title: Identifying mechanistic similarities in drug responses.
abstract: BACKGROUND
  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.
  
  
  BACKGROUND
  The R code for the RLCSS algorithm is available at http://gsp.tamu.edu/Publications/supplementary/zhao12a.
authors.affiliation: Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX 77843, USA.
authors.forename: Chen
authors.initials: C
authors.lastname: Zhao
authors.forename: Jianping
authors.initials: J
authors.lastname: Hua
authors.forename: Michael L
authors.initials: ML
authors.lastname: Bittner
authors.forename: Ivan
authors.initials: I
authors.lastname: Ivanov
authors.forename: Edward R
authors.initials: ER
authors.lastname: Dougherty
mesh_terms: D000465:Algorithms; D045744:Cell Line, Tumor; D015195:Drug Design; D005786:Gene Expression Regulation / Q000187:drug effects*; D006801:Humans; D007091:Image Processing, Computer-Assisted; D011401:Promoter Regions, Genetic; D011506:Proteins / Q000737:chemistry; D014158:Transcription, Genetic / Q000187:drug effects
chemical_list: D011506:Proteins