Ycombinator Announces Support for Biotech Start-ups

April 26th, 2014 Comments off

Interesting post at Derek Lowe’s blog, In The Pipeline, regarding the announcement of Ycombinator’s support for biotech start-ups. Certainly an interesting model for jump starting novel approaches in biopharma research. However, I share Derek’s limited optimism around any one of these ventures rapidly scaling up and generating the kind of meaningful and reproducible results (IP) that it presently takes big pharma lots of money to accomplish.

At NEO Proteomics we have a portfolio of novel approaches to get meaningful results, too. Partly funded by grant awards. But that’s only one part of what it takes to generate meaningful IP. The rest requires good old-fashioned hard, analytical work, interpretation of results, and often re-analysis to find the signal in what is very often noisy biological data.

To say nothing of the necessary follow-up work one has to do to validate results. Of course, you can contract out the validation. As Atul Butte said a while back at a conference I attended, there’s a number of web sites now that permit you to describe the kind of validation experiment(s) you want done, in great detail, and when you’re done you simply “add to cart” and checkout. While doable, that’s never inexpensive.

Good luck to those who get investment. Just remember, having expert methods (analytics) only gets you so far. Eventually you have to deal with real world data, and it can be very messy indeed.

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Out With Old Paradigm, In With New?

May 14th, 2013 Comments off

Interview with Carl Barrett of Astra Zeneca, discussing the promise of systems based approaches related to personalized medicine.


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Watson Dubious re:Genes

March 26th, 2013 Comments off

Co-discoverer of DNA, James Watson, who is no stranger to making controversial remarks, lately is dubious on the usefulness of gene sequencing in the fight against human cancer, and more so for the prospect of advancing a cure. I don’t find these latest remarks to be very controversial, qua system’s biologist I’m a bit dubious myself that the tendentious focus on genes and related technologies, e.g., NGS, will lead to great discoveries in cancer treatments with markedly improved outcomes.

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Lamarck Revisited

October 8th, 2012 Comments off

Can the particular environment in which an organism lives, the associated stresses and/or advantages, cause epigentic marking of the organism’s germ line genome, thus making these modifications inheritable?

An article at the New Scientist considers the possibility that Lamarck may have been on to something.

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Pin Prick Proteomics

August 26th, 2012 Comments off

I’d not heard of Danny Hillis before reading this article. He evidently shares NEO’s optimism around the promise of proteomics in medicine, and the limits of DNA.

Danny Hillis has had a prolific career, to say the least. The co-founder of the Long Now Foundation developed a parallel supercomputer while he was at MIT; started Applied Minds – a technology and design consulting company that works with companies like Intel, Sony and Lockheed Martin; created Metaweb – an “open, shared database of the world’s knowledge” (which Google acquired in 2010); and wrote the book The Pattern on the Stone: The Simple Ideas That Make Computers Work, which breaks down computer science for a mainstream audience. Lately he’s been focusing his efforts on “proteomics”, the large scale study of proteins through its structures and functions – and he believes it’s a “revolution in medicine” in the making.

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New Investment

April 27th, 2012 Comments off

In addition to several new client engagements for our existing suite of systems biology tools, NEO Proteomics recently received an award from the Third Frontier Foundation of Ohio to extend our pipeline to implement new algorithms in network biology. The award will be matched by Case Western, doubling the investment in future development.

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February 19th, 2012 Comments off

I will be talking next week in San Francisco at the Molecular Medicine Tri-Con Confrence, in the Bioinformatics & Cancerinformatics track on Wednesday. Stop by and say hello if you’re there.

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Controls In Computational Biology Experiments

January 20th, 2012 Comments off

I have argued for the importance of positive and negative controls when conducting in silico experiments, common in the field of computational biology. I came to understand the importance of controls during my academic training in pharmacology. Say you want to test the efficacy of a new peptide inhibitor of a biological process that occurs in the nucleus. A process which, when inhibited, re-activates an apoptosis pathway causing the cell to die. It is not sufficient to merely incubate one dish of cells with the inhibitor and another with sham, measure your readout and calculate significance based on replicate experiments. Any competent reviewer of the work will also insist you run a positive control. In this case, a separate experiment that shows the peptide actually enters the nucleus. Why is this important? Because the measured effect (cell death) may be the result of an off-target phenomenon unrelated to the peptide’s activity in the nucleus. Indeed, if you can’t demonstrate that the peptide can actually cross the nuclear membrane, then the effect must be due to an off-target phenomenon!

Similarly in computational experiments. If you propose your new algorithm can discover genes or proteins significantly associated with some disease, it is important to run a positive control. One way to do this is to create a synthetic data set which “embeds” a clear target disease signature. If your algorithm is unable to discover the signature in the synthetic data set, it should reduce your confidence that any signature discovered in the real data set is meaningful.

I was reminded of this while reading a paper recently published in PLoS Computational Biology: Most Random Gene Expression Signatures Are Significantly Associated with Breast Cancer Outcome.

Few studies using the outcome-association argument present negative controls to check whether their signature of interest is indeed more strongly related to outcome than signatures with no underlying oncological rationale. In statistical terms, these studies typically rest on H0 assuming a background of no association with outcome. The negative controls we present here prove this assumption wrong: a random signature is more likely to be correlated with breast cancer outcome than not.

The paper received some additional press coverage.

When relevant to the computational biology papers I review, I now insist that appropriate controls be conducted and included with the results.

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P4 Medicine

May 19th, 2011 Comments off

I recently attended the P4 medicine symposium at the Institute for Systems Biology in Seattle. It was held at their new location in the South Lake Union district, the same place where Amazon recently relocated. Overall, I came away impressed with the quality of speakers, which included Eric Schadt, George Poste, Joe Nadeau and others. The four Ps – Preventive, Predictive, Participatory, Personalized – are laudable pillars, and while I share the enthusiasm for systems biology to erect them I didn’t come away from many of the presentations thinking the approaches they detailed were strikingly different from traditional biological research. Many of them relied on high-dimensional data sets, but using lots of data in and of itself does not systems biology make. Several presentations revealed the great strides that have been made toward cheap and fast full genome sequencing, and the rapidly increasing disk arrays being filled with these data – it’s a good time to be in the LIMS business! – but I thought fell short of showing the way to any of the P4 goals, especially Prediction.

I don’t want to sound overly critical; the P4 pillars are not only laudable but also quite challenging. Yet if we’re serious about systems biology approaches leading the way then considerably more effort will have to be paid to proteomics, metabolomics, informatics and network biology. The computational challenge of integrating all these data, in addition to the genomic data, to achieve P4 will be immense, but necessary. Even if every individual were able to get their personal genome sequenced cheaply and rapidly (imagine a cell phone of the future, with an on-board sequencer and an alignment app), even including epigenetic patterns, I’m afraid would not be sufficient to make P4 a reality. That may get us  most of the way to Personalized and Participatory, but Prediction and Prevention will require we understand much more than we do now about the entire -omic space, not merely the genomic space.

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“The Emperor of All Maladies” won a Pulitzer

April 19th, 2011 Comments off

The book I wrote two reviews (planed write even more) won 2011 Pulitzer award (nonfiction). Here is the official announcement.
“.. an elegant inquiry, at once clinical and personal, into the long history of an insidious disease that, despite treatment breakthroughs, still bedevils medical science. ”
It is a great book, highly recommend for everyone who is interested in cancer.

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