Job scheduling policy for high throughput grid computing

The growing computational power requirements of grand challenge applications has promoted the need for merging high throughput computing and grid computing principles to harness computational resources distributed across multiple organisations. This paper identifies the issues in resource management and scheduling in the emerging high throughput grid computing context. We also survey and study the performance of several space-sharing and time-sharing opportunistic scheduling policies that have been developed for high throughput computing.

EST-PAC HPC - a web portal for high-throughput EST annotation and protein sequence prediction

Expressed Sequence Tags (ESTs) are short DNA sequences generated by sequencing the transcribed cDNAs coming from a gene expression. They can provide significant functional, structural and evolutionary information and thus are a primary resource for gene discovery. EST annotation basically refers to the analysis of unknown ESTs that can be performed by database similarity search for possible identities and database search for functional prediction of translation products. Such kind of annotation typically consists of a series of repetitive tasks which should be automated, and be customizable and amenable to using distributed computing resources. Furthermore, processing of EST data should be done efficiently using a high performance computing platform. In this paper, we describe an EST annotator, EST-PACHPC, which has been developed for harnessing HPC resources potentially from Grid and Cloud systems for high throughput EST annotations. The performance analysis of EST-PACHPC has shown that it provides substantial performance gain in EST annotation.

Unicast and broadcast throughput maximization in amplify-and-forward relay networks

Cooperative communication (CC) offers an efficient and low-cost way to achieve spatial diversity by forming a virtual antenna array among single-antenna nodes that cooperatively share their antennas. It has been well recognized that the selection of relay nodes plays a critical role in the performance of CC. Most existing relay selection strategies focus on optimizing the outage probability or energy consumption. To fill in the vacancy of research on throughput improvement via CC, we study the relay selection problem with the objective of optimizing the throughput in this paper. For unicast, it is a P problem, and an optimal relay selection algorithm is provided with a correctness proof. For broadcast, we show the challenge of relay selection by proving it nonprobabilistic hard (NP-hard). A greedy heuristic algorithm is proposed to effectively choose a set of relay nodes that maximize the broadcast throughput. Simulation results show that the proposed algorithms can achieve high throughput under various network settings.

Exercise and skeletal muscle gene expression

1. Skeletal muscle is a complex and heterogenous tissue capable of remarkable adaptation in response to exercise training. The role of gene transcription, as an initial target to control protein synthesis, is poorly understood.
2. Mature myofibres contain several hundred nuclei, all of which maintain transcriptional competency, although the localized responsiveness of nuclei is not well known. Myofibres are capable of hypertrophy. These processes require the activation and myogenic differentiation of mononuclear satellite cells that fuse with the enlarging or repairing myofibre.
3. A single bout of exercise in human subjects is capable of activating the expression of many diverse groups of genes.
4. The impact of repeated exercise bouts, typical of exercise training, on gene expression has yet to receive systematic investigation.
5. The molecular programme elicited by resistance exercise and endurance exercise differs markedly. Muscular hypertrophy following resistance exercise is dependent on the activation of satellite cells and their subsequent myogenic maturation. Endurance exercise requires the simultaneous activation of mitochondrial and nuclear genes to enable mitochondrial biogenesis.
6. Future analysis of the regulation of genes by exercise may combine high-throughput technologies, such as gene-chips, enabling the rapid detection and analysis of changes in the expression of many thousands of genes.

Preemptive job scheduling policy for distributively-owned workstation clusters

Cluster computing has come to prominence as a cost-effective parallel processing tool for solving many complex computational problems. In this paper, we propose a new timesharing opportunistic scheduling policy to support remote batch job executions over networked clusters to be used in conjunction with the Condor Up-Down scheduling algorithm. We show that timesharing approaches can be used in an opportunistic setting to improve both mean job slowdowns and mean response times with little or no throughput reduction. We also show that the proposed algorithm achieves significant improvement in job response time and slowdown as compared to exiting approaches and some recently proposed new approaches.

Dynamical systems for discovering protein complexes and functional modules from biological networks

Recent advances in high throughput experiments and annotations via published literature have provided a wealth of interaction maps of several biomolecular networks, including metabolic, protein-protein, and protein-DNA interaction networks. The architecture of these molecular networks reveals important principles of cellular organization and molecular functions. Analyzing such networks, i.e., discovering dense regions in the network, is an important way to identify protein complexes and functional modules. This task has been formulated as the problem of finding heavy subgraphs, the Heaviest k-Subgraph Problem (k-HSP), which itself is NPhard. However, any method based on the k-HSP requires the parameter k and an exact solution of k-HSP may still end up as a “spurious” heavy subgraph, thus reducing its practicability in analyzing large scale biological networks. We proposed a new formulation, called the rank-HSP, and two dynamical systems to approximate its results. In addition, a novel metric, called the Standard deviation and Mean Ratio (SMR), is proposed for use in “spurious” heavy subgraphs to automate the discovery by setting a fixed threshold. Empirical results on both the simulated graphs and biological networks have demonstrated the efficiency and effectiveness of our proposal.

Insights into exazaphosphorine resistance and possible approaches to its circumvention

The oxazaphosphorines cyclophosphamide, ifosfamide and trofosfamide remain a clinically useful class of anticancer drugs with substantial antitumour activity against a variety of solid tumors and hematological malignancies. A major limitation to their use is tumour resistance, which is due to multiple mechanisms that include increased DNA repair, increased cellular thiol levels, glutathione S-transferase and aldehyde dehydrogenase activities, and altered cell-death response to DNA damage. These mechanisms have been recently re-examined with the aid of sensitive analytical techniques, high-throughput proteomic and genomic approaches, and powerful pharmacogenetic tools. Oxazaphosphorine resistance, together with dose-limiting toxicity (mainly neutropenia and neurotoxicity), significantly hinders chemotherapy in patients, and hence, there is compelling need to find ways to overcome it. Four major approaches are currently being explored in preclinical models, some also in patients: combination with agents that modulate cellular response and disposition of oxazaphosphorines; antisense oligonucleotides directed against specific target genes; introduction of an activating gene (CYP3A4) into tumor tissue; and modification of dosing regimens. Of these approaches, antisense oligonucleotides and gene therapy are perhaps more speculative, requiring detailed safety and efficacy studies in preclinical models and in patients. A fifth approach is the design of novel oxazaphosphorines that have favourable pharmacokinetic and pharmacodynamic properties and are less vulnerable to resistance. Oxazaphosphorines not requiring hepatic CYP-mediated activation (for example, NSC 613060 and mafosfamide) or having additional targets (for example, glufosfamide that also targets glucose transport) have been synthesized and are being evaluated for safety and efficacy. Characterization of the molecular targets associated with oxazaphosphorine resistance may lead to a deeper understanding of the factors critical to the optimal use of these agents in chemotherapy and may allow the development of strategies to overcome resistance.

Drug bioactivation, covalent binding to target proteins and toxicity relevance

A number of therapeutic drugs with different structures and mechanisms of action have been reported to undergo metabolic activation by Phase I or Phase II drug-metabolizing enzymes. The bioactivation gives rise to reactive metabolites/intermediates, which readily confer covalent binding to various target proteins by nucleophilic substitution and/or Schiff's base mechanism. These drugs include analgesics (e.g., acetaminophen), antibacterial agents (e.g., sulfonamides and macrolide antibiotics), anticancer drugs (e.g., irinotecan), antiepileptic drugs (e.g., carbamazepine), anti-HIV agents (e.g., ritonavir), antipsychotics (e.g., clozapine), cardiovascular drugs (e.g., procainamide and hydralazine), immunosupressants (e.g., cyclosporine A), inhalational anesthetics (e.g., halothane), nonsteroidal anti-inflammatory drugs (NSAIDSs) (e.g., diclofenac), and steroids and their receptor modulators (e.g., estrogens and tamoxifen). Some herbal and dietary constituents are also bioactivated to reactive metabolites capable of binding covalently and inactivating cytochrome P450s (CYPs). A number of important target proteins of drugs have been identified by mass spectrometric techniques and proteomic approaches. The covalent binding and formation of drug-protein adducts are generally considered to be related to drug toxicity, and selective protein covalent binding by drug metabolites may lead to selective organ toxicity. However, the mechanisms involved in the protein adduct-induced toxicity are largely undefined, although it has been suggested that drug-protein adducts may cause toxicity either through impairing physiological functions of the modified proteins or through immune-mediated mechanisms. In addition, mechanism-based inhibition of CYPs may result in toxic drug-drug interactions. The clinical consequences of drug bioactivation and covalent binding to proteins are unpredictable, depending on many factors that are associated with the administered drugs and patients. Further studies using proteomic and genomic approaches with high throughput capacity are needed to identify the protein targetsof reactive drug metabolites, and to elucidate the structure-activity relationships of drug's covalent binding to proteins and their clinical outcomes.

A genomic approach to understanding the cause and effect of annual ryegrass toxicity

Annual Ryegrass Toxicity (ARGT) is a potentially lethal disease affecting livestock grazing on pastures or consuming fodder that include annual ryegrass (Lolium rigidum) contaminated with corynetoxins. The corynetoxins (CTs), among the most lethal toxins produced in nature, are produced by the bacterium Rathayibacter toxicus that uses a nematode vector to attach to and infect the seedheads of L.rigidum. There is little known of the factors that control toxin production. Several studies have speculated that a bacteriophage specific to R.toxicus may be implicated in CT production. We have developed a PCR-based assay to test for both bacterium and phage in ryegrass material and results indicate that there is a correlation between phage and bacterial presence in all toxic ryegrass samples tested so far. This PCR-based technique may ultimately allow for a rapid, high-throughput screening assay to identify potentially toxic pastures and feed in the field. Currently, ~80% of the 45 Kb genome has been sequenced an investigation to further elucidate its potential role in toxin production.Furthermore, specific alterations in gene expression as a result of exposure to CTs or the closely related tunicamycins (TMs), which are commercially available and considered biologically indistinguishable from CTs, will be evaluated for use as biomarkers of exposure. The effects of both toxins will be analysed in vitro using a rat hepatocyte cell line and screened on a low-density DNA micro array “CT-Chip” that contains <100 selected rat hepatic genes. The results are expected to further define the bioequivalence of CTs and TMs and to identify levels of exposure that are related to specific toxic effects or have no adverse effect on livestock.