Noisy information processing through transcriptional regulation.

@article{citeulike:1248417, abstract = {Cells must respond to environmental changes to remain viable, yet the information they receive is often noisy. Through a biochemical implementation of Bayes's rule, we show that genetic networks can act as inference modules, inferring from intracellular conditions the likely state of the extracellular environment and regulating gene expression appropriately. By considering a two-state environment, either poor or rich in nutrients, we show that promoter occupancy is proportional to the (posterior) probability of the high nutrient state given current intracellular information. We demonstrate that single-gene networks inferring and responding to a high environmental state infer best when negatively controlled, and those inferring and responding to a low environmental state infer best when positively controlled. Our interpretation is supported by experimental data from the lac operon and should provide a basis for both understanding more complex cellular decision-making and designing synthetic inference circuits.}, address = {Centre for Nonlinear Dynamics, Department of Physiology, McGill University, 3655 Promenade Sir William Osler, Montreal, QC, Canada H3G 1Y6.}, author = {Libby, Eric and Perkins, Theodore J J. and Swain, Peter S S. }, citeulike-article-id = {1248417}, doi = {http://dx.doi.org/10.1073/pnas.0608963104}, issn = {0027-8424}, journal = {Proc Natl Acad Sci U S A}, keywords = {fluctuations, information-theory, noise, stochasticity, theory}, month = {April}, posted-at = {2007-05-01 18:28:56}, priority = {2}, title = {Noisy information processing through transcriptional regulation.}, url = {http://dx.doi.org/10.1073/pnas.0608963104}, year = {2007} }

TY - JOUR ID - citeulike:1248417 L3 - citeulike-article-id:1248417 N2 - Cells must respond to environmental changes to remain viable, yet the information they receive is often noisy. Through a biochemical implementation of Bayes's rule, we show that genetic networks can act as inference modules, inferring from intracellular conditions the likely state of the extracellular environment and regulating gene expression appropriately. By considering a two-state environment, either poor or rich in nutrients, we show that promoter occupancy is proportional to the (posterior) probability of the high nutrient state given current intracellular information. We demonstrate that single-gene networks inferring and responding to a high environmental state infer best when negatively controlled, and those inferring and responding to a low environmental state infer best when positively controlled. Our interpretation is supported by experimental data from the lac operon and should provide a basis for both understanding more complex cellular decision-making and designing synthetic inference circuits. JF - Proc Natl Acad Sci U S A SN - 0027-8424 AD - Centre for Nonlinear Dynamics, Department of Physiology, McGill University, 3655 Promenade Sir William Osler, Montreal, QC, Canada H3G 1Y6. TI - Noisy information processing through transcriptional regulation. KW - fluctuations KW - information-theory KW - noise KW - stochasticity KW - theory AU - Libby, Eric AU - Perkins, Theodore J AU - Swain, Peter S PY - 2007/04/09/ UR - http://dx.doi.org/10.1073/pnas.0608963104 ER -