Flow cytometry is a useful and efficient method for the rapid characterization of a cell population based on the optical and fluorescence properties of individual cells. Ideally, the cell population would consist of only healthy viable cells as dead cells can confound the analysis. Thus, separating out healthy cells from dying and dead cells, and any potential debris, is an important first step in analysis of flow cytometry data. While gating of debris can be conducted using measured optical properties, identifying dead and dying cells often requires utilizing fluorescent stains (e.g. Sytox, a nucleic acid stain that stains cells with compromised cell membranes) to identify cells that should be excluded from downstream analyses. These stains prolong the experimental preparation process and use a flow cytometer’s fluorescence channels that could otherwise be used to measure additional fluorescent markers within the cells (e.g. reporter proteins). Here we outline a stain-free method for identifying viable cells for downstream processing by gating cells that are dying or dead. AutoGater is a weakly supervised deep learning model that can separate healthy populations from unhealthy and dead populations using only light-scatter channels. In addition, AutoGater harmonizes different measurements of dead cells such as Sytox and CFUs.

Laboratory protocols are critical to biological research and development, yet difficult to communicate and reproduce across projects, investigators, and organizations. While many attempts have been made to address this challenge, there is currently no available protocol representation that is unambiguous enough for precise interpretation and automation, yet simultaneously “human friendly” and abstract enough to enable reuse and adaptation. The Laboratory Open Protocol language (LabOP) is a free and open protocol representation aiming to address this gap, building on a foundation of UML, Autoprotocol, Aquarium, SBOL RDF, and the Provenance Ontology. LabOP provides a linked-data representation both for protocols and for records of their execution and the resulting data, as well as a framework for exporting from LabOP for execution by either humans or laboratory automation. LabOP is currently implemented in the form of an RDF knowledge representation, specification document, and Python library, and supports execution as manual “paper protocols,” by Autoprotocol or by Opentrons. From this initial implementation, LabOP is being further developed as an open community effort.

Computational tools addressing various components of design-build-test-learn loops (DBTL) 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 DART (Design Assemble Round Trip). 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.

We describe an experimental campaign that replicated the performance assessment of logic gates engineered into cells of S. cerevisiae by Gander, et al . Our experimental campaign used a novel high throughput experimentation framework developed under DARPA’s Synergistic Discovery and Design (SD2) 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.

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.

A challenge in the design and construction of synthetic genetic circuits is that they will operate within biological systems that have noisy and changing parameter regimes that are largely unmeasurable. The outcome is that these circuits do not operate within design specifications or have a narrow operational envelope in which they can function. This behavior is often observed as a lack of reproducibility in function from day to day or lab to lab. Moreover, this narrow range of operating conditions does not promote reproducible circuit function in deployments where environmental conditions for the chassis are changing, as environmental changes can affect the parameter space in which the circuit is operating. Here we describe a computational method for assessing the robustness of circuit function across broad parameter regions. Previously designed circuits are assessed by this computational method and then circuit performance is measured across multiple growth conditions in budding yeast. The computational predictions are correlated with experimental findings, suggesting that the approach has predictive value for assessing the robustness of a circuit design.

We are working on an experiment planning system for synthetic biology. As part of this effort, we wish to identify the most informative experiments to perform, based on our current state of knowledge about some design or designs. To do so, we introduce a hierarchical Bayesian model of genetic circuits, assess its predictive adequacy, and explain how it is used to score candidate experiments. We conclude with a discussion of future research directions, including model extensions, improvements to the inference methods, and more ambitious uses of the predictive model.

Organisations conducting research programs often focus the work of their scientists and technologists on challenge problems (CPs). These challenges are designed to ensure that progress is measurable and relevant to the goals of the program sponsor. Generating and selecting pertinent CPs is difficult, as is assessing their value. We describe a method of generating and selecting CPs and its application in a highly collaborative, multi-organisation research program. Thirty-eight biologists, chemists, mathematicians and computer scientists across academic, commercial and government organisations generated and ranked their top choices from among 12 richly described candidate challenge problems. A ranked-choice voting formula was applied. Five CPs were highly scored; the remaining seven were distributed across a lower range of scores. The program sponsor subsequently directed researchers to address six CPs, including the elected five. Analysis of the rationales that participants offered for their CP rankings revealed four domain-independent dimensions of value: capability, speed, impact and synergy. These dimensions of value can help managers of interdisciplinary research programs systematically select a portfolio of CPs that will efficiently apply utilise resources towards program goals and facilitate measurement of scientific progress.

PDDL+ planning has its semantics rooted in hybrid automata (HA) and recent work has shown that it can be modeled as a network of HAs. Addressing the complexity of nonlinear PDDL+ planning as HAs requires both space and time efficient reasoning. Unfortunately, existing solvers either do not address nonlinear dynamics or do not natively support networks of automata. We present a new algorithm, called HNSolve, which guides the variable selection of the dReal Satisfiability Modulo Theories (SMT) solver while reasoning about network encodings of nonlinear PDDL+ planning as HAs. HNSolve tightly integrates with dReal by solving a discrete abstraction of the HA network. HNSolve finds composite runs on the HA network that ignore continuous variables, but respect mode jumps and synchronization labels. HNSolve admissibly detects dead-ends in the discrete abstraction, and posts conflict clauses that prune the SMT solver's search. We evaluate the benefits of our HNSolve algorithm on PDDL+ benchmark problems and demonstrate its performance with respect to prior work.

When engineering an automated planning model, domain authors typically assume a static, unchanging ground-truth world. Unfortunately, this assumption can clash with reality, where domain changes often rapidly occur in best practices, effectors, or known conditions. In these cases, remodeling the domain causes domain experts to ensure newly captured requirements integrate well with the current model. In this work, we address this model maintenance problem in a system called Marshal. Marshal assists model maintainers by reasoning about their model as a (hidden) stochastic process. It issues queries, and learns models by observing query answers, plan solutions, and direct changes to the model. Our results indicate that anticipating model evolution leads to more accurate models over naive approaches.

This paper describes Hy-CIRCA, an architecture for verified, correct-by-construction planning and execution for hybrid systems, including nonlinear continuous dynamics. Hy-CIRCA addresses the high computational complexity of such systems by first planning at an abstract level, and then progressively refining the original plan. Hy-CIRCA integrates the dReal nonlinear SMT solver with enhanced versions of the SHOP2 HTN planner and the CIRCA Controller Synthesis Module (CSM). SHOP2 computes a high level nominal mission plan, the CIRCA CSM develops reactive controllers for the mission steps, accounting for disturbances, and dReal verifies that the plans are correct with respect to continuous dynamics. In this way, Hy-CIRCA decomposes reasoning about the plan and judiciously applies the different solvers to the problems they are best at.

Hybrid planning with nonlinear continuous change is a significant challenge for existing planners. Prior works limit their scope to linear change or base their formalisms in model checking frameworks with inherent limitations. We address nonlinear PDDL+ planning with a new encoding in first order logic over real valued functions. Our planner, PluReal , translates PDDL+ to this logical encoding and applies the dReal Satisfiability Modulo Theories (SMT) solver to construct plans. Unlike prior work that uses dReal in the hybrid system model checking tradition, PluReal is based in the planning as satisfiability (SAT) heritage. Adopting the SAT approach helps lift several unnatural restrictions that are imposed by the translation through hybrid systems and leads to improved scalability even without SMT solver variable selection heuristics.

This paper describes Hy-CIRCA, an architecture for verified, correct-by-construction planning and execution for hybrid systems, including non-linear continuous dynamics. Hy-CIRCA addresses the high computational complexity of such systems by first planning at an abstract level, and then progressively refining the original plan. Hy-CIRCA is an extension of our Playbook 1 approach, which aims to make it easy for users to exert supervisory control over multiple autonomous systems by “calling a play.” The Playbook approach is implemented by combining (1) a human-machine interface for commanding and monitoring the autonomous systems; (2) a hierarchical planner for translating commands into executable plans; and (3) a smart executive to manage plan execution by coordinating the control systems of the individual autonomous agents, tracking plan execution, and triggering replanning when necessary. Hy-CIRCA integrates the dReal non-linear SMT solver, with enhanced versions of the SHOP2 HTN planner and the CIRCA Control Synthesis Module (CSM). Hy-CIRCA’s planning process has 5 steps: (1) Using SHOP2, compute an approximate mission plan. While computing this plan, compute a hybrid automaton model of the plan, featuring more expressive continuous dynamics. (2) Using dReal, solve this hybrid model, establishing the correctness of the plan, and computing values for its continuous parameters. To execute the plan, (3) extract from the plan specifications for closed-loop, hard real-time supervisory controllers for the agents that must execute the plan. (4) Based upon these specifications, use the CIRCA CSM to plan the controllers. To ensure correct execution, (5) verify the CSM-generated controllers with dReal.

PDDL+ planning involves reasoning about mixed discrete-continuous change over time. Nearly all PDDL+ planners assume that continuous change is linear. We present a new technique that accommodates nonlinear change by encoding problems as nonlinear hybrid systems. Using this encoding, we apply a Satisfiability Modulo Theories (SMT) solver to find plans. We show that it is important to use novel planning-specific heuristics for variable and value selection for SMT solving, which is inspired by recent advances in planning as SAT. We show the promising performance of the resulting solver on challenging nonlinear problems.

Building and maintaining large, reusable ontologies is a prerequisite for building automated systems that reason and plan in a changing world. Unfortunately, our needs, best practices, and mental models of the world also change. These factors— in addition to common human mistakes— lead to model drift over time in our computational models and ontologies. As a result, ontologies contain direct inconsistencies, anomalies, partial or duplicate descriptions, and erroneous constraints between components. This paper presents ongoing development on our system, Marshal, designed to help to curate ontologies and task models by making suggestions while users write procedures and plans. We present experiments in three ontology curation settings: (1) inducing new categories; (2) detecting anomalous instances; and (3) classification of unlabeled instances into an existing ontology. On each task, we compare multiple similarity-based inference strategies, and we show that structure-mapping produces favorable results on all three ontology curation tasks.

This paper describes a novel combination of Java program analysis and automated learning and planning architecture to the domain of Java vulnerability analysis. The key feature of our “HACKAR: Helpful Advice for Code Knowledge and Attack Resilience” system is its ability to analyze Java programs at development-time, identifying vulnerabilities and ways to avoid them. HACKAR uses an improved version of NASA’s Java PathFinder (JPF) to execute Java programs and identify vulnerabilities. The system features new Hierarchical Task Network (HTN) learning algorithms that (1) advance stateof-theart HTN learners with reasoning about numeric constraints, failures, and more general cases of recursion, and (2) contribute to problem-solving by learning a hierarchical dataflow representation of the program from the inputs of the program. Empirical evaluation demonstrates that HACKAR was able to suggest fixes for all of our test program suites. It also shows that HACKAR can analyze programs with string inputs that original JPF implementation cannot.

Model checking is an important tool for verifying that sys- tem designs will satisfy their requirements. Model check- ers excel at reasoning about probabilistic systems, but can be improved when explaining counter-examples. Probabilistic counter-examples are sets of verifier traces that demonstrate how and when the probability of satisfying a system property is insufficient. Understanding a single verifier trace can be challenging; reconciling multiple verifier traces is even more difficult. We present a new technique for explaining a set of verifier traces that applies explanation based learning. Our approach extracts causal proofs from verifier traces and then general- izes over the proofs. Generalization is achieved through en- coding and solving a weighted MaxSAT problem that maps trace explanations onto a generalized explanation. We present results on our model of an infantry fighting vehicle and show that the reduction in verifier traces is typically two orders of magnitude.

Prior approaches to generating diverse plans in domain-independent planning seek out variations on plan structure such as actions or causal links used, or states entered. Measuring such syntactic differences between plans can be misleading because syntactically different plans can be semantically identical. We develop a landmark-based plan distance measure that captures semantic differences between plans. The landmark-based distance measure focuses on the disjunctive landmarks satisfied by each plan. We develop a simple algorithm for finding diverse plans that is based upon the LAMA planner. We illustrate that, in comparison with plan distance measures, landmark-based plan distance is not as susceptible to including irrelevant or redundant actions in plans to increase plan distance. Through extensive empirical evaluation, we find that high landmark distance between plans implies high action set distance, but not vice versa. Landmark-based plan distance overcomes some of the weaknesses of syntactic plan distance measures and can be used to find plan sets that are both landmark diverse and action set diverse.