类光捕获蛋白3(Light-harvesting like protein 3,LIL3)是光捕获复合物蛋白超家族(Light-harvesting complex,LHC)的一员,其特征是存在一个保守的LHC motif序列.水稻(Oryza sativa L.)LIL3参与叶绿素、生育酚侧链的生物合成.维生素E包...类光捕获蛋白3(Light-harvesting like protein 3,LIL3)是光捕获复合物蛋白超家族(Light-harvesting complex,LHC)的一员,其特征是存在一个保守的LHC motif序列.水稻(Oryza sativa L.)LIL3参与叶绿素、生育酚侧链的生物合成.维生素E包括生育酚和三烯生育酚2种,是一类重要的脂溶性化合物,具有抗氧化活性.为了探究LIL3对水稻籽粒维生素E含量的影响,在日本晴(Oryza Sativa spp.Japonica.)中过表达LIL3,得到了稳定的转基因株系.分别对野生型和过表达植株穗长、每穗粒数、粒长、粒宽以及籽粒中的维生素E含量做了分析.结果表明:与野生型相比,LIL3过表达植株的穗长、粒长和粒宽没有明显变化,每穗籽粒数略有所下降.籽粒中三烯生育酚含量降低.展开更多
Gene regulatory networks （GRNs） control the production of proteins in cells. It is well-known that this process is not deterministic. Numerous studies employed a non- deterministic transition structure to model thes...Gene regulatory networks （GRNs） control the production of proteins in cells. It is well-known that this process is not deterministic. Numerous studies employed a non- deterministic transition structure to model these networks. However, it is not realistic to expect state-to-state transition probabilities to remain constant throughout an organ- ism＇s lifetime. In this work, we focus on modeling GRN state transition （edge） variability using an ever-changing set of propensities. We suspect that the source of this variation is due to internal noise at the molecular level and can be modeled by introducing addi- tional stochasticity into GRN models. We employ a beta distribution, whose parameters are estimated to capture the pattern inherent in edge behavior with minimum error. Additionally, we develop a method for obtaining propensities from a pre-determined network.展开更多
文摘类光捕获蛋白3(Light-harvesting like protein 3,LIL3)是光捕获复合物蛋白超家族(Light-harvesting complex,LHC)的一员,其特征是存在一个保守的LHC motif序列.水稻(Oryza sativa L.)LIL3参与叶绿素、生育酚侧链的生物合成.维生素E包括生育酚和三烯生育酚2种,是一类重要的脂溶性化合物,具有抗氧化活性.为了探究LIL3对水稻籽粒维生素E含量的影响,在日本晴(Oryza Sativa spp.Japonica.)中过表达LIL3,得到了稳定的转基因株系.分别对野生型和过表达植株穗长、每穗粒数、粒长、粒宽以及籽粒中的维生素E含量做了分析.结果表明:与野生型相比,LIL3过表达植株的穗长、粒长和粒宽没有明显变化,每穗籽粒数略有所下降.籽粒中三烯生育酚含量降低.
文摘Gene regulatory networks （GRNs） control the production of proteins in cells. It is well-known that this process is not deterministic. Numerous studies employed a non- deterministic transition structure to model these networks. However, it is not realistic to expect state-to-state transition probabilities to remain constant throughout an organ- ism＇s lifetime. In this work, we focus on modeling GRN state transition （edge） variability using an ever-changing set of propensities. We suspect that the source of this variation is due to internal noise at the molecular level and can be modeled by introducing addi- tional stochasticity into GRN models. We employ a beta distribution, whose parameters are estimated to capture the pattern inherent in edge behavior with minimum error. Additionally, we develop a method for obtaining propensities from a pre-determined network.
