Studies on factors affecting business performance of biotechnology companies

Biotechnology industries are fast growing, technology based and multibillion dollars business. Biotechnology dealt with using biomaterials to produce bioproducts or to provide service in many industrial sectors of health, food, chemical and environmental industries. Like most of high-tech business, biotechnology is long term investment business with high risk. Therefore, based on the unique nature of this business, factors affecting its performance are different from those of ordinary business. Thus, this study is focused on the determination of the current factors affecting the performance of biotechnology business by 46 worldwide experts using mixed research method (a combination between Expert Opinion Assessment EOA technique and questionnaire of closed ended questions). Two rounds of questioning were conducted to identify, categorize and prioritize these factors by mean ranks. In the first round, experts listed all factors affecting business performance. The results of the first round were grouped and returned to the experts in the second round to score the importance of each factor. The second round showed high results consensus among the experts. Based on the favorable Kendall’s coefficient of consensus, it was not necessary to run the third round. Based on the results obtained from EOA study, a non-Financial Business Performance Indicator (n-FBPI) was developed for quantitative determination of performance of biotechnology companies. To evaluate the company’s performance, another research instrument (questionnaire) was developed. This questionnaire composed of 97 questions to evaluate the company’s external and internal environment and strength in points related to the effective factors uncovered in the first part of this study. The developed instrument was tested by 5 companies and gave quantitative measure for companies performance. Furthermore, a new website (www.biotechhorizon.com) was developed for this study for online assessment of the performance of biotechnology companies. The results of this study will help all stakeholders in biotechnology business either who are new in this type of business or who are well established by providing the current factors which have significant effect on biotechnology business performance. The n-FBPI developed will also be a useful tool for companies to evaluate their non financial performance quantitatively

Dynamic Energy Aware Task Scheduling for Periodic Tasks using Expected Execution Time Feedback

Scheduling dependent tasks is one of the most challenging problems in parallel and distributed systems. It is known to be computationally intractable in its general form as well as several restricted cases. An interesting application of scheduling is in the area of energy awareness for mobile battery operated devices where minimizing the energy utilized is the most important scheduling policy consideration. A number of heuristics have been developed for this consideration. In this paper, we study the scheduling problem for a particular battery model. In the proposed work, we show how to enhance a well know approach of accounting for the slack generated at runtime due to the difference between WCET (Worst Case Execution Time) and AET (Actual Execution Time). Our solution exploits the knowledge gained about the AET of the tasks after the first period, to come up with EET (Expected Execution Time). We then use the EET as an input for the next period to use as much slack as possible and to eliminate wastage of slack generated. This happens because WCET is used to determine if a task should be executed at runtime. Dynamically adjusting the run-queue to use EET as a feedback, which is based on the previous period’s AET eliminates wastage of the slack generated. Based on the outcome of the conducted experiments, the proposed algorithm outperformed or matched the performance of the 2-Phase dynamic task scheduling algorithm and the run-queue peek algorithm all the time

A Dynamic Run-Profile Energy-Aware Approach for Scheduling Computationally Intensive Bioinformatics Applications

High Performance Computing (HPC) resources are housed in large datacenters, which consume exorbitant amounts of energy and are quickly demanding attention from businesses as they result in high operating costs. On the other hand HPC environments have been very useful to researchers in many emerging areas in life sciences such as Bioinformatics and Medical Informatics. In an earlier work, we introduced a dynamic model for energy aware scheduling (EAS) in a HPC environment; the model is domain agnostic and incorporates both the deadline parameter as well as energy parameters for computationally intensive applications. Our proposed EAS model incorporates 2-phases. In the Offline Phase, we use a run profile based approach to generate the initial schedule. In the Online Phase a feedback mechanism is incorporated between the EAS Engine and the master scheduling process. As scheduled tasks are completed, actual execution times are used to adjust the resources required for scheduling remaining tasks using the least number of nodes while meeting a given deadline. In this paper we study the impact of the quality of initial schedule using different run profiles which is the starting point for the EAS algorithm on the number of adjustments which is critical to the overall energy optimization as every adjustment made has an overhead. The conducted experiments show that the proposed approach succeeded in meeting preset deadlines while minimizing the number of nodes; thus reducing overall energy utilized and that choosing the right profile in the Offline phase has an impact on the energy optimization achieved by the EAS algorithm

An efficient and scalable graph modeling approach for capturing information at different levels in next generation sequencing reads

Background: Next generation sequencing technologies have greatly advanced many research areas of the biomedical sciences through their capability to generate massive amounts of genetic information at unprecedented rates. The advent of next generation sequencing has led to the development of numerous computational tools to analyze and assemble the millions to billions of short sequencing reads produced by these technologies. While these tools filled an important gap, current approaches for storing, processing, and analyzing short read datasets generally have remained simple and lack the complexity needed to efficiently model the produced reads and assemble them correctly. Results: Previously, we presented an overlap graph coarsening scheme for modeling read overlap relationships on multiple levels. Most current read assembly and analysis approaches use a single graph or set of clusters to represent the relationships among a read dataset. Instead, we use a series of graphs to represent the reads and their overlap relationships across a spectrum of information granularity. At each information level our algorithm is capable of generating clusters of reads from the reduced graph, forming an integrated graph modeling and clustering approach for read analysis and assembly. Previously we applied our algorithm to simulated and real 454 datasets to assess its ability to efficiently model and cluster next generation sequencing data. In this paper we extend our algorithm to large simulated and real Illumina datasets to demonstrate that our algorithm is practical for both sequencing technologies. Conclusions: Our overlap graph theoretic algorithm is able to model next generation sequencing reads at various levels of granularity through the process of graph coarsening. Additionally, our model allows for efficient representation of the read overlap relationships, is scalable for large datasets, and is practical for both Illumina and 454 sequencing technologies

On the Tradeoff between Speedup and Energy Consumption in High Performance Computing – A Bioinformatics Case Study

High Performance Computing has been very useful to researchers in the Bioinformatics, Medical and related fields. The bioinformatics domain is rich in applications that require extracting useful information from very large and continuously growing sequence of databases. Automated techniques such as DNA sequencers, DNA microarrays & others are continually growing the dataset that is stored in large public databases such as GenBank and Protein DataBank. Most methods used for analyzing genetic/protein data have been found to be extremely computationally intensive, providing motivation for the use of powerful computers or systems with high throughput characteristics. In this paper, we provide a case study for one such bioinformatics application called BLAT running in a high performance computing environment. We use sequences gathered from researchers and parallelize the runs to study the performance characteristics under three different query and data partitioning models. This research highlights the need to carefully develop a parallel model with energy awareness in mind, based on our understanding of the application and then appropriately designing a parallel model that works well for the specific application and domain. We found that the BLAT program is highly parallelizable and a high degree of speedup is achievable. The experiments suggest that the speed up depends on model used for query and database segmentation

A Correlation Network Model for Structural Health Monitoring and Analyzing Safety Issues in Civil Infrastructures

Structural Health monitoring (SHM) is essential to analyze safety issues in civil infrastructures and bridges. With the recent advancements in sensor technology, SHM is moving from the occasional or periodic maintenance checks to continuous monitoring. While each technique, whether it is utilizing assessment or sensors, has their advantages and disadvantages, we propose a method to predict infrastructure health based on representing data streams from multiple sources into a graph model that is more scaleable, flexible and efficient than relational or unstructured databases. The proposed approach is centered on the idea of intelligently determining similarities among various structures based on population analysis that can then be visualized and carefully studied. If some “unhealthy” structures are identified through assessments or sensor readings, the model is capable of finding additional structures with similar parameters that need to be carefully inspected. This can save time, cost and effort in inspection cycles, provide increased readiness, help to prioritize inspections, and in general lead to safer, more reliable infrastructures

On the Tradeoff between Speedup and Energy Consumption in High Performance Computing – A Bioinformatics Case Study

High Performance Computing has been very useful to researchers in the Bioinformatics, Medical and related fields. The bioinformatics domain is rich in applications that require extracting useful information from very large and continuously growing sequence of databases. Automated techniques such as DNA sequencers, DNA microarrays & others are continually growing the dataset that is stored in large public databases such as GenBank and Protein DataBank. Most methods used for analyzing genetic/protein data have been found to be extremely computationally intensive, providing motivation for the use of powerful computers or systems with high throughput characteristics. In this paper, we provide a case study for one such bioinformatics application called BLAT running in a high performance computing environment. We use sequences gathered from researchers and parallelize the runs to study the performance characteristics under three different query and data partitioning models. This research highlights the need to carefully develop a parallel model with energy awareness in mind, based on our understanding of the application and then appropriately designing a parallel model that works well for the specific application and domain. We found that the BLAT program is highly parallelizable and a high degree of speedup is achievable. The experiments suggest that the speed up depends on model used for query and database segmentation