title: A differential equation model for functional mapping of a virus-cell dynamic system.
abstract: The dynamic pattern of viral load in a patient's body critically depends on the host's genes. For this reason, the identification of those genes responsible for virus dynamics, although difficult, is of fundamental importance to design an optimal drug therapy based on patients' genetic makeup. Here, we present a differential equation (DE) model for characterizing specific genes or quantitative trait loci (QTLs) that affect viral load trajectories within the framework of a dynamic system. The model is formulated with the principle of functional mapping, originally derived to map dynamic QTLs, and implemented with a Markov chain process. The DE-integrated model enhances the mathematical robustness of functional mapping, its quantitative prediction about the temporal pattern of genetic expression, and therefore its practical utilization and effectiveness for gene discovery in clinical settings. The model was used to analyze simulated data for viral dynamics, aimed to investigate its statistical properties and validate its usefulness. With an increasing availability of genetic polymorphic data, the model will have great implications for probing the molecular genetic mechanism of virus dynamics and disease progression.
authors.affiliation: Department of Mathematics, University of Florida, Gainesville, FL 32611, USA.
authors.forename: Jiangtao
authors.initials: J
authors.lastname: Luo
authors.forename: William W
authors.initials: WW
authors.lastname: Hager
authors.forename: Rongling
authors.initials: R
authors.lastname: Wu
mesh_terms: D002874:Chromosome Mapping; D003198:Computer Simulation / Q000706:statistics & numerical data; D006801:Humans; D008957:Models, Genetic; D015233:Models, Statistical; D040641:Quantitative Trait Loci; D019562:Viral Load / Q000235:genetics*; D014777:Virus Diseases / Q000235:genetics*
references.citation: Stat Appl Genet Mol Biol. 2009;8:Article 18
references.pmid: 19341352
references.citation: Nat Rev Genet. 2006 Mar;7(3):229-37
references.pmid: 16485021
references.citation: Nature. 1995 Jan 12;373(6510):123-6
references.pmid: 7816094
references.citation: Genetics. 1989 Jan;121(1):185-99
references.pmid: 2563713
references.citation: Genetics. 2004 Dec;168(4):2383-94
references.pmid: 15371347
references.citation: Genetics. 2002 Aug;161(4):1751-62
references.pmid: 12196415
references.citation: Genetics. 1994 Nov;138(3):963-71
references.pmid: 7851788
references.citation: Proc Natl Acad Sci U S A. 2008 Mar 25;105(12):4832-7
references.pmid: 18362342
references.citation: Genetics. 2002 Feb;160(2):779-92
references.pmid: 11861578
references.citation: Genetics. 2004 Mar;166(3):1541-51
references.pmid: 15082567
references.citation: Proc Natl Acad Sci U S A. 1997 Jun 24;94(13):6971-6
references.pmid: 9192676
references.citation: Biometrics. 2004 Sep;60(3):729-38
references.pmid: 15339296
references.citation: Nature. 2003 Dec 18;426(6968):789-96
references.pmid: 14685227
references.citation: Stat Med. 2006 Feb 15;25(3):495-511
references.pmid: 16013038
references.citation: Stat Med. 2006 Nov 30;25(22):3826-49
references.pmid: 16435340
references.citation: Proc Natl Acad Sci U S A. 2000 Jul 18;97(15):8193-5
references.pmid: 10899988
references.citation: Stat Appl Genet Mol Biol. 2005;4:Article33
references.pmid: 16646852
references.citation: Proc Natl Acad Sci U S A. 2000 Jul 5;97(14):7681-6
references.pmid: 10884399
references.citation: Nature. 1997 May 8;387(6629):188-91
references.pmid: 9144290
references.citation: Science. 1996 Mar 15;271(5255):1582-6
references.pmid: 8599114
references.citation: J Virol. 1997 Apr;71(4):3275-8
references.pmid: 9060694
references.citation: Stat Med. 2004 Oct 15;23(19):3033-51
references.pmid: 15351959