title: Testing the limits of gradient sensing.
abstract: The ability to detect a chemical gradient is fundamental to many cellular processes. In multicellular organisms gradient sensing plays an important role in many physiological processes such as wound healing and development. Unicellular organisms use gradient sensing to move (chemotaxis) or grow (chemotropism) towards a favorable environment. Some cells are capable of detecting extremely shallow gradients, even in the presence of significant molecular-level noise. For example, yeast have been reported to detect pheromone gradients as shallow as 0.1 nM/μm. Noise reduction mechanisms, such as time-averaging and the internalization of pheromone molecules, have been proposed to explain how yeast cells filter fluctuations and detect shallow gradients. Here, we use a Particle-Based Reaction-Diffusion model of ligand-receptor dynamics to test the effectiveness of these mechanisms and to determine the limits of gradient sensing. In particular, we develop novel simulation methods for establishing chemical gradients that not only allow us to study gradient sensing under steady-state conditions, but also take into account transient effects as the gradient forms. Based on reported measurements of reaction rates, our results indicate neither time-averaging nor receptor endocytosis significantly improves the cell's accuracy in detecting gradients over time scales associated with the initiation of polarized growth. Additionally, our results demonstrate the physical barrier of the cell membrane sharpens chemical gradients across the cell. While our studies are motivated by the mating response of yeast, we believe our results and simulation methods will find applications in many different contexts.
authors.affiliation: Curriculum in Bioinformatics and Computational Biology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America.|Battelle Center for Mathematical Medicine, Research Institute at Nationwide Children's Hospital, Columbus, Ohio, United States of America.
authors.forename: Vinal
authors.initials: V
authors.lastname: Lakhani
authors.affiliation: Curriculum in Bioinformatics and Computational Biology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America.|Department of Pharmacology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America.
authors.forename: Timothy C
authors.identifier: 0000-0002-4601-2117
authors.initials: TC
authors.lastname: Elston
mesh_terms: D002462:Cell Membrane / Q000737:chemistry / Q000378:metabolism*; D002633:Chemotaxis / Q000187:drug effects / Q000502:physiology*; D003198:Computer Simulation; D004058:Diffusion; D008954:Models, Biological; D008956:Models, Chemical; D015233:Models, Statistical; D010675:Pheromones / Q000737:chemistry / Q000493:pharmacokinetics*; D043563:Receptors, Pheromone / Q000737:chemistry / Q000378:metabolism*; D012441:Saccharomyces cerevisiae / Q000737:chemistry / Q000502:physiology*
chemical_list: D010675:Pheromones; D043563:Receptors, Pheromone