Papers by D. Del Vecchio and E.D. Sontag

Understanding how opposing regulatory factors shape gene expression is essential for interpreting complex biological systems. A motivating observation, drawn from cancer epigenetics, is that removing an activating factor can sometimes lead to higher, not lower, expression of a gene that is also subject to repression. This counterintuitive behavior suggests that competition between activators and repressors for limited genomic binding sites may produce unexpected transcriptional outcomes. Prior theoretical work proposed this mechanism, but it has been difficult to test directly in natural systems, where layers of chromatin regulation obscure causal relationships. This paper introduces a fully synthetic, tunable genetic platform in a prokaryotic model system that isolates this competition mechanism in a clean and interpretable setting. The engineered construct contains a target gene with binding sites for both an activator and a repressor, together with a separate decoy region that carries overlapping binding sites for the same regulators. Activator and repressor functions are implemented using CRISPRa and CRISPRi, which permit independent control of regulator expression levels and binding affinities. Using this minimal system, the paper shows that increasing activator expression can reduce expression of the target gene when both regulators are present, consistent with the prediction that additional activator molecules displace the repressor from decoy sites and allow it to more effectively repress the target. By demonstrating how competition alone can invert expected regulatory responses, this synthetic framework provides a validated model for understanding similar paradoxical behaviors in natural regulatory networks and establishes a foundation for future studies in more complex mammalian contexts.

Synthetic gene circuits require cellular resources, which are often limited. This leads to competition for resources by different genes, which alter a synthetic genetic circuit's behavior. However, the manner in which competition impacts behavior depends on the identity of the "bottleneck" resource which might be difficult to discern from input-output data. In this paper, we aim at classifying the mathematical structures of resource competition in biochemical circuits. We find that some competition structures can be distinguished by their response to different competitors or resource levels. Specifically, we show that some response curves are always linear, convex, or concave. Furthermore, high levels of certain resources protect the behavior from low competition, while others do not. We also show that competition phenotypes respond differently to various interventions. Such differences can be used to eliminate candidate competition mechanisms when constructing models based on given data. On the other hand, we show that different networks can display mathematically equivalent competition phenotypes.