Limits...
Large-scale in silico modeling of metabolic interactions between cell types in the human brain.

Lewis NE, Schramm G, Bordbar A, Schellenberger J, Andersen MP, Cheng JK, Patel N, Yee A, Lewis RA, Eils R, König R, Palsson BØ - Nat. Biotechnol. (2010)

Bottom Line: Transport reactions are used to account for the transfer of metabolites between models of different cell types via the interstitial fluid.Analysis of the models identifies genes and pathways that may explain observed experimental phenomena, including the differential effects of the disease on cell types and regions of the brain.Constraint-based modeling can thus contribute to the study and analysis of multicellular metabolic processes in the human tissue microenvironment and provide detailed mechanistic insight into high-throughput data analysis.

View Article: PubMed Central - PubMed

Affiliation: Department of Bioengineering, University of California, San Diego, La Jolla, California, USA.

ABSTRACT
Metabolic interactions between multiple cell types are difficult to model using existing approaches. Here we present a workflow that integrates gene expression data, proteomics data and literature-based manual curation to model human metabolism within and between different types of cells. Transport reactions are used to account for the transfer of metabolites between models of different cell types via the interstitial fluid. We apply the method to create models of brain energy metabolism that recapitulate metabolic interactions between astrocytes and various neuron types relevant to Alzheimer's disease. Analysis of the models identifies genes and pathways that may explain observed experimental phenomena, including the differential effects of the disease on cell types and regions of the brain. Constraint-based modeling can thus contribute to the study and analysis of multicellular metabolic processes in the human tissue microenvironment and provide detailed mechanistic insight into high-throughput data analysis.

Show MeSH

Related in: MedlinePlus

Model-aided prediction of cholinergic contribution is consistent with experimental acetylcholine productionPercent brain cholinergic neurotransmission was predicted based on 14 sets of experimental data in which brain minces were fed [1-14C]-pyruvate or [2-14C]-pyruvate, followed by measurement of 14C-labeled CO2 and acetylcholine. (a) For each experiment, the feasible amount of the brain that can generate the experimental response was computed, centering at 3.3%. (b) This parameter was employed in the analysis, and the updated model predictions were consistent with experimental data, such as seen in the case of treating the brain minces with [1-14C]-pyruvate and increasing levels of the pyruvate-dehydrogenase inhibitor bromopyruvate. Moreover, the updated model predictions were consistent with measured 14C-labeled CO2 and acetylcholine production for brain minces that were treated with three PDHm inhibitors withheld from previous computations for both supplementation with (c) [1-14C]-pyruvate and (d) [2-14C]-pyruvate. Error bars on the simulation results represent 25th and 75th percentiles. ChAT = choline acetyltransferase.
© Copyright Policy
Related In: Results  -  Collection


getmorefigures.php?uid=PMC3140076&req=5

Figure 6: Model-aided prediction of cholinergic contribution is consistent with experimental acetylcholine productionPercent brain cholinergic neurotransmission was predicted based on 14 sets of experimental data in which brain minces were fed [1-14C]-pyruvate or [2-14C]-pyruvate, followed by measurement of 14C-labeled CO2 and acetylcholine. (a) For each experiment, the feasible amount of the brain that can generate the experimental response was computed, centering at 3.3%. (b) This parameter was employed in the analysis, and the updated model predictions were consistent with experimental data, such as seen in the case of treating the brain minces with [1-14C]-pyruvate and increasing levels of the pyruvate-dehydrogenase inhibitor bromopyruvate. Moreover, the updated model predictions were consistent with measured 14C-labeled CO2 and acetylcholine production for brain minces that were treated with three PDHm inhibitors withheld from previous computations for both supplementation with (c) [1-14C]-pyruvate and (d) [2-14C]-pyruvate. Error bars on the simulation results represent 25th and 75th percentiles. ChAT = choline acetyltransferase.

Mentions: The fraction of cholinergic neurotransmission for the brain was computed by randomly choosing points from both the distributions of experimental data and distributions predicted by the simulations. A scaling factor was subsequently found that reconciles the two. This was repeated for 14 different combinations of pyruvate labeling and pyruvate dehydrogenase inhibitors35, yielding a median predicted cholinergic portion of total brain neurotransmission of 3.3% (Fig. 6.a). After adding this new parameter to the model, the predictions corresponded well with the experimental data sets (Fig. 6.b), including six datasets representing three pyruvate dehydrogenase inhibitors withheld from the previous computations (Fig. 6.c–d). Thus, the model was used in conjunction with experimental data to gain insight into physiological observations and derive important physiological parameters dependent on systems-level activity.


Large-scale in silico modeling of metabolic interactions between cell types in the human brain.

Lewis NE, Schramm G, Bordbar A, Schellenberger J, Andersen MP, Cheng JK, Patel N, Yee A, Lewis RA, Eils R, König R, Palsson BØ - Nat. Biotechnol. (2010)

Model-aided prediction of cholinergic contribution is consistent with experimental acetylcholine productionPercent brain cholinergic neurotransmission was predicted based on 14 sets of experimental data in which brain minces were fed [1-14C]-pyruvate or [2-14C]-pyruvate, followed by measurement of 14C-labeled CO2 and acetylcholine. (a) For each experiment, the feasible amount of the brain that can generate the experimental response was computed, centering at 3.3%. (b) This parameter was employed in the analysis, and the updated model predictions were consistent with experimental data, such as seen in the case of treating the brain minces with [1-14C]-pyruvate and increasing levels of the pyruvate-dehydrogenase inhibitor bromopyruvate. Moreover, the updated model predictions were consistent with measured 14C-labeled CO2 and acetylcholine production for brain minces that were treated with three PDHm inhibitors withheld from previous computations for both supplementation with (c) [1-14C]-pyruvate and (d) [2-14C]-pyruvate. Error bars on the simulation results represent 25th and 75th percentiles. ChAT = choline acetyltransferase.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3140076&req=5

Figure 6: Model-aided prediction of cholinergic contribution is consistent with experimental acetylcholine productionPercent brain cholinergic neurotransmission was predicted based on 14 sets of experimental data in which brain minces were fed [1-14C]-pyruvate or [2-14C]-pyruvate, followed by measurement of 14C-labeled CO2 and acetylcholine. (a) For each experiment, the feasible amount of the brain that can generate the experimental response was computed, centering at 3.3%. (b) This parameter was employed in the analysis, and the updated model predictions were consistent with experimental data, such as seen in the case of treating the brain minces with [1-14C]-pyruvate and increasing levels of the pyruvate-dehydrogenase inhibitor bromopyruvate. Moreover, the updated model predictions were consistent with measured 14C-labeled CO2 and acetylcholine production for brain minces that were treated with three PDHm inhibitors withheld from previous computations for both supplementation with (c) [1-14C]-pyruvate and (d) [2-14C]-pyruvate. Error bars on the simulation results represent 25th and 75th percentiles. ChAT = choline acetyltransferase.
Mentions: The fraction of cholinergic neurotransmission for the brain was computed by randomly choosing points from both the distributions of experimental data and distributions predicted by the simulations. A scaling factor was subsequently found that reconciles the two. This was repeated for 14 different combinations of pyruvate labeling and pyruvate dehydrogenase inhibitors35, yielding a median predicted cholinergic portion of total brain neurotransmission of 3.3% (Fig. 6.a). After adding this new parameter to the model, the predictions corresponded well with the experimental data sets (Fig. 6.b), including six datasets representing three pyruvate dehydrogenase inhibitors withheld from the previous computations (Fig. 6.c–d). Thus, the model was used in conjunction with experimental data to gain insight into physiological observations and derive important physiological parameters dependent on systems-level activity.

Bottom Line: Transport reactions are used to account for the transfer of metabolites between models of different cell types via the interstitial fluid.Analysis of the models identifies genes and pathways that may explain observed experimental phenomena, including the differential effects of the disease on cell types and regions of the brain.Constraint-based modeling can thus contribute to the study and analysis of multicellular metabolic processes in the human tissue microenvironment and provide detailed mechanistic insight into high-throughput data analysis.

View Article: PubMed Central - PubMed

Affiliation: Department of Bioengineering, University of California, San Diego, La Jolla, California, USA.

ABSTRACT
Metabolic interactions between multiple cell types are difficult to model using existing approaches. Here we present a workflow that integrates gene expression data, proteomics data and literature-based manual curation to model human metabolism within and between different types of cells. Transport reactions are used to account for the transfer of metabolites between models of different cell types via the interstitial fluid. We apply the method to create models of brain energy metabolism that recapitulate metabolic interactions between astrocytes and various neuron types relevant to Alzheimer's disease. Analysis of the models identifies genes and pathways that may explain observed experimental phenomena, including the differential effects of the disease on cell types and regions of the brain. Constraint-based modeling can thus contribute to the study and analysis of multicellular metabolic processes in the human tissue microenvironment and provide detailed mechanistic insight into high-throughput data analysis.

Show MeSH
Related in: MedlinePlus