A Machine Learning Method for Genome Engineering Design Tool Attribution.

As the ability to engineer biological systems improves with increasingly advanced technology, the risk of accidental or intentional release of a dangerous genetically modified organism becomes greater. It is important that authorities can carry out attribution for the source of a genetically modified biological agent release. In the absence of evidence that ties a release directly to the individuals responsible, attribution can be carried out in part by discovering the in silico tools used to design the engineered genetic components, which can leave a signature in the DNA of the organism. Previous attribution methods have focused on identifying the laboratory of origin of an engineered organism using machine learning on plasmid signatures. The next logical step is to address attribution using signatures from the tools that are used to create the engineered modifications. A random forest classifier was developed that discriminates between design tools used to optimize coding regions for incorporation into the genome of another organism. To this end, tens of thousands of genes were optimized with 4 different codon optimization methods and relevant features from these sequences were generated for a machine learning classifier. This method achieves more than 97% accuracy in predicting which tools were used to design codon optimized genes for expression in other organisms. The methods presented here lay the groundwork for the creation of effective organism engineering attribution techniques. Such methods can act both as deterrents for future attempts at creating dangerous organisms as well as tools for forensic science.

An in vitro platform for study of the human gut microbiome under an oxygen gradient.

The complex, dynamic environment of the human lower gastrointestinal tract is colonized by hundreds of bacterial species that impact health and performance. Ex vivo study of the functional interactions between microbial community members in conditions representative of those in the gut is an ongoing challenge. We have developed an in vitro 40-plex platform that provides an oxygen gradient to support simultaneous maintenance of microaerobic and anaerobic microbes from the gut microbiome that can aid in rapid characterization of microbial interactions and direct comparison of individual microbiome samples. In this report, we demonstrate that the platform more closely maintained the microbial diversity and composition of human donor fecal microbiome samples than strict anaerobic conditions. The oxygen gradient established in the platform allowed the stratification and subsequent sampling of diverse microbial subpopulations that colonize microaerobic and anaerobic micro-environments. With the ability to run forty samples in parallel, the platform has the potential to be used as a rapid screening tool to understand how the gut microbiome responds to environmental perturbations such as toxic compound exposure, dietary changes, or pharmaceutical treatments.

Standardizing Automated DNA Assembly: Best Practices, Metrics, and Protocols Using Robots.

The advancement of synthetic biology requires the ability to create new DNA sequences to produce unique behaviors in biological systems. Automation is increasingly employed to carry out well-established assembly methods of DNA fragments in a multiplexed, high-throughput fashion, allowing many different configurations to be tested simultaneously. However, metrics are required to determine when automation is warranted based on factors such as assembly methodology, protocol details, and number of samples. The goal of our synthetic biology automation work is to develop and test protocols, hardware, and software to investigate and optimize DNA assembly through quantifiable metrics. We performed a parameter analysis of DNA assembly to develop a standardized, highly efficient, and reproducible MoClo protocol, suitable to be used both manually and with liquid-handling robots. We created a key DNA assembly metric (Q-metric) to characterize a given automation method's advantages over conventional manual manipulations with regard to researchers' highest-priority parameters: output, cost, and time. A software tool called Puppeteer was developed to formally capture these metrics, help define the assembly design, and provide human and robotic liquid-handling instructions. Altogether, we contribute to a growing foundation of standardizing practices, metrics, and protocols for automating DNA assembly.

Rate of serious infection in patients who are prescribed systemic biologic or nonbiologic agents for psoriasis: A large, single center, retrospective, observational cohort study.

BACKGROUND: Systemic biologic and nonbiologic agents used to treat psoriasis may or may not contribute to serious infection (SI) risk. Safety data, particularly for biologic agents, and associated risk for SI, are scarce. The study's aim was to explore the risk for SI in psoriasis patients exposed to systemic biologic or nonbiologic agents. METHODS: A large, single-center electronic medical record repository was searched between January 2010 and December 2014. Records for patients prescribed a systemic agent for psoriasis (SAP) with psoriasis or psoriatic arthritis diagnoses were included (ICD-9 codes 696.1 and 696.0, respectively). SIs were those who required hospitalization, and/or injectable antibacterial, antiviral or antifungal therapy. SIs occurring within 120 days after exposure to a SAP, were included for study. RESULTS: A total of 1,346 patients were exposed to a SAP between January 2010 and December 2014; 27 (2%) had a SI. Comparing biologic and nonbiologic agent exposure, no statistically significant difference for risk of SI was detectable (p = .83). CONCLUSION: In this population, the SI rate for biologic and nonbiologic systemic agents was clinically indistinguishable, thereby supporting consideration of the entire spectrum of available systemic therapeutic agents, both biologic and nonbiologic agents, for management of moderate to severe psoriasis.

Determination of a Screening Metric for High Diversity DNA Libraries.

The fields of antibody engineering, enzyme optimization and pathway construction rely increasingly on screening complex variant DNA libraries. These highly diverse libraries allow researchers to sample a maximized sequence space; and therefore, more rapidly identify proteins with significantly improved activity. The current state of the art in synthetic biology allows for libraries with billions of variants, pushing the limits of researchers' ability to qualify libraries for screening by measuring the traditional quality metrics of fidelity and diversity of variants. Instead, when screening variant libraries, researchers typically use a generic, and often insufficient, oversampling rate based on a common rule-of-thumb. We have developed methods to calculate a library-specific oversampling metric, based on fidelity, diversity, and representation of variants, which informs researchers, prior to screening the library, of the amount of oversampling required to ensure that the desired fraction of variant molecules will be sampled. To derive this oversampling metric, we developed a novel alignment tool to efficiently measure frequency counts of individual nucleotide variant positions using next-generation sequencing data. Next, we apply a method based on the "coupon collector" probability theory to construct a curve of upper bound estimates of the sampling size required for any desired variant coverage. The calculated oversampling metric will guide researchers to maximize their efficiency in using highly variant libraries.

Redirector: designing cell factories by reconstructing the metabolic objective.

Advances in computational metabolic optimization are required to realize the full potential of new in vivo metabolic engineering technologies by bridging the gap between computational design and strain development. We present Redirector, a new Flux Balance Analysis-based framework for identifying engineering targets to optimize metabolite production in complex pathways. Previous optimization frameworks have modeled metabolic alterations as directly controlling fluxes by setting particular flux bounds. Redirector develops a more biologically relevant approach, modeling metabolic alterations as changes in the balance of metabolic objectives in the system. This framework iteratively selects enzyme targets, adds the associated reaction fluxes to the metabolic objective, thereby incentivizing flux towards the production of a metabolite of interest. These adjustments to the objective act in competition with cellular growth and represent up-regulation and down-regulation of enzyme mediated reactions. Using the iAF1260 E. coli metabolic network model for optimization of fatty acid production as a test case, Redirector generates designs with as many as 39 simultaneous and 111 unique engineering targets. These designs discover proven in vivo targets, novel supporting pathways and relevant interdependencies, many of which cannot be predicted by other methods. Redirector is available as open and free software, scalable to computational resources, and powerful enough to find all known enzyme targets for fatty acid production.

Research, collaboration, and open science using web 2.0.

There is little doubt that the Internet has transformed the world in which we live. Information that was once archived in bricks and mortar libraries is now only a click away, and people across the globe have become connected in a manner inconceivable only 20 years ago. Although many scientists and educators have embraced the Internet as an invaluable tool for research, education and data sharing, some have been somewhat slower to take full advantage of emerging Web 2.0 technologies. Here we discuss the benefits and challenges of integrating Web 2.0 applications into undergraduate research and education programs, based on our experience utilizing these technologies in a summer undergraduate research program in synthetic biology at Harvard University. We discuss the use of applications including wiki-based documentation, digital brainstorming, and open data sharing via the Web, to facilitate the educational aspects and collaborative progress of undergraduate research projects. We hope to inspire others to integrate these technologies into their own coursework or research projects.

Large-scale identification of genetic design strategies using local search.

In the past decade, computational methods have been shown to be well suited to unraveling the complex web of metabolic reactions in biological systems. Methods based on flux-balance analysis (FBA) and bi-level optimization have been used to great effect in aiding metabolic engineering. These methods predict the result of genetic manipulations and allow for the best set of manipulations to be found computationally. Bi-level FBA is, however, limited in applicability because the required computational time and resources scale poorly as the size of the metabolic system and the number of genetic manipulations increase. To overcome these limitations, we have developed Genetic Design through Local Search (GDLS), a scalable, heuristic, algorithmic method that employs an approach based on local search with multiple search paths, which results in effective, low-complexity search of the space of genetic manipulations. Thus, GDLS is able to find genetic designs with greater in silico production of desired metabolites than can feasibly be found using a globally optimal search and performs favorably in comparison with heuristic searches based on evolutionary algorithms and simulated annealing.

A pathway and genetic factors contributing to elevated gene expression noise in stationary phase.

Previous studies have identified factors associated with transcription and translation efficiency, such as promoter strength and mRNA sequences, that can affect stochasticity in gene expression. Here we present evidence for a pathway and associated genetic factors (namely, the ribosome modulation factor RMF and ppGpp) in Escherichia coli that contribute to heightened levels of gene expression noise during stationary phase. Endogenous cellular mechanisms that globally affect gene expression noise, such as those identified in this study, could provide phenotypic diversity under adverse conditions such as stationary phase.

A bottom-up approach to gene regulation.

The ability to construct synthetic gene networks enables experimental investigations of deliberately simplified systems that can be compared to qualitative and quantitative models. If simple, well-characterized modules can be coupled together into more complex networks with behaviour that can be predicted from that of the individual components, we may begin to build an understanding of cellular regulatory processes from the 'bottom up'. Here we have engineered a promoter to allow simultaneous repression and activation of gene expression in Escherichia coli. We studied its behaviour in synthetic gene networks under increasingly complex conditions: unregulated, repressed, activated, and simultaneously repressed and activated. We develop a stochastic model that quantitatively captures the means and distributions of the expression from the engineered promoter of this modular system, and show that the model can be extended and used to accurately predict the in vivo behaviour of the network when it is expanded to include positive feedback. The model also reveals the counterintuitive prediction that noise in protein expression levels can increase upon arrest of cell growth and division, which we confirm experimentally. This work shows that the properties of regulatory subsystems can be used to predict the behaviour of larger, more complex regulatory networks, and that this bottom-up approach can provide insights into gene regulation.