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Related Concept Videos

Coordination of Gene Expression Processes in Bacteria01:29

Coordination of Gene Expression Processes in Bacteria

The DNA replication, transcription, and translation processes are intricately coupled in bacteria, allowing efficient gene expression and rapid protein synthesis. While this physical and functional coordination is advantageous, it introduces challenges that bacteria overcome through specific regulatory mechanisms.Coupling of Replication, Transcription, and TranslationThe coupling of replication, transcription, and translation is a hallmark of bacterial gene expression. As the replisome unwinds...
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Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
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Cell-Free Protein Synthesis System for Building Synthetic Cells
07:43

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Published on: April 19, 2024

Coping with complexity: machine learning optimization of cell-free protein synthesis.

Filippo Caschera1, Mark A Bedau, Andrew Buchanan

  • 1ProtoLife, Inc., 57 Post St #513, San Francisco, California 94104, USA.

Biotechnology and Bioengineering
|April 27, 2011
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Summary

Machine learning-guided experiments significantly improved cell-free protein synthesis yield by 350%. This evolutionary design of experiments approach identified novel conditions and revealed distinct kinetic behaviors for innovation.

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Escherichia coli-Based Cell-Free Protein Synthesis: Protocols for a robust, flexible, and accessible platform technology
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Published on: February 25, 2019

Area of Science:

  • Biotechnology
  • Systems Biology
  • Machine Learning

Background:

  • Biological metabolic pathways are complex and difficult to predict.
  • Protein synthesis is a key example of such a complex pathway.

Purpose of the Study:

  • To enhance cell-free protein synthesis (CFPS) efficiency.
  • To apply machine learning and evolutionary design of experiments (Evo-DoE) for optimizing CFPS.

Main Methods:

  • Iterative high-throughput experiments guided by a machine learning algorithm.
  • Evo-DoE utilizing statistical models and stochastic exploration.
  • Defining target product yield as the measure of evolutionary fitness.

Main Results:

  • Achieved approximately 350% greater yield compared to standard conditions.
  • Discovered new experimental conditions that significantly boost protein production.
  • Identified two distinct classes of kinetics under optimal conditions.

Conclusions:

  • Evo-DoE is effective for optimizing complex biological systems like CFPS.
  • The method enables both gradual improvement and significant innovation in experimental design.
  • The discovered kinetics provide new insights into protein synthesis pathways.