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Srimani JK, Diderichsen PM, Hanley MJ, Labotka R, Gupta N. Dose Titration of Ixazomib Maintenance Therapy in Transplant-Ineligible Multiple Myeloma: Exposure-Response Analysis of the TOURMALINE-MM4 Study. Clin Pharmacol Ther 2023. [PMID: 37186295 DOI: 10.1002/cpt.2917] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2023] [Accepted: 04/05/2023] [Indexed: 05/17/2023]
Abstract
Ixazomib has been approved in several countries as single agent maintenance therapy in newly diagnosed multiple myeloma (NDMM), in both post-transplant and transplant-ineligible settings, based on two Phase 3 studies. In these maintenance studies, patients were initially administered 3 mg ixazomib, escalating to 4 mg if the initial dose level was well-tolerated through Cycles 1-4. Here, we report the results of exposure-response analyses of TOURMALINE-MM4, wherein relationships between exposure and clinical response, dose adjustments, and selected adverse events (AEs) were evaluated. Similar progression-free survival (PFS) benefits were observed across the range of ixazomib exposures achieved in the study. Moreover, increased ixazomib exposures corresponded to a higher probability of maintaining complete response (CR). Exposure was not a significant predictor (p>0.05) of hematological adverse events (anemia, neutropenia, thrombocytopenia) and peripheral neuropathy; however, higher exposures did correlate to increased probabilities of experiencing diarrhea, vomiting, nausea, rash, and fatigue. While ixazomib exposure was not predictive of dose reductions, lower apparent clearance values (corresponding to higher systemic exposures) were correlated with a reduced likelihood of escalating to the 4 mg dose. Thus, the dose titration approach balanced patient benefit and risk; it ensured that only patients for whom the 3 mg dose was safe/tolerable escalated to the higher dose, while maximizing the fraction of patients (85%) who were able to derive additional clinical benefit at 4 mg. Collectively, these results highlight the value of safety-driven personalized dosing to maximize patient benefit/risk.
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Affiliation(s)
| | | | - Michael J Hanley
- Takeda Development Center Americas, Inc. (TDCA), Lexington, MA, USA
| | - Richard Labotka
- Takeda Development Center Americas, Inc. (TDCA), Lexington, MA, USA
| | - Neeraj Gupta
- Takeda Development Center Americas, Inc. (TDCA), Lexington, MA, USA
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2
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Srimani JK, Diderichsen PM, Hanley MJ, Venkatakrishnan K, Labotka R, Gupta N. Population pharmacokinetic/pharmacodynamic joint modeling of ixazomib efficacy and safety using data from the pivotal phase III TOURMALINE‐MM1 study in multiple myeloma patients. CPT Pharmacometrics Syst Pharmacol 2022; 11:1085-1099. [PMID: 35598166 PMCID: PMC9381907 DOI: 10.1002/psp4.12815] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2021] [Revised: 04/27/2022] [Accepted: 05/03/2022] [Indexed: 01/04/2023] Open
Abstract
Ixazomib is an oral proteasome inhibitor approved in combination with lenalidomide and dexamethasone for the treatment of relapsed/refractory multiple myeloma (MM). Approval in the United States, Europe, and additional countries was based on results from the phase III TOURMALINE‐MM1 (C16010) study. Here, joint population pharmacokinetic/pharmacodynamic time‐to‐event (TTE) and discrete time Markov models were developed to describe key safety (rash and diarrhea events, and platelet counts) and efficacy (myeloma protein [M‐protein] and progression‐free survival [PFS]) outcomes observed in TOURMALINE‐MM1. Models reliably described observed safety and efficacy results; prior immunomodulatory drug therapy and race were significant covariates for diarrhea and rash events, respectively, whereas M‐protein dynamics were sufficiently characterized using TTE models of relapse and dropout. Moreover, baseline M‐protein was identified as a significant covariate for observed PFS. The developed framework represents an integrated approach to describing safety and efficacy with MM therapy, enabling the simulation of prospective trials and potential alternate dosing regimens.
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Affiliation(s)
- Jaydeep K. Srimani
- Takeda Development Center Americas, Inc. (TDCA) Lexington Massachusetts USA
| | | | - Michael J. Hanley
- Takeda Development Center Americas, Inc. (TDCA) Lexington Massachusetts USA
| | | | - Richard Labotka
- Takeda Development Center Americas, Inc. (TDCA) Lexington Massachusetts USA
| | - Neeraj Gupta
- Takeda Development Center Americas, Inc. (TDCA) Lexington Massachusetts USA
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3
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Dong P, Maddali MV, Srimani JK, Thélot F, Nevins JR, Mathey-Prevot B, You L. Author Correction: Division of labour between Myc and G1 cyclins in cell cycle commitment and pace control. Nat Commun 2018; 9:4766. [PMID: 30425246 PMCID: PMC6233176 DOI: 10.1038/s41467-018-07169-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022] Open
Abstract
This Article contains errors in Supplementary Table 3, which are described in the Author Correction associated with this Article. The simulation results in the Article were based on the correct formula and thus the results are not affected by this correction. The errors have not been fixed in the original Article.
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Affiliation(s)
- Peng Dong
- Computational Biology and Bioinformatics Program, Duke University, Durham, North Carolina, 27708, USA
| | - Manoj V Maddali
- Department of Biomedical Engineering, Duke University, Durham, North Carolina, 27708, USA.,School of Medicine, Johns Hopkins University, Baltimore, Maryland, 21205, USA
| | - Jaydeep K Srimani
- Department of Biomedical Engineering, Duke University, Durham, North Carolina, 27708, USA
| | - François Thélot
- Department of Pharmacology and Cancer Biology, Duke University, Durham, North Carolina, 27708, USA
| | - Joseph R Nevins
- Department of Molecular Genetics and Microbiology, Duke University, Durham, North Carolina, 27708, USA
| | - Bernard Mathey-Prevot
- Department of Pharmacology and Cancer Biology, Duke University, Durham, North Carolina, 27708, USA. .,Department of Pediatrics, Duke University, Durham, North Carolina, 27708, USA.
| | - Lingchong You
- Department of Biomedical Engineering, Duke University, Durham, North Carolina, 27708, USA. .,Center for Genomic and Computational Biology, Duke University, Durham, North Carolina, 27708, USA. .,Duke Center for Systems Biology, Duke University, Durham, North Carolina, 27708, USA.
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4
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Lopatkin AJ, Meredith HR, Srimani JK, Pfeiffer C, Durrett R, You L. Persistence and reversal of plasmid-mediated antibiotic resistance. Nat Commun 2017; 8:1689. [PMID: 29162798 PMCID: PMC5698434 DOI: 10.1038/s41467-017-01532-1] [Citation(s) in RCA: 181] [Impact Index Per Article: 25.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2017] [Accepted: 09/22/2017] [Indexed: 02/07/2023] Open
Abstract
In the absence of antibiotic-mediated selection, sensitive bacteria are expected to displace their resistant counterparts if resistance genes are costly. However, many resistance genes persist for long periods in the absence of antibiotics. Horizontal gene transfer (primarily conjugation) could explain this persistence, but it has been suggested that very high conjugation rates would be required. Here, we show that common conjugal plasmids, even when costly, are indeed transferred at sufficiently high rates to be maintained in the absence of antibiotics in Escherichia coli. The notion is applicable to nine plasmids from six major incompatibility groups and mixed populations carrying multiple plasmids. These results suggest that reducing antibiotic use alone is likely insufficient for reversing resistance. Therefore, combining conjugation inhibition and promoting plasmid loss would be an effective strategy to limit conjugation-assisted persistence of antibiotic resistance. It is unclear whether the transfer of plasmids carrying antibiotic resistance genes can explain their persistence when antibiotics are not present. Here, Lopatkin et al. show that conjugal plasmids, even when costly, are indeed transferred at sufficiently high rates to be maintained in the absence of antibiotics.
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Affiliation(s)
- Allison J Lopatkin
- Department of Biomedical Engineering, Duke University, Durham, NC, 27708, USA
| | - Hannah R Meredith
- Department of Biomedical Engineering, Duke University, Durham, NC, 27708, USA
| | - Jaydeep K Srimani
- Department of Biomedical Engineering, Duke University, Durham, NC, 27708, USA
| | - Connor Pfeiffer
- Department of Biology, Duke University, Durham, NC, 27708, USA
| | - Rick Durrett
- Department of Mathematics, Duke University, Durham, NC, 27708, USA
| | - Lingchong You
- Department of Biomedical Engineering, Duke University, Durham, NC, 27708, USA. .,Center for Genomic and Computational Biology, Duke University, Durham, NC, 27708, USA. .,Department of Molecular Genetics and Microbiology, Duke University School of Medicine, Durham, NC, 27710, USA.
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5
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Abstract
The postantibiotic effect (PAE) refers to the temporary suppression of bacterial growth following transient antibiotic treatment. This effect has been observed for decades for a wide variety of antibiotics and microbial species. However, despite empirical observations, a mechanistic understanding of this phenomenon is lacking. Using a combination of modeling and quantitative experiments, we show that the PAE can be explained by the temporal dynamics of drug detoxification in individual cells after an antibiotic is removed from the extracellular environment. These dynamics are dictated by both the export of the antibiotic and the intracellular titration of the antibiotic by its target. This mechanism is generally applicable for antibiotics with different modes of action. We further show that efflux inhibition is effective against certain antibiotic motifs, which may help explain mixed cotreatment success.
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Affiliation(s)
- Jaydeep K Srimani
- Department of Biomedical Engineering, Duke University, Durham, NC, USA
| | - Shuqiang Huang
- Center for Synthetic Biology Engineering Research, Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences, Shenzhen, China
| | | | - Lingchong You
- Department of Biomedical Engineering, Duke University, Durham, NC, USA .,Center for Genomic and Computational Biology, Duke University, Durham, NC, USA.,Department of Molecular Genetics and Microbiology, Duke University School of Medicine, Durham, NC, USA
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Lopatkin AJ, Huang S, Smith RP, Srimani JK, Sysoeva TA, Bewick S, Karig D, You L. Antibiotics as a selective driver for conjugation dynamics. Nat Microbiol 2016; 1:16044. [PMID: 27572835 PMCID: PMC5010019 DOI: 10.1038/nmicrobiol.2016.44] [Citation(s) in RCA: 175] [Impact Index Per Article: 21.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2015] [Accepted: 03/02/2016] [Indexed: 01/19/2023]
Abstract
It is generally assumed that antibiotics can promote horizontal gene transfer. However, because of a variety of confounding factors that complicate the interpretation of previous studies, the mechanisms by which antibiotics modulate horizontal gene transfer remain poorly understood. In particular, it is unclear whether antibiotics directly regulate the efficiency of horizontal gene transfer, serve as a selection force to modulate population dynamics after such gene transfer has occurred, or both. Here, we address this question by quantifying conjugation dynamics in the presence and absence of antibiotic-mediated selection. Surprisingly, we find that sublethal concentrations of antibiotics from the most widely used classes do not significantly increase the conjugation efficiency. Instead, our modelling and experimental results demonstrate that conjugation dynamics are dictated by antibiotic-mediated selection, which can both promote and suppress conjugation dynamics. Our findings suggest that the contribution of antibiotics to the promotion of horizontal gene transfer may have been overestimated. These findings have implications for designing effective antibiotic treatment protocols and for assessing the risks of antibiotic use.
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Affiliation(s)
- Allison J. Lopatkin
- Department of Biomedical Engineering, Duke University, Durham, North Carolina, USA
| | - Shuqiang Huang
- Department of Biomedical Engineering, Duke University, Durham, North Carolina, USA
| | - Robert P. Smith
- Department of Biological Sciences, Halmos College of Natural Sciences and Oceanography, Nova Southeastern University, Fort Lauderdale FL, USA
| | - Jaydeep K. Srimani
- Department of Biomedical Engineering, Duke University, Durham, North Carolina, USA
| | - Tatyana A. Sysoeva
- Department of Biomedical Engineering, Duke University, Durham, North Carolina, USA
| | - Sharon Bewick
- Department of Biology, University of Maryland, College Park, MD 20742, USA
| | - David Karig
- Johns Hopkins University Applied Physics Laboratory, Laurel, MD, USA
| | - Lingchong You
- Department of Biomedical Engineering, Duke University, Durham, North Carolina, USA
- Center for Genomic and Computational Biology, Duke University, Durham, North Carolina, USA
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Abstract
Numerous bacterial species utilize quorum sensing to communicate, but crosstalk often complicates the dynamics of mixed populations. In this issue of Chemistry & Biology, Wu and colleagues take advantage of synthetic gene circuits to elucidate interactions between two quorum sensing systems, with potential applications to fields from infectious diseases to biosynthesis.
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Affiliation(s)
- Jaydeep K Srimani
- Department of Biomedical Engineering, Center for Genomic and Computational Biology, Center for Systems Biology, Duke University, Durham, NC 27708, USA
| | - Lingchong You
- Department of Biomedical Engineering, Center for Genomic and Computational Biology, Center for Systems Biology, Duke University, Durham, NC 27708, USA.
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Huang S, Srimani JK, Lee AJ, Zhang Y, Lopatkin AJ, Leong KW, You L. Dynamic control and quantification of bacterial population dynamics in droplets. Biomaterials 2015; 61:239-45. [PMID: 26005763 DOI: 10.1016/j.biomaterials.2015.05.038] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2015] [Revised: 05/16/2015] [Accepted: 05/18/2015] [Indexed: 10/23/2022]
Abstract
Culturing and measuring bacterial population dynamics are critical to develop insights into gene regulation or bacterial physiology. Traditional methods, based on bulk culture to obtain such quantification, have the limitations of higher cost/volume of reagents, non-amendable to small size of population and more laborious manipulation. To this end, droplet-based microfluidics represents a promising alternative that is cost-effective and high-throughput. However, difficulties in manipulating the droplet environment and monitoring encapsulated bacterial population for long-term experiments limit its utilization. To overcome these limitations, we used an electrode-free injection technology to modulate the chemical environment in droplets. This ability is critical for precise control of bacterial dynamics in droplets. Moreover, we developed a trapping device for long-term monitoring of population dynamics in individual droplets for at least 240 h. We demonstrated the utility of this new microfluidic system by quantifying population dynamics of natural and engineered bacteria. Our approach can further improve the analysis for systems and synthetic biology in terms of manipulability and high temporal resolution.
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Affiliation(s)
- Shuqiang Huang
- Department of Biomedical Engineering, Duke University, Durham, NC, USA
| | - Jaydeep K Srimani
- Department of Biomedical Engineering, Duke University, Durham, NC, USA
| | - Anna J Lee
- Department of Biomedical Engineering, Duke University, Durham, NC, USA
| | - Ying Zhang
- Department of Biomedical Engineering, Duke University, Durham, NC, USA; Department of Biomedical Engineering, Columbia University, New York, USA
| | | | - Kam W Leong
- Department of Biomedical Engineering, Columbia University, New York, USA
| | - Lingchong You
- Department of Biomedical Engineering, Duke University, Durham, NC, USA; Duke Center for Genomic and Computational Biology, Duke University, Durham, NC, USA.
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Srimani JK, Yao G, Neu J, Tanouchi Y, Lee TJ, You L. Linear population allocation by bistable switches in response to transient stimulation. PLoS One 2014; 9:e105408. [PMID: 25141235 PMCID: PMC4139379 DOI: 10.1371/journal.pone.0105408] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2014] [Accepted: 07/23/2014] [Indexed: 12/19/2022] Open
Abstract
Many cellular decision processes, including proliferation, differentiation, and phenotypic switching, are controlled by bistable signaling networks. In response to transient or intermediate input signals, these networks allocate a population fraction to each of two distinct states (e.g. OFF and ON). While extensive studies have been carried out to analyze various bistable networks, they are primarily focused on responses of bistable networks to sustained input signals. In this work, we investigate the response characteristics of bistable networks to transient signals, using both theoretical analysis and numerical simulation. We find that bistable systems exhibit a common property: for input signals with short durations, the fraction of switching cells increases linearly with the signal duration, allowing the population to integrate transient signals to tune its response. We propose that this allocation algorithm can be an optimal response strategy for certain cellular decisions in which excessive switching results in lower population fitness.
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Affiliation(s)
- Jaydeep K. Srimani
- Department of Biomedical Engineering, Duke University, Durham, North Carolina, United States of America
| | - Guang Yao
- Department of Molecular & Cellular Biology, University of Arizona, Tucson, Arizona, United States of America
| | - John Neu
- Department of Biomedical Engineering, Duke University, Durham, North Carolina, United States of America
| | - Yu Tanouchi
- Department of Biomedical Engineering, Duke University, Durham, North Carolina, United States of America
| | - Tae Jun Lee
- Department of Biomedical Engineering, Duke University, Durham, North Carolina, United States of America
| | - Lingchong You
- Department of Biomedical Engineering, Duke University, Durham, North Carolina, United States of America
- * E-mail:
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10
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Abstract
Quorum sensing (QS) enables bacteria to sense and respond to changes in their population density. It plays a critical role in controlling different biological functions, including bioluminescence and bacterial virulence. It has also been widely adapted to program robust dynamics in one or multiple cellular populations. While QS systems across bacteria all appear to function similarly-as density-dependent control systems-there is tremendous diversity among these systems in terms of signaling components and network architectures. This diversity hampers efforts to quantify the general control properties of QS. For a specific QS module, it remains unclear how to most effectively characterize its regulatory properties in a manner that allows quantitative predictions of the activation dynamics of the target gene. Using simple kinetic models, here we show that the dominant temporal dynamics of QS-controlled target activation can be captured by a generic metric, 'sensing potential', defined at a single time point. We validate these predictions using synthetic QS circuits in Escherichia coli. Our work provides a computational framework and experimental methodology to characterize diverse natural QS systems and provides a concise yet quantitative criterion for selecting or optimizing a QS system for synthetic biology applications.
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Affiliation(s)
- Anand Pai
- Department
of Biomedical Engineering ‡Institute for Genome Sciences and Policy Duke University, Durham, North Carolina 27708, United States
| | - Jaydeep K. Srimani
- Department
of Biomedical Engineering ‡Institute for Genome Sciences and Policy Duke University, Durham, North Carolina 27708, United States
| | - Yu Tanouchi
- Department
of Biomedical Engineering ‡Institute for Genome Sciences and Policy Duke University, Durham, North Carolina 27708, United States
| | - Lingchong You
- Department
of Biomedical Engineering ‡Institute for Genome Sciences and Policy Duke University, Durham, North Carolina 27708, United States
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Tan C, Phillip Smith R, Srimani JK, Riccione KA, Prasada S, Kuehn M, You L. The inoculum effect and band-pass bacterial response to periodic antibiotic treatment. Mol Syst Biol 2012; 8:617. [PMID: 23047527 PMCID: PMC3472685 DOI: 10.1038/msb.2012.49] [Citation(s) in RCA: 74] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2012] [Accepted: 08/29/2012] [Indexed: 12/25/2022] Open
Abstract
The inoculum effect (IE) refers to the decreasing efficacy of an antibiotic with increasing bacterial density. It represents a unique strategy of antibiotic tolerance and it can complicate design of effective antibiotic treatment of bacterial infections. To gain insight into this phenomenon, we have analyzed responses of a lab strain of Escherichia coli to antibiotics that target the ribosome. We show that the IE can be explained by bistable inhibition of bacterial growth. A critical requirement for this bistability is sufficiently fast degradation of ribosomes, which can result from antibiotic-induced heat-shock response. Furthermore, antibiotics that elicit the IE can lead to 'band-pass' response of bacterial growth to periodic antibiotic treatment: the treatment efficacy drastically diminishes at intermediate frequencies of treatment. Our proposed mechanism for the IE may be generally applicable to other bacterial species treated with antibiotics targeting the ribosomes.
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Affiliation(s)
- Cheemeng Tan
- Department of Biomedical Engineering, Duke University, Durham, NC, USA
| | | | - Jaydeep K Srimani
- Department of Biomedical Engineering, Duke University, Durham, NC, USA
| | | | - Sameer Prasada
- Department of Biomedical Engineering, Duke University, Durham, NC, USA
| | - Meta Kuehn
- Department of Biochemistry, Duke University Medical Center, Durham, NC, USA
| | - Lingchong You
- Department of Biomedical Engineering, Duke University, Durham, NC, USA
- Institute for Genome Sciences and Policy, Duke University, Durham, NC, USA
- Center for Systems Biology, Duke University, Durham, NC, USA
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Srimani JK, Wu PY, Phan JH, Wang MD. A distributed system for fast alignment of next-generation sequencing data. IEEE Int Conf Bioinform Biomed Workshops 2010; 2010:579-584. [PMID: 27536739 PMCID: PMC4984844 DOI: 10.1109/bibmw.2010.5703865] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/06/2023]
Abstract
We developed a scalable distributed computing system using the Berkeley Open Interface for Network Computing (BOINC) to align next-generation sequencing (NGS) data quickly and accurately. NGS technology is emerging as a promising platform for gene expression analysis due to its high sensitivity compared to traditional genomic microarray technology. However, despite the benefits, NGS datasets can be prohibitively large, requiring significant computing resources to obtain sequence alignment results. Moreover, as the data and alignment algorithms become more prevalent, it will become necessary to examine the effect of the multitude of alignment parameters on various NGS systems. We validate the distributed software system by (1) computing simple timing results to show the speed-up gained by using multiple computers, (2) optimizing alignment parameters using simulated NGS data, and (3) computing NGS expression levels for a single biological sample using optimal parameters and comparing these expression levels to that of a microarray sample. Results indicate that the distributed alignment system achieves approximately a linear speed-up and correctly distributes sequence data to and gathers alignment results from multiple compute clients.
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Affiliation(s)
- Jaydeep K Srimani
- Department of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, 30332, USA
| | - Po-Yen Wu
- Department of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, 30332, USA
| | - John H Phan
- Department of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, 30332, USA
| | - May D Wang
- Department of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, 30332, USA
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