It’s not just the lives of individuals who eat crap (which is most of the country, frankly, even though they have no idea how toxic what they are eating is).  It’s the happiness and achievement potential of today’s youth.  It’s the emperor with no clothes at the center of the healthcare debate.  And it’s a lynchpin for economic recovery and sustainability.

Watch the premiere, and spread the word…

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3. The Problem With Processed Foods

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Target & Kill Approaches

Biris and Zharov are making some exciting progress in using nanotubes to tag and then track cancer cells inside the body as they move around.  They propose to kill the cancer cells by heating up the nanotubes using lasers, while others are using nanomagnets and still others siRNA. Glazier is in agreement with the target and kill approach and outines a number of such methods in his book, Cure, in which he also argues forcefully for the importance of taking somatic evolution seriously in our approaches to treating cancer.

One potential problem with target and kill, as Glazier points out, is that if you don’t get all cancer cells, you run a high risk of recurrence.  Which belies an even bigger problem: how do you detect which cells are cancerous and which are not?  Glazier calls for behavioral pattern recognition, i.e. looking for cells that are proliferating and also exhibiting invasive behavior at the same time.  But it remains to be seen whether such pattern recognition is possible in practice.  A possible way to keep tabs on cell behavior is to do continuous in situ monitoring or ultrasonic nanotech.

Enhance Immune Response

The immune system is really good at identifying and killing cells behaving badly (although the majority of the time the immune system’s targets are foreign invaders like viruses).  But what if we could boost the immune system so that it was better able to deal with cancer cells?  Essentially create a vaccine for cancer.

The difficulty with immunotherapies for cancer has always been that it’s not in the “charter” of the immune system to fight the body’s own cells; when it does we can get what are know as autoimmune diseases.

Reiter, et al are working on a clever hack of the a class of immune cells called tumor-infiltrating lymphocytes (TILs) wherin they extract TILs from a tumor, enhance their tumor-fighting potential and reinject the enhanced TILs back into the tumor.

The achilles heel of immune enhancement will always be comprehensiveness.  That is, if you don’t get everything, cancer can eventually evolves resistence by becoming too hard for the immune system to detect or by learning how to fight off the immune response.  And if you get overly aggressive, you risk harming the patient in other ways.  And cancer has proven to be extremely tricky in outwitting the immune system.

Genetic Modification Approaches

Modifying genes, either by enhancing tumor suppressors or reducing tumor promotors, has been a popular appoach in recent years.  Often the approach has been to focus on individually important genes or to try to find exhaustive sets of genes which, when modified appropriatly, stop cancer progression.

One problem is that genetic information is not organized into atomic functions or even sets of functions, but rather in complex, multi-scale functional networks with built-in redundancy.  In such networks, you can modify, add or delete many nodes and links without changing the overall network behavior significantly.  Still, recent advances do show promise, as with microRNA replacement.

Another confounding factor is genetic modification is that the genetic code seems to be organized a bit like a toolbox of mix-and-match parts that get shuffled around by evolution.   Thus if a trait or function is adaptive, it might emerge by more than one evolutionary path using different arrangements of genetic code and entirely different mechanisms (this is known as convergent evolution).  Theoretically the malignant behaviors that characterize cancer — unregulated proliferation and invasiveness — could re-evolve, just as happens in organismal evolution; after all, to the cancer cells malignant behaviors are are adaptive, it’s just us mulitcellular beings that view the behavior as bad.  What I mean by this is the following; vision has been achieved a number of different ways by organismal evolution with the genetic toolbox, so what’s to stop somatic evolution from achieving proliferation and invasiveness in different ways than is normally seen in human physiology?

Viewing the problem from a slightly different angle still, consider the following.  Cancer itself works by making massive numbers of changes to individual cells’ genetic networks.  This source of heterogeneity is what provides the grist for the evolutionary mill.  The vast majority of these mutations don’t work out and the cells die off or — more problematically — the mutations remain dormant in successive generations of the cell line.  But every once in a while you end up with a rearrangement of the network that is viable and which creates cells who don’t “play nice” with their neighbors (i.e. cancer).  Thus, if you have created a therapy targeted to a particular gene, there’s a good chance it won’t work anymore because the gene now sits in a different functional context; the original function you were targeting may now be served via different mechanisms.

A more harmonious variant of genetic modification is to replace entire cells with stem cells and allow them to differentiate into the appropriate cell type, effectively cleansing the genome.  This type of work is being done but is very preliminary and the stems cells themselves are prone to becoming cancerous, presumably due to their pluripotency and robust replicative potential.  Still, this line of inquiry seems promising to me, because it honors the body’s own developmental programming to replace badly acting cells with good ones, instead of just, say, killing bad cells and leaving a physical (and behavioral/ecological) void for surrounding cancer cells to exploit.  While currently solid tissue cell replacement requires surgery, down the road we can expect a veritable Cambrian explosion of nanobots that will be able to precisely navigate to targeted areas and do the work of cell replacement and genetic modification.

Prophylactic / Preventative Approaches

Aubrey de Grey works on the radical extension of the human lifespan and believes that there’s no theoretical limit to how long we can live if we hack our biological inheritance appropriately (BTW, many others agree, including Ray Kurzweil).  Organ replacement and regrowing failed body parts is a forgone conclusion (it’s happening already), and de Grey says that the only disease that presents a problem long-term is cancer, due to the relentlessness and “cleverness” of somatic evolution.  De Grey proposes therefore that the only real approach is one of indefinite prophylaxis, i.e. take specific steps to intervene on a regular basis so that somatic evolution stays in check and we don’t get the unregulated proliferation and invasiveness that is cancer.  His WILT approach argues we achieve this by regulating the length of telomeres which are critical to the proliferation process.

Carlo Maley says that the WILT approach should work, but the technology is a far way off and it’s hard work to go this route.  Maley believes that we may be closer on the prophylactic front with by boosting cancer-suppression genes, as in the super p53 approach.

Several months ago I started asking cancer researchers the following question: if we were somehow magically able to replace the DNA in every cell in your body with a clean copy at regular intervals, would that prevent cancer entirely?  While most who answered thought that in theory this would work, some startling research recently has me wondering whether it would.  The discovery of non-genetic forms of persistent heterogeneity (Brock, et al, Spencer et al, and Sigal et al), combined with the logic of somatic evolution and the genetic toolbox, leads me to be fearful that unregulated proliferation and invasiveness might re-emerge without genetic (or genomic) heterogeneity.  Even if non-genetic heterogeneity is not broad enough to provide an “escape hatch” from full DNA replacement, it might be broad enough to thwart a WILT or super p53 approach.

Other preventative approaches focus on detecting pre-cancerous cells — ones that are most likely to turn malignant at some point — and removing them either surgically or with more advanced technology like radio waves.

Hijacking Microorganisms

Then there’s the approach of co-opting existing viruses and bacteria (also here, here,and here) since these microorganisms have exquisitely evolved to be effective at targeting and dismantling individual cells and cell types in multicellular organisms like humans.  There are several issues with this approach though.  First is that in order to “repurpose” these critters to do our therapeutic bidding, we have to simultaneously help them outsmart our immune system while making sure they don’t harm normal cells; not such an easy task.  Second, there is a danger in messing with viruses and bacteria in that these are populations with the potential to evolve (despite whatever measures are taken to avoid this) and as such could get out of control.  Third, there are always unintended and unpredictable consequences when injecting a body with foreign substances, especially ones that are alive….

Fighting Evolution with Evolution

There are a number of ways to approach fighting cancer “with” evolution, one of which was mentioned already (the TIL approach).  Another is to use evolution as a mad tinkerer/designer to create sophisticated biological agents that empirically do the job well.

Maley and Pepper are looking at changing the microenvironment to shape somatic evolution so that there is less selective pressure for cells to compete with one another.  David Basanta and his colleagues at the Moffitt Research Center modeling various aspects of evolution in the hopes to be able to one day shape it’s direction.

David Rasnick suggests that if we are to really take somatic evolution seriously we need to recognize that normal human cells are vastly more robust than cancer cells and that most cancer cells die off with the smallest perturbation to their environment.  The problem is that they mutate and adapt very quickly.  Rasnick’s “perturbation theory” says we should look to induce stresses into the body that normal cells are equipped well to deal with and on a relative basis, cancer cells are not.  While one could think of chemo and radiation in this regard there are two problems: (1) they can damage DNA making the heterogeneity worse; (2) normal cells are not equipped to deal with these perturbations either.  Examples of perturbations normal cells are equipped to deal with include radical changes in various lifestyle dimensions (extreme exercise, extreme diet changes) or inducing natural stress reactions.  Rasnick notes that many cases of “spontaneous remission” occurred after prolonged periods of extremely high fever.  One thing that’s for sure, as technology advances we will have more and more ways to cleverly perturb cells.

Doing Less

In our “Just Do It” society we often forget that sometimes less is more:

• Surgery Not Necessary For Most Late-Stage Colorectal Cancers
• Chemo spurs some cancer cells
• To Survive Cancer, Live With It

Related posts:

1. Cancer as Evolution — 2008 Summary
2. Cancer as Evolution, part 2
3. The Conflict Between Complex Systems and Reductionism

In thinking about this I am reminded about a duet of innovators who spoke at the Pop!Tech conference last year about scaling.  Both Bunker Roy and Paul Polack have some profound lessons to teach us about scalability.  You will learn these lessons by watching Roy talk about Barefoot College and by watching Polack talk about his Out of Poverty approach (also see BarefootCollege.org and PaulPolack.com).  But despite all of the incredible wisdom to be gleaned from observing how Roy and Polack achieve scale, I’ve been wondering about how their success can be translated to other realms.

Replicators

In creating a general theory of scalability, I think there is a key conceptual anchor from Susan Backmore’s TED talk on the third replicator.  Now, I have to pause here because as simple and great as the universal Darwinism principle is, I know from conversations that many people have a really hard viewing evolution in non-biological systems as anything more than a good metaphor.  It’s hard for most people to see that “true evolution” — the kind that Darwin was talking about — is actually what is happening in these non-biological systems.  I will address this in detail in a later post, but ask that you indulge me for the time being so that we can talk about replicators.

When we talk about scaling sociotechnical systems, really we’re talking about one of two things: either growing the original system to handle “more”, or replicating the original system (or enabling it to replicate itself) with appropriate variation for the new context.  Growth models are the more familiar and comforting to governments and policy makers for reasons that should be obvious to anyone who has noticed how scared these types get when faced with systems that scale via replicators.  Formal organizations (corporations, non-profits, governments, anything with a legal structure or formal set of rules) are growers; networks of cooperating agents (open source software, social change movements, revolutionaries, anything that is formed in a grass-roots / bottom-up manner) are replicators.

I am not here to argue that either type of system is dispensable, indeed they are both essential.  I will leave it as an unproven conjecture that we are at a point in history wherein the ecology of sociotechnical systems is dominated by growers that are straining and stretching to the edges of their dynamic range.  Societal edifices are crumbling under their own weight, and are thus vulnerable to subversion by an algal bloom of replicators in their midst.  For those that want the argument and evidence, go read The Chaos Point by the grandfather of complex systems theory, Ervin László.

And I will leave alone in this theory of scalability the entire grower side of the equation.  It’s been systematized and refined since at least the days of Machiavelli;  we know it today as management science.  Instead I want to suggest that there is lacking an entire half of the formalization project for a unified theory of scale, and that’s a formal model for scaling via replication.  The reason this formalism has eluded us for so long is the same reason Darwinian evolution is so hotly contested: it requires a fundamentally different way of thinking than the Western analytic tradition is based on.  That’s not to say that the complex systems paradigm is not scientific, just that the scientific method as it exists today has not yet incorporated the bottom-up, emergent calculus required to be complete.

The first question we must ask is what exactly is being replicated, and only then we can ask how that replication is achieved.  Blackmore names three classes of replicators which I would like to refine by pointing out (as she does) that these are really self-replicators.  In her TED talk she observes that biological self-replicators exist (i.e. what we normally refer to as “life”), that mental self-replicators do indeed exist (though most people don’t take this notion seriously enough yet), and that technological self-replicators are in the process of being born.  If we think about it though, it is easy to see that certain forms of this third replicator already exist: computer viruses, bot nets (e.g. as are used in DDoS attacks), digital agents in artificial life simulations and genetic algorithm systems, and others.  What Blackmore was hinting at with the her more restrictive definition of technological self-replicator is one in which the artifact being replicated has a physical form (as opposed to digital information form).

I must digress here for a moment to point out that it is a red herring to try to neatly circumscribe the system being replicated (the “artifact” or agent) from its environment.  In reality there is no such thing as a true self-replicator; there are always some resources or information that is outside the self-replicator that is required for replication to occur.  Neither the chicken nor the egg can recreate itself.  And if you (rightly) view the chicken/egg system as the thing self-replicating, you only need observe that food is also essential (as are many other things) for replication to occur.  Given this truth in the realm of biology, is it really so far fetched to view digital cameras self-replicating technological agents, that is replicators of the third kind?  Sure they require humans, manufacturing processes and other technology from their environment to replicate, but I’ll reiterate that there are no biological life forms either that are entirely self-replicating.  (This blog post puts an even finer point on it all, if you are still not convinced).

Principles

The scaling brilliance of Bunker Roy and Paul Polack was hard-won, after many years of solving specific problems at the bottom.  It was only after gaining a deep understanding all of the interacting subsystems was it possible for each of them to engineer an overall system that was scalable via replication.  Looking at various attempts to scale sociotechnical systems, both successful and unsuccessful, a pattern starts to emerge of the key principles and dynamics.  Here are a few:

• Counterintuitive: Brilliant solutions are only obvious in retrospect.  Crazy. Crazy. Crazy. Obvious.
• Self-Replicators: It is important to identify the parts of the system that are — or that can be made to be — self-replicating.
• Fecundity: Digital information replicators are more easily replicable than mental constructs (i.e. memes), which are in turn more easily replicable than organizations of humans.
• Mutation: The more fecund the replicator, the easier it is to co-opt for ulterior motives, and the more likely it is that random variation will throw the overall system off course.
• Environment: It is easy to mistakenly believe that a prospective environment is suitable for replication when it’s not.
• Side-effects: With any complex dynamic process there are always side-effects. If ignored, this usually leads to collateral damage, but on the flip side there is usually an opportunity to accomplish other goals and turn side-effects into new benefits.

In thinking about how to engineer a system to bring solar electric installations to rural villages around the world, it is counterintuitive to think that poor, illiterate grandmothers (with no formal education and very little social standing in their village) could learn to be solar engineers.  To further think that they could be taught by illiterate trainers (who don’t speak the same language) is crazy.  Until Bunker Roy proved it was possible.

Microcredit was crazy too, until Muhammad Yunus proved that it wasn’t, and then it was obvious.  So obvious in fact that it became a viral meme and has spread all over the world.  The concept of microcredit is a very fecund self-replicator.  Unfortunately, the practice of microcredit in many places has ignored the nuances of different environmental contexts and unintended side-effects.  Add to that a high mutation rate: the model being tweaked to confer greater benefit to lenders (at the expense of borrowers); the introduction of middlemen who screw up the incentive structure and unwritten social contracts; etc.  The net effect has been that in some areas microcredit has been a net negative to the economy, and especially negative to the borrowers, whom the model was originally designed to help most.

Polack’s franchise model (an indeed all franchise models) are inherently replicators.  They are also good self-replicators because customers and other locals get exposure to the idea of becoming an entrepreneur themselves. And some of them end up as franchisees.  That is replication.  But to move from solving one problem (e.g. clean drinking water) to solving a very different one (e.g. locally available energy), new technologies that are also “radically affordable” have to be created on a regular basis.  And this type of innovation does not self-replicate.  So Polack created an entirely separate institution, the non-profit R&D lab, specifically to tackle the problem of replicating franchises (i.e. going from an electrochlorinator franchise to a solar concentrator franchise).

Applications

With this nascent framework in mind, I’d like to invite you to evaluate some of the social ventures that I encountered at The Feast (and a few of my favorites from Pop!Tech last year) and see if you can predict how scalable their model will be based on the replicator principles above.  And in cases where they have achieved some amount of scale (like charity: water and frontlineSMS), can you explain their success using the theory?

• SHE (Feast talk Session #3 starting 0:46:30)
• charity: water (Feast talk Session #4 starting 0:01:30)
• frontlineSMS (Feast talk Session #3 starting 0:25:45)
• Yellow Brick Road (Feast talk Session #1 0:31:37)
• 9th Ward Field of Dreams (Feast talk Session #4 starting 0:59:20)
• Change.org (Kitchen)
• International Transparency Solutions (Kitchen)
• Madecasse (Kitchen)
• Parent Earth (Kitchen)
• Srina (Kitchen)
• TrustArt (Kitchen)
• VotaVox (Kitchen)
• Global Citizen Year (Pop!Tech)
• Vittana (Pop!Tech)

I would love to hear your thoughts, both on the specifics of these ventures, and on the theory of scaling through replication.

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2. Autocatalytic Systems
3. Daniel Nocera’s Gift

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3. If Rafe Were In Charge: Major Medical Edition

If you ask a cardiologist it’s clear that they believe unequivocally that statins work, mostly because they see what statins to do blood cholesterol levels.  But remember, cholesterol numbers in and of themselves do not matter.  They are a proxy variable for cardiovascular health.  Plaque buildup matters.  At one time blood cholesterol numbers were the only non-invasive indicator we had of plaque buildup, but that’s not true anymore.  However, drug companies are highly incentivized to prove that statins improve health.  So they fund lots of studies.

Notwithstanding the systemic bias when there are profit motives and publication motives, we can turn to these studies and see if statins actually work.  The best way to remove bias is to look at large-scale meta-analyses, like this one.  If you simply read the conclusion you see “statin use was associated with significantly improved survival and large reductions in the risk of major cardiovascular events.”  However, if you decode the numbers you see that “significant” means that, for example, at age 65 you improve your changes of dying within the next four years from 8% down to 7% [hat tip to Kevin for helping me decode the numbers].  Now, is that really significant?

If my cardiologist told me I needed to take statins and I said “prove it” and he produced that study above, I would absolutely not take them.  For one, there are known deleterious side effects (which could possibly account for the small overall mortality risk decrease).  For another, the average length of followup was only 4 years, whereas I am told that I am to take these drugs indefinitely.  How do I know that the long-term side-effects won’t overrun the long-term benefits sometime after the four year mark and thus increase my overall mortality risk?

On the other hand, some small scale studies done by well-respected physicians indicates that you can reverse even advanced heart disease, as measured by the number of cardiac events (i.e. what really matters) through diet and exercise alone.

You can object all you want to the statistical significance of small scale studies.  The real issue is, given the evidence, what would YOU do if you were diagnosed with heart disease or were at high risk of heart disease as measured by your cholesterol numbers?

Since I’m not such a candidate, I made a friendly wager with a good friend of mine about a year ago who is.  He has been under a physician’s care for years because he has abnormally bad cholesterol numbers, and it runs in his family.  His doctors told him that he couldn’t control it with diet and that statins are the only way to go.  Problem is, he has a hard time tolerating statins (i.e. deleterious side effects).  Despite being on various statins, his numbers had barely budged.  The wager was designed to test the hypothesis that through dietary changes and exercise he could improve his numbers, a proposition that he claimed was impossible because he’d changed his diet in the past but it didn’t work.  My claim was that he was misinformed about what he was supposed to be eating and what he was not.  He got some advice from another friend who knows the literature.  Here is the result:

Been on about an 80%ish vegan diet, taking 2 meds (that I’ve taken before to little effect) that are pretty mild as far as lipid management,my cholesterol on a recent test came back lower than it’s ever been:

total chol = 165, hdl = 40, ldl = 110, triglyc = 80

my LDL level was 270 less than a year ago, and my total chol was 350.

Still kind of stunned, and I expect the results are at least somewhat anomalous, but even if they prove to be outliers to my normalized levels it will represent a bigger improvement than I could have ever had taking super-dosage statins.

Here’s my challenge to the medical establishment: produce a meta-analysis of statins that shows absolute age-adjusted mortality rate decrease of greater than 10% over 20 years.*

In the mean time, I’ll be eating healthy, exercising regularly, and making money from people who want to take the same bet as my friend.

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Click here to view the embedded video.

The main takeaways from this that I got are:

1. Cancers for which sunlight deficit is a risk factor are orders of magnitude more prevalent than the few for which overexposure is a risk factor.
2. People who are using sunscreen regularly are precisely the ones who shouldn’t be.
3. We should be very careful and sparing about recommending sunscreen usage or sun avoidance, and always temper such advice with the tradeoffs of not getting enough sunlight.

As someone who wonders on a regular basis whether the public has the right information to make informed decisions about health-related tradeoffs, I am curious… does the above strike you as surprising?  What do you currently do regarding sun exposure, and are you likely to change anything based on the above?  What do you think the overall message that reaches the masses is regarding sun exposure?

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2. Invisible Etiology
3. The Conflict Between Complex Systems and Reductionism

If you’ve listened to this the whole way through (which you should), I’m curious as to how it will affect your habits, if at all.  And why?

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[The incident] is an extraordinary example of what happens when you get… a dozen people with an average IQ of 160… working in a field in which they collectively have 250 years of experience… employing a ton of leverage.

It’s hard to overstate the significance of a [government-led] rescues of a private [corporation].  If a [company], however large was too big to fail, then what large [company] would ever be allowed to collapse?  The government risked becoming the margin of safety.  No serious consequences had come about in the end from the… near-meltdown.

Was the incident:

a) The savings and loan scandal

b) The collapse of Enron

c) The sub-prime mortgage meltdown

d) none of the above

First correct answer gets to invest in an exciting new bridge project I’m involved with in New York!

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2. Chasing the Dragon
3. The Good, The Bad & The Ugly