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Knowledge-driven Vaccine Systems Engineering

Authors:
Leonard Petnga
Surangi Jayawardena

Keywords: Vaccine; Systems Engineering; Semantic; Ontology; Markov Chain.

Abstract:
Current vaccine development approaches are ineffective in capturing, representing and reconciling domains and disciplines knowledge and viewpoints across the vaccine development life cycle. As a result, vaccine development is a long, complex, and costly process that often results (when it succeeds) in vaccines whose potency is hard to predict and efficacy expensive to preserve. State-of-the-art vaccine development approaches fail to (1) integrate the multiplicity of stakeholders perspectives across the Vaccine Development Life Cycle (VDLC), (2) bridge the knowledge gap between multiple disciplines, and (3) formally evaluate stochastic system behaviors of biological systems in a seamless manner. This paper introduces a novel semantically enabled framework for knowledge and behavior specification, modeling and processing in vaccine systems engineering. Data and semantic models of domains supported by sound theories and tightly coupled with Markov Chain models of biological systems are the cornerstone of our solution. Near future work includes optimal vaccine matrix design and experimental vaccine preservation systems. Looking ahead, the capabilities of the framework – demonstrated in a vaccine preservation study – will enable more effective, cheaper and faster time-to-market vaccines.

Pages: 12 to 17

Copyright: Copyright (c) IARIA, 2018

Publication date: April 22, 2018

Published in: conference

ISSN: 2308-4243

ISBN: 978-1-61208-626-2

Location: Athens, Greece

Dates: from April 22, 2018 to April 26, 2018