Home // International Journal On Advances in Systems and Measurements, volume 11, numbers 3 and 4, 2018 // View article

Towards a Knowledge-intensive Framework for Efficient Vaccine Development

Authors:
Leonard Petnga
Surangi Jayawardena

Keywords: Knowledge formalisms; Systems Engineering; Semantic; Ontology; Markov Chain; Multiple Regression Model.

Abstract:
Modern vaccine research & development efforts are complex, long, costly undertakes with high rate of failure. A major cause of inefficiency can be attributed to the mostly unstructured, unorganized, disconnected and diversity of knowledge spread across the Vaccine Development Life Cycle (VDLC) and honed by number of stakeholders with conflicting interests. State-of-the-art approaches have mostly fostered stove-piping knowledge and information within individual disciplines and separation of concerns, with little interest or appetite for cross-domain knowledge integration. In this research, we build on the ability of systems engineering to bridge the gaps between (and integrate) other disciplines to architect and develop a novel knowledge-intensive framework for efficient vaccine development. We formulate a model-based platform that accounts for the need for, (1) formalisms to support unambiguous and correct knowledge representation and reasoning across the VDLC, (2) capturing stochastic system biology behaviors and integrating with stakeholders’ discrete decisions and, (3) models that are formal, reusable, customizable and can be assembled as needed for the purpose of the analysis at hand. Description logic-based formalisms and foundational domain theories support knowledge models of domains tightly coupled with Markov models of biological and chemical processes are the cornerstone of our framework. An example step-by-step implementation procedure illustrates the modularity, flexibility and configuration of the framework for tackling increasingly complex, cross-domain challenges across the VDLC. Vaccine preservation laboratory experiments are conducted to assess some prototype formulations and generate system biology models to be integrated with semantic models in the platform. Results are very encouraging but further work is needed in identifying and mapping all relevant biological system behaviors for the analysis under consideration and improving their characterization and integration in the framework.

Pages: 383 to 395

Copyright: Copyright (c) to authors, 2018. Used with permission.

Publication date: December 30, 2018

Published in: journal

ISSN: 1942-261x