High-Throughput In Vitro, Ex Vivo, and In Vivo Screen of Adeno-Associated Virus Vectors Based on Physical and Functional Transduction

Adeno-associated virus (AAV) vectors are quickly becoming the vectors of choice for therapeutic gene delivery. To date, hundreds of natural isolates and bioengineered variants have been reported. While factors such as high production titer and low immunoreactivity are important to consider, the ability to deliver the genetic payload (physical transduction) and to drive high transgene expression (functional transduction) remains the most important feature when selecting AAV variants for clinical applications. Reporter expression assays are the most commonly used methods for determining vector fitness. However, such approaches are time consuming and become impractical when evaluating a large number of variants. Limited access to primary human tissues or challenging model systems further complicates vector testing. To address this problem, convenient high-throughput methods based on next-generation sequencing (NGS) are being developed. To this end, we built an AAV Testing Kit that allows inherent flexibility in regard to number and type of AAV variants included, and is compatible with in vitro, ex vivo, and in vivo applications. The Testing Kit presented here consists of a mix of 30 known AAVs where each variant encodes a CMV-eGFP cassette and a unique barcode in the 3′-untranslated region of the eGFP gene, allowing NGS-barcode analysis at both the DNA and RNA/cDNA levels. To validate the AAV Testing Kit, individually packaged barcoded variants were mixed at an equal ratio and used to transduce cells/tissues of interest. DNA and RNA/cDNA were extracted and subsequently analyzed by NGS to determine the physical/functional transduction efficiencies. We were able to assess the transduction efficiencies of immortalized cells, primary cells, and induced pluripotent stem cells in vitro, as well as in vivo transduction in naïve mice and a xenograft liver model. Importantly, while our data validated previously reported transduction characteristics of individual capsids, we also identified novel previously unknown tropisms for some AAV variants

ModelPlex: Verified Runtime Validation of Verified Cyber-Physical System Models

Abstract. Formal verification and validation play a crucial role in making cyber-physical systems (CPS) safe. Formal methods make strong guarantees about the system behavior if accurate models of the system can be obtained, including mod-els of the controller and of the physical dynamics. In CPS, models are essential; but any model we could possibly build necessarily deviates from the real world. If the real system fits to the model, its behavior is guaranteed to satisfy the correct-ness properties verified w.r.t. the model. Otherwise, all bets are off. This paper introduces ModelPlex, a method ensuring that verification results about models apply to CPS implementations. ModelPlex provides correctness guarantees for CPS executions at runtime: it combines offline verification of CPS models with runtime validation of system executions for compliance with the model. Model-Plex ensures that the verification results obtained for the model apply to the ac-tual system runs by monitoring the behavior of the world for compliance with the model, assuming the system dynamics deviation is bounded. If, at some point, the observed behavior no longer complies with the model so that offline verifica-tion results no longer apply, ModelPlex initiates provably safe fallback actions. This paper, furthermore, develops a systematic technique to synthesize provably correct monitors automatically from CPS proofs in differential dynamic logic.

Temporal Logic Based Monitoring of Assisted Ventilation in Intensive Care Patients

We introduce a novel approach to automatically detect ineffective breathing efforts in patients in intensive care subject to assisted ventilation. The method is based on synthesising from data temporal logic formulae which are able to discriminate between normal and ineffective breaths. The learning procedure consists in first constructing statistical models of normal and abnormal breath signals, and then in looking for an optimally discriminating formula. The space of formula structures, and the space of parameters of each formula, are searched with an evolutionary algorithm and with a Bayesian optimisation scheme, respectively. We present here our preliminary results and we discuss our future research directions.\&nbsp;</p