Transcription factor network reconstruction using the living cell array

Abstract

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.

Comparison of the cytokine and chemokine dynamics of the early inflammatory response in models of burn injury and infection

“…Although very preliminary, the results of our TF analysis begin to point towards the direction of elucidating the structure and networks of transcription factors as these emerge as major contributors to regulation [57]. The idea of inferring functional interactions by observing the dynamics of regulated signals has been previously explored in the context of liver-specific responses to soluble signals [58], whereas we recently demonstrated how the regulated dynamics can begin to elucidate implicit upstream interactions among transcription factors [59–60]. In this context the information generated through our preliminary analyses enables the initial formulation of putative injury-specific modules of regulatory interactions (Figure 7).…”

Understanding Physiology in the Continuum: Integration of Information from Multiple -Omics Levels

“…(Note: in the analysis that follows, we assume that the base line expression levels, i.e., in the absence of the drug, are represented by mRNA and protein abundance levels prior to drug administration). In a number of previous publications, we have illustrated the analysis of longitudinal data with a time-varying base line ( Androulakis et al, 2007 ; Yang et al, 2007b , 2008a , 2009b , 2012a , b ; Yang E.H. et al, 2009 ; Almon et al, 2008a , b ; Nguyen et al, 2009 , 2010a , b , 2011 , 2014a ; Ovacik et al, 2010 ; Scheff et al, 2010a ; Swiss et al, 2011 ). However, temporal relations among time-varying quantities are known to be non-trivial, extending far beyond the classic view of correlation ( Qian et al, 2001 ).…”

Networks, biology and systems engineering: A case study in inflammation

“…The final constraint controls the expected complexity of the model by setting a limit on the destined number of interactions. More details are discussed in (Yang, Yarmush et al 2009)…”