Epigenetics, what is it?

A recent debate has started after Siddhartha Mukherjee  published an article about epigenetics. The piece, called “Same but different” (subtitle: “How epigenetics can blur the line between nature and nurture”). Mukherjee, a physician, is well known for writing the Pulitzer-Prize-winning book (2011) The Emperor of All Maladies: A Biography of Cancer.

Jerry Coyne published in his blog Why Evolution is True some of the critics scientists had with the Mukherjee piece:

  1. The New Yorker screws up big time with science: researchers criticize the Mukherjee piece on epigenetics
  2. Researchers criticize the Mukherjee piece on epigenetics: Part 2

Then in An Epigenetics Controversy, Siddhartha Muhkerjee responds to the critique of his recent New Yorker piece

Coyne writes again, L’Affaire Mukherjee: the last word , excerpt from the post:

” So, for the record, let me say this: all of us, including Mukherjee, agree on the gist of what follows (though I don’t know if he’d sign off on this wording):

There is absolutely no evidence for any Lamarckian form of evolution based on “epigenetic” markers on the DNA produced by the environment. Further speculations about this—and claims that it shows that the modern theory of evolution is wrong—are misguided and should be ignored pending some real evidence. “

The Vox writes an article on this discussion: Why scientists are infuriated with a New Yorker article on epigenetics

Coyne again Dreadful science journalism at Vox: all interpretations of science are equal, but some are cuter than others

“The problem with Mukherjee’s piece, of course, is that he presented a story—that epigenetic markers and histone-protein modifications are THE mediators of differential gene expression in differentiated cells, working as a kind of “epigenetic code”—for which there is virtually no evidence. This was the cute and intriguing tale that he told readers of the New Yorker, who, of course, loved the good writing and assumed what Mukherjee said was accurate.

But what he left out—to the readers’ detriment—was the true story of gene regulation as we know it: a story identifying protein “transcription factors” and short bits of RNA as the factors that regulate gene expression. As Mark Ptashne and John Greally noted, neither Drosophila nor Caenorhabditis worms have DNA “markers,” yet both organisms—paradigms for the study of genetics and development—develop just fine, thank you. That alone should give pause to people like Mukherjee or the Epigenesis Mavens, and it comes on top of the lack of evidence for epigenetic or histone-regulated control of genes.
“The problem with Mukherjee’s piece, of course, is that he presented a story—that epigenetic markers and histone-protein modifications are THE mediators of differential gene expression in differentiated cells, working as a kind of “epigenetic code”—for which there is virtually no evidence.”

And from the comments in the blog, Coyne himself:

“The problem with Mukherjee’s piece, of course, is that he presented a story—that epigenetic markers and histone-protein modifications are THE mediators of differential gene expression in differentiated cells, working as a kind of “epigenetic code”—for which there is virtually no evidence.”

The New York Time has reviewed Siddhartha Mukherjee latest book, The Genes: an intimate history

Fortune writes Siddhartha Mukherjee, Author Of Bestselling Cancer Book, Starts Biotech Company And Answers Criticism

And one good summary from Nature: Researcher under fire for New Yorker epigenetics article

Michael Eisen a biologist at UC Berkeley writes in his blog The Imprinter of All Maladies

On WhyEvolutionIsTrue blog PLOS Biology weighs in on Mukherjee affair: “Writing for Story distorts and cripples explanatory prose”

Scientific American in Gene Regulation, Illustrated

Senior Editor, Current Biology writes to the New Yorker, Mukherjee replies The Regulators

Looking forward to reading Siddhartha Mukherjee latest book, The Genes: an intimate history

The Rise of Robots

Robots are coming. It is a certainly a great thing. They will be able to do many things for us and help us in many jobs. Probably they will be better than us. Traditionally Robots replaced low skilled workers performing repetitive tasks. This is not the case any more. Robots are better than humans in many cognitive tasks. The questions then becomes, what to do to when Robots will replace us in the workforce. Will only a small set of people benefit leaving many others behind? We need to rethink our society and with it the way we want to move forward with this inevitable revolution.
Some books and articles on the subject:

The History of Tomorrow’s By: Yuval Noah Harari
The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies, by Erik Brynjolfsson, Andrew McAfee
Rise of the Robots: Technology and the Threat of a Jobless Future, by Martin Ford
The Fourth Industrial Revolution: A Davos Reader by Gideon Rose
World After Capital, by Albert Wengel

Deep Learning Is Going to Teach Us All the Lesson of Our Lives: Jobs Are for Machines by Scott Santens

Genetics, the future and implications

A few links and a book to get an understanding of the power of genetics and its implications.

From Scientific American, Scientists Synthesize Bacteria with Smallest Genome Yet

Excerpt:

Genomics entrepreneur Craig Venter has created a synthetic cell that contains the smallest genome of any known, independent organism. Functioning with 473 genes, the cell is a milestone in his team’s 20-year quest to reduce life to its bare essentials and, by extension, to design life from scratch.

Nature on the same topic:  ‘Minimal’ cell raises stakes in race to harness synthetic life

Nature: Governance: Learn from DIY biologists

Very good book on human GMO, GMO Sapiens: The Life-Changing Science of Designer Babies by Paul Knoepfler, his twitter handle @pknoepfler and blog http://www.ipscell.com/paul/

 
Inside the garage labs of DIY gene hackers, whose hobby may terrify you 

Genetics and genomics

Genetics and genomics is an exciting area of scientific research, growing in its importance. It will let us learn more about us and help cure many diseases. A good recent book on this subject is “Genetics and Genomics” by  Tom Strachan, Judith Goodship and Patrick Chinnery.

If you want to stay updated, the US National Center for Biotechnology Information (NCBI) makes available scientific research articles http://ncbi.nlm.nih.gov 

You may also want to check Google Scholar http://scholar.google.com

The OMIM (Online Mendelian Inheritance in Man database) provides more information on single gene disorders http://www.omin.org

For even more info on gene disorders, the University of Washington’s Gene Reviews series are highly recommended and available at http://www.ncbi.nlm.nih.gov/books/NBK1116/

Other very useful resources.
SNP (pronounced ‘snip’) is the short term for Single Nucleotide Polymorphism.
The importance of SNPs comes from their ability to influence disease risk, drug efficacy and side-effects, tell you about your ancestry, and predict aspects of how you look and even act. SNPs are probably the most important category of genetic changes influencing common diseases. And in terms of common diseases, 9 of the top 10 leading causes of death have a genetic component and thus most likely one or more SNPs influence your risk.

The Rs1815739 SNP for example will tell you whether you are likely a sprinter or an endurance athlete.

OpenSNP is a non-profit, open-source project that is about sharing genetical and phenotypic information. You can find info on SNPs https://opensnp.org/

SNPedia is a wiki investigating human genetics. We share information about the effects of variations in DNA, citing peer-reviewed scientific publications. http://www.snpedia.com/

ClinVar aggregates information about genomic variation and its relationship to human health. http://www.ncbi.nlm.nih.gov/clinvar/

Genecards is a searchable, integrative database that provides comprehensive, user-friendly information on all annotated and predicted human genes. http://www.genecards.org/

Free online books at the US National Center for Biotechnology Information (NCBI) http://www.ncbi.nlm.nih.gov/books/

Mayo Clinic http://www.mayoclinic.org/

Data Science books

What is data science and what you need to know to be a data scientist?
Joel Grus, in his book writes “There’s a joke that says a data scientist is someone who knows more statistics than a computer scientist and more computer science than a statistician. (I didn’t say it was a good joke.)”

Data Science from Scratch: First Principles with Python by Joel Grus
Doing Data Science: Straight Talk from the Frontline by Cathy O’Neil and Rachel Schutt
Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython by Wes McKinney

Machine Learning
Artificial Intelligence: A Modern Approach by Stuart Russell and Peter Norvig
Introduction to Machine Learning by Ethem Alpaydin
Introduction to Machine Learning by Alex Smola (free download)
Pattern Recognition and Machine Learning by Christopher Bishop
Programming Collective Intelligence Toby Segaran
Bayesian Reasoning and Machine Learning by David Barber
Bayesian Data Analysis by Andrew Gelman and John B. Carlin

Data Analysis
Data Analysis Using Regression and Multilevel/Hierarchical Models by Andrew Gelman and Jennifer Hill
The Elements of Statistical Learning: Data Mining, Inference, and Prediction by Trevor Hastie and Robert Tibshirani
Statistical Inference by George Casella
Advanced Data Analysis from an Elementary Point of View by Cosma Rohilla Shalizi (draft free download)

Statics and Probability
Introductory Statistics Barbara Illowsky,Susan Dean (free download)
OpenIntro Statistics by David M Diez, Christopher D Barr, Mine Cetinkaya-Rundel (free download)
Introduction to Probability by Charles M. Grinstead and J. Laurie Snell
Introduction to Probability Models by Sheldon M. Ross
A First Course in Probability by Sheldon Ross

Linear Algebra and Calculus
Active Calculus Matthew Boelkins, David Austin, Steven Schlicker (free download)
Convex Optimization by Stephen Boyd and Lieven Vandenberghe (free download)
Linear Algebra and Its Applications by Gilbert Strang
Linear Algebra by Jim Hefferon
Linear Algebra by David Cherney, Tom Denton and Andrew Waldron (free download)
Linear Algebra Done Wrong by Sergei Treil (free download)

Presenting the information
The Visual Display of Quantitative Information by Edward R. Tufte

The apps monetization problem

While app stores are a great distribution channel for developers that want to sell applications, it is not the best monetization tool if you want to get a living.

There are some mechanisms in the current model of the stores that limit developers on how and what they can charge for the application and services and this has an impact on the quality of the applications and limit their potential.

Ben Thomson of Stratechery nicely list 3 problems in his From Products to Platforms post.

The problem for iPad developers is three-fold:

  • First, the lack of trials means that genuinely superior apps are unable to charge higher prices because there is no way to demonstrate to consumers prior to purchase why they should pay more. Some apps can hack around this with in-app purchases, but purposely ruining the user experience is an exceedingly difficult way to demonstrate that your experience is superior
  • Secondly, the lack of a simple upgrade path (and upgrade pricing) makes it difficult to extract additional revenue from your best customers; it is far easier to get your fans to pay more than it is to find completely new customers forever. Again, developers can hack around this by simply releasing completely new apps, but it’s a poor experience at best and there is no way to reward return customers with better pricing, or, more critically, to communicate to them why they should upgrade
  • That there is the third point: Apple has completely intermediated the relationship between developers and their customers. Not only can developers not communicate news about upgrades (or again, hack around it with inappropriate notifications), they also can’t gain qualitative feedback that could inspire the sort of improvements that would make an upgrade attractive in the first place