Organism Coding

I remember when Shannon first introduced to me the idea that states cannot overlap. In data transmission messages must stay distinct. Spheres in high dimensional space cannot touch (channel capacity formula). You work the same way. Molecular states driving reactions / decisions must be distinct for survival. He probably never would’ve dreamed his communications mathematics would be utilized like this.

Organisms encode information within molecules so they can thrive under the constraints of thermodynamics and noise. Coding principles help them transmit and process signals with high fidelity. This is especially clear in enzymes that recognize specific DNA sequences. They act like communication devices that must distinguish between valid and invalid messages. If these messages overlapped, the entire system would lose its efficiency and risk harmful errors.

Shannon’s channel capacity theorem offers a framework for understanding this process. In classical communication, we transmit signals through noisy channels. Biology faces a similar challenge at the molecular level where random thermal motions can disrupt signals. For example, EcoRI (a restriction enzyme) slides along DNA. It scans for the GAATTC sequence yet avoids sticking to slightly different sequences like TAATTC. The enzyme’s molecular states behave as points in a high-dimensional sphere. These states cannot intersect or the enzyme would bind indiscriminately leading to fatal DNA cuts.

Thermodynamics sets strict limits on how much energy can be used to maintain distinct molecular states. The second law dictates that noise will always exist. Shannon’s theorem then shows how many distinct signals can coexist before the system becomes confused. EcoRI and other molecular machines operate near 70% efficiency, which implies that evolution has optimized their capacity to manage noise. This is done by balancing energy dissipation and signal clarity so that error rates remain low enough for survival.

Evolution refines these coding strategies over many generations. Organisms that fail to maintain clear molecular signals cannot reliably pass on genetic material. As a result, nature promotes enzymes like EcoRI that can decode sequences with precision. This same principle applies widely in biology, from gene regulation to neural signaling. The key is preserving distinct molecular states. Although cells operate in chaotic environments, they manage to preserve codes that ensure accurate information flow.

Studying these coding principles may lead to technologies that mimic the way nature maintains signal fidelity. Molecular machines could be harnessed or designed to transmit data with minimal error and optimal energy use. Looking at molecular coding through the lens of channel capacity allows us to see how evolution and thermodynamics converge to create life’s insance efficiency.

Further Reading:

Julyan H. E. Cartwright, Jitka Čejková, Elena Fimmel, Simone Giannerini, Diego Luis Gonzalez, Greta Goracci, Clara Grácio, Jeanine Houwing-Duistermaat, Dragan Matić, Nataša Mišić, Frans A. A. Mulder, Oreste Piro; Information, Coding, and Biological Function: The Dynamics of Life. Artif Life 2024; 30 (1): 16–27. doi: https://doi.org/10.1162/artl_a_00432