatic reaction according to the Michaelis-Menten kinetic formalism.S, E, C and P denote the concentrations of the Substrate, Enzyme, substrate-enzyme Complex and Product respectively, kC and kP denote the rate constants of complex formation and product formation respectively, kCR and kPR the reverse reaction rate constants of complex dissociation into the enzyme and substrate and product reversibility to complex, respectively. φI corresponds to the substrate influx, φO to the product efflux, φEI to the influx of enzyme, φEO to the degradation of enzyme and f denotes the distribution of the total metabolic substrate flux into alternative pathways.
Mentions: A systems biology library of objects corresponding to biological entities was utilised to compile the SteatoNet (Steatosis Network) in a systematic workflow (Fig. 1) to form a closed multi-pathway metabolic network (Fig. 2). The dynamics of each reaction in the SteatoNet (Fig. 3) is described by a set of differential algebraic equations (DAEs). The novelty in the approach utilized to generate SteatoNet is the definition of model parameters as a mathematical formalism based on reaction reversibility r, the distribution of the metabolic influx f into alternative pathways, the total influx φI and the ratio between bound and free enzyme, w. This methodology transforms classical Michaelis-Menten kinetic parameters into a notation that differentiates static (r, f) and kinetic parameters (w, φI). The model notation used in SteatoNet reduces the number of model parameters that must be derived from data or prior knowledge. In the presented model, 1046 or 25% of the total model parameters that describe the network must be manually set, the rest are calculated from the steady-state relations. Table 1 summarizes the model structure statistics.

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