Engineering biological networks using cooperative transcriptional assembly
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Eukaryotic genes are often regulated by multivalent transcription factor (TF) complexes. Through the process of cooperative self-assembly, these complexes carry out non-linear regulatory operations involved in cellular decision-making and signal processing. In this thesis, we apply this natural design principle to artificial networks, testing whether engineered cooperative TF assemblies can be used to program non-linear synthetic circuit behavior in yeast. Using a model-guided approach, we show that specifying strength and number of interactions in an assembly enables predictive tuning between regimes of linear and non-linear regulatory response for single- and multi-input circuits. We demonstrate that synthetic assemblies can be adjusted to control circuit dynamics, shaping the timing of activation. We harness this capability to engineer circuits that perform dynamic filtering, enabling frequency-dependent decoding in cell populations. Thru this work, we find that cooperative assembly provides a versatile way to tune nonlinearity of network connections, dramatically expanding the range engineerable behaviors available to synthetic circuits. We then extend our modeling-framework to predict genome-wide binding of our TF assemblies and find that cooperative complexes made of weakly-interacting proteins can reduce unintended activation of endogenous genes. Thus, we are able to introduce synthetic regulatory components with low fitness costs on the cell, ensuring long-term stability of our integrated circuits over time. Taken together, this dissertation outlines a synthetic framework for building cooperative transcriptional complexes in vivo in order to engineer complex regulatory behaviors that are functionally orthogonal to the host cell.