While tremendous strides in increasing throughput and reducing cost have enabled multiomics studies through collaborative research, integration of simultaneous measurements from multiple biological components is not currently possible. Platforms of the various omics techniques including genomics, proteomics, transcriptomics, metabolomics, and cytomics are used in coordinated studies to produce snapshots of data with an intention of creating a holistic system level understanding. These studies can be quite difficult, normally requiring months or years to accomplish. There are outstanding difficulties in experiment planning, data integration, and cost control when performing multiomics studies. Even with the heroic efforts of these studies, the dynamic nature of biology is not addressed by current approaches, and computer models are required to simulate dynamic responses between individual time points.
At the same time that multiomics was being developed as an experimental foundation for systems biology, nanoelectronics was being developed as a foundation to directly link digital electronics to active biological systems. The combination of a unifying multiomics analytical biosensor with machine learning algorithms would be a very effective tool for complex biological data collection and decisive interpretation. Of the various designs and materials demonstrated over the 20 years of this effort, graphene transistor-based sensors are uniquely positioned to provide the capability to detect multiple analytes via an integrative device enabling greater accessibility to an academic research lab on a wider commercial scale.