BIOE 402. Medical Technology Assessment. 2 or 3 hours. Bioentrepreneur course. Assessment of medical technology in the context of commercialization. Objectives, competition, market share, funding, pricing, manufacturing, growth, and intellectual property; many issues unique to biomedical products. Course Information: 2 undergraduate hours. 3 graduate hours. Prerequisite(s): Junior standing or above and consent of the instructor.

“Bioinformatics” 특집을 내면서

KEGG(Kyoto Encyclopedia of Genes and Genomes)〔和文〕 (特集 ゲノム医学の現在と未来--基礎と臨床) -- (データベース)

■ Abstract Apoptosis is a genetically programmed, morphologically distinct form of cell death that can be triggered by a variety of physiological and pathological stimuli. Studies performed over the past 10 years have demonstrated that proteases play critical roles in initiation and execution of this process. The caspases, a family of cysteine-dependent aspartate-directed proteases, are prominent among the death proteases. Caspases are synthesized as relatively inactive zymogens that become activated by scaffold-mediated transactivation or by cleavage via upstream proteases in an intracellular cascade. Regulation of caspase activation and activity occurs at several different levels: ( a) Zymogen gene transcription is regulated; ( b) antiapoptotic members of the Bcl-2 family and other cellular polypeptides block proximity-induced activation of certain procaspases; and ( c) certain cellular inhibitor of apoptosis proteins (cIAPs) can bind to and inhibit active caspases. Once activated, caspases cleave a variety of intracellular polypeptides, including major structural elements of the cytoplasm and nucleus, components of the DNA repair machinery, and a number of protein kinases. Collectively, these scissions disrupt survival pathways and disassemble important architectural components of the cell, contributing to the stereotypic morphological and biochemical changes that characterize apoptotic cell death.

Mammalian caspases: structure ,a ctivation ,s ubstrates, and functions during apoptosis

https://dialnet.unirioja.es/descarga/articulo/4511387.pdf

The origins of order. Self-organization and selection in evolution

The fungus Fusarium fujikuroi causes “bakanae” disease of rice due to its ability to produce gibberellins (GAs), but it is also known for producing harmful mycotoxins. However, the genetic capacity for the whole arsenal of natural compounds and their role in the fungus' interaction with rice remained unknown. Here, we present a high-quality genome sequence of F. fujikuroi that was assembled into 12 scaffolds corresponding to the 12 chromosomes described for the fungus. We used the genome sequence along with ChIP-seq, transcriptome, proteome, and HPLC-FTMS-based metabolome analyses to identify the potential secondary metabolite biosynthetic gene clusters and to examine their regulation in response to nitrogen availability and plant signals. The results indicate that expression of most but not all gene clusters correlate with proteome and ChIP-seq data. Comparison of the F. fujikuroi genome to those of six other fusaria revealed that only a small number of gene clusters are conserved among these species, thus providing new insights into the divergence of secondary metabolism in the genus Fusarium. Noteworthy, GA biosynthetic genes are present in some related species, but GA biosynthesis is limited to F. fujikuroi, suggesting that this provides a selective advantage during infection of the preferred host plant rice. Among the genome sequences analyzed, one cluster that includes a polyketide synthase gene (PKS19) and another that includes a non-ribosomal peptide synthetase gene (NRPS31) are unique to F. fujikuroi. The metabolites derived from these clusters were identified by HPLC-FTMS-based analyses of engineered F. fujikuroi strains overexpressing cluster genes. In planta expression studies suggest a specific role for the PKS19-derived product during rice infection. Thus, our results indicate that combined comparative genomics and genome-wide experimental analyses identified novel genes and secondary metabolites that contribute to the evolutionary success of F. fujikuroi as a rice pathogen.

/pdf/deciphering-the-cryptic-genome-genome-wide-analyses-of-the-20083t1bpx.pdf

Deciphering the cryptic genome : genome-wide analyses of the rice pathogen Fusarium fujikuroi reveal complex regulation of secondary metabolism and novel metabolites

Subcellular location prediction of proteins is an important and well-studied problem in bioinformatics. This is a problem of predicting which part in a cell a given protein is transported to, where an amino acid sequence of the protein is given as an input. This problem is becoming more important since information on subcellular location is helpful for annotation of proteins and genes and the number of complete genomes is rapidly increasing. Since existing predictors are based on various heuristics, it is important to develop a simple method with high prediction accuracies. In this paper, we propose a novel and general predicting method by combining techniques for sequence alignment and feature vectors based on amino acid composition. We implemented this method with support vector machines on plant data sets extracted from the TargetP database. Through fivefold cross validation tests, the obtained overall accuracies and average MCC were 0.9096 and 0.8655 respectively. We also applied our method to other datasets including that of WoLF PSORT. Although there is a predictor which uses the information of gene ontology and yields higher accuracy than ours, our accuracies are higher than existing predictors which use only sequence information. Since such information as gene ontology can be obtained only for known proteins, our predictor is considered to be useful for subcellular location prediction of newly-discovered proteins. Furthermore, the idea of combination of alignment and amino acid frequency is novel and general so that it may be applied to other problems in bioinformatics. Our method for plant is also implemented as a web-system and available on http://sunflower.kuicr.kyoto-u.ac.jp/~tamura/slpfa.html
 .

/pdf/subcellular-location-prediction-of-proteins-using-support-4daipivw3w.pdf

Subcellular location prediction of proteins using support vector machines with alignment of block sequences utilizing amino acid composition

Background
Precise quantitative growth measurements and detection of small growth changes in high-throughput manner is essential for fundamental studies of bacterial cell. However, an inherent tradeoff for measurement quality in high-throughput methods sacrifices some measurement quality. A key challenge has been how to enhance measurement quality without sacrificing throughput.

/pdf/colony-live-a-high-throughput-method-for-measuring-microbial-4gl5qv1r0w.pdf

Colony-live —a high-throughput method for measuring microbial colony growth kinetics— reveals diverse growth effects of gene knockouts in Escherichia coli

In this paper, we study the problem of finding a periodic attractor of a Boolean network (BN), which arises in computational systems biology and is known to be NP-hard. Since a general case is quite hard to solve, we consider special but biologically important subclasses of BNs. For finding an attractor of period 2 of a BN consisting of n OR functions of positive literals, we present a polynomial time algorithm. For finding an attractor of period 2 of a BN consisting of n AND/OR functions of literals, we present an O(1.985^n) time algorithm. For finding an attractor of a fixed period of a BN consisting of n nested canalyzing functions and having constant treewidth w, we present an O(n^{2p(w+1)} poly(n)) time algorithm.

/pdf/finding-a-periodic-attractor-of-a-boolean-network-4r4j8b6l0y.pdf

Finding a Periodic Attractor of a Boolean Network

The Boolean network (BN) is a mathematical model of genetic networks. It is known that detecting a singleton attractor, which is also called a fixed point, is NP-hard even for AND/OR BNs (i.e., BNs consisting of AND/OR nodes), where singleton attractors correspond to steady states. Though a naive algorithm can detect a singleton attractor for an AND/OR BN in O(n2 n ) time, no O((2 ― ∈) n ) (∈ > 0) time algorithm was known even for an AND/OR BN with non-restricted indegree, where n is the number of nodes in a BN. In this paper, we present an O(1.787 n ) time algorithm for detecting a singleton attractor of a given AND/OR BN, along with related results. We also show that detection of a singleton attractor in a BN with maximum indegree two is NP-hard and can be polynomially reduced to a satisfiability problem.

Detecting a Singleton Attractor in a Boolean Network Utilizing SAT Algorithms

Background
Boolean network (BN) is a mathematical model for genetic network and control of genetic networks has become an important issue owing to their potential application in the field of drug discovery and treatment of intractable diseases. Early researches have focused primarily on the analysis of attractor control for a randomly generated BN. However, one may also consider how anti-cancer drugs act in both normal and cancer cells. Thus, the development of controls for multiple BNs is an important and interesting challenge.

/pdf/on-control-of-singleton-attractors-in-multiple-boolean-37w4qevfh3.pdf

Takeyuki Tamura

Papers

Subcellular location prediction of proteins using support vector machines with alignment of block sequences utilizing amino acid composition

Colony-live —a high-throughput method for measuring microbial colony growth kinetics— reveals diverse growth effects of gene knockouts in Escherichia coli

Finding a Periodic Attractor of a Boolean Network

Detecting a Singleton Attractor in a Boolean Network Utilizing SAT Algorithms

On control of singleton attractors in multiple Boolean networks: integer programming-based method