Manolis Kellis


Manolis Kellis is a professor of Computer Science at the Massachusetts Institute of Technology in the area of Computational Biology and a member of the Broad Institute of MIT and Harvard. He is the head of the Computational Biology Group at MIT and is a Principal Investigator in the Computer Science and Artificial Intelligence Lab at MIT.
Kellis is known for his contributions to genomics, human genetics, epigenomics, gene regulation, and genome evolution. He co-led the NIH Roadmap Epigenomics Project effort to create a comprehensive map of the human epigenome, the comparative analysis of 29 mammals to create a comprehensive map of conserved elements in the human genome, the ENCODE, GENCODE, and modENCODE projects to characterize the genes, non-coding elements, and circuits of the human genome and model organisms. A major focus of his work is understanding the effects of genetic variations on human disease, with contributions to obesity, diabetes, Alzheimer's disease, schizophrenia, and cancer.

Education and early career

Kellis was born in Greece, moved with his family to France when he was 12, and came to the U.S. in 1993. He obtained his PhD from MIT, where he worked with Eric Lander, founding director of the Broad Institute, and Bonnie Berger, professor at MIT and received the Sprowls award for the best doctorate thesis in Computer Science, and the first Paris Kanellakis graduate fellowship. Prior to computational biology, he worked on artificial intelligence, sketch and image recognition, robotics, and computational geometry, at MIT and at the Xerox Palo Alto Research Center.

Research and career

As of July 2018, Manolis Kellis has authored 187 journal publications that have been cited 68,380 times. He has helped direct several large-scale genomics projects, including the Roadmap Epigenomics project, the Encyclopedia of DNA Elements project, the Genotype Tissue-Expression project.

Comparative genomics

Kellis started comparing the genomes of yeast species as an MIT graduate student. As part of this work, which was published in Nature in 2003, he developed computational methods to pinpoint patterns of similarity and difference between closely related genomes. The goal was to develop methods for understanding genomes with a view to apply them to the human genome.
He turned from yeast to flies and ultimately to mammals, comparing multiple species to explore genes, their control elements, and their deregulation in human disease. Kellis led several comparative genomics projects in human, mammals, flies, and yeast.

Epigenomics

Kellis co-led the NIH government-funded project to catalogue the human epigenome. He said during an interview with MIT Technology Review “If the genome is the book of life, the epigenome is the complete set of annotations and bookmarks.” His lab now uses this map to further the understanding of fundamental processes and disease in humans.

Obesity

Kellis and colleagues used epigenomic data to investigate the mechanistic basis of the strongest genetic association with obesity. They showed that this mechanism operates in the fat cells of both humans and mice and detailed how changes within the relevant genomic regions cause a shift from dissipating energy as heat to storing energy as fat. A full understanding of the phenomenon may lead to treatments for people whose 'slow metabolism' cause them to gain excessive weight.

Alzheimer's disease

Kellis, Li-Huei Tsai, and others at MIT used epigenomic markings in human and mouse brains to study the mechanisms leading to Alzheimer’s disease. They showed that immune cell activation and inflammation, which have long been associated with the condition, are not simply the result of neurodegeneration, as some researchers have argued. Rather, in mice engineered to develop Alzheimer’s-like symptoms, they found that immune cells start to change even before neural changes are observed

Genotype-Tissue Expression (GTEx)

Kellis is a member of the Genotype-Tissue Expression project that seeks to elucidate the basis of disease predisposition. It is an NIH-sponsored project that seeks to characterize genetic variation in human tissues with roles in diabetes, heart disease, and cancer.
To date, his lab has developed specific domain expertise in obesity, diabetes, Alzheimer's disease, schizophrenia, and cancer.

Teaching

In addition to his research, Kellis co-taught for several years MIT's required undergraduate introductory algorithm courses 6.006: Introduction to Algorithms and 6.046: Design and Analysis of Algorithms with Profs. Ron Rivest, Erik Demaine, Piotr Indyk, Srinivas Devadas and others.
He is also teaching a computational biology course at MIT, titled "Computational Biology: Genomes, Networks, Evolution." The course is geared towards advanced undergraduate and early graduate students, seeking to learn the algorithmic and machine learning foundations of computational biology, and also be exposed to current frontiers of research in order to become active practitioners of the field. He started 6.881: Computational Personal Genomics: Making sense of complete genomes. This course is aimed at exploring the computational challenges associated with interpreting how sequence differences between individuals lead to phenotypic differences such as gene expression, disease predisposition, or response to treatment.

Awards and honors

Kellis received the US Presidential Early Career Award for Scientists and Engineers, the National Science Foundation CAREER award, a Sloan Research Fellowship, the Gregor Mendel medal for Outstanding Achievements in Science by the Mendel Lectures committee, the Athens Information Technology Niki Award for Science and Engineering, the Ruth and Joel Spira Teaching award, and the George M. Sprowls Award for the best Ph.D. thesis in Computer Science at MIT. He was named as one of Technology Review's Top 35 Innovators Under 35 for his research in comparative genomics

Media appearances