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Metabolic modeling helps interpret transcriptomic changes during malaria.

Yan Tang1, Anuj Gupta2, Swetha Garimalla3

  • 1School of Chemical and Biomolecular Engineering, Georgia Tech, Atlanta, GA 30332, USA.

Biochimica Et Biophysica Acta. Molecular Basis of Disease
|October 26, 2017
PubMed
Summary
This summary is machine-generated.

Dynamic metabolic models can interpret complex transcriptomic data from infectious diseases like malaria. This approach reveals consistent purine pathway alterations across different Plasmodium species and hosts, aiding in understanding disease mechanisms.

Keywords:
Biochemical systems theoryDynamic modelGeneralized mass action systemMalariaMetabolic modelingTranscriptomics

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Area of Science:

  • Systems biology
  • Computational biology
  • Infectious disease research

Background:

  • Identifying molecular causes of disease is challenging, even with omics data.
  • Transcriptome analysis alone often yields unreliable predictions of metabolic consequences.
  • Understanding molecular profiles is crucial for developing targeted treatments.

Purpose of the Study:

  • To demonstrate dynamic metabolic models as a tool for interpreting disease-related transcriptomic data.
  • To analyze gene expression changes in purine metabolism during malaria infections.

Main Methods:

  • Utilized dynamic models of metabolic pathways.
  • Interpreted transcriptomic profiles from rhesus macaques infected with Plasmodium cynomolgi and P. coatneyi.
  • Compared findings with human data from Plasmodium falciparum infections.

Main Results:

  • Model-based interpretation revealed consistent purine pathway flux redistribution patterns.
  • These patterns were conserved between P. cynomolgi and P. coatneyi infections.
  • Similar alterations were observed in human P. falciparum malaria data.

Conclusions:

  • Dynamic metabolic models offer a robust framework for interpreting complex omics data in disease.
  • This approach enhances understanding of molecular mechanisms underlying infectious diseases.
  • The findings highlight conserved metabolic adaptations during malaria infection across different Plasmodium species.