Showing 1 - 10 of 10 results
Synthetic Biological Approaches for Optogenetics and Tools for Transcriptional Light‐Control in Bacteria.
Light has become established as a tool not only to visualize and investigate but also to steer biological systems. This review starts by discussing the unique features that make light such an effective control input in biology. It then gives an overview of how light‐control came to progress, starting with photoactivatable compounds and leading up to current genetic implementations using optogenetic approaches. The review then zooms in on optogenetics, focusing on photosensitive proteins, which form the basis for optogenetic engineering using synthetic biological approaches. As the regulation of transcription provides a highly versatile means for steering diverse biological functions, the focus of this review then shifts to transcriptional light regulators, which are presented in the biotechnologically highly relevant model organism Escherichia coli.
Synthetic gene networks recapitulate dynamic signal decoding and differential gene expression.
Cells live in constantly changing environments and employ dynamic signaling pathways to transduce information about the signals they encounter. However, the mechanisms by which dynamic signals are decoded into appropriate gene expression patterns remain poorly understood. Here, we devise networked optogenetic pathways that achieve novel dynamic signal processing functions that recapitulate cellular information processing. Exploiting light-responsive transcriptional regulators with differing response kinetics, we build a falling-edge pulse-detector and show that this circuit can be employed to demultiplex dynamically encoded signals. We combine this demultiplexer with dCas9-based gene networks to construct pulsatile-signal filters and decoders. Applying information theory, we show that dynamic multiplexing significantly increases the information transmission capacity from signal to gene expression state. Finally, we use dynamic multiplexing for precise multidimensional regulation of a heterologous metabolic pathway. Our results elucidate design principles of dynamic information processing and provide original synthetic systems capable of decoding complex signals for biotechnological applications.
Exploiting natural chemical photosensitivity of anhydrotetracycline and tetracycline for dynamic and setpoint chemo-optogenetic control.
The transcriptional inducer anhydrotetracycline (aTc) and the bacteriostatic antibiotic tetracycline (Tc) are commonly used in all fields of biology for control of transcription or translation. A drawback of these and other small molecule inducers is the difficulty of their removal from cell cultures, limiting their application for dynamic control. Here, we describe a simple method to overcome this limitation, and show that the natural photosensitivity of aTc/Tc can be exploited to turn them into highly predictable optogenetic transcriptional- and growth-regulators. This new optogenetic class uniquely features both dynamic and setpoint control which act via population-memory adjustable through opto-chemical modulation. We demonstrate this method by applying it for dynamic gene expression control and for enhancing the performance of an existing optogenetic system. We then expand the utility of the aTc system by constructing a new chemical bandpass filter that increases its aTc response range. The simplicity of our method enables scientists and biotechnologists to use their existing systems employing aTc/Tc for dynamic optogenetic experiments without genetic modification.
Cell-in-the-loop pattern formation with optogenetically emulated cell-to-cell signaling.
Designing and implementing synthetic biological pattern formation remains challenging due to underlying theoretical complexity as well as the difficulty of engineering multicellular networks biochemically. Here, we introduce a cell-in-the-loop approach where living cells interact through in silico signaling, establishing a new testbed to interrogate theoretical principles when internal cell dynamics are incorporated rather than modeled. We present an easy-to-use theoretical test to predict the emergence of contrasting patterns in gene expression among laterally inhibiting cells. Guided by the theory, we experimentally demonstrate spontaneous checkerboard patterning in an optogenetic setup, where cell-to-cell signaling is emulated with light inputs calculated in silico from real-time gene expression measurements. The scheme successfully produces spontaneous, persistent checkerboard patterns for systems of sixteen patches, in quantitative agreement with theoretical predictions. Our research highlights how tools from dynamical systems theory may inform our understanding of patterning, and illustrates the potential of cell-in-the-loop for engineering synthetic multicellular systems.
Pulsatile inputs achieve tunable attenuation of gene expression variability and graded multi-gene regulation.
Many natural transcription factors are regulated in a pulsatile fashion, but it remains unknown whether synthetic gene expression systems can benefit from such dynamic regulation. Here we find, using a fast-acting, optogenetic transcription factor in Saccharomyces cerevisiae, that dynamic pulsatile signals reduce cell-to-cell variability in gene expression. We then show that by encoding such signals into a single input, expression mean and variability can be independently tuned. Further, we construct a light-responsive promoter library and demonstrate how pulsatile signaling also enables graded multi-gene regulation at fixed expression ratios, despite differences in promoter dose-response characteristics. Pulsatile regulation can thus lead to beneficial functional behaviors in synthetic biological systems, which previously required laborious optimization of genetic parts or the construction of synthetic gene networks.
An Optogenetic Platform for Real-Time, Single-Cell Interrogation of Stochastic Transcriptional Regulation.
Transcription is a highly regulated and inherently stochastic process. The complexity of signal transduction and gene regulation makes it challenging to analyze how the dynamic activity of transcriptional regulators affects stochastic transcription. By combining a fast-acting, photo-regulatable transcription factor with nascent RNA quantification in live cells and an experimental setup for precise spatiotemporal delivery of light inputs, we constructed a platform for the real-time, single-cell interrogation of transcription in Saccharomyces cerevisiae. We show that transcriptional activation and deactivation are fast and memoryless. By analyzing the temporal activity of individual cells, we found that transcription occurs in bursts, whose duration and timing are modulated by transcription factor activity. Using our platform, we regulated transcription via light-driven feedback loops at the single-cell level. Feedback markedly reduced cell-to-cell variability and led to qualitative differences in cellular transcriptional dynamics. Our platform establishes a flexible method for studying transcriptional dynamics in single cells.
Dynamic Blue Light-Inducible T7 RNA Polymerases (Opto-T7RNAPs) for Precise Spatiotemporal Gene Expression Control.
Light has emerged as a control input for biological systems due to its precise spatiotemporal resolution. The limited toolset for light control in bacteria motivated us to develop a light-inducible transcription system that is independent from cellular regulation through the use of an orthogonal RNA polymerase. Here, we present our engineered blue light-responsive T7 RNA polymerases (Opto-T7RNAPs) that show properties such as low leakiness of gene expression in the dark state, high expression strength when induced with blue light, and an inducible range of more than 300-fold. Following optimization of the system to reduce expression variability, we created a variant that returns to the inactive dark state within minutes once the blue light is turned off. This allows for precise dynamic control of gene expression, which is a key aspect for most applications using optogenetic regulation. The regulators, which only require blue light from ordinary light-emitting diodes for induction, were developed and tested in the bacterium Escherichia coli, which is a crucial cell factory for biotechnology due to its fast and inexpensive cultivation and well understood physiology and genetics. Opto-T7RNAP, with minor alterations, should be extendable to other bacterial species as well as eukaryotes such as mammalian cells and yeast in which the T7 RNA polymerase and the light-inducible Vivid regulator have been shown to be functional. We anticipate that our approach will expand the applicability of using light as an inducer for gene expression independent from cellular regulation and allow for a more reliable dynamic control of synthetic and natural gene networks.
Automated optogenetic feedback control for precise and robust regulation of gene expression and cell growth.
Dynamic control of gene expression can have far-reaching implications for biotechnological applications and biological discovery. Thanks to the advantages of light, optogenetics has emerged as an ideal technology for this task. Current state-of-the-art methods for optical expression control fail to combine precision with repeatability and cannot withstand changing operating culture conditions. Here, we present a novel fully automatic experimental platform for the robust and precise long-term optogenetic regulation of protein production in liquid Escherichia coli cultures. Using a computer-controlled light-responsive two-component system, we accurately track prescribed dynamic green fluorescent protein expression profiles through the application of feedback control, and show that the system adapts to global perturbations such as nutrient and temperature changes. We demonstrate the efficacy and potential utility of our approach by placing a key metabolic enzyme under optogenetic control, thus enabling dynamic regulation of the culture growth rate with potential applications in bacterial physiology studies and biotechnology.
Iterative experiment design guides the characterization of a light-inducible gene expression circuit.
Systems biology rests on the idea that biological complexity can be better unraveled through the interplay of modeling and experimentation. However, the success of this approach depends critically on the informativeness of the chosen experiments, which is usually unknown a priori. Here, we propose a systematic scheme based on iterations of optimal experiment design, flow cytometry experiments, and Bayesian parameter inference to guide the discovery process in the case of stochastic biochemical reaction networks. To illustrate the benefit of our methodology, we apply it to the characterization of an engineered light-inducible gene expression circuit in yeast and compare the performance of the resulting model with models identified from nonoptimal experiments. In particular, we compare the parameter posterior distributions and the precision to which the outcome of future experiments can be predicted. Moreover, we illustrate how the identified stochastic model can be used to determine light induction patterns that make either the average amount of protein or the variability in a population of cells follow a desired profile. Our results show that optimal experiment design allows one to derive models that are accurate enough to precisely predict and regulate the protein expression in heterogeneous cell populations over extended periods of time.
In silico feedback for in vivo regulation of a gene expression circuit.
We show that difficulties in regulating cellular behavior with synthetic biological circuits may be circumvented using in silico feedback control. By tracking a circuit's output in Saccharomyces cerevisiae in real time, we precisely control its behavior using an in silico feedback algorithm to compute regulatory inputs implemented through a genetically encoded light-responsive module. Moving control functions outside the cell should enable more sophisticated manipulation of cellular processes whenever real-time measurements of cellular variables are possible.