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Transcription Factors02:16

Transcription Factors

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Tissue-specific transcription factors contribute to diverse cellular functions in mammals. For example, the gene for beta globin, a major component of hemoglobin, is present in all cells of the body. However, it is only expressed in red blood cells because the transcription factors that can bind to the promoter sequences of the beta globin gene are only expressed in these cells. Tissue-specific transcription factors also ensure that mutations in these factors may impair only the function of...
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Transcription elongation is a dynamic process that alters depending upon the sequence heterogeneity of the DNA being transcribed. Hence, it is not surprising that the elongation complex's composition also varies along the way while transcribing a gene.
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Pyruvate Oxidation01:15

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After glycolysis, the charged pyruvate molecules enter the mitochondria via active transport and undergo three enzymatic reactions. These reactions ensure that pyruvate can enter the next metabolic pathway so that energy stored in the pyruvate molecules can be harnessed by the cells.
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Efficient Sampling of Genetically Encoded Biosensor Design Space Enabled with a Design of Experiments and Automation Workflow
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Design-Build-Test-Learn-Guided Engineering of a Whole-Cell Pyruvate Biosensor Based on a Transcription Factor.

Zihan Gao1, Maria Suarez-Diez1, Pieter Candry1

  • 1Laboratory of Systems and Synthetic Biology, Wageningen University & Research, Stippeneng 4, 6708 WE Wageningen, The Netherlands.

ACS Synthetic Biology
|February 8, 2026
PubMed
Summary
This summary is machine-generated.

This study engineered a whole-cell pyruvate biosensor using iterative Design-Build-Test-Learn cycles and statistical modeling. The optimized biosensor significantly improved dynamic range and reduced leaky expression for precise metabolite monitoring.

Keywords:
DBTL cycledesign of experimentsdynamic rangestatistical modelingtranscription factor-based pyruvate biosensorwhole-cell biosensor

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Area of Science:

  • Synthetic Biology
  • Metabolic Engineering
  • Biosensor Development

Background:

  • Whole-cell biosensors are crucial for metabolite monitoring but face limitations like narrow dynamic range and high leaky expression.
  • Optimizing biosensor performance is essential for broader applications in metabolic regulation and screening.

Purpose of the Study:

  • To systematically develop and optimize a transcription factor-based pyruvate biosensor in Escherichia coli.
  • To enhance biosensor dynamic range and minimize leaky expression using a Design-Build-Test-Learn (DBTL) workflow.

Main Methods:

  • Employed two iterative Design-Build-Test-Learn (DBTL) cycles for biosensor optimization.
  • Utilized Design of Experiments (DoE) and statistical modeling (Akaike Information Criterion) to explore promoter and ribosome-binding site (RBS) effects.
  • Quantified intracellular pyruvate levels to confirm biosensor operational range.

Main Results:

  • The first DBTL cycle yielded a biosensor responsive to pyruvate in the 0.05-10 mM range.
  • The second DBTL cycle, guided by DoE, identified key RBS effects on dynamic range and signal span.
  • The optimized strain showed an 18.54-fold increase in dynamic range and a 37.22-fold reduction in leaky expression.
  • Confirmed an operational range of 1.23-6.81 μmol/g DCW for intracellular pyruvate quantification.

Conclusions:

  • DBTL cycles combined with statistical modeling provide a powerful framework for biosensor engineering.
  • The developed pyruvate biosensor offers improved performance for precise metabolic regulation and screening applications.
  • This systematic approach advances the development of robust whole-cell biosensors.