Quantum-Inspired Models for Cellular Signaling Networks and Energy Transfer Efficiency in Biological Systems

Abstract

Understanding how living cells move information and energy so quickly and efficiently is a key question in modern biophysics. In this project, I use quantum-inspired computational models to study cellular signaling networks and energy transfer pathways. The main idea is to treat signaling molecules like “walkers” on a graph and compare how signals spread under classical rules versus quantum-inspired rules.

Using Python, I built simple graph-based simulations of signaling and energy transfer. In the quantum-inspired version, nodes can be in superposed states and transitions are probabilistic, similar to quantum walks. I then compared how fast and how reliably signals reach target nodes in the two cases. The results suggest that quantum-inspired dynamics can support faster and more robust signal propagation, echoing coherence effects reported in photosynthetic complexes.

I also reviewed recent work in quantum biology, biophotonics, and molecular computation to relate the simulations to real biological systems. Finally, I briefly reflect on ethical and societal questions that arise when quantum ideas are applied to biology and cognition. Overall, the project shows how quantum-inspired models can deepen our understanding of biological communication and open new directions in biomedical engineering and computational science.