Designing diagnostic platforms for analysis of disease patterns and probing disease emergence

TL;DR

This paper discusses designing point-of-care diagnostic platforms that analyze disease patterns and emergence, integrating complex biological signals to support personalized medicine with minimal user intervention.

Contribution

It introduces a multistep systems-level approach for developing point-of-care systems that analyze cellular and biomolecular profiles for personalized disease management.

Findings

Framework for integrating complex signals in point-of-care devices

Methodology for quantifying disease states and therapy progression

Design principles for minimal-intervention diagnostic systems

Abstract

The emerging era of personalized medicine relies on medical decisions, practices, and products being tailored to the individual patient. Point-of-care systems, at the heart of this model, play two important roles. First, they are required for identifying subjects for optimal therapies based on their genetic make-up and epigenetic profile. Second, they will be used for assessing the progression of such therapies. Central to this vision is designing systems that, with minimal user-intervention, can transduce complex signals from biosystems in complement with clinical information to inform medical decision within point-of-care settings. To reach our ultimate goal of developing point-of-care systems and realizing personalized medicine, we are taking a multistep systems-level approach towards understanding cellular processes and biomolecular profiles, to quantify disease states and external interventions.