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 .
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. 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.
Lester will investigate how to model and test these noisy biological systems. One method is to use the Caldeira Leggett model of open quantum systems using path integrals to trace out the environment  and this is the subject of some of our projects. An alternative approach would be to use the stochastic Schrödinger equations to build up a quantum master equation , the stochastic Schrodinger equation is the quantum equivalent to the Langevin equation and has been used to reconstruct quantum Brownian motion.
Once Lester has constructed a model for noisy open quantum systems the task is then to apply these approaches to biological systems and experimentally verify the models. This could be in regards to noise-assisted transport in light harvesting complexes, fluctuating potentials in DNA mutation or many other areas of interest in quantum biology.
Noise, defined as the standard deviation normalised to the mean of any set of observations, is a fundamental challenge in quantitative models of molecular biology. Michael will focus on characterising noise in a model organism and connecting observed noise to a novel cause of noise, post-transcriptional regulation. The 3’ untranslated region (UTR) of mRNA transcripts has been highlighted in previous work as a possible region for translational auto-regulation by corresponding RNA-binding proteins. He will generate next-generation mNeonGreen-tagged strains in the model organism Saccharomyces cerevisiae. The strains will improve on previous strains by preserving the endogenous 3’ UTRs while allowing sensitive quantification of autoregulatory RNA-binding proteins. Quantification will be done using high-throughput flow cytometry, to provide powerful and reliable results. This basic research will give insights into the central dogma of biology and constitutes a missing link in modern models of gene expression for biomedical research and synthetic biosyntheses which focus on transcriptional noise but not yet translation.
 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.
 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.
 Strunz WT, Yu T. Convolutionless non-Markovian master equations and quantum trajectories: Brownian motion. Physical Review A. 2004 May 24;69(5):052115.
 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.
 Elowitz MB, Levine AJ, Siggia ED, Swain PS. Stochastic gene expression in a single cell. Science. 2002 Aug 16;297(5584):1183–6.