Frequency Analysis of Two-Component Light SensorsView all posters
Rice University Bioengineering, United States
In response to the construction of complex electrical circuits and control systems, the engineering community has developed a range of analytical tools to robustly characterize and model integrated systems that would otherwise be unpredictable. Broadly labeled system identification, it provides a framework for the parameterization of such systems and the construction of a “black box” input-output relationship, called a transfer function. Transfer function models are used to determine the operating limits of newly-engineered components and reliably predict the behavior of those components when interfaced with others. Such analysis has previously been applied to cellular systems using step and oscillatory inputs, most recently using microfluidic apparatus to modulate chemical inputs. While valuable, these methods are impractical for interfacing with cells on fast (< 1 minute) timescales and are not amenable to spatial patterning, in contrast to our previously published light-sensing two component systems, which can be modulated quickly and patterned easily. In order to parameterize a transfer function input-output model of signal transduction via these light sensors, we have performed frequency analysis on two different sensors by subjecting cells to sinusoidally oscillating light inputs spanning a wide range of amplitudes and frequencies, and monitoring gene expression output precisely and with high temporal resolution. Our model has yielded a better understanding of the sensors’ filtering characteristics, noise, and signal transduction limits, suggesting methods of improving sensor performance via engineering. Improved sensors would be ideally suited for applications requiring high-fidelity spatio-temporal patterning of gene expression, for example in the study of developmental processes by patterning precise morphogen gradients with light. This characterization will also enable the light sensors to be used to assess the frequency response of other genes or gene networks, leveraging all the advantages of light induction to address the challenge of analyzing more complex genetic regulatory systems, synthetic or otherwise.