On the heels of the $3bn Human Genome Project, our co-founders Dr. Joe Loscalzo and Dr. Albert-Laszlo Barabasi started building the map of human biology that explains how proteins expressed from genes interact to cause specific disease phenotypes (physical characteristics), providing the wiring diagram needed to interpret the genetic list developed by the Human Genome Project.
After 10 years of extensive experimental and computational lab work, the map has grown to cover 89% of all physically proven protein – protein interactions in human cells, revealing a profound network of underlying biological processes regulating diseases. Termed Network Medicine, our approach of using network science and graph theory to solve some of the most challenging problems in healthcare today is transforming they way we diagnose and treat patients.
Genes are static and poor predictors of disease given that most diseases are driven by environmental risk factors that can’t be captured by analyzing a patient’s DNA. An individual may be predisposed to a certain disease but the environment in many cases has a greater influence on whether the disease develops or not.
Our genes carry the information needed for creating over 18,000 proteins in human cells, but it is our dynamic, or continually changing, RNA that signals to the cell which and how much of a protein should be produced. Since most diseases are caused by proteins not being produced or controlled correctly, RNA data can show in real time which proteins make up and drive the biology of the disease, while capturing both genetic and environmental drivers.
While the ability to interpret RNA data has eluded scientists for decades, we combined patient RNA data with our map of protein interactions to create a platform capable of deciphering an individual’s unique disease biology.
By analyzing RNA from a simple blood sample, we can identify the patient’s disease biology and determine if a particular drug targets the right proteins to be an effective treatment or not.
Collecting RNA data from a growing patient population also allows us to identify common biological trends amongst sub-groups of patients, opening the opportunity to find novel targets and therapies for patients not responding to current standard of care drugs.