In the post-genomic biology era,the reconstruction of gene regulatory networks from microarray gene expression data is very important to understand the underlying biological system,and it has been a challenging task i...In the post-genomic biology era,the reconstruction of gene regulatory networks from microarray gene expression data is very important to understand the underlying biological system,and it has been a challenging task in bioinformatics.The Bayesian network model has been used in reconstructing the gene regulatory network for its advantages,but how to determine the network structure and parameters is still important to be explored.This paper proposes a two-stage structure learning algorithm which integrates immune evolution algorithm to build a Bayesian network.The new algorithm is evaluated with the use of both simulated and yeast cell cycle data.The experimental results indicate that the proposed algorithm can find many of the known real regulatory relationships from literature and predict the others unknown with high validity and accuracy.展开更多
Objective Environmental estrogens at an elevated concentration are known to produce adverse effects on human and animal life. However, the majority of researches have been focused on industrial discharges, while the i...Objective Environmental estrogens at an elevated concentration are known to produce adverse effects on human and animal life. However, the majority of researches have been focused on industrial discharges, while the impact of livestock wastes as a source of endocrine disrupters in aquatic environments has been rarely elucidated. In order to investigate the contribution of environmental estrogens from livestock, the estrogenic activity in water samples from a farm wastewater treatment plant was analyzed by a recombinant yeast screening method. Methods The extracts prepared from 15 selected water samples from the farm wastewater treatment plant, among which 6 samples were from pre-treatment section （influents） and 9 from post-treatment section （effluents）, were analyzed for estrogenic activity by cellar bioassay. Yeast cells transfected with the expression plasmid of human estrogen receptor and the Lac Z reporter plasmid encoding β-galactossidase, were used to measure the estrogen-like compounds in the farm wastewater treatment plant. Results The wastewater samples from influents showed a higher estrogenic potency than the effluent samples showing a low induction of β-galactossidase relative to solvent control condition. By comparison with a standard curve for 17β-estradiol （E2）, estrogenic potency in water samples from the influents was calculated as E2-equivalent and ranged from 0.1 to 150 pM E2-equivalent. The estrogenic potency in water samples from the effluents was significantly lower than that in the influents, and 7 water samples had less detectable limit in the total of 9 samples. Conclusion Yeast bioassay of estrogenic activity in most of the samples from the farm wastewater after disposal by traditional sewage treatment showed negative results.展开更多
基金supported by National Natural Science Foundation of China(Grant Nos.60433020,60175024 and 60773095)European Commission under grant No.TH/Asia Link/010(111084)the Key Science-Technology Project of the National Education Ministry of China(Grant No.02090),and the Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education,Jilin University,P.R.China
文摘In the post-genomic biology era,the reconstruction of gene regulatory networks from microarray gene expression data is very important to understand the underlying biological system,and it has been a challenging task in bioinformatics.The Bayesian network model has been used in reconstructing the gene regulatory network for its advantages,but how to determine the network structure and parameters is still important to be explored.This paper proposes a two-stage structure learning algorithm which integrates immune evolution algorithm to build a Bayesian network.The new algorithm is evaluated with the use of both simulated and yeast cell cycle data.The experimental results indicate that the proposed algorithm can find many of the known real regulatory relationships from literature and predict the others unknown with high validity and accuracy.
基金the Natural Science foundation of Jiangsu Education Bureau (03KJB610168)
文摘Objective Environmental estrogens at an elevated concentration are known to produce adverse effects on human and animal life. However, the majority of researches have been focused on industrial discharges, while the impact of livestock wastes as a source of endocrine disrupters in aquatic environments has been rarely elucidated. In order to investigate the contribution of environmental estrogens from livestock, the estrogenic activity in water samples from a farm wastewater treatment plant was analyzed by a recombinant yeast screening method. Methods The extracts prepared from 15 selected water samples from the farm wastewater treatment plant, among which 6 samples were from pre-treatment section （influents） and 9 from post-treatment section （effluents）, were analyzed for estrogenic activity by cellar bioassay. Yeast cells transfected with the expression plasmid of human estrogen receptor and the Lac Z reporter plasmid encoding β-galactossidase, were used to measure the estrogen-like compounds in the farm wastewater treatment plant. Results The wastewater samples from influents showed a higher estrogenic potency than the effluent samples showing a low induction of β-galactossidase relative to solvent control condition. By comparison with a standard curve for 17β-estradiol （E2）, estrogenic potency in water samples from the influents was calculated as E2-equivalent and ranged from 0.1 to 150 pM E2-equivalent. The estrogenic potency in water samples from the effluents was significantly lower than that in the influents, and 7 water samples had less detectable limit in the total of 9 samples. Conclusion Yeast bioassay of estrogenic activity in most of the samples from the farm wastewater after disposal by traditional sewage treatment showed negative results.
