From Performance to Viability: A Bootstrap Framework for Latent-Space Representation Learning in Adaptive Biological Systems
arXiv:2606.01374v1 Announce Type: new Abstract: Observable performance is commonly used to characterize biological systems. In adaptive systems, however, similar performances may arise from distinct organizations, and configurations that appear comparable at a given time may follow different longitudinal trajectories. This limitation motivates a methodological framework for moving beyond performance-based interpretation without assuming a complete mechanistic model in advance. This article proposes a bootstrap framework for latent-space representation learning in adaptive biological systems. H
The paper contributes to ongoing research at the intersection of AI/ML and biological systems, driven by advancements in both fields that allow for more sophisticated modeling.
This framework offers a new approach to understanding complex adaptive biological systems by focusing on latent-space representation, potentially enabling better prediction and intervention without needing full mechanistic models.
The focus shifts from merely observable performance to deeper, latent organizational principles, offering a more robust understanding of biological system dynamics.
- · AI/ML researchers in biology
- · Synthetic biology companies
- · Drug discovery and development
- · Biotechnology sector
- · Purely performance-based biological modeling approaches
Improved understanding and predictive power for complex biological systems, such as disease progression or ecosystem dynamics.
Acceleration of synthetic biology design cycles and therapeutic development through better modeling of biological adaptability.
Potential for new AI-driven platforms that can 'learn' the underlying rules of complex biological systems for autonomous engineering.
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Read at arXiv cs.LG