title: Trade-offs and constraints in allosteric sensing.
abstract: Sensing extracellular changes initiates signal transduction and is the first stage of cellular decision-making. Yet relatively little is known about why one form of sensing biochemistry has been selected over another. To gain insight into this question, we studied the sensing characteristics of one of the biochemically simplest of sensors: the allosteric transcription factor. Such proteins, common in microbes, directly transduce the detection of a sensed molecule to changes in gene regulation. Using the Monod-Wyman-Changeux model, we determined six sensing characteristics--the dynamic range, the Hill number, the intrinsic noise, the information transfer capacity, the static gain, and the mean response time--as a function of the biochemical parameters of individual sensors and of the number of sensors. We found that specifying one characteristic strongly constrains others. For example, a high dynamic range implies a high Hill number and a high capacity, and vice versa. Perhaps surprisingly, these constraints are so strong that most of the space of characteristics is inaccessible given biophysically plausible ranges of parameter values. Within our approximations, we can calculate the probability distribution of the numbers of input molecules that maximizes information transfer and show that a population of one hundred allosteric transcription factors can in principle distinguish between more than four bands of input concentrations. Our results imply that allosteric sensors are unlikely to have been selected for high performance in one sensing characteristic but for a compromise in the performance of many.
authors.affiliation: Centre for Systems Biology at Edinburgh, The University of Edinburgh, Edinburgh UK. bruno.martins@ed.ac.uk
authors.forename: Bruno M C
authors.initials: BM
authors.lastname: Martins
authors.forename: Peter S
authors.initials: PS
authors.lastname: Swain
mesh_terms: D000494:Allosteric Regulation; D018407:Bacterial Physiological Phenomena; D005786:Gene Expression Regulation; D008954:Models, Biological; D015398:Signal Transduction; D049490:Systems Biology / Q000379:methods*; D014157:Transcription Factors / Q000502:physiology
chemical_list: D014157:Transcription Factors
references.citation: Nat Rev Mol Cell Biol. 2002 Sep;3(9):639-50
references.pmid: 12209124
references.citation: Nucleic Acids Res. 2002 Jan 1;30(1):56-8
references.pmid: 11752253
references.citation: Proc Natl Acad Sci U S A. 2006 Apr 11;103(15):5735-40
references.pmid: 16571657
references.citation: Biophys J. 2000 Dec;79(6):2801-17
references.pmid: 11106590
references.citation: Phys Rev E Stat Nonlin Soft Matter Phys. 2008 Jul;78(1 Pt 1):011910
references.pmid: 18763985
references.citation: Trends Pharmacol Sci. 1995 Mar;16(3):89-97
references.pmid: 7540781
references.citation: Science. 2005 Mar 25;307(5717):1965-9
references.pmid: 15790857
references.citation: Mol Syst Biol. 2008;4:196
references.pmid: 18463620
references.citation: J Mol Biol. 1965 May;12:88-118
references.pmid: 14343300
references.citation: Curr Opin Cell Biol. 2002 Apr;14(2):140-8
references.pmid: 11891111
references.citation: Mol Syst Biol. 2008;4:221
references.pmid: 18854820
references.citation: Mol Syst Biol. 2009;5:326
references.pmid: 19920811
references.citation: Cell. 2010 Jun 25;141(7):1117-34
references.pmid: 20602996
references.citation: Proc Natl Acad Sci U S A. 2001 Jul 17;98(15):8614-9
references.pmid: 11438714
references.citation: BMC Syst Biol. 2007 Aug 03;1:36
references.pmid: 17683566
references.citation: PLoS Comput Biol. 2006 Jul 28;2(7):e59
references.pmid: 16863396
references.citation: Science. 2002 Feb 1;295(5556):813-8
references.pmid: 11823631
references.citation: J Mol Biol. 1984 Jul 5;176(3):349-67
references.pmid: 6748079
references.citation: PLoS Comput Biol. 2009 Dec;5(12):e1000620
references.pmid: 20041206
references.citation: BMC Syst Biol. 2009 Feb 24;3:27
references.pmid: 19239699
references.citation: Science. 2002 Mar 1;295(5560):1664-9
references.pmid: 11872830
references.citation: Biochemistry. 1966 Jan;5(1):365-85
references.pmid: 5938952
references.citation: Phys Rev Lett. 2009 May 29;102(21):218101
references.pmid: 19519137
references.citation: Science. 2005 Mar 25;307(5717):1962-5
references.pmid: 15790856
references.citation: Annu Rev Genet. 2007;41:121-45
references.pmid: 18076326
references.citation: Nat Rev Genet. 2004 Jan;5(1):34-42
references.pmid: 14708014
references.citation: Proc Natl Acad Sci U S A. 1998 Jul 21;95(15):8420-7
references.pmid: 9671692
references.citation: Trends Biochem Sci. 1997 Aug;22(8):288-9
references.pmid: 9270299
references.citation: Proc Natl Acad Sci U S A. 2005 Mar 8;102(10):3581-6
references.pmid: 15738412
references.citation: Proc Natl Acad Sci U S A. 2002 Oct 1;99(20):12795-800
references.pmid: 12237400
references.citation: Proc Natl Acad Sci U S A. 2009 Apr 21;106(16):6435-40
references.pmid: 19279208