Model‐based systems engineering for life‐sciences instrumentation development

Abstract

Next-generation genome sequencing machines and Point-of-Care (PoC) in vitro diagnostics devices are precursors of an emerging class of Cyber-Physical Systems (CPS), one that harnesses biomolecular-scale mechanisms to enable novel "wet-technology" applications in medicine, biotechnology, and environmental science. Although many such applications exist, testifying the importance of innovative life-sciences instrumentation, recent events have highlighted the difficulties that designing organizations face in their attempt to guarantee safety, reliability, and performance of this special class of CPS. New regulations and increasing competition pressure innovators to rethink their design and engineering practices, and to better address the above challenges. The pace of innovation will be determined by how organizations manage to ensure the satisfaction of aforementioned constraints while also streamlining product development, maintaining high cost-efficiency and shortening time-to-market. Model-Based Systems Engineering provides a valuable framework for addressing these challenges. In this paper, we demonstrate that existing and readily available model-based development frameworks can be adopted early in the life-sciences instrumentation design process. Such frameworks are specifically helpful in describing and characterizing CPS including elements of a biological nature both at the architectural and performance level. We present the SysML model of a smartphone-based PoC diagnostics system designed for detecting a particular molecular marker. By modeling components and behaviors spanning across the biological, physical-nonbiological, and computational domains, we were able to characterize the important systemic relations involved in the specification of our system's Limit of Detection. Our results illustrate the suitability of such an approach and call for further work toward formalisms enabling the formal verification of systems including biomolecular components. K E Y W O R D Scyber-physical systems, life-sciences, model-based systems engineering, model-based systems design, sensitivity analysis, SysML 98

Design of Cyber Bio-analytical Physical Systems: Formal methods, architectures, and multi-system interaction strategies

“…Another research [10] proposed an evolvable system architecture for lab-on-a-chip (LOC) devices. That work was extended to define platformbased design for LOC devices [11] and later a model-based system design framework was proposed for life sciences instruments [12]. The works [9,12] motivated model-based system design methodology in bio-analytical and diagnostic devices and modeled the system using SysML.…”

“…That work was extended to define platformbased design for LOC devices [11] and later a model-based system design framework was proposed for life sciences instruments [12]. The works [9,12] motivated model-based system design methodology in bio-analytical and diagnostic devices and modeled the system using SysML. It is possible to model system behavior using SysML; however, as mentioned earlier in this paper, non-functional properties are required for the synchronized operation of the system under constraints; and cannot be fully handled with this approach.…”

“…This paper seeks to address this research gap by providing a wireless control model for bioanalytical devices. Figure 1 depicts a simplified architecture concept for wireless control of a bio-analytical CPS, also known as CBPS [12] and CPBS [9] by introducing the communication domain, in addition to the physical, fluidic, and cyber domains. The cyber domain handles process/system control, computation, decisionmaking, and data storage and discarding.…”

Integration of systems design and risk management through model‐based systems development

“…Model‐based systems engineering (MBSE) has been utilized for developing safety‐critical systems in defense, aviation, and automotive industries, where compliance with stringent standards is required. MBSE deals with complexity, improve communications between the stakeholders, increases quality, productivity, and reduces risk and cost 1–3 . In addition to these benefits, many researchers have been trying to integrate model‐based system development and safety risk assessment in various ways to create a unique source of information for the certification during the last years.…”

“…MBSE deals with complexity, improve communications between the stakeholders, increases quality, productivity, and reduces risk and cost. [1][2][3] In addition to these benefits, many researchers have been trying to integrate model-based system development and safety risk assessment in various ways to create a unique source of information for the certification during the last years. The use of system model structure as a single source of truth for dependability (safety, security, reliability) analysis is not only critical in providing a consistent and complete risk assessment but also during the life cycle of the development.…”