One of the hallmark events regulating the process of osteogenesis is the transition of undifferentiated human mesenchymal stem cells (hMSCs) found in the bone marrow into mineralized-matrix producing osteoblasts (hOSTs) through mechanisms that are not entirely understood. With recent developments in mass spectrometry and its potential application to the systematic definition of the stem cell proteome, proteins that govern cell fate decisions can be identified and tracked during this differentiation process. We hypothesize that protein profiling of hMSCs and hOSTs will identify potential osteogenic marker proteins associated with hMSC commitment and hOST differentiation. To identify markers for each cell population, we analyzed the expression of hMSC proteins and compared them to that of hOST by two-dimensional gel electrophoresis and two-dimensional liquid chromatography tandem mass spectrometry (2D LC-MS/MS). The 2D LC-MS/MS data sets were analyzed using the Database for Annotation, Visualization and Integrated Discovery (DAVID). Only 34% of the spots in 2D gels were found in both cell populations; of those that differed between populations, 65% were unique to hOST cells. Of the 755 different proteins identified by 2D LCMS/ MS in both cell populations, two sets of 247 and 158 proteins were found only in hMSCs and hOST cells, respectively. Differential expression of some of the identified proteins was further confirmed by Western blot analyses. Substantial differences in clusters of proteins responsible for calcium- based signaling and cell adhesion were found between the two cell types. Osteogenic differentiation is accompanied by a substantial change in the overall protein expression profile of hMSCs. This study, using gene ontology analysis, reveals that these changes occur in clusters of functionally related proteins. These proteins may serve as markers for identifying stem cell differentiation into osteogenic fates because they promote differentiation by mechanisms that remain to be defined.

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To overcome the obstacle of the fascinating relation in predicting animal phenotype value, we have developed a neural network model to detect the complex non-linear relationships between the genotypes and phenotypes and the possible interactions that cannot be expressed with equations. In this paper, back-propagation neural network is used to discuss the influences of different allele frequencies on estimating the polygenic phenotype value. To ensure the precision of prediction, normalization was needed to train the prediction model. The results show that back-propagation artificial neural networks can be used to predict the phenotype value and perform very well in allele frequency from 0.2 to 0.8, when the allele frequency is very small (less than 0.2) or big (more than 0.8); however, the prediction model was not reliable and the predicted value should be carefully tested.

Recently, largescale, high-density single-nucleotide polymorphism (SNP) marker information has become available. However, the simple relation was not enough for describing the relation between markers and genotype value, and the genetic diversity should be carefully monitored as genomic selection for quantitative traits as a routine technology for animal genetic improvement. In this paper, back-propagation neural network is used to simulate and predict the genotype values, and the different gene effects were used to discuss the influences on estimating the polygenic genotype value. The results showed that after phenotype value being normalized, optimization network could be established for predicting the phenotype value without fearing that the gene effect is too large. If the number of hidden neurons is large enough, the stability of back-propagation artificial neural network established for predicting phenotype value is very well. the gene effect could not affected the precise of optimum neural network for estimating the animal phenotype, the optimum neural network could be selected for predicting the phenotype values of quantitative traits controlled by genes with small or large effects.