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

Profile hidden Markov models (profile HMMs) and probabilistic inference methods have made important contributions to the theory of sequence database homology search. However, practical use of profile HMM methods has been hindered by the computational expense of existing software implementations. Here I describe an acceleration heuristic for profile HMMs, the "multiple segment Viterbi" (MSV) algorithm. The MSV algorithm computes an optimal sum of multiple ungapped local alignment segments using a striped vector-parallel approach previously described for fast Smith/Waterman alignment. MSV scores follow the same statistical distribution as gapped optimal local alignment scores, allowing rapid evaluation of significance of an MSV score and thus facilitating its use as a heuristic filter. I also describe a 20-fold acceleration of the standard profile HMM Forward/Backward algorithms using a method I call "sparse rescaling". These methods are assembled in a pipeline in which high-scoring MSV hits are passed on for reanalysis with the full HMM Forward/Backward algorithm. This accelerated pipeline is implemented in the freely available HMMER3 software package. Performance benchmarks show that the use of the heuristic MSV filter sacrifices negligible sensitivity compared to unaccelerated profile HMM searches. HMMER3 is substantially more sensitive and 100- to 1000-fold faster than HMMER2. HMMER3 is now about as fast as BLAST for protein searches.

/pdf/accelerated-profile-hmm-searches-5f0frwnmca.pdf

Accelerated Profile HMM Searches

▪ Abstract Most prokaryotic signal-transduction systems and a few eukaryotic pathways use phosphotransfer schemes involving two conserved components, a histidine protein kinase and a response regul...

Two-component signal transduction

Gene families are growing rapidly, but standard methods for inferring phylogenies do not scale to alignments with over 10,000 sequences. We present FastTree, a method for constructing large phylogenies and for estimating their reliability. Instead of storing a distance matrix, FastTree stores sequence profiles of internal nodes in the tree. FastTree uses these profiles to implement neighbor-joining and uses heuristics to quickly identify candidate joins. FastTree then uses nearest-neighbor interchanges to reduce the length of the tree. For an alignment with N sequences, L sites, and a different characters, a distance matrix requires O(N^2) space and O(N^2 L) time, but FastTree requires just O( NLa + N sqrt(N) ) memory and O( N sqrt(N) log(N) L a ) time. To estimate the tree's reliability, FastTree uses local bootstrapping, which gives another 100-fold speedup over a distance matrix. For example, FastTree computed a tree and support values for 158,022 distinct 16S ribosomal RNAs in 17 hours and 2.4 gigabytes of memory. Just computing pairwise Jukes-Cantor distances and storing them, without inferring a tree or bootstrapping, would require 17 hours and 50 gigabytes of memory. In simulations, FastTree was slightly more accurate than neighbor joining, BIONJ, or FastME; on genuine alignments, FastTree's topologies had higher likelihoods. FastTree is available at http://microbesonline.org/fasttree.

/pdf/fast-tree-computing-large-minimum-evolution-trees-with-4grq0gwfpy.pdf

Fast Tree: Computing Large Minimum-Evolution Trees with Profiles instead of a Distance Matrix

Laccases of fungi attract considerable attention due to their possible involvement in the transformation of a wide variety of phenolic compounds including the polymeric lignin and humic substances. So far, more than a 100 enzymes have been purified from fungal cultures and characterized in terms of their biochemical and catalytic properties. Most ligninolytic fungal species produce constitutively at least one laccase isoenzyme and laccases are also dominant among ligninolytic enzymes in the soil environment. The fact that they only require molecular oxygen for catalysis makes them suitable for biotechnological applications for the transformation or immobilization of xenobiotic compounds.

Fungal laccases - occurrence and properties.

PAS domains are newly recognized signaling domains that are widely distributed in proteins from members of the Archaea and Bacteria and from fungi, plants, insects, and vertebrates. They function as input modules in proteins that sense oxygen, redox potential, light, and some other stimuli. Specificity in sensing arises, in part, from different cofactors that may be associated with the PAS fold. Transduction of redox signals may be a common mechanistic theme in many different PAS domains. PAS proteins are always located intracellularly but may monitor the external as well as the internal environment. One way in which prokaryotic PAS proteins sense the environment is by detecting changes in the electron transport system. This serves as an early warning system for any reduction in cellular energy levels. Human PAS proteins include hypoxia-inducible factors and voltage-sensitive ion channels; other PAS proteins are integral components of circadian clocks. Although PAS domains were only recently identified, the signaling functions with which they are associated have long been recognized as fundamental properties of living cells.

PAS Domains: Internal Sensors of Oxygen, Redox Potential, and Light

Bacteria of the genus Shewanella are known for their versatile electron-accepting capacities, which allow them to couple the decomposition of organic matter to the reduction of the various terminal electron acceptors that they encounter in their stratified environments. Owing to their diverse metabolic capabilities, shewanellae are important for carbon cycling and have considerable potential for the remediation of contaminated environments and use in microbial fuel cells. Systems-level analysis of the model species Shewanella oneidensis MR-1 and other members of this genus has provided new insights into the signal-transduction proteins, regulators, and metabolic and respiratory subsystems that govern the remarkable versatility of the shewanellae.

/pdf/towards-environmental-systems-biology-of-shewanella-3hg342cb6v.pdf

Towards environmental systems biology of Shewanella

One-component systems dominate signal transduction in prokaryotes.

PAS domain S-boxes in archaea, bacteria and sensors for oxygen and redox

The molecular machinery that controls chemotaxis in bacteria is substantially more complex than any other signal transduction system in prokaryotes, and its origins and variability among living species are unknown. We found that this multiprotein “chemotaxis system” is present in most prokaryotic species and evolved from simpler two-component regulatory systems that control prokaryotic transcription. We discovered, through genomic analysis, signaling systems intermediate between two-component systems and chemotaxis systems. Evolutionary genomics established central and auxiliary components of the chemotaxis system. While tracing its evolutionary history, we also developed a classification scheme that revealed more than a dozen distinct classes of chemotaxis systems, enabling future predictive modeling of chemotactic behavior in unstudied species.

Igor B. Zhulin

Papers

PAS Domains: Internal Sensors of Oxygen, Redox Potential, and Light

Towards environmental systems biology of Shewanella

One-component systems dominate signal transduction in prokaryotes.

PAS domain S-boxes in archaea, bacteria and sensors for oxygen and redox

Origins and Diversification of a Complex Signal Transduction System in Prokaryotes