Curated Optogenetic Publication Database

Abstract: The bacterial stress response is an intricately regulated system that plays a critical role in cellular resistance to drug treatment. The complexity of this response is further complicated by cell-to-cell heterogeneity in the expression of bacterial stress response genes. These genes are often organized into networks comprising one or more transcriptional regulators that control expression of a suite of downstream genes. While the expression heterogeneity of many of these upstream regulators has been characterized, the way in which this variability affects the larger downstream stress response remains hard to predict, prompting two key questions. First, how does heterogeneity and expression noise in stress response regulators propagate to the diverse downstream genes in their regulons. Second, when expression levels vary, how do multiple downstream genes act together to protect cells from stress. To address these questions, we focus on the transcription factor PhoP, a critical virulence regulator which coordinates pathogenicity in several gram-negative species. We use optogenetic stimulation to precisely control PhoP expression levels and examine how variations in PhoP affect the downstream activation of genes in the PhoP regulon. We find that these downstream genes exhibit differences both in mean expression level and sensitivity to increasing levels of PhoP. These response functions can also vary between individual cells, increasing heterogeneity in the population. We tie these variations to cell survival when bacteria are exposed to a clinically-relevant antimicrobial peptide, showing that high expression of the PhoP-regulon gene pmrD provides a protective effect against Polymyxin B. Overall, we demonstrate that even subtle heterogeneity in expression of a stress response regulator can have clear consequences for enabling bacteria to survive stress.

Abstract: Gene expression is inherently dynamic, due to complex regulation and stochastic biochemical events. However, the effects of these dynamics on cell phenotypes can be difficult to determine. Researchers have historically been limited to passive observations of natural dynamics, which can preclude studies of elusive and noisy cellular events where large amounts of data are required to reveal statistically significant effects. Here, using recent advances in the fields of machine learning and control theory, we train a deep neural network to accurately predict the response of an optogenetic system in Escherichia coli cells. We then use the network in a deep model predictive control framework to impose arbitrary and cell-specific gene expression dynamics on thousands of single cells in real time, applying the framework to generate complex time-varying patterns. We also showcase the framework's ability to link expression patterns to dynamic functional outcomes by controlling expression of the tetA antibiotic resistance gene. This study highlights how deep learning-enabled feedback control can be used to tailor distributions of gene expression dynamics with high accuracy and throughput without expert knowledge of the biological system.

Abstract: Gene regulatory networks and the dynamic responses they produce offer a wealth of information about how biological systems process information about their environment. Recently, researchers interested in dissecting these networks have been outsourcing various parts of their experimental workflow to computers. Here we review how, using microfluidic or optogenetic tools coupled with fluorescence imaging, it is now possible to interface cells and computers. These platforms enable scientists to perform informative dynamic stimulations of genetic pathways and monitor their reaction. It is also possible to close the loop and regulate genes in real time, providing an unprecedented view of how signals propagate through the network. Finally, we outline new tools that can be used within the framework of cell-machine interfaces.