Reliable mechanistic operator recovery with biologically-informed neural networks: principles for architecture and optimisation design
arXiv:2607.07425v1 Announce Type: cross Abstract: Many biological processes are governed by complex dynamical mechanisms that remain incompletely understood despite increasing volumes of experimental data. Biologically-informed neural networks (BINNs) seek to address this challenge by embedding mechanistic differential equations into neural network training, enabling interpretable constitutive operators to be recovered directly from sparse and noisy observations. However, reliable operator recovery depends sensitively on network architecture, optimisation strategy, and data informativeness. He
The proliferation of experimental biological data combined with advancements in neural network capabilities provides the impetus for developing more sophisticated tools like Biologically-Informed Neural Networks (BINNs).
This research outlines principles for designing BINNs to reliably recover mechanistic operators, addressing a core challenge in understanding complex biological processes which is foundational for drug discovery and synthetic biology.
The ability to reliably extract interpretable constitutive operators from noisy biological data using AI will accelerate drug development, materials science, and our fundamental understanding of life.
- · Synthetic Biology
- · Pharmaceutical Industry
- · Biotech Startups
- · AI Researchers
- · Traditional high-throughput screening methods
- · Less efficient biological modeling techniques
BINNs will enable faster and more accurate identification of mechanisms underlying biological diseases and processes.
This improved understanding will lead to the development of novel therapeutics, diagnostic tools, and programmable biological systems.
The integration of reliable mechanistic AI with biological data could fundamentally transform the pace of scientific discovery and engineering in life sciences.
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Read at arXiv cs.LG