Parallel Load Balancing Strategies for Ensembles of Stochastic Biochemical Simulations

The evolution of biochemical systems where some chemical species are present with only a small number of molecules, is strongly influenced by discrete and stochastic effects that cannot be accurately captured by continuous and deterministic models. The budding yeast cell cycle provides an excellent example of the need to account for stochastic effects in biochemical reactions. To obtain statistics of the cell cycle progression, a stochastic simulation algorithm must be run thousands of times with different initial conditions and parameter values. In order to manage the computational expense involved, the large ensemble of runs needs to be executed in parallel. The CPU time for each individual task is unknown before execution, so a simple strategy of assigning an equal number of tasks per processor can lead to considerable work imbalances and loss of parallel efficiency. Moreover, deterministic analysis approaches are ill suited for assessing the effectiveness of load balancing algorithms in this context. Biological models often require stochastic simulation. Since generating an ensemble of simulation results is computationally intensive, it is important to make efficient use of computer resources. This paper presents a new probabilistic framework to analyze the performance of dynamic load balancing algorithms when applied to large ensembles of stochastic biochemical simulations. Two particular load balancing strategies (point-to-point and all-redistribution) are discussed in detail. Simulation results with a stochastic budding yeast cell cycle model confirm the theoretical analysis. While this work is motivated by cell cycle modeling, the proposed analysis framework is general and can be directly applied to any ensemble simulation of biological systems where many tasks are mapped onto each processor, and where the individual compute times vary considerably among tasks

A Framework to Analyze the Performance of Load Balancing Schemes for Ensembles of Stochastic Simulations

Ensembles of simulations are employed to estimate the statistics of possible future states of a system, and are widely used in important applications such as climate change and biological modeling. Ensembles of runs can naturally be executed in parallel. However, when the CPU times of individual simulations vary considerably, a simple strategy of assigning an equal number of tasks per processor can lead to serious work imbalances and low parallel efficiency. This paper presents a new probabilistic framework to analyze the performance of dynamic load balancing algorithms for ensembles of simulations where many tasks are mapped onto each processor, and where the individual compute times vary considerably among tasks. Four load balancing strategies are discussed: most-dividing, all-redistribution, random-polling, and neighbor-redistribution. Simulation results with a stochastic budding yeast cell cycle model is consistent with the theoretical analysis. It is especially significant that there is a provable global decrease in load imbalance for the local rebalancing algorithms due to scalability concerns for the global rebalancing algorithms. The overall simulation time is reduced by up to 25%, and the total processor idle time by 85%

Cell Cycle Modeling for Budding Yeast with Stochastic Simulation Algorithms

For biochemical systems, where some chemical species are represented by small numbers of molecules, discrete and stochastic approaches are more appropriate than continuous and deterministic approaches. The continuous deterministic approach using ordinary differential equations is adequate for understanding the average behavior of cells, while the discrete stochastic approach accurately captures noisy events in the growth-division cycle. Since the emergence of the stochastic simulation algorithm (SSA) by Gillespie, alternative algorithms have been developed whose goal is to improve the computational efficiency of the SSA. This paper explains and empirically compares the performance of some of these SSA alternatives on a realistic model. The budding yeast cell cycle provides an excellent example of the need for modeling stochastic effects in mathematical modeling of biochemical reactions. This paper presents a stochastic approximation of the cell cycle for budding yeast using Gillespie’s stochastic simulation algorithm. To compare the stochastic results with the average behavior, the simulation must be run thousands of times. Many of the proposed techniques to accelerate the SSA are not effective on the budding yeast problem, because of the scale of the problem or because underlying assumptions are not satisfied. A load balancing algorithm improved overall performance on a parallel supercomputer

Customized Energy Down-Shift using Iridium Complexes for Enhanced Performance of Polymer Solar Cells

School of Molecular Sciences(Chemistry)For the higher performance of polymer solar cells (PSCs), many researchers tried to develop new polymers that can absorb broader range of spectrum. However, there are some limits to absorb broader range with single donor. Therefore, multi donor systems and energy transfer systems have been researched. With two different donors it is easier to enhance absorption range. As a result, multi donor and energy transfer was successful to increase performance. However, the existing systems are applying polymer-polymer systems. When two different polymers are mixed, the compatibility between two polymers is critical to morphology of blend film. Also, in polymer-polymer energy transfer, the boundary between charge transfer and energy transfer is unclear. Therefore, for the first time, we developed customized iridium (Ir(III)) complexes, with Ir(III) complex incorporated into the active materials poly(thieno[3,4-b]-thiophene/benzodithiophene) (PTB7, amorphous) or poly(3-hexylthiophene) (P3HT, high crystalline) as energy donor additives. The Ir(III) complex with the 2-phenyl quinolone ligand energy donor increased the power conversion efficiency of the corresponding devices by approximately 20%. The enhancements are attributed to the improved molecular compatibility and energy level between the Ir(III) complex and the active materials, long F??rster resonance energy transfer radius, and high energy down-shift efficiency. Overall, we reveal Ir(III) complex additives for amorphous and highly crystalline polymer active materialsthese additives would enable efficient energy transfer in polymer solar cells, while retaining the desirable active layer morphology, thereby resulting in improved light absorption and conversion.ope

Delayed Infrarenal Aortic Pseudoaneurysm Treated by Endovascular Stent Graft in Pyogenic Spondylitis

A 61-year-old male patient with pyogenic spondylodiscitis and epidural and psoas abscesses underwent posterior decompression, debridement, and instrumented fusion, followed by anterior debridement and reconstruction. Sudden onset flank pain was diagnosed 7 weeks postoperatively and was determined to be a pseudoaneurysm located at the aorta inferior to the renal artery and superior to the aortic bifurcation area. An endovascular stent graft was applied to successfully treat the pseudoaneurysm. Postoperative recovery was uneventful and infection status was stabilized

LONGO: An R package for interactive gene length dependent analysis for neuronal identity

Motivation: Reprogramming somatic cells into neurons holds great promise to model neuronal development and disease. The efficiency and success rate of neuronal reprogramming, however, may vary between different conversion platforms and cell types, thereby necessitating an unbiased, systematic approach to estimate neuronal identity of converted cells. Recent studies have demonstrated that long genes (\u3e100 kb from transcription start to end) are highly enriched in neurons, which provides an opportunity to identify neurons based on the expression of these long genes. Results: We have developed a versatile R package, LONGO, to analyze gene expression based on gene length. We propose a systematic analysis of long gene expression (LGE) with a metric termed the long gene quotient (LQ) that quantifies LGE in RNA-seq or microarray data to validate neuronal identity at the single-cell and population levels. This unique feature of neurons provides an opportunity to utilize measurements of LGE in transcriptome data to quickly and easily distinguish neurons from non-neuronal cells. By combining this conceptual advancement and statistical tool in a user-friendly and interactive software package, we intend to encourage and simplify further investigation into LGE, particularly as it applies to validating and improving neuronal differentiation and reprogramming methodologies. Availability and implementation: LONGO is freely available for download at https://github.com/biohpc/longo. Supplementary information: Supplementary data are available at Bioinformatics online

Charge and dielectric effects of biomolecules on electrical characteristics of nanowire FET biosensors

The sensing mechanism of nanowire field effect transistor (NWFET) biosensors is investigated by taking into consideration both the charge and dielectric effects of biomolecules. The dielectric effect of the biomolecules is dominantly reflected in the linear regime, whereas the charge property is manifested in the subthreshold regime. The findings are supported by bio-experiments and numerical simulations. This study provides a rudimentary means of understanding interactions between biomolecules and NWFET biosensors

Pitfalls and Important Issues in Testing Reliability Using Intraclass Correlation Coefficients in Orthopaedic Research

Background: Intra-class correlation coeffi cients (ICCs) provide a statistical means of testing the reliability. However, their interpretationis not well documented in the orthopedic fi eld. The purpose of this study was to investigate the use of ICCs in the orthopedicliterature and to demonstrate pitfalls regarding their use. Methods: First, orthopedic articles that used ICCs were retrieved from the Pubmed database, and journal demography, ICC modelsand concurrent statistics used were evaluated. Second, reliability test was performed on three common physical examinationsin cerebral palsy, namely, the Thomas test, the Staheli test, and popliteal angle measurement. Thirty patients were assessed bythree orthopedic surgeons to explore the statistical methods testing reliability. Third, the factors affecting the ICC values were examinedby simulating the data sets based on the physical examination data where the ranges, slopes, and interobserver variabilitywere modifi ed. Results: Of the 92 orthopedic articles identifi ed, 58 articles (63%) did not clarify the ICC model used, and only 5 articles (5%)described all models, types, and measures. In reliability testing, although the popliteal angle showed a larger mean absolute differencethan the Thomas test and the Staheli test, the ICC of popliteal angle was higher, which was believed to be contrary to thecontext of measurement. In addition, the ICC values were affected by the model, type, and measures used. In simulated data sets,the ICC showed higher values when the range of data sets were larger, the slopes of the data sets were parallel, and the interobservervariability was smaller. Conclusions: Care should be taken when interpreting the absolute ICC values, i.e., a higher ICC does not necessarily mean lessvariability because the ICC values can also be affected by various factors. The authors recommend that researchers clarify ICCmodels used and ICC values are interpreted in the context of measurement.N