Handle URI:
http://hdl.handle.net/10754/598553
Title:
Identifying mechanistic similarities in drug responses
Authors:
Zhao, C.; Hua, J.; Bittner, M. L.; Ivanov, I.; Dougherty, a. E. R.
Abstract:
Motivation: In early drug development, it would be beneficial to be able to identify those dynamic patterns of gene response that indicate that drugs targeting a particular gene will be likely or not to elicit the desired response. One approach would be to quantitate the degree of similarity between the responses that cells show when exposed to drugs, so that consistencies in the regulation of cellular response processes that produce success or failure can be more readily identified.Results: We track drug response using fluorescent proteins as transcription activity reporters. Our basic assumption is that drugs inducing very similar alteration in transcriptional regulation will produce similar temporal trajectories on many of the reporter proteins and hence be identified as having similarities in their mechanisms of action (MOA). The main body of this work is devoted to characterizing similarity in temporal trajectories/signals. To do so, we must first identify the key points that determine mechanistic similarity between two drug responses. Directly comparing points on the two signals is unrealistic, as it cannot handle delays and speed variations on the time axis. Hence, to capture the similarities between reporter responses, we develop an alignment algorithm that is robust to noise, time delays and is able to find all the contiguous parts of signals centered about a core alignment (reflecting a core mechanism in drug response). Applying the proposed algorithm to a range of real drug experiments shows that the result agrees well with the prior drug MOA knowledge. © The Author 2012. Published by Oxford University Press. All rights reserved.
Citation:
Zhao C, Hua J, Bittner ML, Ivanov I, Dougherty a. ER (2012) Identifying mechanistic similarities in drug responses. Bioinformatics 28: 1902–1910. Available: http://dx.doi.org/10.1093/bioinformatics/bts290.
Publisher:
Oxford University Press (OUP)
Journal:
Bioinformatics
KAUST Grant Number:
KUS-C1-016-04
Issue Date:
15-May-2012
DOI:
10.1093/bioinformatics/bts290
PubMed ID:
22592382
Type:
Article
ISSN:
1367-4803; 1460-2059
Sponsors:
This publication is based in part on work supported by Award No. KUS-C1-016-04, made by King Abdullah University of Science and Technology (KAUST).
Appears in Collections:
Publications Acknowledging KAUST Support

Full metadata record

DC FieldValue Language
dc.contributor.authorZhao, C.en
dc.contributor.authorHua, J.en
dc.contributor.authorBittner, M. L.en
dc.contributor.authorIvanov, I.en
dc.contributor.authorDougherty, a. E. R.en
dc.date.accessioned2016-02-25T13:32:03Zen
dc.date.available2016-02-25T13:32:03Zen
dc.date.issued2012-05-15en
dc.identifier.citationZhao C, Hua J, Bittner ML, Ivanov I, Dougherty a. ER (2012) Identifying mechanistic similarities in drug responses. Bioinformatics 28: 1902–1910. Available: http://dx.doi.org/10.1093/bioinformatics/bts290.en
dc.identifier.issn1367-4803en
dc.identifier.issn1460-2059en
dc.identifier.pmid22592382en
dc.identifier.doi10.1093/bioinformatics/bts290en
dc.identifier.urihttp://hdl.handle.net/10754/598553en
dc.description.abstractMotivation: In early drug development, it would be beneficial to be able to identify those dynamic patterns of gene response that indicate that drugs targeting a particular gene will be likely or not to elicit the desired response. One approach would be to quantitate the degree of similarity between the responses that cells show when exposed to drugs, so that consistencies in the regulation of cellular response processes that produce success or failure can be more readily identified.Results: We track drug response using fluorescent proteins as transcription activity reporters. Our basic assumption is that drugs inducing very similar alteration in transcriptional regulation will produce similar temporal trajectories on many of the reporter proteins and hence be identified as having similarities in their mechanisms of action (MOA). The main body of this work is devoted to characterizing similarity in temporal trajectories/signals. To do so, we must first identify the key points that determine mechanistic similarity between two drug responses. Directly comparing points on the two signals is unrealistic, as it cannot handle delays and speed variations on the time axis. Hence, to capture the similarities between reporter responses, we develop an alignment algorithm that is robust to noise, time delays and is able to find all the contiguous parts of signals centered about a core alignment (reflecting a core mechanism in drug response). Applying the proposed algorithm to a range of real drug experiments shows that the result agrees well with the prior drug MOA knowledge. © The Author 2012. Published by Oxford University Press. All rights reserved.en
dc.description.sponsorshipThis publication is based in part on work supported by Award No. KUS-C1-016-04, made by King Abdullah University of Science and Technology (KAUST).en
dc.publisherOxford University Press (OUP)en
dc.titleIdentifying mechanistic similarities in drug responsesen
dc.typeArticleen
dc.identifier.journalBioinformaticsen
dc.contributor.institutionTexas A and M University, College Station, United Statesen
dc.contributor.institutionTranslational Genomics Research Institute, Phoenix, United Statesen
dc.contributor.institutionUniversity of Texas M. D. Anderson Cancer Center, Houston, United Statesen
kaust.grant.numberKUS-C1-016-04en

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