We report stochastic simulations of the yeast mating signal transduction pathway.The effects of intrinsic and external noise,the influence of cell-to-cell difference in the pathway capacity,and noise propagation in the pathway have been examined.The stochastic temporal behaviour of the pathway is found to be robust to the influence of inherent fluctuations,and intrinsic noise propagates in the pathway in a uniform pattern when the yeasts are treated with pheromones of different stimulus strengths and of varied fluctuations.In agreement with recent experimental findings, extrinsic noise is found to play a more prominent role than intrinsic noise in the variability of proteins.The occurrence frequency for the reactions in the pathway are also examined and a more compact network is obtained by dropping most of the reactions of least occurrence.
Flux balance analysis, based on the mass conservation law in a cellular organism, has been extensively employed to study the interplay between structures and functions of cellular metabolic networks. Consequently, the phenotypes of the metabolism can be well elucidated. In this paper, we introduce the Expanded Flux Variability Analysis (EFVA) to characterize the intrinsic nature of metabolic reactions, such as flexibility, modularity and essentiality, by exploring the trend of the range, the maximum and the minimum flux of reactions. We took the metabolic network of Escherichia coli as an example and analyzed the variability of reaction fluxes under different growth rate constraints. The average variabil-ity of all reactions decreases dramatically when the growth rate increases. Consider the noise effect on the metabolic system, we thus argue that the microorganism may practically grow under a suboptimal state. Besides, under the EFVA framework, the reactions are easily to be grouped into catabolic and anabolic groups. And the anabolic groups can be further assigned to specific biomass constitute. We also discovered the growth rate dependent essentiality of reactions.
为便于大规模代谢网络的计算,发展了一款方便实用的工具:MetaGen,对Kyoto Encyclopedia of Genesand Genomes(KEGG)中物种特异的各层次代谢系统进行建模,生成的代谢网络以酶图和通路图的方式表示.利用该工具,对人类代谢系统的bow-tie结构进行了初步研究,并以此为例展示了该工具广阔的应用前景.MetaGen利用KEGGweb服务保证建模数据的可靠性,依靠本地关系数据库加速网络建模过程并提供更多的数据管理和利用方式,并结合高级JAVA技术提高代码的可扩展性.MetaGen完全开源,可直接从http://bnct.sourceforge.net/下载.
With the developments of international human transcriptome data and our ESTs of human fetal liver aged 22 weeks (wk) of gestation (HFL22w), the former research must be renewed. In this work, the EST data were firstly clustered by blasting against the ESTs of HFL22w, UniGene, DoTS, MGC and Twinscan-predicted human transcriptome. Then, after EST assembly and gene identification, the known genes were classified by GO (gene ontology), and the unknown genes were predicted by Pfam and ScanProsite to clarify their functions. In the end, the relations of 5 tissues including fetal liver, adult liver, bone marrow, thymus and lymph node that possess hemopoiesis or can indicate fetal liver characteristics were analyzed by hierarchical clustering. The results show that: (i) By comparing the 5 newest human transcriptome databases, we can largely reduce the probability that the ESTs belonging to unconnected parts of one gene were probably divided into different clusters, so it is recommended to blast against the newest databases when clustering EST data; (ii) some previous unknown ESTs had been identified as function-known genes, and 1379 genes were identified as fully new sequences possessed in our lab; (iii) through GO classification, we got a rough understanding of HFL22w, and obtained 6 cell migration genes and 6 hemopoiesis genes; (iv) prediction of gene function had enabled us to obtain 277 profiles, among them, there are 5 categories distributed in more than 10 genes; (v) five tissue relations analyzed by hierarchical clustering are related to their functions; (vi) We have built the world's largest EST database on HFL22w. Renewal and preliminary analysis of EST database on HFL22w will help to understand hemopoiesis and cell migration mechanism, and promote future research on human fetal liver.
