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EXTRACTION OF TRANSCRIPTION FACTOR NETWORKS VIA GLOBALLY OPTIMAL
 

Summary: EXTRACTION OF TRANSCRIPTION FACTOR
NETWORKS VIA GLOBALLY OPTIMAL
BICLUSTERING
E. Yang1
, P.T. Foteinou1
, K.R. King2
, M.L. Yarmush1,2
and I.P Androulakis*
1
1
Rutgers University, Department of Biomedical Engineering, Piscataway, NJ 08853
2
Center for Engineering in Medicine/Surgical Services, Massachusetts General
Hospital, Harvard Medical School, Boston, MA 02114, USA
Abstract
We present an optimization-based framework for identifying and quantifying interactions transcription
factor networks. We explore the availability of high-temporal resolution expression data using the
Living Cell Array and we formulate to critical problems. First, we demonstrate how to rigorously obtain
bi-clusters of transcription factor and condition through a novel MILP formulation and subsequently
demonstrate how such networks can be quantify using appropriate deconvolution schemes based on

  

Source: Androulakis, Ioannis (Yannis) - Biomedical Engineering Department & Department of Chemical and Biochemical Engineering, Rutgers University

 

Collections: Engineering; Biology and Medicine