Bayesian experts in exploring reaction kinetics of transcription circuits.
BACKGROUND
Biochemical reactions in cells are made of several types of biological circuits. In current systems biology, making differential equation (DE) models simulatable in silico has been an appealing, general approach to uncover a complex world of biochemical reaction dynamics. Despite of a need for simulation-aided studies, our research field has yet provided no clear answers: how to specify kinetic values in models that are difficult to measure from experimental/theoretical analyses on biochemical kinetics.


RESULTS
We present a novel non-parametric Bayesian approach to this problem. The key idea lies in the development of a Dirichlet process (DP) prior distribution, called Bayesian experts, which reflects substantive knowledge on reaction mechanisms inherent in given models and experimentally observable kinetic evidences to the subsequent parameter search. The DP prior identifies significant local regions of unknown parameter space before proceeding to the posterior analyses. This article reports that a Bayesian expert-inducing stochastic search can effectively explore unknown parameters of in silico transcription circuits such that solutions of DEs reproduce transcriptomic time course profiles.


BACKGROUND
A sample source code is available at the URL http://daweb.ism.ac.jp/~yoshidar/lisdas/.
Yoshida
Ryo
R
The Institute of Statistical Mathematics, Research Organization of Information and Systems, Tokyo, Japan. yoshidar@ism.ac.jp
Saito
Masaya M
MM
Nagao
Hiromichi
H
Higuchi
Tomoyuki
T
D000465:Algorithms; D001499:Bayes Theorem; D002940:Circadian Rhythm; D003198:Computer Simulation; D007700:Kinetics; D008957:Models, Genetic; D013269:Stochastic Processes; D014158:Transcription, Genetic
Genome Inform. 2006;17(1):46-61
17503355
Bioinformatics. 2008 Jul 15;24(14):1619-24
18505754
PLoS Biol. 2009 Mar 10;7(3):e52
19278294
Bioinformatics. 2003 Oct;19 Suppl 2:ii138-48
14534183
Nat Genet. 2005 Feb;37(2):187-92
15665827
Mol Biol Cell. 2004 Aug;15(8):3841-62
15169868
Bioinformatics. 2006 Jul 15;22(14):e271-80
16873482
Pac Symp Biocomput. 2009;:227-38
19209704
Bioinformatics. 2008 Dec 15;24(24):2901-7
18974172
Bioinformatics. 2003 Mar 22;19(5):643-50
12651723
Pac Symp Biocomput. 2000;:341-52
10902182
Bioinformatics. 2010 May 1;26(9):1239-45
20305266
IEEE/ACM Trans Comput Biol Bioinform. 2007 Jul-Sep;4(3):322-335
17666754
Cell. 2009 Apr 3;137(1):172-81
19327819
Bioinformatics. 2005 Apr 1;21(7):1154-63
15514004
Nature. 2002 Aug 1;418(6897):534-9
12152080
Bioinformatics. 2007 Dec 1;23(23):3209-16
18042557
Bioinformatics. 2008 Nov 15;24(22):2592-601
18818216