Biology subjects

Murray, R. M.

Publications and source records attributed to Murray, R. M..

At least 19 recordsLinked to original sources

bioRxiv2026

The Compositional Encoding of Hand-Eye Coordinated Movements for Single Neurons in the Posterior Parietal Cortex

Human posterior parietal cortex (PPC) is thought to play an important role in hand-eye coordination, yet the underlying encoding mechanisms remain uncertain. We recorded 412 single neurons across 11 sessions from motor cortex (MC; n=251) and PPC (n=161) in a single human participant performing a hand-eye (H-E) coordinated center-out task. While MC neurons showed little to no modulation by eye movements, 79% of PPC neurons had neural representations that were additively separable into independent hand- and eye-movement tuning curves. Due to this separability, neural representations could be separated and additively recomposed while maintaining structure similarity. Consequently, compositional decoders trained solely on single-effector movements could match the performance of decoders trained on coordinated H-E movements (hand: 66% vs 69%; eye: 34% vs 36%). These results show that, during simple center-out tasks, MC hand movement codes are unaffected by eye movements and that compositionality can be used to modularly decode H-E coordinated movements in PPC.

neuroscience↗

bioRxiv2026

Nucleotide-level chemical reaction network modeling enables quantitative prediction of reconstituted cell-free expression system

Cell-free expression systems offer a method for rapid prototyping of DNA circuits and functional protein synthesis. While crude extracts remain a black box with many components carrying out unknown reactions, PURE contains only the required transcription and translation components for protein production. All proteins and small molecules are at known concentrations, enabling detailed modeling for reliable computational predictions. However, there is little to no experimental data supporting the expression of target proteins for PURE-based models. In this work, we generalized the PURE detailed translation model for proteins with arbitrary amino acid compositions and lengths. We then built a chemical reaction network for transcription in PURE, validating the transcription models using DNA expression for the malachite-green aptamer (MGapt) to measure mRNA production. Lastly, we coupled the transcription and the generalized translation models to create a PURE protein synthesis model built purely of mass-action reactions. We used the combined model to capture the kinetics of MGapt and deGFP expressed from plasmids at varying concentrations.

synthetic biology↗

bioRxiv2026

Quantitative measurement of synthetic repression curves reveals design challenges for genetic circuit engineering under growth arrest

Despite the fact that microbes in natural environments spend most of their time in growth arrest, we understand little about how this physiological state affects the performance of engineered genetic circuits. Here, we measure repression curves from a library of genetic NOT gates at single-cell resolution in Escherichia coli under both active growth and growth arrest to systematically investigate how growth arrest affects circuit behavior. We find that the impact of growth arrest on circuit performance is almost entirely dominated by a single effect: a >100-fold reduction in unrepressed expression levels. Growth arrest caused gene expression noise to increase moderately and had only minimal impacts on the sensitivity and sharpness of the repression curves. Our work shows both that conventional genetic circuit design paradigms are currently insufficient to develop circuits that can function properly under growth arrest, but also that addressing the reduction in just a single performance parameter would be sufficient to resolve this problem. This work expands our understanding of bacterial gene regulation under growth arrest and lays the groundwork for new design paradigms that will be essential in ensuring the safe and reliable performance of synthetic biology systems in real-world environments. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=87 SRC="FIGDIR/small/703179v1_ufig1.gif" ALT="Figure 1"> View larger version (14K): org.highwire.dtl.DTLVardef@104ecd2org.highwire.dtl.DTLVardef@57f992org.highwire.dtl.DTLVardef@1c215c2org.highwire.dtl.DTLVardef@122a8c5_HPS_FORMAT_FIGEXP M_FIG C_FIG

synthetic biology↗

bioRxiv2025

Engineering Synthetic Phase Variation for Control of Microbial consortium

Some biochemical functions can be performed more efficiently when split into different tasks, each performed by distinct strains within a microbial consortium. Due to the distinct growth dynamics of each strain, uncontrolled consortia have unstable composition dynamics, leading to the rapid loss of the community level function. Several approaches have been developed to stabilize consortia, with most relying on communicated-mediated growth and death feedback. These approaches require accurate communication between strains to maintain control, something which is not guaranteed under non-well-mixed conditions. As such, these methods are of limited utility in consortia applications with poorly mixed environments e.g. industrial scale bioreactors or soil. Here, inspired by microbial phase variation dynamics, we introduce an alternative, communication-free approach in which a set of genetically identical cells switch stochastically between distinct phenotypes. In this scheme, the population composition is dynamically stable and determined by the rates of transitions between states. Mathematical modeling indicates that this approach can stabilize consortia. Experimentally, we used reversible DNA inversions catalyzed by serine recombinases to implement a dynamic consortium. We then characterized the dynamic properties of the consortium at the single cell and bulk levels, and demonstrated control in 2- and 3-state consortia. These results provide a composition control approach that does not rely on cell to cell communication, providing a foundation for deployment of engineered consortia in complex, and sometimes non-mixed, environments such as industrial scale bioreactors or the human gut.

synthetic biology↗

bioRxiv2025

Association of E. coli Nissle with insect-parasitic nematode Steinernema hermaphroditum

Steinernema species are soil-dwelling and insect-parasitic nematodes that associate with symbiotic Xenorhabdus bacteria. During the infective juvenile (IJ) stage, Steinernema nematodes package species-specific Xenorhabdus bacteria in the anterior intestinal pockets. The nematodes can survive in the soil for months as they seek insect prey. The mechanisms of how these nematodes associate with environmental microbes other than their Xenorhabdus symbionts is barely known. Here, we report a new mechanism of E. coli Nissle (EcN) association with the nematode Steinernema hermaphroditum. We show that EcN cells are enclosed and lysed in at least four pairs of coelomocytes, suggesting these immune cells respond to bacterial invasion. During the IJ stage of nematode development, EcN cells localize to posterior intestinal vacuoles and enter the inter-cuticular space, where they proliferate, aggregate, then lyse. EcN cells expressed proteins in the cell lysates were maintained in the nematode cuticle over eight weeks in non-sterile soil. These observations suggest sequential steps of EcN colonization in the host nematodes involving an immune response that is distinctive from interactions with mutualistic symbiont. Our work establishes a novel framework of nematode-bacteria interaction with potential applications in environmental bioengineering.

microbiology↗

bioRxiv2025

Metabolomics-informed coarse-grained model enables prediction of cell-free protein expression dynamics

Cell-free expression systems have gained considerable attention for their potential in biomanufac-turing, biosensing, and circuit prototyping. All these endeavors make use of the innate metabolic activity of cell lysates. However, our knowledge of the underlying nature of cell-free metabolism remains lacking. In this work, we use non-targeted mass spectrometry to generate time-course measurements of small molecules in E. coli cell lysates (metabolomics) during cell-free protein synthesis (CFPS) to show that the majority of E. colis metabolism is active in cell lysate. Fur-thermore, these data indicate that protein synthesis is a relatively small metabolic burden on cell lysates compared to the background metabolism, and that the build-up of multiple metabolic waste products is fundamentally responsible for stopping cell-free protein expression as opposed to depletion of fuel sources. We use these insights coupled with high-throughput CFPS experiments to develop a foundational coarse-grained mechanistic model of CFPS which we show can be easily recalibrated using Bayesian parameter inference techniques to provide accurate models across hundreds of novel experimental conditions. This work provides new experimental insights into the effects of cell-free metabolism on CFPS and establishes a novel framework for leveraging existing CFPS models in new experimental contexts, opening the avenue to future cell-free applications aimed at building complex systems, from multilayered biological circuits to synthetic biological cells.

synthetic biology↗

bioRxiv2025

A DNA part library for reliable engineering of the emerging model nematode symbiotic bacterium Xenorhabdus griffiniae HGB2511

Xenorhabdus griffiniae is a bacterium that lives inside the intestine of the entomopathogenic nematode Steinernema hermaphroditum and partners with the nematode to infect and kill insect larvae in soil. The construction of gene circuits, like reporters, in X. griffiniae would provide tools to study and better understand the symbiotic relationship it has with its host. However, because X. griffiniae is not a model organism, information about gene circuit construction in X. griffiniae is limited. We develop and characterize a DNA part library similar to the CIDAR MoClo extension library for E. coli to allow more efficient construction of genetic circuits in X. griffiniae. TurboRFP expressing strains with different constitutive Anderson promoters and different ribosome binding sites (RBS) were constructed to quantify promoter and RBS strengths in X. griffiniae. Furthermore, two fluorescent proteins sfGFP and sfYFP, as well as the bioluminescent luxCDABE operon were added to the part library and successfully expressed in X. griffiniae. We then used the characterized parts to build and characterize IPTG inducible constructs.

synthetic biology↗

bioRxiv2025

Recruiting ESCRT to single-chain heterotrimer peptide-MHCI releases antigen-presenting vesicles that stimulate T cells selectively

Immune cells naturally secrete extracellular antigen-presenting vesicles (APVs) displaying peptide:MHC complexes to facilitate the initiation, expansion, maintenance, or silencing of immune responses. Previous work has sought to manufacture and purify these vesicles for cell-free immunotherapies. In this study, APV assembly and release is achieved in non-immune cells by transfecting HEK293T or Expi293F cells with a single-chain heterotrimer (SCT) peptide/major histocompatibility complex I (pMHCI) construct containing an ESCRT- and ALIX-binding region (EABR) sequence appended to the cytoplasmic tail; this EABR sequence recruits ESCRT proteins to induce the budding of APVs displaying SCT pMHCI. A comparison of multiple pMHCI constructs shows that inducing the release of APVs by the addition of an EABR sequence generalizes across SCT pMHCI constructs. Purified pMHCI/EABR APVs selectively stimulate IFN-{gamma} release from T cells presenting their cognate T cell receptor, demonstrating the potential use of these vesicles as a form of cell-free immunotherapy. Significance StatementImmune cells are known to naturally release pMHC-displaying extracellular vesicles (EVs), called antigen-presenting vesicles (APVs), which can orchestrate immune responses either directly or with the aid of antigen-presenting cells (APCs). For decades, researchers have pursued ways to replicate these APVs for immunotherapy by using chemically modified nanoparticles or by engineering the increased expression of APVs from immune cells which are typically low yield. Here we presents a broadly applicable platform for generating high concentrations of pMHCI-displaying APVs that can selectively modulate T cells, demonstrating a significant advance in the engineering of APVs for cell-free immunotherapy. The APVs presented here, and related APVs, could be translated into clinical therapies for modulating cancer progression or regulating autoimmunity in addition to their use as a tool to help characterize how endogenous extracellular vesicles influence the immune system.

immunology↗

bioRxiv2024

A Field-Deployable Arsenic Sensor Integrating Bacillus Megaterium with CMOS Technology

Bacteria innately monitor their environment by dynamically regulating gene expression to respond to fluctuating conditions. Through synthetic biology, we can harness this natural capability to design cell-based sensors. Bacillus megaterium, a soil bacterium, stands out due to its remarkable heavy metal tolerance and sporulation ability, making it an ideal candidate for heavy metal detection with low transportation costs. However, challenges persist: the synthetic biology toolkit for this strain is underdeveloped and conventional whole-cell sensors necessitate specialized laboratory equipment to read the output. In our study, we genetically modified B. megaterium for arsenic detection, establishing a detection threshold below the EPA recommendation of 10 ppb for drinking water in both vegetative cell form and spore form. Additionally, we integrated both engineered B. megaterium living cells and spores with CMOS chip for field-deployable arsenic detection. We show that the limit of detection of our integrated sensor is applicable in soil and air arsenic contamination testing. As a proof of concept, this work paves the way for deploying our sensor in resource-limited settings, ensuring real-time arsenic detection in challenging environments. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=108 SRC="FIGDIR/small/604150v1_ufig1.gif" ALT="Figure 1"> View larger version (29K): org.highwire.dtl.DTLVardef@1940e83org.highwire.dtl.DTLVardef@144da0aorg.highwire.dtl.DTLVardef@146e906org.highwire.dtl.DTLVardef@6cb8c1_HPS_FORMAT_FIGEXP M_FIG C_FIG

synthetic biology↗

bioRxiv2024

Optimizing protein expression in the One-Pot PURE system: insights into reaction composition and translation efficiency

The One-Pot PURE (Protein synthesis Using Recombinant Elements) system simplifies the preparation of traditional PURE systems by co-culturing and purifying 36 essential proteins for gene expression in a single step, thereby improving accessibility and affordability for widespread laboratory adoption and customization. However, replicating this protocol to match the productivity of traditional PURE systems can take considerable time and effort due to uncharacterized variability in the systems biochemical composition. In this work, we observed unstable PURE protein expression in E. coli strains M15/pREP4 and BL21(DE3) and addressed this using glucose-mediated catabolite repression to minimize burdensome background expression. We also identified differences in optimal protein induction timing between these two strains, leading to growth incompatibility in co-culture, and observed proteolysis of PURE proteins expressed in M15/pREP4. We showed that consolidating all expression vectors into a protease-deficient BL21(DE3) strain could minimize proteolysis. This single-strain system also led to more uniform cell growth at the time of protein induction, improving the stoichiometry of critical translation initiation factors in the PURE reaction for efficient protein production. In addition to optimizing One-Pot PURE protein composition, we found that variations in commercial energy solution formulations could compensate for suboptimal PURE protein stoichiometry. Moreover, our study revealed significant differences in the expression capacity of commercially available E. coli tRNAs, suggesting the potential of optimizing tRNA codons to improve protein translation. Taken together, this work highlights the complex biochemical interplay influencing protein expression capacity in the One-Pot PURE system and presents strategies to improve its robustness and productivity.

synthetic biology↗

bioRxiv2024

Single-cell genotype-phenotype mapping identifies therapeutic vulnerabilities in VEXAS syndrome

Somatic evolution leads to the emergence of clonal diversity across tissues with broad implications for human health. A striking example of somatic evolution is the VEXAS (Vacuoles E1 enzyme X-linked Autoinflammatory Somatic) syndrome, caused by somatic UBA1 mutations in hematopoietic stem cells (HSCs), inducing treatment-refractory, systemic inflammation. However, the mechanisms that lead to survival and expansion of mutant HSCs are unknown, limiting the development of effective therapies. The lack of animal or cellular models of UBA1-mutant HSCs has hindered such mechanistic understanding, mandating analysis of primary human VEXAS samples, which harbor admixtures of wild-type and UBA1-mutant HSCs. To address these challenges, we applied single-cell multi-omics to comprehensively define mutant UBA1-induced transcriptome, chromatin accessibility and signaling pathway alterations in VEXAS patients, allowing for the direct comparison of mutant versus wild-type cells within the same environment. We confirmed the expected enrichment of UBA1M41V/T mutations in myeloid cells, and additionally discovered that these mutations were also prevalent in Natural Killer (NK) cells in VEXAS patients, providing new insights into disease phenotypes. Through mapping genotypes to molecular phenotypes, including transcriptome, chromatin accessibility, cell surface protein or intracellular protein profiles, in HSCs, we found that UBA1M41V/T-mutant cells showed an increased inflammation signature (interferon alpha and gamma response pathways), as well as activation of unfolded protein response (UPR) via pro-survival, but not pro-apoptotic, mediators of the PERK pathway, compared to UBA1 wild-type HSCs. Ex vivo validation experiments showed that inhibiting UBA1 in normal CD34+ or using UBA1-mutant HSCs led to PERK pathway up-regulation, increased myeloid differentiation and cell survival, which was reversed by PERK inhibition. Thus, we demonstrated that human VEXAS HSCs show cell-intrinsic inflammatory phenotypes and survive the proteomic stress caused by compromised ubiquitination through PERK-mediated activation of the UPR. Together, these analyses nominate PERK pathway inhibition as a potential new therapeutic strategy for eradicating the VEXAS-inducing clone, demonstrating the power of single-cell multi-omics profiling of primary patient sample to enable genotype-to-phenotype somatic mapping for the discovery of novel candidates for clinical intervention.

genomics↗

bioRxiv2024

Impact of Chemical Dynamics of Commercial PURE Systems on Malachite Green Aptamer Fluorescence

The malachite green aptamer (MGapt) is known for its utility in RNA measurement in vivo and lysate-based cell-free protein systems. However, MGapt fluorescence dynamics do not accurately reflect mRNA concentration. Our study finds that MGapt fluorescence is unstable in commercial PURE systems. We discovered that the chemical composition of the cell-free reaction strongly influences MGapt fluorescence, which leads to inaccurate RNA calculations. Specific to the commercial system, we posit that MGapt fluorescence is significantly affected by the systems chemical properties, governed notably by the presence of dithiothreitol (DTT). We propose a model that, on average, accurately predicts MGapt measurement within a 10% margin, leveraging DTT concentration as a critical factor. This model sheds light on the complex dynamics of MGapt in cell-free systems and underscores the importance of considering environmental factors in RNA measurements using aptamers.

synthetic biology↗

bioRxiv2023

Systematic review and meta-analysis on the effects of chronic peri-adolescent cannabinoid exposure on schizophrenia-like behaviour in rodents

BackgroundThe link between cannabis use and schizophrenia is well-established in epidemiological studies, especially among adolescents with early-onset use. However, this association in rodent models is less clear. This meta-analysis examined the effects of adolescent cannabinoid exposure on distinct schizophrenia-like behaviours in rodents and how experimental variations influence outcomes. MethodsFollowing a pre-registered protocol (CRD42022338761), we searched PubMed, Ovid Medline, Embse and APA PsychInfo for English-language original studies until 2022. We synthesised data from experiments on schizophrenia-like behaviour in rats and mice after repeated peri-pubertal (onset between P23-P45) cannabinoid exposure. Risk of bias was assessed using the SYRCLEs tool. ResultsWe included 291 experiments from 91 articles across 9 behavioural tests. We found meta-analytic evidence supporting that CB1R agonists, both natural and synthetic, elicited broad schizophrenia-like behavioural alterations, including impaired working memory (g =-0.58 [CI: -1.00,-0.16]), novel object recognition (g=-0.63 [CI: -0.97,-0.30]), novel object location recognition (g=-0.70 [CI: -1.22,-0.28]), social motivation (g=-0.40 [CI: -0.63, -0.16]), pre-pulse inhibition (g=-0.48 [CI: -0.89, -0.08]), and sucrose preference (g=-0.92 [CI: -1.87,0.04]). By contrast, effects on novelty-induced locomotion were negligible. Subgroup analyses revealed similar effects across sexes and species. Substantial variance in the protocols and moderate-to-high heterogeneity in behavioural outcomes were observed. We found CBD may attenuate novelty-induced locomotion in an open field and enhance fear memory recall, but data was limited. DiscussionThis is the first meta-analysis to comprehensively assess the link between cannabinoids and schizophrenia-like behaviours in rodents. Our results support epidemiological links between early cannabis use and schizophrenia-like phenotypes, confirming the utility of animal models. Standardising protocols will optimise models to strengthen reproducibility and comparisons, our work provides a framework for refining rodent models to elucidate biological pathways linking cannabis and schizophrenia.

animal behavior and cognition↗

bioRxiv2023

TRILL: Orchestrating Modular Deep-Learning Workflows for Democratized, Scalable Protein Analysis and Engineering

AO_SCPLOWBSTRACTC_SCPLOWDeep-learning models have been rapidly adopted by many fields, partly due to the deluge of data humanity has amassed. In particular, the petabases of biological sequencing data enable the unsupervised training of protein language models that learn the "language of life." However, due to their prohibitive size and complexity, contemporary deep-learning models are often unwieldy, especially for scientists with limited machine learning backgrounds. TRILL (TRaining and Inference using the Language of Life) is a platform for creative protein design and discovery. Leveraging several state-of-the-art models such as ESM-2, DiffDock, and RFDiffusion, TRILL allows researchers to generate novel proteins, predict 3-D structures, extract high-dimensional representations of proteins, functionally classify proteins and more. What sets TRILL apart is its ability to enable complex pipelines by chaining together models and effectively merging the capabilities of different models to achieve a sum greater than its individual parts. Whether using Google Colab with one GPU or a supercomputer with hundreds, TRILL allows scientists to effectively utilize models with millions to billions of parameters by using optimized training strategies such as ZeRO-Offload and distributed data parallel. Therefore, TRILL not only bridges the gap between complex deep-learning models and their practical application in the field of biology, but also simplifies the orchestration of these models into comprehensive workflows, democratizing access to powerful methods. Documentation: https://trill.readthedocs.io/en/latest/home.html.

bioengineering↗

bioRxiv2023

Engineering the soil bacterium Pseudomonas synxantha 2-79 into a ratiometric bioreporter for phosphorus limitation

Microbial bioreporters hold promise for addressing challenges in medical and environmental applications. However, the difficulty of ensuring their stable persistence and function within the target environment remains a challenge. One strategy is to integrate information about the host strain and target environment into the design-build-test cycle of the bioreporter itself. Here, we present a case study for such an environmentally-motivated design process by engineering the wheat commensal bacterium Pseudomonas synxantha 2-79 into a ratiometric bioreporter for phosphorus limitation. Comparative analysis showed that an exogenous P-responsive promoter outperformed its native counterparts. This reporter can selectively sense and report phosphorus limitation at plant-relevant concentrations of 25-100 {micro}M without cross-activation from carbon or nitrogen limitation or high cell densities. Its performance is robust over a field-relevant pH range (5.8-8), and it responds only to inorganic phosphorus, even in the presence of common soil organic P. Finally, we used fluorescein-calibrated flow cytometry to assess whether the reporters performance in shaken liquid culture predict its performance in soil, finding that although the reporter is still functional at the bulk level, its variability in performance increases when grown in a soil slurry as compared to planktonic culture, with a fraction of the population not expressing the reporter proteins. Together, our environmentally-aware design process provides an example of how laboratory bioengineering efforts can generate microbes with greater promise to function reliably in their applied contexts.

synthetic biology↗

bioRxiv2023

A chemical reaction network model of PURE

Cell-free expression systems provide a method for rapid DNA circuit prototyping and functional protein synthesis. While crude extracts remain a black box with many components carrying out unknown reactions, the PURE system contains only the required transcription and translation components for protein production. All proteins and small molecules are at known concentrations, opening up the possibility of detailed modeling for reliable computational predictions. However, there is little to no experimental data supporting the expression of target proteins for detailed protein models PURE models. In this work, we build a chemical reaction network transcription model for PURE protein synthesis. We compare the transcription models using DNA encoding for the malachite-green aptamer (MGapt) to measure mRNA production. Furthermore, we expand the PURE detailed translation model for an arbitrary set of amino acids and length. Lastly, we combine the transcription and the expanded translation models to create a PURE protein synthesis model built purely from mass-action reactions. We use the combined model to capture the translation of a plasmid encoding MGapt and deGFP under a T7-promoter and a strong RBS. The model accurately predicts the MGapt mRNA production for the first two hours, the dynamics of deGFP expression, and the total protein production with an accuracy within 10 %.

synthetic biology↗

bioRxiv2023

Development of cell-free transcription-translation systems in three soil Pseudomonads

In vitro transcription-translation (TX-TL) can enable faster engineering of biological systems. This speed-up can be significant, especially in difficult-to-transform chassis. This work shows the successful development of TX-TL systems using three soil-derived wild-type Pseudomonads known to promote plant growth: Pseudomonas synxantha, Pseudomonas chlororaphis, and Pseudomonas aureofaciens. One, P. synxantha, was further characterized. A lysate test of P. synxantha showed a maximum protein yield of 2.5 M at 125 proteins per DNA template and a maximum protein synthesis rate of 20 nM/min. A set of different constitutive promoters driving mNeonGreen expression were tested in TX-TL and integrated into the genome, showing similar normalized strengths for in vivo and in vitro fluorescence. This correspondence between the TX-TL derived promoter strength and the in vivo promoter strength indicates these lysate-based cell-free systems can be used to characterize and engineer biological parts without genome integration, enabling a faster designbuild-test cycle.

synthetic biology↗

bioRxiv2023

Probing metabolism in an E. coli-based cell-free system reveals a trade-off between transcription and translation

Cell-free transcription-translation (TX-TL) systems have been used for diverse applications, but their performance and scope are limited by variability and poor predictability. To understand the drivers of this variability, we explored the effects of metabolic perturbations to an E. coli Rosetta2 TX-TL system. We targeted three classes of molecules: energy molecules, in the form of nucleotide triphosphates (NTPs); central carbon "fuel" molecules, which regenerate NTPs; and magnesium ions (Mg2+). Using malachite green mRNA aptamer (MG aptamer) and destabilized enhanced Green Fluorescent Protein (deGFP) as transcriptional and translational readouts, respectively, we report the presence of a trade-off between optimizing total protein yield and optimizing total mRNA yield, as measured by integrating the area under the curve for mRNA time-course dynamics. We found that a systems position along the trade-off curve is strongly determined by Mg2+ concentration, fuel type and concentration, and cell lysate batch, and that variability can be reduced by modulating these components. Our results further suggest the trade-off arises from limitations in translation regulation and inefficient energy regeneration. This work advances our understanding of the effects of fuel and energy metabolism on TX-TL in cell-free systems and lays a foundation for improving TX-TL performance, lifetime, standardization, and prediction. For Table of Contents Use Only O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=74 SRC="FIGDIR/small/533877v4_ufig1.gif" ALT="Figure 1"> View larger version (16K): org.highwire.dtl.DTLVardef@d6ffd7org.highwire.dtl.DTLVardef@1368a05org.highwire.dtl.DTLVardef@19f5b31org.highwire.dtl.DTLVardef@11c14c0_HPS_FORMAT_FIGEXP M_FIG C_FIG

synthetic biology↗