|dc.description.abstract||Variation is omnipresent in biological systems. Organisms must survive in fluctuating environments, genetic diversity drives evolution, and finite molecule counts challenge signal processing accuracy. However, in classic cellular biology, this variation is often ignored. While a dose-response curve may show error bars, these are often attributed to measurement error and not assumed to be true biological variation, or noise. Recently, however, sufficiently powerful technology has been developed to measure response of many cells at the single-cell level. These studies have raised significant questions in cellular and systems biology, requiring the development of new methods and revisiting of old models.
In this manuscript, we first develop a novel method based on organism symmetry for differentiating between sources of noise. We apply this method to Drosophila early morphogenesis dorsal-ventral (D-V) cell differentiation. We identify significant position-dependent structure to the noise. Given commonality of organisms exhibiting bilateral symmetry, this method can find very wide applicability. Further, by leveraging information, we are able to demonstrate which components of the noise have a significant effect on Drosophila development. We then investigate noise in the context of a multicellular organism performing gradient detection. In contrast to previous studies, which have focused only on noise outside of the cell, we find that noise inside the cell contributes significantly to gradient detection accuracy. In particular, as a cell grows however, internal communication noise is also expected to grow, complicating comparison of concentrations. We discover this effect through a combination of using linear mathematics, nonlinear simulations, and in vivo experiments on multicellular organoids, we show that results obtained by ignoring internal system variation are incomplete. Finally, we consider noise in the context of a system which must detect signal duration despite variation in the signal amplitude. We show that the Incoherent Type 1 Feed Forward Loop (I1FFL), one of the most common network motifs, is capable of accurately performing this detection function. Taken together, these contributions provide new analytical tools, shed additional light on the importance of biological noise, and support an increasingly wide-held view that variation is a fundamental aspect of biology.||