A Full Bayesian Approach for Boolean Genetic Network Inference

Abstract

Boolean networks are a simple but efficient model for describing gene regulatory systems. A number of algorithms have been proposed to infer Boolean networks. However, these methods do not take full consideration of the effects of noise and model uncertainty. In this paper, we propose a full Bayesian approach to infer Boolean genetic networks. Markov chain Monte Carlo algorithms are used to obtain the posterior samples of both the network structure and the related parameters. In addition to regular link addition and removal moves, which can guarantee the irreducibility of the Markov chain for traversing the whole network space, carefully constructed mixture proposals are used to improve the Markov chain Monte Carlo convergence. Both simulations and a real application on cell-cycle data show that our method is more powerful than existing methods for the inference of both the topology and logic relations of the Boolean network from observed data.

The Agropyron cristatum karyotype, chromosome structure and cross-genome homoeology as revealed by fluorescence in situ hybridization with tandem repeats and wheat single-gene probes

“…The inverted order of cDNA probes on the long arm of chromosome 6P when compared to their order on homoeologous wheat chromosomes (Danilova et al 2014 ) indicates the presence of a P genome-specific paracentric inversion. A similar observation on chromosome 6P from the tetraploid A. cristatum was made by Han et al ( 2014 ). This phenomenon of rearrangements can be found in the genomes of wild relatives of wheat (Kishii et al 2004 ; Said and Cabrera 2009 ; Wang et al 2010 ; Wang 2011 ; Hu et al 2012 ; McArthur et al 2012 ; Said et al 2012 ; Han et al 2014 ).…”

“…A similar observation on chromosome 6P from the tetraploid A. cristatum was made by Han et al ( 2014 ). This phenomenon of rearrangements can be found in the genomes of wild relatives of wheat (Kishii et al 2004 ; Said and Cabrera 2009 ; Wang et al 2010 ; Wang 2011 ; Hu et al 2012 ; McArthur et al 2012 ; Said et al 2012 ; Han et al 2014 ). Although our data showed chromosomal rearrangements in A. cristatum with respect to wheat, it seems that this does not hamper alien chromosome introgression into wheat.…”

“…Among wild relatives of cultivated Triticeae, the genus Agropyron is a promising donor of important traits for improvement of bread wheat ( Triticum aestivum L., 2n = 6x = 42, genome AABBDD). The most widely distributed species in the genus, Agropyron cristatum (L.) Gaertn (Yang et al 2014 ), known as crested wheatgrass, deserves particular attention because of its high crossability with wheat and other Triticeae and because it is a source of a number of genes controlling resistance to biotic and abiotic stress, such as resistance to barley yellow dwarf virus (Sharma et al 1984 ; Shukle et al 1987 ), wheat streak mosaic virus (Sharma et al 1984 ; Shukle et al 1987 ; Brettell et al 1988 ; Friebe et al 1991 ), yellow rust, leaf rust and stem rust (Knott 1964 , 1968 ; Cauderon and Rhind 1976 ), and powdery mildew (Wu et al 2006 ; Luan et al 2010 ; Han et al 2014 ; Ye et al 2015 ; Zhang et al 2015 ; Song et al 2016 ), as well as cold tolerance (Limin and Fowler 1990 ), salinity tolerance and drought tolerance (McGuire and Dvorak 1981 ), and genes affecting yield (Song et al 2013 ).…”

“…Only a few attempts were made to characterize the A. cristatum karyotype on the basis of chromosome morphology (Asghari et al 2007 ; Yang et al 2014 ). Han et al ( 2014 ) found that the A. cristatum chromosome 6P differs from wheat homoeologs by large rearrangements. This observation underlines the need for detailed analysis of the structures of all A. cristatum chromosomes.…”

Predicting Variabilities in Cardiac Gene Expression with a Boolean Network Incorporating Uncertainty

“…BNs are useful as a first approach when it comes to model complex networks with many genes and their interactions [ 7 ]. Often the BN is modeled from known regulatory interactions that are manually derived from qualitative wet-lab experiments [ 8 ] or computationally determined with BN reconstruction methods [ 9 , 10 ]. Additionally, simulated Boolean states of genes from the simulation allow an intuitive interpretation of the results.…”