Applying finely tuned minimal models to high-dimensional biological systems

Develop a principled methodology for applying finely tuned minimal models to study complex biological systems composed of numerous heterogeneous, interacting components, enabling these models to make reliable predictions and mechanistic inferences in high-dimensional contexts.

Background

The perspective highlights a long tradition of successful small-circuit models in biology (e.g., Hodgkin–Huxley, Lotka–Volterra, and network motifs) that provide tractable mechanistic insights. However, modern high-dimensional biological systems, such as cortical circuits, ecosystems, and cellular networks, involve thousands of interacting, heterogeneous components, making direct extension of finely tuned models challenging.

The authors propose the random-with-constraints paradigm as one path forward but explicitly note that it remains unclear how to use traditional finely tuned minimal models in such high-dimensional contexts. This open question concerns bridging classic low-dimensional mechanistic modeling with the realities of complex biological data and interactions.