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Efficient Modeling and Simulation of Space-Dependent Biological Systems.

Elise Rosati1, Morgan Madec1, Jean-Baptiste Kammerer1

  • 11 Laboratoire des Sciences pour l'Ingénieur, de l'Informatique et de l'Imagerie (ICube), UMR 7357 (Université de Strasbourg/CNRS), 300 bd Sébastien Brandt, 67412 ILLKIRCH, France.

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Summary

This study introduces a novel hardware description language (HDL) approach for simulating molecular diffusion in biological systems. The new method effectively models space-dependent phenomena, bridging biology and electronics.

Keywords:
SPICEcompact modelingmesherspace-and-time modelingsystems and synthetic biology

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Area of Science:

  • Computational Biology
  • Biophysics
  • Microelectronics Engineering

Background:

  • Hardware description languages (HDLs) are traditionally used for microelectronics simulation.
  • Existing HDLs are unsuitable for space-dependent biological models described by partial differential equations.
  • Space- and time-dependent models are increasingly crucial in biological applications.

Purpose of the Study:

  • To develop a new modeling approach for simulating molecular diffusion on a mesoscopic scale using HDLs.
  • To adapt existing electrothermal simulation tools for biological and thermodynamic analogies.
  • To create a transdisciplinary simulation tool integrating biology with other physics domains.

Main Methods:

  • Utilized a mesher to divide space into adaptable-sized parallelepipeds (or rectangles in 2D).
  • Developed interconnected biological models integrated within a SPICE simulator.
  • Employed Python scripts for interfacing the mesher, biological models, and SPICE simulator.

Main Results:

  • Validated simulation results against analytical solutions for simple cases.
  • Compared simulation outcomes with experimental data from existing literature.
  • Demonstrated the tool's capability to simulate molecular diffusion on a mesoscopic scale.

Conclusions:

  • The developed HDL-based simulator offers a direct interface between diffusion and biological models.
  • Leverages a powerful SPICE simulation core for robust performance.
  • Enables the study of transdisciplinary systems by interfacing biological models with other physics domains.