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Computational Synthetic Biology Enabled through JAX: A Showcase.

Olivia Gallup1, Kirill Sechkar1, Sebastian Towers1

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|September 4, 2024
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Summary

Mathematical modeling in synthetic biology is enhanced by the JAX library, enabling faster, scalable simulations on GPUs. This work democratizes advanced computational tools for broader biological research applications.

Keywords:
JAXcomputationalmachine learningmodelingsimulationsynthetic biology

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Area of Science:

  • Computational Biology
  • Synthetic Biology
  • Artificial Intelligence

Background:

  • Mathematical modeling is crucial but underutilized in synthetic biology.
  • Existing AI frameworks facilitate access to computational hardware.
  • JAX enables diverse computations beyond deep learning.

Purpose of the Study:

  • Demonstrate the utility of the JAX library in computational biology.
  • Showcase JAX's application in synthetic biology and directed evolution.
  • Promote wider adoption of JAX for faster, scalable biological simulations.

Main Methods:

  • Utilized the JAX library for computational tasks.
  • Developed three example projects in synthetic biology and directed evolution.
  • Provided accompanying Jupyter notebooks for reproducibility and learning.

Main Results:

  • Successfully applied JAX to diverse computational biology problems.
  • Achieved faster run-times and flexible scaling using GPUs.
  • Demonstrated ease of GPU portability and mathematical enhancements like automatic differentiation.

Conclusions:

  • JAX offers significant advantages for computational biology research.
  • The provided tutorials democratize the use of JAX for scalable simulations.
  • JAX facilitates larger-scale experiments and improved code usability in biology.