Robustness and reproducibility of simple and complex synthetic logic circuit designs using a DBTL loop.

Computational tools addressing various components of design-build-test-learn (DBTL) loops for the construction of synthetic genetic networks exist but do not generally cover the entire DBTL loop. This manuscript introduces an end-to-end sequence of tools that together form a DBTL loop called Design Assemble Round Trip (DART). DART provides rational selection and refinement of genetic parts to construct and test a circuit. Computational support for experimental process, metadata management, standardized data collection and reproducible data analysis is provided via the previously published Round Trip (RT) test-learn loop. The primary focus of this work is on the Design Assemble (DA) part of the tool chain, which improves on previous techniques by screening up to thousands of network topologies for robust performance using a novel robustness score derived from dynamical behavior based on circuit topology only. In addition, novel experimental support software is introduced for the assembly of genetic circuits. A complete design-through-analysis sequence is presented using several OR and NOR circuit designs, with and without structural redundancy, that are implemented in budding yeast. The execution of DART tested the predictions of the design tools, specifically with regard to robust and reproducible performance under different experimental conditions. The data analysis depended on a novel application of machine learning techniques to segment bimodal flow cytometry distributions. Evidence is presented that, in some cases, a more complex build may impart more robustness and reproducibility across experimental conditions. Graphical Abstract.

Neural network based integration of assays to assess pathogenic potential.

Limited data significantly hinders our capability of biothreat assessment of novel bacterial strains. Integration of data from additional sources that can provide context about the strain can address this challenge. Datasets from different sources, however, are generated with a specific objective and which makes integration challenging. Here, we developed a deep learning-based approach called the neural network embedding model (NNEM) that integrates data from conventional assays designed to classify species with new assays that interrogate hallmarks of pathogenicity for biothreat assessment. We used a dataset of metabolic characteristics from a de-identified set of known bacterial strains that the Special Bacteriology Reference Laboratory (SBRL) of the Centers for Disease Control and Prevention (CDC) has curated for use in species identification. The NNEM transformed results from SBRL assays into vectors to supplement unrelated pathogenicity assays from de-identified microbes. The enrichment resulted in a significant improvement in accuracy of 9% for biothreat. Importantly, the dataset used in our analysis is large, but noisy. Therefore, the performance of our system is expected to improve as additional types of pathogenicity assays are developed and deployed. The proposed NNEM strategy thus provides a generalizable framework for enrichment of datasets with previously collected assays indicative of species.

Highly-automated, high-throughput replication of yeast-based logic circuit design assessments.

We describe an experimental campaign that replicated the performance assessment of logic gates engineered into cells of Saccharomyces cerevisiae by Gander et al. Our experimental campaign used a novel high-throughput experimentation framework developed under Defense Advanced Research Projects Agency's Synergistic Discovery and Design program: a remote robotic lab at Strateos executed a parameterized experimental protocol. Using this protocol and robotic execution, we generated two orders of magnitude more flow cytometry data than the original experiments. We discuss our results, which largely, but not completely, agree with the original report and make some remarks about lessons learned. Graphical Abstract.

Phenotype-Based Threat Assessment.

Bacterial pathogen identification, which is critical for human health, has historically relied on culturing organisms from clinical specimens. More recently, the application of machine learning (ML) to whole-genome sequences (WGSs) has facilitated pathogen identification. However, relying solely on genetic information to identify emerging or new pathogens is fundamentally constrained, especially if novel virulence factors exist. In addition, even WGSs with ML pipelines are unable to discern phenotypes associated with cryptic genetic loci linked to virulence. Here, we set out to determine if ML using phenotypic hallmarks of pathogenesis could assess potential pathogenic threat without using any sequence-based analysis. This approach successfully classified potential pathogenetic threat associated with previously machine-observed and unobserved bacteria with 99% and 85% accuracy, respectively. This work establishes a phenotype-based pipeline for potential pathogenic threat assessment, which we term PathEngine, and offers strategies for the identification of bacterial pathogens.

Round Trip: An Automated Pipeline for Experimental Design, Execution, and Analysis.

Synthetic biology is a complex discipline that involves creating detailed, purpose-built designs from genetic parts. This process is often phrased as a Design-Build-Test-Learn loop, where iterative design improvements can be made, implemented, measured, and analyzed. Automation can potentially improve both the end-to-end duration of the process and the utility of data produced by the process. One of the most important considerations for the development of effective automation and quality data is a rigorous description of implicit knowledge encoded as a formal knowledge representation. The development of knowledge representation for the process poses a number of challenges, including developing effective human-machine interfaces, protecting against and repairing user error, providing flexibility for terminological mismatches, and supporting extensibility to new experimental types. We address these challenges with the DARPA SD2 Round Trip software architecture. The Round Trip is an open architecture that automates many of the key steps in the Test and Learn phases of a Design-Build-Test-Learn loop for high-throughput laboratory science. The primary contribution of the Round Trip is to assist with and otherwise automate metadata creation, curation, standardization, and linkage with experimental data. The Round Trip's focus on metadata supports fast, automated, and replicable analysis of experiments as well as experimental situational awareness and experimental interpretability. We highlight the major software components and data representations that enable the Round Trip to speed up the design and analysis of experiments by 2 orders of magnitude over prior ad hoc methods. These contributions support a number of experimental protocols and experimental types, demonstrating the Round Trip's breadth and extensibility. We describe both an illustrative use case using the Round Trip for an on-the-loop experimental campaign and overall contributions to reducing experimental analysis time and increasing data product volume in the SD2 program.

Prediction of whole-cell transcriptional response with machine learning.

MOTIVATION: Applications in synthetic and systems biology can benefit from measuring whole-cell response to biochemical perturbations. Execution of experiments to cover all possible combinations of perturbations is infeasible. In this paper, we present the host response model (HRM), a machine learning approach that maps response of single perturbations to transcriptional response of the combination of perturbations. RESULTS: The HRM combines high-throughput sequencing with machine learning to infer links between experimental context, prior knowledge of cell regulatory networks, and RNASeq data to predict a gene's dysregulation. We find that the HRM can predict the directionality of dysregulation to a combination of inducers with an accuracy of >90% using data from single inducers. We further find that the use of prior, known cell regulatory networks doubles the predictive performance of the HRM (an R2 from 0.3 to 0.65). The model was validated in two organisms, Escherichia coli and Bacillus subtilis, using new experiments conducted after training. Finally, while the HRM is trained with gene expression data, the direct prediction of differential expression makes it possible to also conduct enrichment analyses using its predictions. We show that the HRM can accurately classify >95% of the pathway regulations. The HRM reduces the number of RNASeq experiments needed as responses can be tested in silico prior to the experiment. AVAILABILITY AND IMPLEMENTATION: The HRM software and tutorial are available at https://github.com/sd2e/CDM and the configurable differential expression analysis tools and tutorials are available at https://github.com/SD2E/omics_tools. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Intent Parser: A Tool for Codification and Sharing of Experimental Design.

Communicating information about experimental design among a team of collaborators is challenging because different people tend to describe experiments in different ways and with different levels of detail. Sometimes, humans can interpret missing information by making assumptions and drawing inferences from information already provided. Doing so, however, is error-prone and typically requires a high level of interpersonal communication. In this paper, we present a tool that addresses this challenge by providing a simple interface for incremental formal codification of experiment designs. Users interact with a Google Docs word-processing interface with structured tables, backed by assisted linking to machine-readable definitions in a data repository (SynBioHub) and specification of available protocols and requests for execution in the Open Protocol Interface Language (OPIL). The result is an easy-to-use tool for generating machine-readable descriptions of experiment designs with which users in the DARPA SD2 program have collected data from 80 208 samples using a variety of protocols and instruments over the course of 181 experiment runs.

Immuno-Electrophysiological Mechanisms of Functional Electrical Connections Between Recipient and Donor Heart in Patients With Orthotopic Heart Transplantation Presenting With Atrial Arrhythmias.

[Figure: see text].

Monitoring cell-mediated immunity during immunosuppression reduction in heart transplant recipients with severe systemic infections.

BACKGROUND: Treatment for severe systemic infections in heart transplantation is reduction in immunosuppression while treating the infection. An assay that measures adenosine triphosphate production in activated lymphocytes (ImmuKnow® ) objectively monitors cellular immunity of transplant recipients. In this study, we used ImmuKnow® to adjust immunosuppression in heart transplant recipients with severe systemic infections. METHODS: Heart transplant recipients were followed with ImmuKnow® at the time of biopsy and diagnosis of systemic infection. Patients who developed an infection were monitored by ImmuKnow® assay with adjustments in immunosuppression based upon the results of the assay. Maintenance immunosuppression was reinstituted when the ImmuKnow® increased to >225 ng/mL of ATP. RESULTS: Two or more ImmuKnow® assays were performed in 80 patients. Thirteen patients developed severe systemic infections. ImmuKnow® mean value at the time of diagnosis of infection was 109 ± 49.2 ng/mL. Reduction in immunosuppression and treatment of infection resulted in normalization of ImmuKnow® level, resolution of infection, and no episodes of rebound rejection. CONCLUSION: Heart transplant recipients with severe systemic infections presented with a decreased ImmuKnow® , suggesting over immunosuppression. ImmuKnow® can be used as an objective measurement in withdrawing immunosuppression in heart transplant recipients with severe systemic infections.

Outcomes of Induction Therapy with Rabbit Anti-Thymocyte Globulin in Heart Transplant Recipients: A Single Center Retrospective Cohort Study.

BACKGROUND Induction immunosuppression is used in transplantation to prevent early acute rejection. The survival benefit of rabbit anti-thymocyte globulin (rATG) induction has not been established yet. We sought to determine the role of rATG in preventing rejection and improving overall survival. MATERIAL AND METHODS A retrospective cohort study was conducted from 2005 to 2009 and data of consecutive 268 heart transplant recipients were reviewed. RESULTS The data of 144 patients who received induction with rATG were compared to 124 patients who did not. Although overall survival was not different between the 2 groups (P=0.12), there was a significant difference in restricted mean survival time (RMST) at 5 years (RMST=4.8 months; 95% CI: 1.0-8.6, P=0.01) and 10 years (RMST=10.4 months; 95% CI: 1.6-19.3, P=0.02) in favor of the non-induced patients. No difference was observed between induced and non-induced patients who developed de novo donor specific antibodies. There was a significant difference in median days to first rejection in favor of the induced group (P<0.001). CONCLUSIONS Induction with rATG adds no survival benefit in heart transplant recipients. Patients who did not receive induction therapy had higher life expectancy at 5 years and 10 years. Although there was significant delay in the first rejection episode in favor of the rATG induced group, no difference was observed in donor specific antibodies. This study indicates a need for separate analysis of peri-transplantation co-morbidities and mainly the incidence of acute kidney injury, which could affect long-term survival.

Translucent poly(vinyl alcohol) cryogel dosimeters for simultaneous dose buildup and monitoring during chest wall radiation therapy.

Chest wall radiation therapy treatment delivery was monitored using a 5 mm thick radiochromic poly(vinyl alcohol) cryogel that also provided buildup material. The cryogels were used to detect positioning errors and measure the impact of shifts for a chest wall treatment that was delivered to a RANDO phantom. The phantom was shifted by ± 2, ± 3, and ± 5 mm from the planned position in the anterior/posterior (A/P) direction; these shifts represent setup errors and the uncertainty associated with lung filling during breath-hold. The two-dimensional absolute dose distributions measured in the cryogel at the planned position were compared with the distributions at all shifts from this position using gamma analysis (3%/3 mm, 10% threshold). For shifts of ± 2, ± 3, and ± 5 mm the passing rates ranged from 94.3% to 95.6%, 74.0% to 78.8%, and 17.5% to 22.5%, respectively. These results are consistent with the same gamma analysis performed on dose planes calculated in the middle of the cryogel and on the phantom surface using our treatment plan-ning system, which ranged from 94.3% to 95.0%, 76.8% to 77.9%, and 23.5% to 24.3%, respectively. The Pinnacle dose planes were then scaled empirically and compared to the cryogel measurements. Using the same gamma metric, the pass rates ranged from 97.0% to 98.4%. The results of this study suggest that cryogels may be used as both a buildup material and to evaluate errors in chest wall treat-ment positioning during deep-inspiration breath-hold delivery. The cryogels are sensitive to A/P chest wall shifts of less than 3 mm, which potentially allows for the detection of clinically relevant errors.

Tackling malnutrition: a systematic review of 15-year research evidence from INDEPTH health and demographic surveillance systems.

BACKGROUND: Nutrition is the intake of food in relation to the body's dietary needs. Malnutrition results from the intake of inadequate or excess food. This can lead to reduced immunity, increased susceptibility to disease, impaired physical and mental development, and reduced productivity. OBJECTIVE: To perform a systematic review to assess research conducted by the International Network for the Demographic Evaluation of Populations and their Health (INDEPTH) of health and demographic surveillance systems (HDSSs) over a 15-year period on malnutrition, its determinants, the effects of under and over nutrition, and intervention research on malnutrition in low- and middle-income countries (LMICs). METHODS: Relevant publication titles were uploaded onto the Zotero research tool from different databases (60% from PubMed). Using the keywords 'nutrition', 'malnutrition', 'over and under nutrition', we selected publications that were based only on data generated through the longitudinal HDSS platform. All titles and abstracts were screened to determine inclusion eligibility and full articles were independently assessed according to inclusion/exclusion criteria. For inclusion in this study, papers had to cover research on at least one of the following topics: the problem of malnutrition, its determinants, its effects, and intervention research on malnutrition. One hundred and forty eight papers were identified and reviewed, and 67 were selected for this study. RESULTS: The INDEPTH research identified rising levels of overweight and obesity, sometimes in the same settings as under-nutrition. Urbanisation appears to be protective against under-nutrition, but it heightens the risk of obesity. Appropriately timed breastfeeding interventions were protective against malnutrition. CONCLUSIONS: Although INDEPTH has expanded the global knowledge base on nutrition, many questions remain unresolved. There is a need for more investment in nutrition research in LMICs in order to generate evidence to inform policies in these settings.

Validation and application of a model of oxygen consumption and diffusion during photodynamic therapy in vitro.

The photophysical parameters for the photosensitizer Pd(II) meso-Tetra(4-carboxyphenyl) porphine (PdT790) acquired in a previous study were incorporated into the PDT oxygen diffusion models for cell suspensions and cell monolayers. The time-dependent phosphorescence signals generated by the diffusion models are shown to match signals previously measured by M.A.W. and M.S.P. when reasonable physical and photophysical parameters are used. Simulations were performed to investigate the effects of metabolic and photodynamic oxygen consumption rates on the PDT dose in each of the treatment geometries. It was found that in cell suspensions of <1 million cells per mL, PDT should not be inhibited by hypoxia if the photodynamic consumption rate is <1 mm s(-1). For cell monolayers the optimal photodynamic oxygen consumption rate was found to depend on the metabolic rate of oxygen consumption. If cells remained well oxygenated in the absence of PDT, then maximum PDT dose was delivered with the lowest practical photodynamic oxygen consumption rate. Simulations of PDT treatments for multicell tumor spheroids showed that large anoxic cores develop within the spheroids and, as a consequence, less PDT dose is delivered in comparison with similar treatments in cell suspensions and cell monolayers.

Measurement of intracellular oxygen concentration during photodynamic therapy in vitro.

A technique is introduced that monitors the depletion of intracellular ground state oxygen concentration ([(3)O(2)]) during photodynamic therapy of Mat-LyLu cell monolayers and cell suspensions. The photosensitizer Pd(II) meso-tetra(4-carboxyphenyl)porphine (PdT790) is used to manipulate and indicate intracellular [(3)O(2)] in both of the in vitro models. The Stern-Volmer relationship for PdT790 phosphorescence was characterized in suspensions by flowing nitrogen over the suspension while short pulses of 405 nm light were used to excite the sensitizer. The bleaching of sensitizer and the oxygen consumption rate were also measured during continuous exposure of the cell suspension to the 405 nm laser. Photodynamic therapy (PDT) was conducted in both cell suspensions and in cell monolayers under different treatment conditions while the phosphorescence signal was acquired. The intracellular [(3)O(2)] during PDT was calculated by using the measured Stern-Volmer relationship and correcting for sensitizer photobleaching. In addition, the amount of oxygen that was consumed during the treatments was calculated. It was found that even at large oxygen consumption rates, cells remain well oxygenated during PDT of cell suspensions. For monolayer treatments, it was found that intracellular [(3)O(2)] is rapidly depleted over the course of PDT.

Effect of 1O2 quencher depletion on the efficiency of photodynamic therapy.

Al(III) phthalocyanine chloride tetrasulfonic acid (AlPcS4) fluorescence and photodynamic oxygen consumption were monitored during AlPcS4-photodynamic therapy (PDT) of Mat LyLu cells in suspension. These measurements were used to calculate the PDT efficiency, which is defined as the oxygen consumption rate divided by the sensitizer concentration. As a function of the intracellular oxygen concentration consumed by PDT, the normalized PDT efficiency fell off more quickly at lower photosensitizer concentrations. The changes in PDT efficiency were compared to models of PDT in which the photosensitizer (PS) and singlet oxygen quencher (A) were either free to diffuse or were fixed. The model in which PS and A are free to diffuse did not agree with the experimental data because this model predicts that the reduction in PDT efficiency is independent of [PS]. A Monte Carlo model was written to simulate PDT when both PS and A are stationary. This model was found to describe the experimental data when the initial intracellular [A] = 90 mM and when the initial and final (i.e. after all A has been depleted) singlet oxygen lifetimes were 0.4 and 1.2 µs respectively.

Monitoring oxygen concentration during photodynamic therapy using prompt photosensitizer fluorescence.

A novel technique is described that uses either time-resolved or steady state prompt photosensitizer fluorescence to measure local oxygen concentration. Solution experiments conducted with Al(III) phthalocyanine chloride tetrasulfonic acid confirmed that the steady state fluorescence signal is dependent on the oxygen concentration and fluence rate. A relationship between prompt sensitizer fluorescence and sensitizer triplet quenching efficiency is derived which does not require knowledge of the Stern-Volmer constant. Similar relationships are also derived for sensitizer delayed fluorescence and phosphorescence. An explicit photodynamic therapy (PDT) dose metric that incorporates light dosimetry, sensitizer dosimetry, and triplet quenching efficiency is introduced. All components of this metric can be determined by optical measurements.

Peer workers' role in smoking-cessation groups for people with mental illness.

OBJECTIVES: The objective of this article is to evaluate the impact of peer workers' involvement as co-leaders in smoking-cessation programmes provided within mental health services. METHOD: Group smoking-cessation programmes were provided for people living with mental illness. Peer workers were involved in the development and delivery of these programmes. Group participants and mental health workers were asked to respond to a questionnaire about their experience of the peer workers. The questionnaire included both Likert scales and qualitative responses. RESULTS: Thirty-three mental health workers and 108 group participants completed the questionnaire. The majority of participants believed that the peer workers increased their confidence, helped them to learn about smoking cessation and promoted well-being. Mental health workers were also positive about the role of peer workers in the groups. CONCLUSIONS: This study supports the role of peer workers providing support and guidance within smoking-cessation programmes for people with mental illness. The results suggest that peer workers make a substantial contribution and that greater peer worker involvement in such programmes is likely to improve their acceptability and efficacy.

Calculation of singlet oxygen dose using explicit and implicit dose metrics during benzoporphyrin derivative monoacid ring A (BPD-MA)-PDT in vitro and correlation with MLL cell survival.

Photodynamic therapy (PDT) oxygen consumption, clonogenic cell survival, fluorescence photobleaching and photoproduct formation were investigated during benzoporphyrin derivative monoacid (BPD-MA)-PDT of MAT-LyLu cells in vitro. Cells were incubated with BPD-MA concentrations of 0.1, 0.5 or 2.5 µg mL(-1) for 2 h and then treated with 405 nm light under oxygenated and hypoxic conditions. Fluorescence spectra were acquired during treatment, and photobleaching and photoproduct generation were quantified using singular value decomposition of the spectra. Cell survival was measured at set times during the treatment using a colony-forming assay. The amount of oxygen consumed by PDT per photon absorbed decreased with BPD-MA intracellular concentration. Survival was correlated with the total amount of oxygen consumed by PDT per unit volume, which is assumed to be equivalent to the amount of singlet oxygen that reacted. A photobleaching-based singlet oxygen dose metric was also found to predict survival independent of intracellular BPD-MA concentration. The BPD-MA photoproduct was bleached during the treatment. Two singlet oxygen dose metrics based on photoproduct kinetics could not be correlated with cell survival over the full range of intracellular BPD-MA concentrations used.