Some 72 years ago today the Japanese attacked Peal Harbor. The above was take on the USS Albert W Grant (DD-649) in 1945 when it came to Japan after its travails during the long war. It is always good to remember the good and bad. Many thanks to the men and women who saved the day then.
Stanford Researchers have completed a Study which shows that technical people are just adept if not more so in start ups. The summary is:
New research on entrepreneurship shows that diverse business skills
are not always the secret to success in the world of tech start-ups.
While different strengths matter sometimes, researchers found that a
tech-focused founding team is almost always best. ... The research revealed that a technically focused team can more
quickly reach market milestones, from design and prototype completion –
all the way to product launch. On the other hand, more diverse founding
teams are better prepared to compete against mature companies, which
similarly have well-established diverse skills in areas like marketing,
operations, sales, engineering and other skills.
It is good that this is now known to the Business School Academics. After all, if one looks outside the Stanford Campus there lies Silicon Valley.
But wait, my sarcasm may be not on point. Again I am reminded of my students a few years ago remarking after a dinner with a MIT PhD CEO that they did not know that a MIT PhD could start and run his own company. They believed they needed to get a Harvard Law or Business person. My response was, "What do I look like, chopped liver?"
That also may have been a bit much, since I have no idea how that transliterates to Mandarin. Now the issue of competing against large mature companies may also be best done by technical entrepreneurs. Evidence also demonstrates this as well. The Business types will develop strategies whereas the Technical types will just attack at the weakest points.
The problem is that all too often the Technical types get too entrenched and secure and/or the Business types take over. The classic example of both happening was DEC. A great company but it just grew mold.
There is a recent paper by Prof. Dougherty from Texas
A&M which bemoans the state of some parts of science in the current
As Dougherty so clearly states:
…science concerns relations between measurable variables
and it is these relations that constitute the subject matter of science,
scientific knowledge ipso facto is mathematically constituted…
Let me give a couple of examples of how this applies.
First, let us look at the world of genomics which I have
been discussing herein for a while. The introduction of the microarray has
allowed an explosion of data that has then allowed scientists to putatively
argue some relationship between genes and cancers. Namely they go about
examining say 9,000 prostate cancer patients and using microarrays primed for
say 500 genes they conclude that say some 50 of these gene are seen in prostate
cancer. They then allege that there is some actionable clinical relationship
between the presence of the gene and the cancer. There is no underlying system
model identifying this, just a microarray demonstrating that “oftentimes” these
genes are under or over expressed.
Second, let us look at the BRAF V600 melanoma cases. Here
unlike the above we have a case where one knows the RAF pathway and that loss
of control of certain elements of that pathway lead to gene instabilities and
thus a malignant expression. Therefore one targets the mutated RAF gene, the
BRAF V600, and it results in a suppression of the malignancy, for a while. Then
we had squamous cell carcinomas, but since the full pathway was known, go down
one step and there was MEK and controlling it controlled the sequella. In this
case there was a model, a system, and by logically following the system one
found what the next step should be.
The above are two examples of how “science” is being done
today in the area of gene related results. The second example is a Dougherty
like science, namely it connects data to an underlying model which is
predictable, and by using that the cancer is controllable, at least until another
instability results. The first model, data collecting, is not really science as
we accept it today. It is more akin to 19th century Botany, at best,
where one goes out and collects specimens of plants and then tries to sew
together a quilt of understanding to explain nature.
What Dougherty is focusing on is the Why question. When I
recall Medical School, one is taught What and How. What disease is it and How
do I treat it. In contrast Engineering is first Why and then How. There is a
strong dissonance when an Engineer is studying Medicine. At least forty years
ago. An Engineer all too often keeps asking Why, what is the underlying set of
basic scientific principles that explain the phenomenon and how can I express them
in a manner in which they can be used on a predictable basis. Why would drive
many a Medical Professor to apoplexy. Medicine was for a long while the
transfer down of “facts” and not validatable principles. The old adage at
graduation that fifty percent of what one had just learned in Medical School
was now invalid was a bit of a joke but sadly it was also true.
But as we move to Genomics we sadly see this trait arise
again. There is a tension between those who want to have basic repeatable
principles to build upon and those who believe that collecting data is the sine
qua non. Let me give an example of a recent experience. Prof Lander at MIT is
teaching an EdX course on Biology. Now Lander is brilliant and his style of
teaching is in many ways classic MIT. Namely he highlights the basic principles,
and then the student works through the Problem Sets developing the details for
themselves. So far so good. His first two three fourths of the course was
fantastic. Then I noticed a subtle change, a change that, unless you were
prepared to recognize would have slipped through the cracks. He slowly started
giving a mixture or core predictable principles and cook book recipes. For
example, we know that we can denature DNA because the base pair bonds are
Hydrogen bonds, relatively weak, and the backbone Phosphate bonds are strong
because they are ionic. Thus by heating the molecule we break the Hydrogen
bonds first and then before we break the ionic bonds we can do our
complementary additions, thus PCR works well.
On the other hand as he progressed to a discussion of Knock
Out genes there were a collection of “tricks” or cook book recipes that were
used. Why, for example did one get the modified DNA into the denatured gene the
way he said? Well it just happens. Well nothing just happens. Fortunately bench
Biologists have developed many “tricks”, like alchemists, and as a result they
have become a bit too comfortable with this unexplained bevy of tools, albeit indispensable,
but in the long run self-defeating.
As Dougherty states when he examines data mining as an
example of the Biologist’s flair for data at all costs:
Data mining and Copernicus share a lack of experimental design;
however, in contradistinction to data mining, Copernicus thought about
unplanned data and changed the world, the key word being ‘thought.’ Copernicus
was not an algorithm numerically crunching data until some stopping point, very
often with no adequate theory of convergence or accuracy. Copernicus had a mind
and ideas. William Barrett writes, ‘The absence of an intelligent
idea in the grasp of a problem cannot be redeemed by the elaborateness of the
machinery one subsequently employs’. Or as M. L. Bittner and I have asked,
‘Does anyone really believe that data mining could produce the general theory
of relativity’? Data mining represents a regression from the achievements
of three and a half centuries of epistemological progress to a radical
empiricism, in regard to which Reichenbach writes, ‘A mere report of relations
observed in the past cannot be called knowledge. If knowledge is to reveal
objective relations of physical objects, it must include reliable predictions.
A radical empiricism, therefore, denies the possibility of knowledge’. A
collection of measurements together with statements about the measurements is
not scientific knowledge, unless those statements are tied to verifiable
predictions concerning the phenomena to which the measurements pertain.
What is Dougherty getting at? Simply, to reiterate the first
quote: Science demands a marriage between data and models, to be true science
it must be predictable and predictable based upon an embodiment in an
Let me now apply this to genomics. Consider prostate cancer.
The question is complex but can be asked; what is the first set of steps that
lead to prostate cancer? Let us examine what we know:
First, we know many of the pathways. We know that the AKT pathway
is critical, we know that c-MYC is a critical control element, we know that
PTEN is often mutated, and we know that AR (Androgen Receptors) ultimately get
mutated and we have metastatic growth. We pathways, we have relationships; we
can demonstrate causality and results. Thus a modicum of a basis in reality
exists. If one would use this pathway model and then search using microarrays
matched against the model one arguable could iterate to improved models and
improved predictability. The data without the model is useless and the model
without the data is unverifiable.
Second, we can ask what sets the process off. Are all the
changes due to mutations or more likely due to epigenetic insults? Thus when we
look at MDS for example, we are looking at a hypermethylated set of blood stem
cells. Something hypermethylated them and we know that since they are
hypermethylated that the gene expression is repressed and thus cell
proliferation of immature cells is a result. In prostate cancer, is the control
mechanism lost because of a mutation, methylation, both, and in what order?
Having a model allows one to validate and then iterate along a consistent trajectory
What does Dougherty have to say here?
While ignorance of basic scientific method is a serious problem,
it is necessary to probe further than simply methodological ignorance to get at
the full depth of the educational problem. Science does not stand alone,
disjoint from the rest of culture. Science takes place within the general human
intellectual condition. Biology cannot be divorced from physics, nor can either
be divorced from mathematics and philosophy. One’s total intellectual repertoire affects the direction
of inquiry: the richer one’s knowledge, the more questions that can be asked.
Schrodinger comments, ‘A selection has been made on which the present structure
of science is built. That selection must have been influenced by circumstances
that are other than purely scientific’
The point I believe he is making is that in the new world of
Genomics, it is necessary to have a foundation that exceeds just the Laboratory
and its tricks. One must understand that no matter what we think that every
time we look at a cell, at an organism, we are looking at a system, at some
stochastic dynamical process wherein things move forward, albeit randomly, but
in a way controlled by principles. We must look at the world wherein data is
used not as an end in itself but as an iterative process with our mathematical
world view. Thus the tools needed to view this world are extensive yet
available. Engineers are trained to use them daily. Perhaps Genomics will grow
to appreciate their essential import.
In a recent piece in the NY Times by some Professor at some Midwest school he bemoans the state of Liberal Arts education.
Let us jump to his logic. He bemoans the fact that those who major in Liberal Arts, and he means it in the most narrow sense, earn less. He states: Is the crisis rather one of harsh economic reality? Humanities majors
on average start earning $31,000 per year and move to an average of
$50,000 in their middle years. (The figures for writers and performing
artists are much lower.) By contrast, business majors start with
salaries 26 percent higher than humanities majors and move to salaries
51 percent higher.
But this data does not show that business majors earn more because
they majored in business. Business majors may well be more interested in
earning money and so accept jobs that pay well even if they are not
otherwise fulfilling, whereas people interested in the humanities and
the arts may be willing to take more fulfilling but lower-paying jobs.
First, young people should go to college and find a major that will get them a job, unless they are already independently wealthy. If you end up $200,000 in debt you owe it to those of us funding the debt to get to some point at which you can pay it back. If you want to be a performance artist what good is college, go out on the street and perform. So you do not get paid, what is new?
Second, the Liberal Arts has substantial value. As an undergraduate I minored in Philosophy. Why, because it was interesting, a challenge, and it was fulfilling. Yet I knew that I could not get a job with that alone. Thus engineering, and a job. Yet engineering is also fulfilling, it is a profession, and has legs that last a lifetime as well, and it teaches a mental discipline that is essential in today's world.
Third, Liberal Arts is like a fine quality desert. It is wonderful, but you cannot live on deserts alone. One needs the protein of a sustainable diet. Thus balance in all things. Also one should not demand, expect, but be realistic.
Then the author really goes off the track. He states:
We could open up a large number of fulfilling jobs for humanists if we developed an elite, professional faculty in our K-12 schools.
Provide good salaries and good working conditions, and many humanists
would find teaching immensely rewarding. Meeting the needs of this part
of the cultural middle class could, in fact, be the key to saving our
Fair treatment for writers and artists is an even more difficult matter,
which will ultimately require a major change in how we think about
support for the arts. Fortunately, however, we already have an excellent
model, in our support of athletics. Despite our general preference for
capitalism, our support for sports is essentially socialist, with local
and state governments providing enormous support for professional teams.
To cite just one striking example, the Minnesota State Legislature
recently appropriated over $500 million to help build the Vikings a new
stadium. At the same time, the Minnesota Orchestra
is close to financial disaster because it can’t erase a $6 million
deficit. If the Legislature had diverted only 10 percent of its support
for football, it would have covered that deficit for the next eight
That's right, do not allow scientists or engineers to teach K-12, allow performance artists. That should really improve our scores on the world stage. Secondly, I have to admit I have never seen a football game, but my daughter went to WVU, but football pays, millions watch it, and also football and other sports have figured out how to get me to pay whether I watch it or not. They have made me pay on my cable channels! Never watch it but ESPN gets a $10 a month fee from me. Thanks Washington! Let's try to get the Philosophy Departments to see if they can do this.
When I see articles like this I often wonder what world the author is living in. Students go to college to get a job. Later on in life one can expand themselves, based upon a lifetime of experience.
Then there is the arrogance of demanding special treatment. As if these folks are something special. They are not. Ultimately all decisions are economic decisions, and yes with a moral undertone. If one decides to be a Classical French Major then one is making the decision to either attempt to to be the best in the world and get one of those three to five job slots, like the best violin player at some major world class orchestra. There just are not that many slots. Nor I gather are there that many slots for Quarterbacks in the NFL.
The book by Dale, Von Schantz and Platt, From Genes to Genomes, is almost perfect. It is a 350 or so page exceptionally well written
book describing all the introductory materials one would need to become current
with genomes and genomics efforts. As with many of the other books I had around
I first looked at this and at a glance set it aside. Then came the moment when
I wanted to re-understand something and I opened this book up and I was hooked.
It in a clean and clear manner takes the reader from basic DNA principles and
through all of the key techniques used in genomic studies today. It avoids
getting to complex into any one area and it reads in a straightforward and
consistent manner. It is a superb asset for “catching up” and I suspect for first
learning the materials.
Chapter 1 is the basic introduction to genes and genomes. It
is DNA 101 but it contains little tidbits of essential materials that are all
well integrated. One thus starts with a clear understanding where the authors
are taking the reader.
Chapter 2 is the material on basic gene cloning. It uses the
plasmid approach with bacteriophage and does so without burdening the reader
with too much overhead and history. This Chapter discusses technique and
technology and the reader is given a logical approach to the basics of cloning.
Restriction enzymes are introduced and the material is adequate to have enough
depth to see how they can be applied. There are, of course, a lot of
implementation questions that are left hanging but that is typical of this
study. There is a section on ligation and I would like to have seen this
carried over a bit when discussing gene knock-outs. We can understand how the
genes ligate but the question is how well does this carry-over the later
Chapter 3 discusses DNA libraries. This is a wonderful
summary of the concept. The graphics supplement the text without over powering
it. One example of what I call the cook book facts is demonstrated in p 95 when
discussing hybridization. Here is the curve showing how as temperature
increases the DNA starts to break apart. This is the denaturing of DNA, a
concept again used with the PCR analysis. This is less a theoretical or
structure issue but one of those cook-book facts that have been added to the
tool chest of the Genome builder.
Chapter 4 is the PCR process. Simply it is the separating of
DNA, then tagging one end and the other end and going through a temperature
sensitive denaturing and rebuilding until what is left is millions of copies of
a desired DNA segment. My only complaint here is that the graphic, good, albeit
it could be made a bit better with color.
Chapter 5 discusses sequencing and it gives a superb
discussion of the Sanger approach. Namely ddNTPs are used with segments and
then measured in a gel electrophoresis. I assume that the reader may have had
some understanding of the physical details but overall it is clear and exceptionally
The text continues developing other elements used in current
day genomics. Chapter 9 is an attempt to provide an overview of microarrays,
SNP, and even GWAS and phylogenetics. My problem here is that they are trying
to stretch it a bit too far. These are reasonable summaries but to do
microarrays justice it may take a bit more detail, and yes color, and the
phylogenetics is much too much just a high level summary.
Finally the Glossary is fantastic and worth every page.
The strengths and weakness of the book are simple. On the
strength side it covers all the key issues superbly. On the negative side, and
this may be perhaps me, I find that almost like Organic Chemistry, in Gene
manipulation there still are many cookbook rules that are scattered between the
facts and logical constructs. If somehow there could be a clarification of the
cook book rule and the well understood logical steps that would be a help.
Overall I would highly recommend this book for almost
anyone, from beginner to professional. My focus is clinical and theoretical
modelling and analysis, and I have avoided bench work as much as possible. But
by reading this book I can see again how much work has been done over the past
Terry has spent most of his career in industry, half in corporate executive positions, and half involved in his start ups. He started on the Faculty at MIT in 1967 and was there until 1975, and he had returned to MIT from 2005 to 2011 to assist groups of doctoral and post doc students. Terry has focused on a broad set of industries from cable, to satellite, wireless, and even health care software and medical imaging. Terry has published extensively in a broad set of areas as well as having written several books. Terry's view is that of an entrepreneur who has built companies in over twenty countries.
Copyright 2008-2013 Terrence P McGarty all rights reserved.
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