GlycoData: GlycoMIP's data sharing platform
In-House Research Program
Scope: convergence of scalable synthesis, automated characterization, and advanced modeling to accelerate rational glycomaterial design.
Intellectual Merit: ability to rationally design glycomaterials does not currently exist.
- Innovations are required for predicting, synthesizing and characterizing carbohydrate self-assembly (relevant to aggregation, gelation, film formation, etc).
National and International Impact: discovery of completely novel materials and methods.
- Applications in areas as diverse as medicine, aerospace, renewable resources, and defense.
Iterative Approach: transdisciplinary, iterative, closed-loop approach.
- MGI approach integrates synthesis, characterization, and modeling to surmount scientific and technological barriers in glycomaterials research and innovation.
Major Goals of the In-House Program
- Develop new chemical and enzymatic methods and protocols for the automated synthesis of glycopolymers with the guidance of advanced machine-learning techniques
- Develop advanced automated modeling protocols that better account for the effects of molecular environment on the conformations and physical properties of glycopolymers.
- Implement autonomous and automated computational methods for predicting and assigning the spectra (ROA, VCD, MS) of glycopolymers.
- Develop high-throughput characterization methods for measuring the properties of glycomaterials.
MGI Approach to In-House Research
In-house research domains ( “Loops”) were created to address the Major Goals.
Loop 1: Novel Glycomaterials
(Lead: John Matson / Sanket Deshmukh)
Loop 2: Biocatalysis in Glycomaterial Creation
(Lead: Maren Roman)
Loop 3: Rational Glycomaterial Design
(Lead: Rob Woods)
Loop 4: Machine learning in Glycoscience
(Lead: Pengyu Hong)
Within each loop, the research program contains three core elements: synthesis, characterization, and theory that together synergize to advance research efficiency