The ability to control the biochemical processes of the cell is fundamental to the goals of synthetic biology. This control implies the ability to specify the spatial, temporal, and amount of gene expression and protein activity in individual cells and populations. Hardware, wetware, and methods which advance the ability to control these processes then increase the ability and precision of researchers to perturb and study natural biological systems and of engineers to build useful biological systems and products.

In optogenetics, researchers use light and genetically encoded photoreceptors to control biological processes with unmatched precision. However, outside of neuroscience, the impact of optogenetics has been limited by a lack of user-friendly, flexible, accessible hardware. In the first portion of this work, we engineer the Light Plate Apparatus (LPA), a device that can deliver two independent 310 to 1550 nm light signals to each well of a 24-well plate with intensity control over three orders of magnitude and millisecond resolution. Signals are programmed using an intuitive web tool named Iris. All components can be purchased for under $400 and the device can be assembled and calibrated by a non-expert in one day. We use the LPA to precisely control gene expression from blue, green, and red light responsive optogenetic tools in bacteria, yeast, and mammalian cells and simplify the entrainment of cyanobacterial circadian rhythm. The LPA dramatically reduces the entry barrier to optogenetics and photobiology experiments.

In the second portion of this work, we describe a general method for independently controlling population mean and noise of cellular protein copy number by convolution of gene expression distributions. In this method, a gene is simultaneously expressed from both a low- and high-noise source, resulting in summation of the gene products within cells and convolution of the gene expression distributions within the population. By tuning the amount and ratio of expression from each source, the mean and noise, respectively, of the convolution can be independently controlled. In principle, this method can be applied in any host using any two sources of low- and high-noise gene expression. We demonstrate this method in \textit{Escherichia coli} by engineering and co-expressing a high-noise, LuxR-based, positive feedback transcriptional unit, and a low-noise, TetR-based, transcriptional unit without feedback. By exposing cells to different amounts and ratios of inducer we can independently control mean and noise in gene expression over a “dynamic area” of 1.1 with a maximum fold-change in CV of 6.9; performance metrics we propose and describe in this study. A mathematical model for mean and noise of a two-gene convolution accurately captures the observed behavior and can be used to predict mean and noise from inducer concentrations. Finally, we use our method to measure the effect of variability in the expression of a toxin on bacterial growth dynamics. We predict that the fraction of cells in the toxin-induced dormant state is a function of mean, while sensitivity to the mean depends on noise. Under some conditions we observe behavior consistent with this prediction, but our results generally suggest more complex and potentially interesting underlying dynamics.