Manolis Kellis

Manolis is an Associate Professor of Computer Science at MIT, a member of the Computer Science and Artificial Intelligence Laboratory and of the Broad Institute of MIT and Harvard, where he directs the MIT Computational Biology Group.      


He obtained his Ph.D. from MIT, where he 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. He lived in Greece and France before moving to the US.

Professor, MIT Department of Computer Science 

Head, MIT Computational Biology group 

Institute Member, Broad Institute of MIT and Harvard Member, 

MIT Computer Science and Artificial Intelligence Lab


Stata Center - 32D.524 - 617.253.2419 


Awards:  - US Presidential Early Career Award (PECASE)  - Alfred P. Sloan Foundation Award  - National Science Foundation CAREER Award  - AIT Niki Award for Science and Engineering  - Technology Review TR35 Top Young Innovators  - Ruth and Joel Spira Teaching Award  - MIT Sprowls Award for Best PhD Thesis in Computer Science


Read more: Curriculum Vitae (pdf)

Decoding a Genomic Revolution

In The News

Discovery may advance neural stem cell treatments for brain disorders  Study reveals novel cross-talk between RNA and histonesNew research reveals a novel gene regulatory system that may advance stem cell therapies and gene-targeting treatments for neurological diseases such as Alzheimer's disease, Parkinson's disease, and mental health disorders that affect cognitive abilities; January 24, 2018 Sanford-Burnham Prebys Medical Discovery Institute


Scientists just made the first map of the human epigenome. Here’s why that’s awesome; The Washington Post, By Rachel Feltman February 18, 2015


Reinterpreting the Human Genome

 MIT Technology Review, by  Amanda Schaffer   June 21, 2016


A metabolic master switch underlying human obesity Fat cells in the human body.  Researchers find pathway that controls metabolism by prompting fat cells to store or burn fat.       Helen Knight | MIT News correspondent  August 19, 2015


New approach to genetic analysis yields markers linked to complex diseases. Study identifies new gene variants that may be targets for treating arrhythmia. Anne Trafton | MIT News Office  May 10, 2016



Ongoing Projects

Variation and Disease

Understanding the effects of genetic variation on molecular phenotypes and human disease. We develop methods for integrating diverse functional genomic datasets of transcription, chromatin modifications, regulator binding, and their changes across multiple conditions to interpret genetic associations, identify causal variants, and predict the effects of genetic perturbations.

More on: Variation and Disease

Genome Interpretation

We have developed comparative genomics methods which can directly discover diverse functional genomic elements based on their characteristic patterns of evolutionary change across related species. We have used such signatures in the human, fly, and yeast genomes to recognize protein-coding genes and exons, RNA genes and structures, microRNAs and their targets, and diverse classes of regulatory elements. 

More on: Genome Interpretation, Protein-coding Genes, Non-coding RNAs

Disease Area

The disease areas that we're focusing on include cancer, metabolic, neurodegenerative, psychiatric, and immune disorders. 


We've developped specific domain expertise in obesity, type 2 diabetes, Alzheimer's Disease, immune cells, Schizophrenia and cancer. 

Gene regulation

Epigenomics, chromatin regulation, and developmental programs. Beyond the primary sequence of the genome, a wide variety of post-translational modifications play key roles in genome function, cellular differentiation, and human disease. . We have developed new methods for addressing these challenges, using genome-wide maps to discover recurrent and spatially-coherent combinations of marks, or 'chromatin states'. We also developed data integration methods to systematically characterize chromatin state function, revealing diverse classes of enhancers, promoters, and insulators, which we use to discover new functional elements, to study dynamics across cell types and development, and to reveal motifs and regulators governing epigenetic changes in development, differentiation and disease.

More on: Chromatin - Regulatory Motifs - Biological Networks

Epigenomics

With the recent availability of genome-wide maps of histone modifications, we have developed new methods for the systematic discovery of recurrent combinations of chromatin marks, or "chromatin signatures," which we found to be associated with very specific types of functional elements, including diverse classes of enhancers, promoters, and insulators. We have used these signatures to discover new elements, including novel non-coding RNA genes, and to systematically study the dynamics of chromatin state across tissues and during development, and to discover the sequence elements and grammars governing those changes. We are currently also exploring the role of small non-coding RNAs in the establishment, maintenance, and targeting of chromatin state.

More on: Epigenomics - Regulatory RNAs

Genome evolution

We have also developed methods to study systematic differences between the species compared, and uncovered important evolutionary mechanisms for the emergence of new functions. To further understand the evolutionary processes leading to new functions, we developed a phylogenomic framework for studying gene family evolution in the context of complete genomes, revealing two largely independent evolutionary forces, dictating gene- and species-specific mutation rates. De-coupling these two rates also allowed us to develop the first machine-learning approach to phylogeny, resulting in drastically higher accuracies than any existing phylogenetic method.   

More on: Evolution - Phylogenomics.