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Related Concept Videos

Reporter Genes02:11

Reporter Genes

Reporter genes are a type of protein-coding gene that are often tagged to a gene of interest. Once inside a target cell, reporter genes usually produce visually identifiable characteristics like fluorescence and luminescence when expressed along with the gene of interest. Thus, reporter genes “report” the presence or absence of genes of interest in an organism, determine the gene expression pattern, or track the physical location of a DNA segment or protein in the cell.
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High-Throughput Metabolic Profiling for Model Refinements of Microalgae
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Predicting metabolic fluxes using gene expression differences as constraints.

Rogier J P van Berlo1, Dick de Ridder, Jean-Marc Daran

  • 1Delft Bioinformatics Lab, Faculty of Electrical Engineering, Mathematics and Computer Science, Delft University of Technology, Mekelweg 4, 2628 CD Delft, The Netherlands. r.j.p.vanberlo@tudelft.nl

IEEE/ACM Transactions on Computational Biology and Bioinformatics
|November 13, 2010
PubMed
Summary
This summary is machine-generated.

This study introduces a new algorithm, transcriptomics-informed flux-balance analysis (tFBA), which uses gene expression data to improve intracellular flux predictions. tFBA demonstrates that gene expression changes accurately predict flux changes, leading to more biologically relevant results.

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Area of Science:

  • Systems Biology
  • Metabolic Engineering
  • Computational Biology

Background:

  • Flux-balance analysis (FBA) is a standard method for estimating genome-scale intracellular fluxes.
  • FBA model performance relies on objective functions and constraints, with gene expression data offering potential for improved accuracy.
  • Previous methods used gene expression for on/off regulatory constraints, which can be overly stringent.

Purpose of the Study:

  • To develop a novel algorithm, tFBA, integrating gene expression data directly into FBA optimization.
  • To introduce less stringent 'up/down' regulatory constraints based on gene expression changes.
  • To assess the predictive power of gene expression for metabolic flux alterations.

Main Methods:

  • Developed transcriptomics-informed flux-balance analysis (tFBA) algorithm.
  • Incorporated regulatory up/down constraints derived from gene expression data into FBA.
  • Applied tFBA to analyze yeast metabolic fluxes across nine different cultivation conditions.
  • Allowed for violation of constraints to account for biological noise and post-transcriptional regulation.

Main Results:

  • Demonstrated that changes in gene expression are predictive of changes in metabolic fluxes.
  • tFBA successfully defined approximately 5,000 regulatory up/down constraints across yeast cultivation conditions.
  • Flux distributions predicted by tFBA showed better agreement with transcriptomics data compared to previous methods.
  • tFBA yielded more biologically relevant flux predictions than standard FBA.

Conclusions:

  • tFBA offers a more accurate and biologically relevant approach to metabolic flux estimation by integrating gene expression data.
  • The 'up/down' constraint strategy effectively leverages transcriptomics information without being overly restrictive.
  • This method enhances the predictive power of constraint-based modeling for understanding cellular metabolism under various conditions.