2012/03/24 by Pankaj Mehta, David J. Schwab · 6 voices · 245 citations
Biochemistry, Genetics and Molecular Biology · Engineering · Mathematics · Physics and Astronomy · #Advanced Thermodynamics and Statistical Mechanics #Algorithm #Biological system #Biology #Cell biology #Cellular network #Computation #Computer science #Constraint (computer-aided design) #Ecology #Energy (signal processing) #Energy consumption #Function (biology) #Gene Regulatory Network Analysis #Mathematical optimization #Mathematics #Molecular Communication and Nanonetworks #Physics #Quantum mechanics #Simple (philosophy) #Statistical physics #Telecommunications #cond-mat.stat-mech #q-bio.MN
paper · pdf · doi:10.1073/pnas.1207814109
published in Proceedings of the National Academy of Sciences 109(44), 17978-17982 (National Academy of Sciences) · 9 Pages (including Appendix); 4 Figures; v3 corrects even more typos
arxiv created 2012/04/10 · openalex publication_date 2012/10/08 · arxiv updated 2015/06/04 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/06
Cells often perform computations in order to respond to environmental cues. A simple example is the classic problem, first considered by Berg and Purcell, of determining the concentration of a chemical ligand in the surrounding media. On general theoretical grounds, it is expected that such computations require cells to consume energy. In particular, Landauer's principle states that energy must be consumed in order to erase the memory of past observations. Here, we explicitly calculate the energetic cost of steady-state computation of ligand concentration for a simple two-component cellular network that implements a noisy version of the Berg-Purcell strategy. We show that learning about external concentrations necessitates the breaking of detailed balance and consumption of energy, with greater learning requiring more energy. Our calculations suggest that the energetic costs of cellular computation may be an important constraint on networks designed to function in resource poor environments, such as the spore germination networks of bacteria.