Probing coexistence of robust threshold and ultrasensitivity in molecular switches and cascades

Molecular switches are fundamental building blocks in many biophysical processes. These switches arise from dissimilar molecular interactions, affected by intrinsic and extrinsic noise, but exhibit a common ON-OFF behavior that suffices for signal transduction, amplification, and relay. Repertoires of different mechanisms pose a fundamental question: which mechanisms in a noisy environment confer ultrasensitivity while also ensuring robust decoding of the concentration threshold required for information interpretation? In this paper, we design molecular switches based on molecular-exchange mechanisms (MEM) and dimeric ligand formation, comprising reversible interactions, and compare them with other switch-forming mechanisms. Our results reveal that either the MEM or dimer mechanism, or both, forms a more robust threshold that information theoretically appears more predictable. This performance enhancement is reflected in their switch-like behavior, making them less prone to flipping states in noisy environments. Extending beyond single-switch comparisons, we also explore alternative switch cascades and assess their performance against noise. Alongside this, we design a Physics-Informed Neural Network (PINN) that predicts switching behavior for variation in input signal and is amenable so that any agentic-AI driven design adheres to the biological and physical constraints indispensable in a molecular switch. Insights from this study may be helpful in drug design and synthetic circuits aimed at therapeutics and other aqueous applications.