Genome-scale transcriptional dynamics and environmental biosensing.

Genome-scale technologies have enabled mapping of the complex molecular networks that govern cellular behavior. An emerging theme in the analyses of these networks is that cells use many layers of regulatory feedback to constantly assess and precisely react to their environment. The importance of complex feedback in controlling the real-time response to external stimuli has led to a need for the next generation of cell-based technologies that enable both the collection and analysis of high-throughput temporal data. Toward this end, we have developed a microfluidic platform capable of monitoring temporal gene expression from over 2,000 promoters. By coupling the "Dynomics" platform with deep neural network (DNN) and associated explainable artificial intelligence (XAI) algorithms, we show how machine learning can be harnessed to assess patterns in transcriptional data on a genome scale and identify which genes contribute to these patterns. Furthermore, we demonstrate the utility of the Dynomics platform as a field-deployable real-time biosensor through prediction of the presence of heavy metals in urban water and mine spill samples, based on the the dynamic transcription profiles of 1,807 unique Escherichia coli promoters.

Wink-controlled polarization-switched telescopic contact lenses.

We describe a wink-controlled hands-free switching system for eye-borne telescopic vision, based on a previously tested fixed-magnification telescope embedded within scleral contact lenses. Here we integrate orthogonal polarizers into the contact lens covering the F/9.1 refractive 1× and F/9.6 catadioptric 2.8× vision paths, to allow switching via external liquid crystal shutters. We provide hands-free control by an infrared wink/blink monitor, using passive retroreflectors embedded within the contact lenses. We demonstrate system operation of the self-contained switching eyewear and the modified contact lenses with a life-size human eye model with mechanical "eyelids."

Gene transfer in Leptolyngbya sp. strain BL0902, a cyanobacterium suitable for production of biomass and bioproducts.

Current cyanobacterial model organisms were not selected for their growth traits or potential for the production of renewable biomass, biofuels, or other products. The cyanobacterium strain BL0902 emerged from a search for strains with superior growth traits. Morphology and 16S rRNA sequence placed strain BL0902 in the genus Leptolyngbya. Leptolyngbya sp. strain BL0902 (hereafter Leptolyngbya BL0902) showed robust growth at temperatures from 22°C to 40°C and tolerated up to 0.5 M NaCl, 32 mM urea, high pH, and high solar irradiance. Its growth rate under outdoor conditions rivaled Arthrospira ("pirulina" strains. Leptolyngbya BL0902 accumulated higher lipid content and a higher proportion of monounsaturated fatty acids than Arthrospira strains. In addition to these desirable qualities, Leptolyngbya BL0902 is amenable to genetic engineering that is reliable, efficient, and stable. We demonstrated conjugal transfer from Escherichia coli of a plasmid based on RSF1010 and expression of spectinomycin/streptomycin resistance and yemGFP reporter transgenes. Conjugation efficiency was investigated in biparental and triparental matings with and without a "elper"plasmid that carries DNA methyltransferase genes, and with two different conjugal plasmids. We also showed that Leptolyngbya BL0902 is amenable to transposon mutagenesis with a Tn5 derivative. To facilitate genetic manipulation of Leptolyngbya BL0902, a conjugal plasmid vector was engineered to carry a trc promoter upstream of a Gateway recombination cassette. These growth properties and genetic tools position Leptolyngbya BL0902 as a model cyanobacterial production strain.

Cell cycle-dependent variations in protein concentration.

Computational modeling of biological systems has become an effective tool for analyzing cellular behavior and for elucidating key properties of the intricate networks that underlie experimental observations. While most modeling techniques rely heavily on the concentrations of intracellular molecules, little attention has been paid to tracking and simulating the significant volume fluctuations that occur over each cell division cycle. Here, we use fluorescence microscopy to acquire single cell volume trajectories for a large population of Saccharomyces cerevisiae cells. Using this data, we generate a comprehensive set of statistics that govern the growth and division of these cells over many generations, and we discover several interesting trends in their size, growth and protein production characteristics. We use these statistics to develop an accurate model of cell cycle volume dynamics, starting at cell birth. Finally, we demonstrate the importance of tracking volume fluctuations by combining cell division dynamics with a minimal gene expression model for a constitutively expressed fluorescent protein. The significant oscillations in the cellular concentration of a stable, highly expressed protein mimic the observed experimental trajectories and demonstrate the fundamental impact that the cell cycle has on cellular functions.

A fast, robust and tunable synthetic gene oscillator.

One defining goal of synthetic biology is the development of engineering-based approaches that enable the construction of gene-regulatory networks according to 'design specifications' generated from computational modelling. This approach provides a systematic framework for exploring how a given regulatory network generates a particular phenotypic behaviour. Several fundamental gene circuits have been developed using this approach, including toggle switches and oscillators, and these have been applied in new contexts such as triggered biofilm development and cellular population control. Here we describe an engineered genetic oscillator in Escherichia coli that is fast, robust and persistent, with tunable oscillatory periods as fast as 13 min. The oscillator was designed using a previously modelled network architecture comprising linked positive and negative feedback loops. Using a microfluidic platform tailored for single-cell microscopy, we precisely control environmental conditions and monitor oscillations in individual cells through multiple cycles. Experiments reveal remarkable robustness and persistence of oscillations in the designed circuit; almost every cell exhibited large-amplitude fluorescence oscillations throughout observation runs. The oscillatory period can be tuned by altering inducer levels, temperature and the media source. Computational modelling demonstrates that the key design principle for constructing a robust oscillator is a time delay in the negative feedback loop, which can mechanistically arise from the cascade of cellular processes involved in forming a functional transcription factor. The positive feedback loop increases the robustness of the oscillations and allows for greater tunability. Examination of our refined model suggested the existence of a simplified oscillator design without positive feedback, and we construct an oscillator strain confirming this computational prediction.

Biomechanical ordering of dense cell populations.

The structure of bacterial populations is governed by the interplay of many physical and biological factors, ranging from properties of surrounding aqueous media and substrates to cell-cell communication and gene expression in individual cells. The biomechanical interactions arising from the growth and division of individual cells in confined environments are ubiquitous, yet little work has focused on this fundamental aspect of colony formation. We analyze the spatial organization of Escherichia coli growing in a microfluidic chemostat. We find that growth and expansion of a dense colony of cells leads to a dynamical transition from an isotropic disordered phase to a nematic phase characterized by orientational alignment of rod-like cells. We develop a continuum model of collective cell dynamics based on equations for local cell density, velocity, and the tensor order parameter. We use this model and discrete element simulations to elucidate the mechanism of cell ordering and quantify the relationship between the dynamics of cell proliferation and the spatial structure of the population.

Monitoring dynamics of single-cell gene expression over multiple cell cycles.

Recent progress in reconstructing gene regulatory networks has established a framework for a quantitative description of the dynamics of many important cellular processes. Such a description will require novel experimental techniques that enable the generation of time-series data for the governing regulatory proteins in a large number of individual living cells. Here, we utilize microfabrication to construct a Tesla microchemostat that permits single-cell fluorescence imaging of gene expression over many cellular generations. The device is used to capture and constrain asymmetrically dividing or motile cells within a trapping region and to deliver nutrients and regulate the cellular population within this region. We illustrate the operation of the microchemostat with Saccharomyces cerevisiae and explore the evolution of single-cell gene expression and cycle time as a function of generation. Our findings highlight the importance of novel assays for quantifying the dynamics of gene expression and cellular growth, and establish a methodology for exploring the effects of gene expression on long-term processes such as cellular aging.