Akihiko Konagaya

Inference of S-system models of genetic networks using a cooperative coevolutionary algorithm

MOTIVATION To resolve the high-dimensionality of the genetic network inference problem in the S-system model, a problem decomposition strategy has been proposed. While this strategy certainly shows promise, it cannot provide a model readily applicable to the computational simulation of the genetic network when the given time-series data contain measurement noise. This is a significant limitation of the problem decomposition, given that our analysis and understanding of the genetic network depend on the computational simulation. RESULTS We propose a new method for inferring S-system models of large-scale genetic networks. The proposed method is based on the problem decomposition strategy and a cooperative coevolutionary algorithm. As the subproblems divided by the problem decomposition strategy are solved simultaneously using the cooperative coevolutionary algorithm, the proposed method can be used to infer any S-system model ready for computational simulation. To verify the effectiveness of the proposed method, we apply it to two artificial genetic network inference problems. Finally, the proposed method is used to analyze the actual DNA microarray data.

High Speed Homology Search with FPGAs

We will introduce a way how we can achieve high speed homology search by only adding one off-the-shelf PCI board with one Field Programmable Gate Array (FPGA) to a Pentium based computer system in use. FPGA is a reconfigurable device, and any kind of circuits, such as pattern matching program, can be realized in a moment. The performance is almost proportional to the size of FPGA which is used in the system, and FPGAs are becoming larger and larger following Moores law. We can easily obtain latest/larger FPGAs in the form off-the-shelf PCI boards with FPGAs, at low costs. The result which we obtained is as follows. The performance is most comparable with small to middle class dedicated hardware systems when we use a board with one of the latest FPGAs and the performance can be furthermore accelerated by using more number of FPGA boards. The time for comparing a query sequence of 2,048 elements with a database sequence of 64 million elements by the Smith-Waterman algorithm is about 34 sec, which is about 330 times faster than a desktop computer with a 1 GHz Pentium III. We can also accelerate the performance of a laptop computer using a PC card with one smaller FPGA. The time for comparing a query sequence (1,024) with the database sequence (64 million) is about 185 sec, which is about 30 times faster than the desktop computer.

Circle formation for oblivious anonymous mobile robots with no common sense of orientation

This paper proposes a distributed algorithm by which a collection of mobile robots roaming on a plane move to form a circle. The algorithm operates under the premises that robots (1) are unable to recall past actions and observations (i.e., oblivious), (2) cannot be distinguished from each others (i.e., anonymous), (3) share no common sense of direction, and (4) are unable to communicate in any other ways than by observing each others position.

AN EFFECTIVE METHOD FOR SELECTING SIRNA TARGET SEQUENCES IN MAMMALIAN CELLS

RNA interference is a gene-silencing phenomenon triggered by dsRNA (double-stranded RNA) and has been widely used for studying gene functions. The short interfering RNA (siRNA) responsible for RNA interference, however, varies markedly in its gene-silencing efficacy. Because this efficacy depends on the selected target sequences, we developed an effective selection method based on the gene degradation measure (priority score) defined by positional features of individual nucleotides. We tested this method experimentally by using it to select new siRNA target sequences in the homo sapiens cyclin B1 gene (CCNB1) and confirmed that it selected highly effective gene-silencing sequences. The proposed method will therefore be useful for selecting new siRNA target sequences in mammalian cells.

RARGE: a large-scale database of RIKEN Arabidopsis resources ranging from transcriptome to phenome

The RIKEN Arabidopsis Genome Encyclopedia (RARGE) database houses information on biological resources ranging from transcriptome to phenome, including RIKEN Arabidopsis full-length (RAFL) complementary DNAs (cDNAs), their promoter regions, Dissociation (Ds) transposon-tagged lines and expression data from microarray experiments. RARGE provides tools for searching by resource code, sequence homology or keyword, and rapid access to detailed information on the resources. We have isolated 245 946 RAFL cDNA clones and collected 11 933 transposon-tagged lines, which are available from the RIKEN Bioresource Center and are stored in RARGE. The RARGE web interface can be accessed at http://rarge.gsc.riken.jp/. Additionally, we report 90 000 new RAFL cDNA clones here.

Novel peroxisomal protease Tysnd1 processes PTS1- and PTS2-containing enzymes involved in β-oxidation of fatty acids

Peroxisomes play an important role in β‐oxidation of fatty acids. All peroxisomal matrix proteins are synthesized in the cytosol and post‐translationally sorted to the organelle. Two distinct peroxisomal signal targeting sequences (PTSs), the C‐terminal PTS1 and the N‐terminal PTS2, have been defined. Import of precursor PTS2 proteins into the peroxisomes is accompanied by a proteolytic removal of the N‐terminal targeting sequence. Although the PTS1 signal is preserved upon translocation, many PTS1 proteins undergo a highly selective and limited cleavage. Here, we demonstrate that Tysnd1, a previously uncharacterized protein, is responsible both for the removal of the leader peptide from PTS2 proteins and for the specific processing of PTS1 proteins. All of the identified Tysnd1 substrates catalyze peroxisomal β‐oxidation. Tysnd1 itself undergoes processing through the removal of the presumably inhibitory N‐terminal fragment. Tysnd1 expression is induced by the proliferator‐activated receptor α agonist bezafibrate, along with the increase in its substrates. A model is proposed where the Tysnd1‐mediated processing of the peroxisomal enzymes promotes their assembly into a supramolecular complex to enhance the rate of β‐oxidation.

Molecular Robotics: A New Paradigm for Artifacts

The rapid progress of molecular nanotechnology has opened the door to molecular robotics, which uses molecules as robot components. In order to promote this new paradigm, the Molecular Robotics Research Group was established in the Systems and Information Division of the Society of Instrument and Control Engineers (SICE) in 2010. The group consists of researchers from various fields including chemistry, biophysics, DNA nanotechnology, systems science and robotics, challenging this emerging new field. Last year, the group proposed a research project focusing on molecular robotics, and it was recently awarded a Grant-in-Aid for Scientific Research on Innovative Areas (FY2012-16), one of the large-scale research projects in Japan, by MEXT (Ministry of Education, Culture, Sports, Science and Technology, JAPAN). Here, we wish to clarify the fundamental concept and research direction of molecular robotics. For this purpose, we present a comprehensive view of molecular robotics based on the discussions held in the Molecular Robotics Research Group.

Microchromosomal deletions involving SCN1A and adjacent genes in severe myoclonic epilepsy in infancy.

Purpose: Genetic abnormalities of the gene encoding α1 subunit of the sodium channel (SCN1A), which can be detected by direct sequencing, are present in more than 60% of patients with severe myoclonic epilepsy in infancy (SMEI) or its borderline phenotype (SMEB). Microchromosomal deletions have been recently reported as additional causes of SMEI. This study examines whether such microdeletions are associated with SMEI as well as with SMEB.

Novel Mechanism of Interaction of p85 Subunit of Phosphatidylinositol 3-Kinase and ErbB3 Receptor-derived Phosphotyrosyl Peptides

Ligand-activated and tyrosine-phosphorylated ErbB3 receptor binds to the SH2 domain of the p85 subunit of phosphatidylinositol 3-kinase and initiates intracellular signaling. Here, we studied the interactions between the N- (N-SH2) and C- (C-SH2) terminal SH2 domains of the p85 subunit of the phosphatidylinositol 3-kinase and eight ErbB3 receptor-derived phosphotyrosyl peptides (P-peptides) by using molecular dynamics, free energy, and surface plasmon resonance (SPR) analyses. In SPR analysis, these P-peptides showed no binding to the C-SH2 domain, but P-peptides containing a phospho-YXXM or a non-phospho-YXXM motif did bind to the N-SH2 domain. The N-SH2 domain has two phosphotyrosine binding sites in its N- (N1) and C- (N2) terminal regions. Interestingly, we found that P-peptides of pY1180 and pY1241 favored to bind to the N2 site, although all other P-peptides showed favorable binding to the N1 site. Remarkably, two phosphotyrosines, pY1178 and pY1243, which are just 63 amino acids apart from the pY1241 and pY1180, respectively, showed favorable binding to the N1 site. These findings indicate a possibility that the pair of phosphotyrosines, pY1178-pY1241 or pY1243-pY1180, will fold into an appropriate configuration for binding to the N1 and N2 sites simultaneously. Our model structures of the cytoplasmic C-terminal domain of ErbB3 receptor also strongly supported the speculation. The calculated binding free energies between the N-SH2 domain and P-peptides showed excellent qualitative agreement with SPR data with a correlation coefficient of 0.91. The total electrostatic solvation energy between the N-SH2 domain and P-peptide was the dominant factor for its binding affinity.

High dimensional function optimization using a new genetic local search suitable for parallel computers

In this paper, we propose a new genetic local search named GLSDC (a genetic local search with distance independent diversity control) by extending the basic idea of DIDC (a genetic algorithm with distance independent diversity control) to coarse grained parallelization. GLSDC employs a local search method as a search operator. GLSDC also uses genetic operators, i.e., a crossover operator and a generation alternation model. However, in GLSDC, the crossover operator is not used as a search operator, but is used only for converging the population. GLSDC has an ability to find multiple optima simultaneously by stacking good individuals that have been found by the local search. Finding multiple optima is often required when we try to solve real world problems. The effectiveness of the proposed method is verified through numerical experiments on several high dimensional benchmark problems.