Gene networks inference using dynamic Bayesian networks

This article deals with the identification of gene regulatory networks from experimental data using a statistical machine learning approach. A stochastic model of gene interactions capable of handling missing variables is proposed. It can be described as a dynamic Bayesian network particularly well suited to tackle the stochastic nature of gene regulation and gene expression measurement. Parameters of the model are learned through a penalized likelihood maximization implemented through an extended version of EM algorithm. Our approach is tested against experimental data relative to the S.O.S. DNA Repair network of the Escherichia coli bacterium. It appears to be able to extract the main regulations between the genes involved in this network. An added missing variable is found to model the main protein of the network. Good prediction abilities on unlearned data are observed. These first results are very promising: they show the power of the learning algorithm and the ability of the model to capture gene interactions.     

重建基因调控网络

分子生物学的主要挑战是如何更好的理解基因间的调控机理。重建基因网络有助于探索生命系统的本质问题。这里对研究基因调控网络的起源、发展动向、目的和方法及目前所面临的挑战进行了综述。     

A Bayesian regression approach to the inference of regulatory networks from gene expression data

MOTIVATION: There is currently much interest in reverse-engineering regulatory relationships between genes from microarray expression data. We propose a new algorithmic method for inferring such interactions between genes using data from gene knockout experiments. The algorithm we use is the Sparse Bayesian regression algorithm of Tipping and Faul. This method is highly suited to this problem as it does not require the data to be discretized, overcomes the need for an explicit topology search and, most importantly, requires no heuristic thresholding of the discovered connections. RESULTS: Using simulated expression data, we are able to show that this algorithm outperforms a recently published correlation-based approach. Crucially, it does this without the need to set any ad hoc threshold on possible connections.     

A novel cytoplasmic structure containing DNA networks in Entamoeba histolytica trophozoites

We report here the presence of cytoplasmic DNA arranged in networks in the trophozoites of the human parasite Entamoeba histolytica. Cytoplasmic DNA was detected in live trophozoites in a structure that we called EhkO, using the fluorescent dye acridine orange, and by in situ hybridization to trophozoites with a rDNA probe. The EhkO was found in the axenically grown clones A, L6 (strain HM1:IMSS) and MAVax (strain MAV) and in the polyxenically grown clone MAVpx (strain MAV). Bacteria present in MAVpx did not cross hybridize with the DNA probe neither in in situ hybridization or in Southern blot experiments. Autoradiography of metabolically [³H]thymidine-labeled trophozoites showed the presence of EhkO, and an EhkO-enriched fraction, purified from a nuclei-free extract and examined by light microscopy, exhibited [³H]thymidine incorporation into this structure. DNA was purified from the EhkO and enriched nuclear fractions and analyzed by transmission electron microscopy. The EhkO fraction contained DNA networks resembling those of trypanosome kDNA, whereas nuclear DNA was present mainly as linear molecules and some circles. Our findings imply that E. histolytica may be taxonomically more closely related to the Trypanosomatidae than previously suspected.