This thesis explores the interplay between quantum physics and biological organization across two frontiers: Quantum Biology, where genuine quantum mechanical processes influence macroscopic life, and Quantum-Like systems, where classical systems exhibit mathematical structures formally resembling quantum mechanics. By investigating both domains, I aim to illuminate how quantum principles manifest both directly in biological function and indirectly through emergent organizational patterns. I begin by deriving a generalized Hamiltonian for the Radical Pair Mechanism underlying avian magnetoreception, capturing how hyperfine interactions and Zeeman interactions with the Earth’s magnetic field modulate singlet-triplet interconversion in cryptochrome proteins. Analytical and numerical treatments demonstrate how coherent spin dynamics can influence global navigation behavior, providing a tractable model for quantum biological sensing. Then, I traverse scales and investigate the emergence of Quantum-Like (QL) structures within the human brain. Drawing inspiration from modern whole-brain modeling techniques, particularly Connectome Harmonic Decomposition, I map real neurophysiological data onto QL graphs, constructing a framework where physical brain dynamics are represented by robust, scalable QL state spaces. Together, these investigations suggest that quantum mechanics may not only shape specialized biological functions, but also that classical systems like the brain can mirror quantum architectures, hinting at deeper symmetries between the fundamental and living worlds.

A central challenge in biophysics is to find the general principles underlying universal biological structures. Here, we focus on genetic networks, fundamental regulatory units that control gene expression. Inherent in the notion of control is the idea that genetic networks transmit information, and, in some limits, this can be quantified using information theory. A large body of theoretical and experimental work suggests that some genetic networks are tuned close to optimality, such that they transmit the maximum possible amount of information given a set of physical constraints and irreducible sources of noise. Following this principle, here we optimize information transmission in a class of idealized genetic networks. We explore the optimal network structures and derive a scaling relation for the information transmission in terms of the relevant biological parameters. Counterintuitively, this scaling suggests that the optimal strategy is to deploy a large number of noisy channels. Additionally, we find that the system should have a preponderance of weak binding interactions. Next, we show that the optimal solutions are surprisingly stable to a wide range of biologically relevant perturbations. Finally, we connect the predictions of the model to experimental data. Our model represents a step towards understanding the structure and reliability of large genetic networks.