Search for: All records

Not AvailableSynthetic cells represent a rapidly advancing frontier in engineering biology, offering new ways to understand living systems and developing innovative solutions across health, environmental management, agriculture, and manufacturing. Constructed from biomolecular components and designed to perform life-like functions, synthetic cells have the potential to enable more predictable, controllable, and adaptable biological technologies with wide-ranging societal and economic benefits. Supporting Responsible Innovation of Synthetic Cells: Biosafety, Biosecurity, and Environmental Considerations examines how this emerging field can advance while addressing biosafety, biosecurity, and environmental considerations. The report finds that many risks associated with synthetic cells are similar to those posed by existing chemical and microbial systems but may arise in new ways due to novel combinations of features, boundary-blurring system architectures, and emerging applications, creating uncertainty for risk-benefit evaluation and oversight. To support responsible innovation, the report outlines a path forward grounded in adaptive, evidence-based, and proportionate governance. Key recommendations include establishing a coordinated national biotechnology governance strategy, strengthening the empirical data and research infrastructure for risk assessment, modernizing biosafety and biosecurity oversight, and promoting transparency, public engagement, and international collaboration. By aligning scientific advancement with adaptive and coordinated governance, this report provides a framework for ensuring that synthetic cell research develops in ways that are safe, secure, and responsive to societal needs, helping realize the promise of this emerging technology while managing uncertainty and risk. 

This paper studies the evaluation of learning-based object detection models in conjunction with model-checking of formal specifications defined on an abstract model of an autonomous system and its environment. In particular, we define two metrics – proposition-labeled and class-labeled confusion matrices – for evaluating object detection, and we incorporate these metrics to compute the satisfaction probability of system-level safety requirements. While confusion matrices have been effective for comparative evaluation of classification and object detection models, our framework fills two key gaps. First, we relate the performance of object detection to formal requirements defined over downstream high-level planning tasks. In particular, we provide empirical results that show that the choice of a good object detection algorithm, with respect to formal requirements on the overall system, significantly depends on the downstream planning and control design. Secondly, unlike the traditional confusion matrix, our metrics account for variations in performance with respect to the distance between the ego and the object being detected. We demonstrate this framework on a car-pedestrian example by computing the satisfaction probabilities for safety requirements formalized in Linear Temporal Logic (LTL). 

Abstract Model reduction methods usually focus on the error performance analysis; however, in presence of uncertainties, it is important to analyze the robustness properties of the error in model reduction as well. This problem is particularly relevant for engineered biological systems that need to function in a largely unknown and uncertain environment. We give robustness guarantees for structured model reduction of linear and nonlinear dynamical systems under parametric uncertainties. We consider a model reduction problem where the states in the reduced model are a strict subset of the states of the full model, and the dynamics for all of the other states are collapsed to zero (similar to quasi‐steady‐state approximation). We show two approaches to compute a robustness guarantee metric for any such model reduction—a direct linear analysis method for linear dynamics and a sensitivity analysis based approach that also works for nonlinear dynamics. Using the robustness guarantees with an error metric and an input‐output mapping metric, we propose an automated model reduction method to determine the best possible reduced model for a given detailed system model. We apply our method for the (1) design space exploration of a gene expression system that leads to a new mathematical model that accounts for the limited resources in the system and (2) model reduction of a population control circuit in bacterial cells.