Matching Items (3)
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- All Subjects: bio-manufacturing
- All Subjects: Safety Requirements
- Creators: Pedrielli, Giulia
Description
Autonomous Vehicles (AV) are inevitable entities in future mobility systems thatdemand safety and adaptability as two critical factors in replacing/assisting human
drivers. Safety arises in defining, standardizing, quantifying, and monitoring requirements
for all autonomous components. Adaptability, on the other hand, involves
efficient handling of uncertainty and inconsistencies in models and data. First, I address
safety by presenting a search-based test-case generation framework that can be
used in training and testing deep-learning components of AV. Next, to address adaptability,
I propose a framework based on multi-valued linear temporal logic syntax and
semantics that allows autonomous agents to perform model-checking on systems with
uncertainties. The search-based test-case generation framework provides safety assurance
guarantees through formalizing and monitoring Responsibility Sensitive Safety
(RSS) rules. I use the RSS rules in signal temporal logic as qualification specifications
for monitoring and screening the quality of generated test-drive scenarios. Furthermore,
to extend the existing temporal-based formal languages’ expressivity, I propose
a new spatio-temporal perception logic that enables formalizing qualification specifications
for perception systems. All-in-one, my test-generation framework can be
used for reasoning about the quality of perception, prediction, and decision-making
components in AV. Finally, my efforts resulted in publicly available software. One
is an offline monitoring algorithm based on the proposed logic to reason about the
quality of perception systems. The other is an optimal planner (model checker) that
accepts mission specifications and model descriptions in the form of multi-valued logic
and multi-valued sets, respectively. My monitoring framework is distributed with the
publicly available S-TaLiRo and Sim-ATAV tools.
ContributorsHekmatnejad, Mohammad (Author) / Fainekos, Georgios (Thesis advisor) / Deshmukh, Jyotirmoy V (Committee member) / Karam, Lina (Committee member) / Pedrielli, Giulia (Committee member) / Shrivastava, Aviral (Committee member) / Yang, Yezhou (Committee member) / Arizona State University (Publisher)
Created2021
Description
With the increased demand for genetically modified T-cells in treating hematological malignancies, the need for an optimized measurement policy within the current good manufacturing practices for better quality control has grown greatly. There are several steps involved in manufacturing gene therapy. These steps are for the autologous-type gene therapy, in chronological order, are harvesting T-cells from the patient, activation of the cells (thawing the cryogenically frozen cells after transport to manufacturing center), viral vector transduction, Chimeric Antigen Receptor (CAR) attachment during T-cell expansion, then infusion into patient. The need for improved measurement heuristics within the transduction and expansion portions of the manufacturing process has reached an all-time high because of the costly nature of manufacturing the product, the high cycle time (approximately 14-28 days from activation to infusion), and the risk for external contamination during manufacturing that negatively impacts patients post infusion (such as illness and death).
The main objective of this work is to investigate and improve measurement policies on the basis of quality control in the transduction/expansion bio-manufacturing processes. More specifically, this study addresses the issue of measuring yield within the transduction/expansion phases of gene therapy. To do so, it was decided to model the process as a Markov Decision Process where the decisions being made are optimally chosen to create an overall optimal measurement policy; for a set of predefined parameters.
The main objective of this work is to investigate and improve measurement policies on the basis of quality control in the transduction/expansion bio-manufacturing processes. More specifically, this study addresses the issue of measuring yield within the transduction/expansion phases of gene therapy. To do so, it was decided to model the process as a Markov Decision Process where the decisions being made are optimally chosen to create an overall optimal measurement policy; for a set of predefined parameters.
ContributorsStarkey, Michaela (Author) / Pedrielli, Giulia (Thesis advisor) / Li, Jing (Committee member) / Wu, Teresa (Committee member) / Arizona State University (Publisher)
Created2020
Description
The success of genetically-modified T-cells in treating hematological malignancies has accelerated the research timeline for Chimeric Antigen Receptor-T (CAR-T) cell therapy. Since there are only two approved products (Kymriah and Yescarta), the process knowledge is limited. This leads to a low efficiency at manufacturing stage with serious challenges corresponding to high cost and scalability. In addition, the individualized nature of the therapy limits inventory and creates a high risk of product loss due to supply chain failure. The sector needs a new manufacturing paradigm capable of quickly responding to individualized demands while considering complex system dynamics.
The research formulates the problem of Chimeric Antigen Receptor-T (CAR-T) manufacturing design, understanding the performance for large scale production of personalized therapies. The solution looks to develop a simulation environment for bio-manufacturing systems with single-use equipment. The result is BioMan: a discrete-event simulation model that considers the role of therapy's individualized nature, type of processing and quality-management policies on process yield and time, while dealing with the available resource constraints simultaneously. The tool will be useful to understand the impact of varying factor inputs on Chimeric Antigen Receptor-T (CAR-T) cell manufacturing and will eventually facilitate the decision-maker to finalize the right strategies achieving better processing, high resource utilization, and less failure rates.
The research formulates the problem of Chimeric Antigen Receptor-T (CAR-T) manufacturing design, understanding the performance for large scale production of personalized therapies. The solution looks to develop a simulation environment for bio-manufacturing systems with single-use equipment. The result is BioMan: a discrete-event simulation model that considers the role of therapy's individualized nature, type of processing and quality-management policies on process yield and time, while dealing with the available resource constraints simultaneously. The tool will be useful to understand the impact of varying factor inputs on Chimeric Antigen Receptor-T (CAR-T) cell manufacturing and will eventually facilitate the decision-maker to finalize the right strategies achieving better processing, high resource utilization, and less failure rates.
ContributorsSharma, Gaurav (Author) / Pedrielli, Giulia (Thesis advisor) / Fainekos, Georgios (Committee member) / Fowler, John (Committee member) / Arizona State University (Publisher)
Created2020