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The role of decoherence and noise in biological systems

Noise is an inherent part of science, often referring to random fluctuations affecting data. It is usually considered to be a hindrance to be overcome as efficiently as possible. However, by understanding the way noise manifests, we can use it to improve efficiency of systems - for example, by noise-assisted transport.


In quantum mechanics noise is usually ignored so that the system of interest is simpler and the equations associated with it are easier to solve. But quantum mechanics is still applicable even in noisy environments, where the system of interest is coupled to a heat bath, which we refer to as an open quantum system. This is vital when we start looking at biological systems which are warm and messy and therefore noisy. A question we are asking is, could biological systems be using their noisy environments to aid processes like photosynthesis. There is evidence to suggest that this may be the case [1].

In molecular biology, it is often observed that there is some variance in the expression of particular mRNA and respective proteins between individual isogenic cells in a homogenous population of cells. This is called noise, and is measured as the coefficient of variation (CV):

CV = σ/μ

There are two components of noise: extrinsic and intrinsic[5]. Extrinsic noise can be thought of as failures in ensuring homogeneity, the development of micro-environments, fluctuations of conditions, and other uncontrolled factors. Intrinsic noise, however, is caused by the inherent randomness inside cells. Stochasticity is the property of, for example, transcriptional gene expression. Either the gene for protein ‘A’ is converted into RNA or it isn’t. The system is either on or off. Cells rely on the semi-random (Brownian) movements of molecules to cause reactions and interactions, however, which means that the activation of gene ‘A’ can be modelled as a probability. The observation that important biological mechanisms are governed by rates set by probability is an intrinsic source of noise.

Research team

Jim Al-Khalili profile image

Professor Jim Al-Khalili

Distinguished Chair in Physics, Professor of Public Engagement in Science, Quantum Foundations and Technologies Research Group Leader

Marian Florescu profile image

Professor Marian Florescu

Professor of Physics

André Gerber profile image

Professor Andre Gerber

Professor of RNA Biology

Brendan Howlin profile image

Professor Brendan Howlin

Professor of Computational Chemistry

Ben Murdin profile image

Professor Benedict Murdin

Professor of Physics, Head of the Photonics and Quantum Sciences Group

Andrea Rocco profile image

Dr Andrea Rocco

Associate Professor (Reader) in Physics and Mathematical Biology

Marco Sacchi profile image

Dr Marco Sacchi

Associate Professor and Royal Society University Research Fellow in Physical and Computational Chemistry, Theme Leader in Sustainable Energy and Materials Research


[1] Caruso F, Chin AW, Datta A, Huelga SF, Plenio MB. Highly efficient energy excitation transfer in light-harvesting complexes: The fundamental role of noise-assisted transport. The Journal of Chemical Physics. 2009 Sep 14;131(10):09B612.

[2] Caldeira AO, Leggett AJ. Path integral approach to quantum Brownian motion. Physica A: Statistical mechanics and its Applications. 1983 Sep 1;121(3):587-616.

[3] Strunz WT, Yu T. Convolutionless non-Markovian master equations and quantum trajectories: Brownian motion. Physical Review A. 2004 May 24;69(5):052115.

[4] Mittal N, Scherrer T, Gerber AP, Janga SC. Interplay between posttranscriptional and posttranslational interactions of RNA-binding proteins. J Mol Biol. 2011;409(3):466–79.

[5] Elowitz MB, Levine AJ, Siggia ED, Swain PS. Stochastic gene expression in a single cell. Science. 2002 Aug 16;297(5584):1183–6.

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