biology

Manolis Kellis

Manolis Kellis is a biology topic covered in the lgStudy science library. This page brings together a partial reference excerpt, illustrations, worked examples, real-world applications and a short study plan, so you can understand Manolis Kellis rather than just read about it. In short: Manolis Kellis (Greek: Μανώλης Καμβυσέλλης; born 1977) is a professor of Computer Science and Computational Biology at the Massachusetts Institute of Technology (MIT) 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 (CSAIL) at MIT.

Key takeaways

Manolis Kellis belongs to biology; place it in that map before memorising details.
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Connect Manolis Kellis to a quantity you can measure, compute or draw — that is where exam questions come from.
Reproduce the core statement of Manolis Kellis from memory before moving on to harder problems.

Reference excerpt

Manolis Kellis (Greek: Μανώλης Καμβυσέλλης; born 1977) is a professor of Computer Science and Computational Biology at the Massachusetts Institute of Technology (MIT) 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 (CSAIL) at MIT. Kellis is known for his contributions to genomics, human genetics, epigenomics, gene regulation, genome evolution, disease mechanism, and single-cell genomics. 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 B.S., M.Eng., and Ph.D. 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 April 2025, Manolis Kellis has authored 250 journal publications that have been cited 190,000 times. He has helped direct several large-scale genomics projects, including the Roadmap Epigenomics project, the Encyclopedia of DNA Elements (ENCODE) project, the Genotype Tissue-Expression (GTEx) 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, published in the New England Journal of Medicine. 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 (thermogenesis) 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, published in Nature in 2015. 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.

Single-cell Genomics The Kellis Lab has profiled a large number of human post-mortem brains at single-cell resolution, studying inter-individual variation associated with genetic differences and disease phenotypes, including the first single-cell transcriptomic analysis of Alzheimer's disease (Nature, 2019).

Genotype-Tissue Expression (GTEx) Kellis is a member of the Genotype-Tissue Expression (GTEx) 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. Kellis is also a Principal Investigator of the enhancing GTEx (eGTEx) consortium, studying epigenomic changes of regulatory elements and epitranscriptomic changes of RNA transcripts across multiple human tissues.

Disease Mechanism To date, his lab has developed specific domain expertise in obesity, diabetes, Alzheimer's disease, schizophrenia, heart disease, ALS and FTLD, 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 (6.047/6.878) 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, and 6.883/9.S99: Neurogenomics: Computational Molecular Neuroscience 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.

… excerpt ends here. Continue reading the full article.

Illustrations

Worked examples

Example 1 — a first encounter with Manolis Kellis

Start with the simplest possible case. Write down what Manolis Kellis claims or describes in one sentence, then invent the smallest concrete situation in which that sentence is true. In biology, the smallest case is usually a single object, a single equation or a single measurement. Check that every symbol or term in your sentence has a meaning in that case.

Example 2 — changing one variable

Take the situation from Example 1 and change exactly one quantity: double it, halve it, or set it to zero. Predict what should happen to Manolis Kellis before you calculate. Comparing your prediction with the result is the fastest way to find out whether you understand the idea or only the words.

Example 3 — an exam-style question

Typical questions about Manolis Kellis ask you to (a) state it precisely, (b) apply it to given data, and (c) explain a limitation. Practise writing all three answers in under five minutes; the third part is what separates a full-mark answer from an average one.

Applications of Manolis Kellis

In research: Manolis Kellis appears in biology research whenever the underlying quantities have to be modelled precisely. Papers usually cite it as a starting assumption and then explore where it breaks down.
In technology and industry: Engineering practice reuses Manolis Kellis in design rules, simulations and safety margins. Knowing the idea lets you read a specification sheet and understand why the numbers look the way they do.
In the classroom: Manolis Kellis is common in secondary-school and first-year university syllabi. It links to neighbouring topics 1977 births, 21st-century American biologists, Biotechnologists, so understanding it makes those chapters shorter.
In everyday life: Look for Manolis Kellis outside the textbook — in sport, cooking, traffic, electronics or the sky above you. An example you found yourself is remembered far longer than one you were given.

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How to study Manolis Kellis in 20 minutes

Read the reference excerpt below once, without taking notes.
Close the page and write down what Manolis Kellis means in your own words.
Compare your version with the excerpt and mark what you missed.
Work through the three examples above with pen and paper.
Explain Manolis Kellis out loud to somebody else — or to Teacher Smith in the lgStudy chat.

Frequently asked questions

What is Manolis Kellis in simple terms?

Why does Manolis Kellis matter?

Because it connects several biology ideas at once: it gives you a definition you can apply, a quantity you can calculate, and a way to check whether a result is plausible.

How should I study Manolis Kellis?

Read the excerpt, restate it from memory, then work through the examples and applications listed on this page. The five-step study plan above takes about twenty minutes.

What does this page cover?

It gives you a compact reference excerpt plus original lgStudy explanations, examples, applications and study material on Manolis Kellis.