title: The fidelity of dynamic signaling by noisy biomolecular networks.
abstract: Cells live in changing, dynamic environments. To understand cellular decision-making, we must therefore understand how fluctuating inputs are processed by noisy biomolecular networks. Here we present a general methodology for analyzing the fidelity with which different statistics of a fluctuating input are represented, or encoded, in the output of a signaling system over time. We identify two orthogonal sources of error that corrupt perfect representation of the signal: dynamical error, which occurs when the network responds on average to other features of the input trajectory as well as to the signal of interest, and mechanistic error, which occurs because biochemical reactions comprising the signaling mechanism are stochastic. Trade-offs between these two errors can determine the system's fidelity. By developing mathematical approaches to derive dynamics conditional on input trajectories we can show, for example, that increased biochemical noise (mechanistic error) can improve fidelity and that both negative and positive feedback degrade fidelity, for standard models of genetic autoregulation. For a group of cells, the fidelity of the collective output exceeds that of an individual cell and negative feedback then typically becomes beneficial. We can also predict the dynamic signal for which a given system has highest fidelity and, conversely, how to modify the network design to maximize fidelity for a given dynamic signal. Our approach is general, has applications to both systems and synthetic biology, and will help underpin studies of cellular behavior in natural, dynamic environments.
authors.affiliation: School of Mathematics, University of Bristol, Bristol, United Kingdom. c.bowsher@bristol.ac.uk
authors.forename: Clive G
authors.initials: CG
authors.lastname: Bowsher
authors.forename: Margaritis
authors.initials: M
authors.lastname: Voliotis
authors.forename: Peter S
authors.initials: PS
authors.lastname: Swain
mesh_terms: D019295:Computational Biology; D025461:Feedback, Physiological / Q000502:physiology*; D015870:Gene Expression; D008954:Models, Biological; D015398:Signal Transduction / Q000502:physiology*
references.citation: Proc Natl Acad Sci U S A. 2011 Sep 13;108(37):15190-5
references.pmid: 21876136
references.citation: J R Soc Interface. 2011 Feb 6;8(55):186-200
references.pmid: 20685691
references.citation: Neural Comput. 2001 Nov;13(11):2409-63
references.pmid: 11674845
references.citation: Nucleic Acids Res. 2012 Aug;40(15):7084-95
references.pmid: 22581772
references.citation: Proc Natl Acad Sci U S A. 2011 Jul 19;108(29):12167-72
references.pmid: 21730172
references.citation: Mol Syst Biol. 2008;4:196
references.pmid: 18463620
references.citation: J Mol Biol. 2006 Jan 27;355(4):619-27
references.pmid: 16330045
references.citation: Phys Rev Lett. 2009 Dec 18;103(25):258101
references.pmid: 20366291
references.citation: Phys Rev E Stat Nonlin Soft Matter Phys. 2010 Sep;82(3 Pt 1):031914
references.pmid: 21230115
references.citation: Nat Rev Mol Cell Biol. 2009 Jun;10(6):410-22
references.pmid: 19461664
references.citation: Nature. 2010 Sep 9;467(7312):167-73
references.pmid: 20829787
references.citation: Cell. 2011 Mar 18;144(6):910-25
references.pmid: 21414483
references.citation: Phys Rev Lett. 2010 Jun 4;104(22):228104
references.pmid: 20867209
references.citation: J Mol Biol. 2002 Nov 8;323(5):785-93
references.pmid: 12417193
references.citation: Phys Rev E Stat Nonlin Soft Matter Phys. 2010 Jun;81(6 Pt 1):061917
references.pmid: 20866450
references.citation: Mol Syst Biol. 2009;5:326
references.pmid: 19920811
references.citation: Biophys J. 2000 Dec;79(6):2801-17
references.pmid: 11106590
references.citation: EMBO Rep. 2009 Aug;10(8):881-6
references.pmid: 19575012
references.citation: J Mol Biol. 2006 Jun 16;359(4):1107-24
references.pmid: 16701695
references.citation: Science. 2000 Aug 25;289(5483):1352-5
references.pmid: 10958782
references.citation: Science. 2011 Oct 21;334(6054):354-8
references.pmid: 21921160
references.citation: Phys Rev Lett. 2006 Aug 11;97(6):068102
references.pmid: 17026206
references.citation: Proc Natl Acad Sci U S A. 2001 Jul 17;98(15):8614-9
references.pmid: 11438714
references.citation: Science. 2002 Feb 1;295(5556):813-8
references.pmid: 11823631
references.citation: Ann Stat. 2010 Aug 1;38(4):2242-2281
references.pmid: 21278808
references.citation: Proc Natl Acad Sci U S A. 2008 May 20;105(20):7165-70
references.pmid: 18480263
references.citation: Proc Natl Acad Sci U S A. 2007 Apr 24;104(17):7151-6
references.pmid: 17420464
references.citation: Proc Natl Acad Sci U S A. 2008 Nov 11;105(45):17256-61
references.pmid: 18988743
references.citation: Phys Rev Lett. 2009 May 29;102(21):218101
references.pmid: 19519137
references.citation: Phys Rev Lett. 2011 Sep 30;107(14):148101
references.pmid: 22107236
references.citation: Proc Natl Acad Sci U S A. 2012 May 15;109(20):E1320-8
references.pmid: 22529351
references.citation: Proc Natl Acad Sci U S A. 2002 Oct 1;99(20):12795-800
references.pmid: 12237400