Gene regulatory networks form the basis for cellular decision-making processes. A description of the flow of information through such networks is essential to understanding their function. Such a description is complicated by the non-instantaneous nature of signal transmission. Signals, in the form of transcription factors, undergo a sequential assembly of first mRNA and then protein. This """"""""transcriptional delay"""""""" is important because it influences the simultaneity at dual-input transcriptional logic gates, and other motifs whose functions rely on the timing of arriving signals. Delay can also lead to nontrivial dynamics such as oscillations in gene networks containing feedback. Mathematical models of gene networks that incorporate delay have been used in the past. However, to date there has been no systematic effort to experimentally and theoretically characterize transcriptional delay and its effects on network behavior. The work outlined in this proposal will experimentally establish the effects of delay on transcriptional signaling and provide novel theoretical techniques for generating and analyzing models of gene networks incorporating delay. The work is highly interdisciplinary, incorporating aspects of synthetic biology, microfluidc engineering, theoretical biology, and dynamical systems. Specifically, the PIs will: 1) create novel synthetic gene networks consisting of common network motifs and variable length transcriptional cascades;2) characterize the dynamics of the synthetic networks at the single-cell level using microfluidic devices and time-lapse fluorescence microscopy;3) mathematically characterize the amount and variability of transcriptional delay as a function of cascade length;4) examine the impact of experimentally characterized delay on the dynamics and computation in simple gene networks;5) experimentally test these theoretical predictions in small synthetic gene networks, and 6) generate and analyze novel mathematical reduction techniques for incorporating transcriptional delay into models of gene regulatory networks.
This work is relevant to public health because it examines the impact of delayed processes in the regulation of gene networks critical to cellular survival. This enables the creation of better computational models of gene regulation that enhance understanding of living systems. Therefore, the proposed research is relevant to the part of NIH's mission to develop fundamental knowledge leading to an understanding of life processes.
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