Microbial engineering often requires fine control over protein expression; for example, to connect genetic circuits 1-7 or control flux through a metabolic pathway 8-13. We have developed a predictive design method for synthetic ribosome binding sites that enables the rational control of a protein's production rate on a proportional scale. Experimental validation of over 100 predictions in Escherichia coli shows that the method is accurate to within a factor of 2.3 over a range of 100,000-fold. The design method also correctly predicts that reusing a ribosome binding site sequence in different genetic contexts can result in different protein expression levels. We demonstrate the method's utility by rationally optimizing a protein's expression level to connect a genetic sensor to a synthetic circuit. The proposed forward engineering approach will accelerate the construction and systematic optimization of large genetic systems.

The ribosome’s interactions with mRNA govern its translation rate and the effects of post-transcriptional regulation. Long, structured 5′ untranslated regions (5′ UTRs) are commonly found in bacterial mRNAs, though the physical mechanisms that determine how the ribosome binds these upstream regions remain poorly defined. Here, we systematically investigate the ribosome’s interactions with structured standby sites, upstream of Shine–Dalgarno sequences, and show that these interactions can modulate translation initiation rates by over 100-fold. We find that an mRNA’s translation initiation rate is controlled by the amount of single-stranded surface area, the partial unfolding of RNA structures to minimize the ribosome’s binding free energy penalty, the absence of cooperative binding and the potential for ribosomal sliding. We develop a biophysical model employing thermodynamic first principles and a four-parameter free energy model to accurately predict the ribosome’s translation initiation rates for 136 synthetic 5′ UTRs with large structures, diverse shapes and multiple standby site modules. The model predicts and experiments confirm that the ribosome can readily bind distant standby site modules that support high translation rates, providing a physical mechanism for observed context effects and long-range post-transcriptional regulation.

Summary Edge detection is a signal processing algorithm common in artificial intelligence and image recognition programs. We have constructed a genetically encoded edge detection algorithm that programs an isogenic community of E.coli to sense an image of light, communicate to identify the light-dark edges, and visually present the result of the computation. The algorithm is implemented using multiple genetic circuits. An engineered light sensor enables cells to distinguish between light and dark regions. In the dark, cells produce a diffusible chemical signal that diffuses into light regions. Genetic logic gates are used so that only cells that sense light and the diffusible signal produce a positive output. A mathematical model constructed from first principles and parameterized with experimental measurements of the component circuits predicts the performance of the complete program. Quantitatively accurate models will facilitate the engineering of more complex biological behaviors and inform bottom-up studies of natural genetic regulatory networks.

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The dynamical solution of a well-mixed, nonlinear stochastic chemical kinetic system, described by the Master equation, may be exactly computed using the stochastic simulation algorithm. However, because the computational cost scales with the number of reaction occurrences, systems with one or more "fast" reactions become costly to simulate. This paper describes a hybrid stochastic method that partitions the system into subsets of fast and slow reactions, approximates the fast reactions as a continuous Markov process, using a chemical Langevin equation, and accurately describes the slow dynamics using the integral form of the "Next Reaction" variant of the stochastic simulation algorithm. The key innovation of this method is its mechanism of efficiently monitoring the occurrences of slow, discrete events while simultaneously simulating the dynamics of a continuous, stochastic or deterministic process. In addition, by introducing an approximation in which multiple slow reactions may occur within a time step of the numerical integration of the chemical Langevin equation, the hybrid stochastic method performs much faster with only a marginal decrease in accuracy. Multiple examples, including a biological pulse generator and a large-scale system benchmark, are simulated using the exact and proposed hybrid methods as well as, for comparison, a previous hybrid stochastic method. Probability distributions of the solutions are compared and the weak errors of the first two moments are computed. In general, these hybrid methods may be applied to the simulation of the dynamics of a system described by stochastic differential, ordinary differential, and Master equations.

Riboswitches are shape-changing regulatory RNAs that bind chemicals and regulate gene expression, directly coupling sensing to cellular actuation. However, it remains unclear how their sequence controls the physics of riboswitch switching and activation, particularly when changing the ligand-binding aptamer domain. We report the development of a statistical thermodynamic model that predicts the sequence-structure-function relationship for translation-regulating riboswitches that activate gene expression, characterized inside cells and within cell-free transcription–translation assays. Using the model, we carried out automated computational design of 62 synthetic riboswitches that used six different RNA aptamers to sense diverse chemicals (theophylline, tetramethylrosamine, fluoride, dopamine, thyroxine, 2,4-dinitrotoluene) and activated gene expression by up to 383-fold. The model explains how aptamer structure, ligand affinity, switching free energy and macromolecular crowding collectively control riboswitch activation. Our model-based approach for engineering riboswitches quantitatively confirms several physical mechanisms governing ligand-induced RNA shape-change and enables the development of cell-free and bacterial sensors for diverse applications.

Developing predictive models of multi-protein genetic systems to understand and optimize their behavior remains a combinatorial challenge, particularly when measurement throughput is limited. We developed a computational approach to build predictive models and identify optimal sequences and expression levels, while circumventing combinatorial explosion. Maximally informative genetic system variants were first designed by the RBS Library Calculator, an algorithm to design sequences for efficiently searching a multi-protein expression space across a > 10,000-fold range with tailored search parameters and well-predicted translation rates. We validated the algorithm's predictions by characterizing 646 genetic system variants, encoded in plasmids and genomes, expressed in six gram-positive and gram-negative bacterial hosts. We then combined the search algorithm with system-level kinetic modeling, requiring the construction and characterization of 73 variants to build a sequence-expression-activity map (SEAMAP) for a biosynthesis pathway. Using model predictions, we designed and characterized 47 additional pathway variants to navigate its activity space, find optimal expression regions with desired activity response curves, and relieve rate-limiting steps in metabolism. Creating sequence-expression-activity maps accelerates the optimization of many protein systems and allows previous measurements to quantitatively inform future designs.

BackgroundEnergy from remote methane reserves is transformative; however, unintended release of this potent greenhouse gas makes it imperative to convert methane efficiently into more readily transported biofuels. No pure microbial culture that grows on methane anaerobically has been isolated, despite that methane capture through anaerobic processes is more efficient than aerobic ones.ResultsHere we engineered the archaeal methanogen Methanosarcina acetivorans to grow anaerobically on methane as a pure culture and to convert methane into the biofuel precursor acetate. To capture methane, we cloned the enzyme methyl-coenzyme M reductase (Mcr) from an unculturable organism, anaerobic methanotrophic archaeal population 1 (ANME-1) from a Black Sea mat, into M. acetivorans to effectively run methanogenesis in reverse. Starting with low-density inocula, M. acetivorans cells producing ANME-1 Mcr consumed up to 9 ± 1 % of methane (corresponding to 109 ± 12 µmol of methane) after 6 weeks of anaerobic growth on methane and utilized 10 mM FeCl3 as an electron acceptor. Accordingly, increases in cell density and total protein were observed as cells grew on methane in a biofilm on solid FeCl3. When incubated on methane for 5 days, high-densities of ANME-1 Mcr-producing M. acetivorans cells consumed 15 ± 2 % methane (corresponding to 143 ± 16 µmol of methane), and produced 10.3 ± 0.8 mM acetate (corresponding to 52 ± 4 µmol of acetate). We further confirmed the growth on methane and acetate production using 13C isotopic labeling of methane and bicarbonate coupled with nuclear magnetic resonance and gas chromatography/mass spectroscopy, as well as RNA sequencing.ConclusionsWe anticipate that our metabolically-engineered strain will provide insights into how methane is cycled in the environment by Archaea as well as will possibly be utilized to convert remote sources of methane into more easily transported biofuels via acetate.Electronic supplementary materialThe online version of this article (doi:10.1186/s12934-015-0397-z) contains supplementary material, which is available to authorized users.