Parallel, scalable, and cost-effective genome engineering to enable the use of allogeneic cell sources, as opposed to patient-specific sources

Develop and rapidly produce biomolecule-based materials (biomaterials) that have improved physiological properties.

Develop biopolymers with physical durability to resist long-term wear and tear.

Achieve minimally-invasive control of synthetic gene and protein networks with light-programmable macromolecules (advanced optogenetics).

Engineer cellular pathways, extracellular matrices, and connective tissues that enhance prosthetic compatibility without compromising health.

Identify predictive, detectable, micro-scale biosignatures (biological outputs) that correlate with health, damage, or disease.

Develop systems for reliable genomic integration of reporters that will sense specific cell states in high-risk populations, where the molecules/states can be sensed, analyzed, and acted upon externally (electronic or optic signaling).

Develop sensing and reporting systems that enable in situ detection of toxins and disease indicators, where the info can be sensed, analyzed, and acted upon externally (electronic or optic signaling) or in a more integrated fashion.

Develop probiotics and similar cell systems that can report to external devices.

Tune select cells or tissues to interact with stimuli from external (electronic) devices in a highly controlled manner.

Develop and advance modeling and analytics to integrate information from wearable tech, medical sensors (like those for continuous glucose monitoring), and eventually in vivo sensors, to predict health, physical performance, toxin exposure, disease, other states of interest.

Use novel machine learning approaches to integrate different types of sensor data and address variation between people and populations.

Design and model systems that both sense and act upon threats, with reliable communication and data integration.

Expand and improve algorithms for estimating health states based on a limited set of measurable data.

Achieve highly efficient, rapid genetic or epigenetic editing of the allograft genome with synthetic gene cassettes or whole chromosomes.

Achieve efficient co-editing of human leukocyte antigen (HLA) gene clusters to prevent allograft rejection.

Generate potent gene delivery vehicles for immune gene clusters (such as HLAs) and whole synthetic chromosomes.

Develop macromolecules to neutralize or mask non-self protein markers.

Remove potent non-self antigens from allograft tissues/organs.

Replace non-self with “self” markers in allograft cells.

Achieve data-driven molecular profiling of key antigens to identify engineerable donor tissue and support patient-to-allograft matching.