Optimizing information flow in small genetic networks Export

@article{citeulike:5861927, abstract = {In order to survive, reproduce, and (in multicellular organisms) differentiate, cells must control the concentrations of the myriad different proteins that are encoded in the genome. The precision of this control is limited by the inevitable randomness of individual molecular events. Here we explore how cells can maximize their control power in the presence of these physical limits; formally, we solve the theoretical problem of maximizing the information transferred from inputs to outputs when the number of available molecules is held fixed. We start with the simplest version of the problem, in which a single transcription factor protein controls the readout of one or more genes by binding to DNA. We further simplify by assuming that this regulatory network operates in steady state, that the noise is small relative to the available dynamic range, and that the target genes do not interact. Even in this simple limit, we find a surprisingly rich set of optimal solutions. Importantly, for each locally optimal regulatory network, all parameters are determined once the physical constraints on the number of available molecules are specified. Although we are solving an oversimplified version of the problem facing real cells, we see parallels between the structure of these optimal solutions and the behavior of actual genetic regulatory networks. Subsequent papers will discuss more complete versions of the problem.}, author = {Tka\v{c}ik, Ga\v{s}per and Walczak, Aleksandra M. and Bialek, William}, citeulike-article-id = {5861927}, citeulike-linkout-0 = {http://scitation.aip.org/getabs/servlet/GetabsServlet?prog=normal&id=PLEEE8000080000003031920000001&idtype=cvips&gifs=yes}, citeulike-linkout-1 = {http://link.aps.org/abstract/PRE/v80/e031920}, citeulike-linkout-2 = {http://dx.doi.org/10.1103/PhysRevE.80.031920}, doi = {10.1103/PhysRevE.80.031920}, journal = {Physical Review E (Statistical, Nonlinear, and Soft Matter Physics)}, keywords = {gene\_expression, gene\_network, information, noise, optimization}, number = {3}, pages = {031920+}, posted-at = {2009-09-30 22:00:57}, priority = {2}, publisher = {APS}, title = {Optimizing information flow in small genetic networks}, url = {http://dx.doi.org/10.1103/PhysRevE.80.031920}, volume = {80}, year = {2009} }

TY - JOUR ID - citeulike:5861927 L3 - citeulike-article-id:5861927 N2 - In order to survive, reproduce, and (in multicellular organisms) differentiate, cells must control the concentrations of the myriad different proteins that are encoded in the genome. The precision of this control is limited by the inevitable randomness of individual molecular events. Here we explore how cells can maximize their control power in the presence of these physical limits; formally, we solve the theoretical problem of maximizing the information transferred from inputs to outputs when the number of available molecules is held fixed. We start with the simplest version of the problem, in which a single transcription factor protein controls the readout of one or more genes by binding to DNA. We further simplify by assuming that this regulatory network operates in steady state, that the noise is small relative to the available dynamic range, and that the target genes do not interact. Even in this simple limit, we find a surprisingly rich set of optimal solutions. Importantly, for each locally optimal regulatory network, all parameters are determined once the physical constraints on the number of available molecules are specified. Although we are solving an oversimplified version of the problem facing real cells, we see parallels between the structure of these optimal solutions and the behavior of actual genetic regulatory networks. Subsequent papers will discuss more complete versions of the problem. IS - 3 JF - Physical Review E (Statistical, Nonlinear, and Soft Matter Physics) TI - Optimizing information flow in small genetic networks PB - APS VL - 80 SP - 031920 KW - gene_expression KW - gene_network KW - information KW - noise KW - optimization AU - Tkačik, Gašper AU - Walczak, Aleksandra AU - Bialek, William PY - 2009/// UR - http://dx.doi.org/10.1103/PhysRevE.80.031920 ER -