Transcription factor network reconstruction using the living cell array.
The objective of identifying transcriptional regulatory networks is to provide insights as to what governs an organism's long term response to external stimuli. We explore the coupling of the living cell array (LCA), a novel microfluidics device which utilizes fluorescence levels as a surrogate for transcription factor activity with reverse Euler deconvolution (RED) a computational technique proposed in this work to decipher the dynamics of the interactions. It is hypothesized that these two methods will allow us to first assess the underlying network architecture associated with the transcription factor network as well as specific mechanistic consequences of transcription factor activation such as receptor dimerization or tolerance. The overall approach identifies evidence of time-lagged response which may be indicative of mechanisms such as receptor dimerization, tolerance mechanisms which are evidence of various receptor mediated dynamics, and feedback loops which regulate the response of an organism to changing environmental conditions. Furthermore, through the exploration of multiple network architectures, we were able to obtain insights as to the role each transcription factor plays in the overall response and their overall redundancy in the organism's response to external perturbations. Thus, the LCA along with the proposed analysis technique is a valuable tool for identifying the possible architectures and mechanisms underlying the transcriptional response.
Yang
Eric
E
Department of Biomedical Engineering, Rutgers University, 599 Taylor Road, Piscataway, NJ 08854, USA. eyang@eden.rutgers.edu
Yarmush
Martin L
ML
Androulakis
Ioannis P
IP
D002477:Cells / Q000378:metabolism*; D003198:Computer Simulation; D020869:Gene Expression Profiling / Q000379:methods; D053263:Gene Regulatory Networks; D044085:Microfluidics; D008957:Models, Genetic; D020411:Oligonucleotide Array Sequence Analysis; D014157:Transcription Factors / Q000235:genetics / Q000378:metabolism; D014158:Transcription, Genetic; D015533:Transcriptional Activation
D014157:Transcription Factors