Precision engineering of biological function with large-scale measurements and machine learning.

As synthetic biology expands and accelerates into real-world applications, methods for quantitatively and precisely engineering biological function become increasingly relevant. This is particularly true for applications that require programmed sensing to dynamically regulate gene expression in response to stimuli. However, few methods have been described that can engineer biological sensing with any level of quantitative precision. Here, we present two complementary methods for precision engineering of genetic sensors: in silico selection and machine-learning-enabled forward engineering. Both methods use a large-scale genotype-phenotype dataset to identify DNA sequences that encode sensors with quantitatively specified dose response. First, we show that in silico selection can be used to engineer sensors with a wide range of dose-response curves. To demonstrate in silico selection for precise, multi-objective engineering, we simultaneously tune a genetic sensor's sensitivity (EC50) and saturating output to meet quantitative specifications. In addition, we engineer sensors with inverted dose-response and specified EC50. Second, we demonstrate a machine-learning-enabled approach to predictively engineer genetic sensors with mutation combinations that are not present in the large-scale dataset. We show that the interpretable machine learning results can be combined with a biophysical model to engineer sensors with improved inverted dose-response curves.

Effects of DNA template preparation on variability in cell-free protein production.

DNA templates for protein production remain an unexplored source of variability in the performance of cell-free expression (CFE) systems. To characterize this variability, we investigated the effects of two common DNA extraction methodologies, a postprocessing step and manual versus automated preparation on protein production using CFE. We assess the concentration of the DNA template, the quality of the DNA template in terms of physical damage and the quality of the DNA solution in terms of purity resulting from eight DNA preparation workflows. We measure the variance in protein titer and rate of protein production in CFE reactions associated with the biological replicate of the DNA template, the technical replicate DNA solution prepared with the same workflow and the measurement replicate of nominally identical CFE reactions. We offer practical guidance for preparing and characterizing DNA templates to achieve acceptable variability in CFE performance.

Best Practices for DNA Template Preparation Toward Improved Reproducibility in Cell-Free Protein Production.

Performance variability is a common challenge in cell-free protein production and hinders a wider adoption of these systems for both research and biomanufacturing. While the inherent stochasticity and complexity of biology likely contributes to variability, other systematic factors may also play a role, including the source and preparation of the cell extract, the composition of the supplemental reaction buffer, the facility at which experiments are conducted, and the human operator (Cole et al. ACS Synth Biol 8:2080-2091, 2019). Variability in protein production could also arise from differences in the DNA template-specifically the amount of functional DNA added to a cell-free reaction and the quality of the DNA preparation in terms of contaminants and strand breakage. Here, we present protocols and suggest best practices optimized for DNA template preparation and quantitation for cell-free systems toward reducing variability in cell-free protein production.

The genotype-phenotype landscape of an allosteric protein.

Allostery is a fundamental biophysical mechanism that underlies cellular sensing, signaling, and metabolism. Yet a quantitative understanding of allosteric genotype-phenotype relationships remains elusive. Here, we report the large-scale measurement of the genotype-phenotype landscape for an allosteric protein: the lac repressor from Escherichia coli, LacI. Using a method that combines long-read and short-read DNA sequencing, we quantitatively measure the dose-response curves for nearly 105 variants of the LacI genetic sensor. The resulting data provide a quantitative map of the effect of amino acid substitutions on LacI allostery and reveal systematic sequence-structure-function relationships. We find that in many cases, allosteric phenotypes can be quantitatively predicted with additive or neural-network models, but unpredictable changes also occur. For example, we were surprised to discover a new band-stop phenotype that challenges conventional models of allostery and that emerges from combinations of nearly silent amino acid substitutions.

Measurements drive progress in directed evolution for precise engineering of biological systems.

Precise engineering of biological systems requires quantitative, high-throughput measurements, exemplified by progress in directed evolution. New approaches allow high-throughput measurements of phenotypes and their corresponding genotypes. When integrated into directed evolution, these quantitative approaches enable the precise engineering of biological function. At the same time, the increasingly routine availability of large, high-quality data sets supports the integration of machine learning with directed evolution. Together, these advances herald striking capabilities for engineering biology.

Cyberbiosecurity for Biopharmaceutical Products.

Cyberbiosecurity is an emerging discipline that addresses the unique vulnerabilities and threats that occur at the intersection of cyberspace and biotechnology. Advances in technology and manufacturing are increasing the relevance of cyberbiosecurity to the biopharmaceutical manufacturing community in the United States. Threats may be associated with the biopharmaceutical product itself or with the digital thread of manufacturing of biopharmaceuticals, including those that relate to supply chain and cyberphysical systems. Here, we offer an initial examination of these cyberbiosecurity threats as they stand today, as well as introductory steps toward paths for mitigation of cyberbiosecurity risk for a safer, more secure future.

Gradient elution moving boundary electrophoresis with channel current detection.

Gradient elution moving boundary electrophoresis (GEMBE) is a recently described technique for electrophoretic separations in short (1-3 cm) capillaries or microchannels. With GEMBE, the electrophoretic migration of analytes is opposed by a bulk counterflow of separation buffer through the separation channel. The counterflow velocity is varied over the course of a separation so that analytes with different electrophoretic mobilities enter the separation channel at different times and are detected as moving boundary, stepwise increases in the detector response. The resolution of a GEMBE separation is thus dependent on the rate at which the counterflow velocity is varied (rather than the length of the separation channel), and relatively high resolution separations can be performed with short microfluidic channels or capillaries. In this paper we describe an implementation of the GEMBE technique in which a very short (2.5-3.5 mm) capillary or microchannel is used as both the separation channel and a conductivity detection cell. Because the channel is so short, only a single moving boundary "step" is present in the channel at any given time, and the measured current through the channel can therefore be used to give a signal comparable to what is normally generated by more complicated detector arrangements. A theoretical description of the new technique is given along with simulation and experimental data relevant to the optimization of the method parameters such as channel length, counterflow acceleration, and applied field strength. A key theoretical prediction is that although this technique is expected to be a factor of 10 or 20 slower than conventional capillary zone electrophoresis, separation times of the order 1 s or less can still be achieved, making it applicable for ultrahigh-throughput analyses when implemented in a multiplexed format.