Taking advantage of the human genome
The MIT Computational Biology Group strives to understand the genetic component of diseases to ultimately reverse their effects
There is no doubt that computational methods have found a home in several diverse areas of study. For example, researchers in disciplines such as biology, finance, and even literature have been turning to computation for solutions to domain-specific problems. Manolis Kellis, professor of computer science, applies his computer science background to find unique solutions to problems in biology. Kellis believes that biology and computer science have a special bond, saying, “There’s a fundamental connection between biology and computation which is that humans are the descendants of the first digital computer. That is, every single one of our cells is a digital computer. Biology is fundamentally computational.” Kellis’s research lab at MIT, the Computational Biology Group, focuses on analyzing patterns in genetic data to better understand the genetic component of disease. The differences in our DNA can cause us to contract diseases with different probabilities and at different rates. With the rising accessibility of human genome sequencing, scientists can utilize this valuable tool as a basis for understanding the genetic underpinnings of disease.
The Computational Biology Group’s research begins by gathering large amounts of genomic data from patients with disorders such as Alzheimer’s disease, schizophrenia, obesity, and diabetes. They use these large samples to study the DNA differences between the patients and find all the regions of the genome that are associated with these differences. By doing so, these genomic regions can be related to the phenotypic traits they contribute and the physiological differences that exist between individuals, which builds a better understanding of a disease. “We can study pathways and processes that we did not previously suspect were associated with a certain disease, and once we find these processes we can better understand them,” says Kellis.
For example, Kellis and his team study the genomic data from postmortem brain samples across multiple regions of the brain and many cell types. They analyze molecular signatures, such as RNA expression and DNA methylation, to gain insight into diseases including Alzheimer’s and schizophrenia. With the collected genomic data, the lab builds computational models to predict the path through which genetic variation leads to a specific disease. Once this path is found, the genes and cell types that are involved in the disease can be uncovered as well. As a result of finding these genetic and cellular players, the genetic processes underlying the disease can be uncovered and potentially reversed. Therefore, by studying the genetic component of diseases, the scientific community can understand how genetic variation impacts gene expression and human diseases.
One of Kellis’s notable works is his research involving the influence of genetics on obesity. Obesity is genetically determined by whether fat cells burn or store a certain amount of energy. This choice is determined by some genetic variation that exists in a large genomic region, but Kellis and his group traced this difference to a single nucleotide letter. With the knowledge of the effects of the gene on the disease, Kellis and his lab reversed the effects of obesity in mice. “We ultimately manipulated that pathway to reverse disease phenotypes. We were able to make human cells burn more fat, make mice lose weight, and make them immune to a high fat diet all by understanding the genome,” says Kellis.
The recent work of the Computational Biology Group has been focused toward systematically mining electronic health records. There are massive amounts of phenotypic data available about each of us: doctor’s visits, prescriptions, online activity, and more. These data are valuable insights into our health and can be used to predict how we do on our next visit to the doctor, what kind of diseases we may develop in the future, or uncover information that may have gone unnoticed from our previous checkups and lab tests. By examining the data, a complete medical record can potentially be completed for each person. These detailed medical records can allow researchers to see which genomic groups different people fit in and may ultimately lead to treatments for diseases of genetic origin later in life.
In the future, the lab hopes to gain a molecular understanding of many phenomena that we know to be effective but do not have enough biological information to understand why. For example, meditation is a practice that helps with stress, anxiety, and overall focus. Similarly, people often feel better after they are given a placebo medication, in spite of the fact that it contains no active drugs. However, it is still unclear to why these methods work from a molecular standpoint. Professor Manolis Kellis and his group hope to uncover the molecular bases for phenomena that people have experienced for millennia but have yet to understand at a fundamental biological level.