Eugenia F. Romantseva

Nucleic acid circuits are powerful tools for programming biology, but the design principles for operating these circuits in complex cellular environments remain poorly understood compared to simple in vitro settings. Cell-free expression systems (CFES) are uniquely suited to address this challenge, as they can integrate measurements ideal for either in vitro or in cyto settings . Further, because CFES are open systems, they enable a level of control over component concentration unattainable in cells. Here, we use CFES to characterize genetically encoded RNA circuits that operate via toehold-mediated strand exchange (TMSE). These circuits have been extensively characterized in vitro and were recently deployed as translational riboregulators in E. coli , revealing key differences between the two environments. By systematically modulating the design parameters of these RNA circuits in both purified protein and lysate-based CFES, we elucidate the mechanisms linking TMSE to protein expression. Further, we combine measurements and alterations in CFES composition that are infeasible in cells to investigate interactions between cellular components and RNA circuit components, identifying a potential interaction with ribosomes that informs circuit design. Our results establish a unified set of principles for designing and operating genetically encoded TMSE circuits across in vitro , CFE, and bacterial environments, which should catalyze the widespread adoption of this platform for new applications in molecular programming, synthetic biology, and biotechnology.

Characterization of cell-free expression (CFE) systems must expand beyond single spectrophotometric measurements of a green fluorescent protein to provide meaningful metrics of system performance during a CFE reaction and enable the development of predictable and reproducible CFE technologies. To date, comprehensive characterization of these systems has posed a formidable measurement challenge, as it requires time-course measurements of reactions involving endogenous components in addition to transcription and translation of a target genetic circuit added exogenously to the CFE reaction. To provide more informative characterization that is still easy to conduct and complements current practices, we demonstrate a measurement framework for transcription and translation dynamics. We use different nucleic acid templates to characterize a suite of Escherichia coli extracts prepared in house, as well as extracts and reconstituted systems available commercially. Notably, we include measurements of low-performing systems to assess the sensitivity of our measurement framework and elucidate metrics indicative of system performance. For all these CFE systems, we compute reaction metrics to enable quantitative comparison. We believe this is an accessible measurement framework that can complement existing characterization, provide informative data for developing CFE technologies, and be adopted for routine characterization.

The National Institute of Standards and Technology (NIST) and the National Center for Advancing Translational Sciences (NCATS) convened the workshop Advancing Cell-free Manufacturing: Challenges in Scale-up and Automation in Rockville, Maryland in February 2024. This workshop brought together over fifty participants, representing the interests and needs of stakeholders in academic, industrial, and government settings. Together, through various plenary discussions, case studies, and working groups, participants broadly surveyed the field and focused on identifying both near-term and long-term needs to support cell-free expression systems. This report synthesizes the workshop discussion and presents actionable recommendations aimed at removing the remaining barriers to realizing the full impact of CFE systems on biomanufacturing and applications of biotechnology.

Biofabricated tissue-engineered constructs have the potential to transform personalized medicine. However, characterizing these constructs post-fabrication and throughout preclinical use remains challenging. On December 1, 2022, the National Institute of Standards and Technology (NIST) held a one-day, virtual workshop on measurement needs for biofabricated constructs that contain cells. The workshop focused on metrology for the structure of the constructs, cell viability in the constructs, and functional capacity of the constructs, with the aim of enabling future research directions, standards development, and the adoption of these constructs for clinical use.

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.

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 ( EC ₅₀ ) and saturating output to meet quantitative specifications. In addition, we engineer sensors with inverted dose-response and specified EC ₅₀ . 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.

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.

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 10 ⁵ 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.

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 10 ⁵ 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.