Our discovery platform for genomic medicine relies heavily on integrated data analysis and modeling

Choose Disease Locus

Unsurprisingly, a majority of disease-associated variants discovered in GWAS studies fall within the cis-regulatory elements (CREs) such as promoters and enhancers serve to define the transcriptional regulatory landscape of the neighboring genes . When defective, CREs cause transcriptional misregulation, which cause imbalances which can eventually lead to disease

Our first step is to first identify a CRE of interest through the use of public and private data. Due to the foundational principles used to construct our platform, it can be applied to any region of the genome.

Build Regulatory Architecture

Integrate information about DNA methylation state, nucleosome positions, histone modifications and chromatin looping by building upon public data with new healthy and disease single cell data, 

Incorporate of machine learning models to predict epigenetic status of genomic locations where high quality assay data is limited or not available.

Use differential mapping and identification of the local chromatin interactions with transcription factors and noncoding RNAs in disease and healthy tissues.

Design Molecular Assembly

Construct quantized 3D models to atomic resolution of the locus influencing expression of the gene of interest to guide the engineering of a therapeutic protein/RNA assembly,

Structure-guided molecular assembly provides increased specificity for the target locus.

Our versatile platform has potential for composing therapeutic programs which include repression and/or activation of more than one gene using the same major components