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Energy Aware Technology Mapping of Genetic Logic Circuits.

Erik Kubaczka1, Maximilian Gehri1, Jérémie J M Marlhens1,2

  • 1Department of Electrical Engineering and Information Technology, TU Darmstadt, Darmstadt, 64283, Germany.

Biorxiv : the Preprint Server for Biology
|October 10, 2024
PubMed
Summary
This summary is machine-generated.

We developed Energy Aware Technology Mapping to design genetic logic circuits that are both functional and energy-efficient. This approach optimizes circuits for reduced energy consumption, improving efficiency by an average of 37.2%.

Keywords:
Computer Aided DesignEnergyEntropy Production RateGene-ExpressionGenetic Design AutomationMetabolic BurdenNon-EquilibriumSynthetic BiologyTechnology MappingThermodynamics

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Area of Science:

  • Synthetic Biology
  • Systems Biology
  • Bioengineering

Background:

  • Energy dissipation is crucial for all living systems, including cellular functions.
  • Genetic circuits can impose an energy burden, impairing cell survival and circuit functionality.
  • Existing genetic design automation (GDA) software lacks non-equilibrium models for energy dissipation and response curves.

Purpose of the Study:

  • Introduce Energy Aware Technology Mapping (EATM) for automated genetic logic circuit design.
  • Optimize circuits for both energy efficiency and functional performance.
  • Incorporate non-equilibrium thermodynamics into GDA software.

Main Methods:

  • Developed an energy-aware non-equilibrium steady state (NESS) model for gene expression.
  • Modeled energy dissipation (linked to entropy production rate) and transcriptional bursting.
  • Evaluated EATM against functionally optimized circuits using benchmark genetic logic circuits.

Main Results:

  • Functional performance and energy efficiency are often disjoint optimization goals for genetic circuits.
  • EATM improved energy efficiency by an average of 37.2% compared to functionally optimized variants.
  • EATM enables designing circuits with the energy efficiency of circuits that are one to two gates smaller, with structural variants offering further improvements.

Conclusions:

  • Energy Aware Technology Mapping enables energy efficiency by design in genetic logic circuits.
  • This approach extends current GDA tools and complements methods for managing cellular burden *in vivo*.
  • Incorporating energy demand into the design process is key for developing robust and efficient synthetic biological systems.