

What Google's Calico Anti-aging initiative will actually do - drcode

The biggest missing piece in curing most diseases IMHO is that we do not yet have any good computational model for a cell. If we had a way to simulate an individual cell for a specific person in a computer program (which is a hard problem) we could actually cure cancers pretty easily (by testing millions of pharmaceutical compounds on the spot against each person&#x27;s cancer cell model) and also make great strides into all other human diseases.<p>Only a very few scientists have worked in the field of whole cell simulation so far (see http:&#x2F;&#x2F;goo.gl&#x2F;heurd for one effort) and this problem is a PERFECT application for Google&#x27;s big data expertise.<p>Here&#x27;s what I&#x27;m guessing Calico will do:<p>1. Fund scientists who work on technologies that can detect various chemical concentrations of cells in vivo.<p>2. Build a multi-petabyte database of chemical concentration timelines generated in this way, which are run against human cell cultures experiencing varying environmental conditions.<p>3. Run complex big data statistical algorithms to determine quantitative measures of causation between all these chemicals and with DNA sequences in the cell.<p>4. Use this information to build an accurate computational model of a human cell.<p>This is an area of research that (1) would improve therapies across the board for all major diseases at once (2) has few short-term financial incentives, meaning only a huge research initiative could attempt it, and (3) requires significant big data expertise.<p>Thoughts?
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matthudson
Mapping, modeling, and indexing a cell's structure is a very different problem
than indexing hyperlink structure on the internet.

A wholesale computational model of the cell is an incredibly ambitious task to
achieve via a broad, unfocused approach to building a chem-concentration
database.

The main issue they would face is in your bullet #1: What is your "Pagerank"
for cell structure and chem-concentration analysis, what/who do you put your
weight behind?

How do you merge these data sets? This research is distributed in labs all
over the planet with different quality standards, research objectives, lab
conditions, data hygiene, statistical significance, etc.

What you've written seems like a plausible approach in a pure research setting
within a single lab, but if their end goal is a computational model of the
cell with commercial side-effects along the way-- the best way to achieve that
would be to chip away at it piecemeal. Not through an unfocused merger of cell
"big data".

E.g. identify specific protein binding sites and conformations that correlate
with a specific type of breast cancer, track their conformations under
different conditions--- prove that your data is superior to data yielded by
existing models.

Of course, you would also have to monetize somehow.

The specific thing you chose is less important than not attempting to paint
the whole model at once.

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cprncus
Although something akin to this should occur, I think your post understates
the computational enormity of the issue. Many diseases are not caused by
single genes or single environmental conditions, but interactions between
dozens of genes and the environment, even epigenetic factors. Big Data (huge
data) approaches will begin to chip away at the mountain, but even with
Google's super powers, it's a ways off.

> (1) would improve therapies across the board for all major diseases at once

How? Unless if by "at once" you mean they would begin the era of using this
approach on any disease. But surely each disease will have its own puzzle.

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drcode
Re: Computationally enormity:

Well, I agree the computational enormity is a big problem, but certainly some
biochemical pathways will be more amenable to this type of approach than
others, and so it should be possible to make incremental progress by solving
one puzzle at a time.

Re: Using on any disease

I agree having this sort of technology is not an immediate solution to each
arbitrary disease. However, being able to accurately predict how different
cells in a specific person's body behave to different drug cocktails would
give doctors "super powers" that are almost unimaginable today. A doctor with
a patient that has cholesterol plaques might say "If we give him these ten
drugs in this dosage his T lymphocytes will aggressively attack the plaques in
the LAD artery only, and the LAD arterial cells will increase division to
reduce risk of subsequent aneurysm from autoimmune side-effects".

If the accuracy of the computational model is good enough, it might be just as
effective as the old "nanobots in the bloodstream" concept, but much easier to
implement. In effect, the patient's own cells could function as the "nanobots"
by reprogramming them

