Learning large network (with hundreds of variables) is gaining interest of many researchers with the emergence of high-throughput biological data sources such as micro-array data. In this paper, we investigated the two popular large scale network structure learning algorithms, sparse candidate hill climbing (SCHC) and Grow-Shrinkage(GS) algorithm. The experiments show that in fact both of them have serious effectiveness problems when the number of variables(genes) is large compared to the number of instances(experimental conditions), which is a common case in micro-array data. We further propose a new large scale structure learning algorithm based on Lasso regression. Theoretical analysis in [10] suggested that the L1-norm in lasso regression could make our algorithm especially suitable in the cases that the number of variables and instances is unbalance. Our algorithm achieves much better results than SCHC and GS on the synthetic data. We also show the effectiveness of our algorithm by learning genetic regulatory network modules from a real micro-array data (with more than 6000 genes), combined with the genome-wide location analysis data. The learned results are consistent well with biological knowledge.

“PubMed Central (PMC) is the U.S. National Library of Medicine’s digital archive of life sciences journal literature. Access to PMC is free and unrestricted.” [3]. It covers 78 journals and also 57 journals of BioMed Central [1]. It provides not only bibliographic information and abstracts but also full texts including figures and tables. Thus PMC is a rich and valuable information resource for research and development in biology and medicine.

We propose a new methodology for "soft'' docking unbound protein molecules (reported at the isolated state). The methodology is characterized by its simplicity and easiness of embedment in any rigid body docking process based on point complementarity. It is oriented to allow limited free but not unrealistic interpenetration of the side chains of protein surface amino acid residues. The central step to the technique is a filtering process similar to those in image processing. The methodology assists in deletion of atomic-scale details on the surface of the interacting monomers, leading to the extraction of the most characteristic flattened shape for the molecule as well as the definition of a soft layer of atoms to allow smooth interpenetration of the interacting molecules during the docking process. Although the methodology does not perform structural or conformational rearrangements in the interacting monomers, results output by the algorithm are in fair agreement with the relative position of the monomer in experimentally reported complexes. The algorithm performs especially well in cases where the complexity of the protein surfaces is high, that is in hetero dimmer complex prediction. The algorithm is oriented to play the role of a fast screening engine for proteins known to interact but for which no information other than that of the structures at the isolated state is available. Consequently the importance of the methodology will increase in structural-function studies of thousand of proteins derived from large scale genome sequencing projects being executed all around the globe.

Recently, the study of dynamics for many kinds of cellular molecules is a topic, because we can notunderstand an entire picture of cell, regardless of rapid increase of information for it individually.Knowing the dynamics, we can nd many insights about mechanics of what happens in cell, cell cycle,apoptosis, development, and transformation.A cell cycle is a critical phenomenon for transformation, apoptosis, and development, which forma complex and highly integrated network [3]. Although many of the regulatory molecules have beenidenti ed, dynamics of the complicated network is too complex to be understood by intuition alone.Therefore, by using a simulation based on a mathematical model of cell cycle G1-to-S phasetransition which consists of di erential equations, we shall understand the key feature which controlsthecellcycle. Untilnow, manymathematical modelsofregulationofcell cycleG1-to-Sphasetransitionhave been previously proposed and simulated [1, 2, 4]. The most detailed model of models is by Agudaand Tang [1]. We simulated cell cycle G1-to-S phase transition based on this model, and analyze thedynamics of cell cycle.

The availability of a growing number of fully sequenced genomes makes it possible for us to conduct a large-scale comparative genomic research. In recent years, functional prediction and genome tree construction methods based on phylogenetic proles have been developed. The phylogenetic prole is dened as a bit pattern that encodes the presence or absence of orthologous genes in a set of organisms with fully sequenced genomes, so the phylogenetic prole can be regarded as the evolutionary pattern of gene acquisition and gene loss across organisms. The genome tree methods based on phylogenetic proles stem from the assumption that evolutionarily correlated organisms have similar gene inheritance patterns. The purpose of this paper is to conduct a whole genome scale phylogenetic analysis in order to extract major organism groups from the comparison of fully sequenced genomes using the independent component analysis, at the same, to identify genes that are characteristic to a certain organism group. In the experiment, we show that 24 major organism groups are extracted from phylogenetic proles composed of orthologous gene clusters, and our genome tree corresponds to the simplied classication of KEGG organism groups and suggests that two prokaryotic kingdoms, Archaea and Bacteria, are closer to each other than to Eukaryotes on the whole genome level. We also show a possibility for predicting biological functions of hypothetical genes by examining the correspondence between the extent of their aliation and specic properties of certain organism groups.

Gene expression data has been rapidly accumulated and the methods of these data analysis are required. Statistical methods are used in these data analysis. However, the biological interpretation of the data and the result of the statistical analysis are difficult. Thus we are developing a method of analysis to interpret the data easily. We propose a PCA based analysis method and developed tools based on our proposal.