{"id":762,"date":"2009-04-02T15:26:30","date_gmt":"2009-04-02T15:26:30","guid":{"rendered":"http:\/\/scientopia.org\/blogs\/goodmath\/2009\/04\/02\/can-simulations-replace-animal-testing-alas-no\/"},"modified":"2009-04-02T15:26:30","modified_gmt":"2009-04-02T15:26:30","slug":"can-simulations-replace-animal-testing-alas-no","status":"publish","type":"post","link":"http:\/\/www.goodmath.org\/blog\/2009\/04\/02\/can-simulations-replace-animal-testing-alas-no\/","title":{"rendered":"Can simulations replace animal testing? Alas, no."},"content":{"rendered":"

My good friend and blogfather, Orac, posted something yesterday about animal testing<\/a>
\nin medical laboratories. I’ve been meaning to write something about that for a while; now
\nseems like a good time.<\/p>\n

I’m not someone who thinks that being cruel to animals is no big deal. I have known
\nsome people like that, but thankfully they’re very<\/em> rare, and none of them
\nwere scientists whose work involves doing animal testing in real laboratories. <\/p>\n

But animal testing isn’t about pointless cruelty. It’s about understanding things that we
\nsimply cannot<\/em> learn about in any other way. It’s extremely important to minimize the
\ncruelty we inflict on the subjects of animal tests: there’s no benefit in torturing an animal;
\nthere’s no good reason to inflict unnecessary pain on any living thing. When we
\ncan<\/em> study something without using animals, we should. When we must use animals
\nin scientific study, we should be as kind to them as we possibly can. But the fact remains that in many cases, animal studies are necessary.<\/p>\n

I don’t want to get into a long discussion of the ethics of it here; that’s a discussion
\nwhich has been had hundreds of times in plenty of other places, and there’s really no sense
\nrepeating it yet again. But there is one thing I can contribute to this discussion. One of the
\nconstant refrains of animal-rights protesters arguing against animal testing is: “Animal
\ntesting isn’t necessary. We can use computer simulations instead.” <\/p>\n

As a computer scientist who’s spent some time studying simulation, I feel qualified
\nto comment on that aspect of this argument.<\/p>\n

The simplest answer to that is the old programmers mantra: “Garbage in, Garbage out”.<\/p>\n

<\/p>\n

To be a tad more precise, like any other computer program, a simulation can only do what
\nyou tell it to. If you don’t already know how something works, you can’t simulate it. If you
\nthink<\/em> you know how something works but you made a tiny, miniscule error, then the
\nsimulation can diverge dramatically from reality.<\/p>\n

Simulations are an incredibly useful technique. They can be used in several different ways:<\/p>\n

    \n
  1. If we know how something works, we can simulate it, and do experiments simply by
    \nchanging parameters to the code. This can allow us to perform experiments that
    \nwould be impossible in the real world, or to run multiple experiments much faster
    \nthan we could, in a more controlled fashion, than we could in the
    \nreal world. For example, we can run orbital simulations of our solar system which
    \nare astonishingly precise, and which allow us to try scenarios that would be impossible
    \nto test in the real world. <\/li>\n
  2. If we don’t<\/em> know how something works, but we have a theory, we can test it
    \nby implementing a simulation according to our theory, and comparing the results of
    \nthe simulation to observed real-world results. If our simulation closely matches what
    \nwe observe in the real world, it’s a great piece of supporting evidence. If it doesn’t,
    \nit means we got it wrong. For example, people have been running massive simulations of
    \nthe experiments that hope to produce the Higgs boson, which allows them to make
    \npredictions about how they’ll be able to recognize it if their experiments manage to
    \ncreate one.<\/li>\n
  3. If we know part of how something works, we can build simulations give us a way
    \nof experimenting specifically on that part without the added complexity of
    \na complete system. For example, we can study specific parts of cellular metabolism:
    \nwe know the basic chemistry of how a mitochondria produces energy for the cell. We
    \ncan focus our attention on simulations of that specific process, without dealing
    \nwith all of the details of cellular metabolism.<\/li>\n<\/ol>\n

    It might sound like I’m saying that simulations can’t surprise us – because they
    \ncan only produce things that we already know. That’s not the case – accurate<\/em> simulations can be extremely surprising. The most common cause of that is a phenomenon called
    \nemergence. Emergent phenomena are things where some thing behaves one way at one scale,
    \nbut changes dramatically when you put together huge numbers of those things and look
    \nat them at a different scale.<\/p>\n

    The best example of emergent phenomena is our macro-scale universe. When we look at
    \nthe world, things seem concrete and predictable. When you watch a baseball game,
    \nyou can see the baseball fly from the hand of the pitcher to the bat, and it’s obvious
    \nthat you can precisely describe both the position and the velocity of the baseball when it’s
    \nin flight. But the baseball is made up of a huge number of particles which do not<\/em> behave in such well-mannered ways. They’re unpredictable, erratic. Their behavior can’t
    \nbe described precisely, only probabilistically. And yet, when we put together quadrillions of quadrillions of unpredictable, probabilistic particles, we get something concrete, comprehensible, and extremely predictable.<\/p>\n

    Simulations can (and frequently do) surprise us due to emergence. We
    \nmay understand the how some thing works, without understanding what’s going to happen when we
    \nsplice together a billion of them. (Simulations can produce surprises due to things
    \nother than emergence; the reason that I stress that one is that it’s the one I’ve experienced.)<\/p>\n

    But getting back to the topic at hand: when it comes to animal testing, most of the
    \ntime, we can’t use simulations – because we don’t understand enough to be able to
    \ndo an accurate simulation. We can’t simulate what we don’t know. And when it comes to
    \nmedicine, there’s an awful lot that we don’t know.<\/p>\n

    A few examples: we know that a lot of non-coding DNA has function doing various things
    \nlike regulating coding DNA. We don’t know all of the functions of non-coding DNA. Of the non-coding regions we basically understand, we don’t understand how they all work. Getting
    \naway from DNA, the basic day-to-day functioning of our cells includes tons of
    \nprocesses that we simply don’t understand. There’s so much going on in a single cell
    \nthat we don’t understand yet, that we don’t have a chance of producing a dead-on accurate simulation of it.<\/p>\n

    And to try to simulate more than a single cell is even harder – because the cells of
    \nour body have an extremely complicated set of interdependencies and interactions. To do a simulated drug test, we need to simulate both the intra- and inter-cellular processes that would be affected by that drug. And the fact is, we don’t.<\/p>\n

    To throw out another example: as I’ve mentioned before, I have clinical depression.
    \nI manage it by taking a selective serotonin reuptake inhibitor called “zoloft”. For me,
    \nand for many other people with depression, SSRIs are almost miracle drugs – they
    \ncompletely eliminate the symptoms of depression. But: We don’t know why zoloft works<\/em>.
    \nIt was designed under a theory – the “serotonin theory” – that thought that depression was
    \ncaused by an inadequate supply of serotonin in synapses of the brain; zoloft was
    \ndesigned specifically to target the cellular mechanisms by which serotonin is
    \nremoved from those synapses. But that theory has, largely, been discredited. It appears
    \nthat the mechanism that zoloft was designed<\/em> to target is not<\/em> the mechanism
    \nby which it works! Why? No one is sure, but a lot of people are doing a lot of work to try to understand it. <\/p>\n

    For the people trying to understand that, in order to develop better treatments
    \nfor depression, simulations are no good – because we don’t know the mechanism by which
    \nthe drugs work<\/em>. We know that they do<\/em>, but we don’t know why or how. <\/p>\n

    When it comes to other things, we’re in a similar situation. Will a new antibiotic work? Maybe. Maybe not. How do we know? We need to test it. We start with tests in cell cultures. But lots of drugs that work in cell culture don’t work in a living creature. There are numerous chemicals that kill HIV in cell culture, but don’t work in an infected animal. There are tons of chemotheraputic agents that kill cancer in culture, but that don’t work in
    \nan animal or a person. We can’t do tests of those using simulations, because we simply
    \ndon’t know enough about the underlying mechanisms. And if we don’t understand a process
    \nor mechanism, we can’t simulate it.<\/p>\n

    Life is still very mysterious to us. There’s so much about living things and the
    \nbasic physical and chemical processes that occur inside of them that we don’t understand. Those mysteries are what we try to study with science, in order to develop more understanding. Simulations can be a useful tool in the process of exploring. But it can’t replace observations and experiments with real living things. There’s still no substitute
    \nfor reality.<\/p>\n","protected":false},"excerpt":{"rendered":"

    My good friend and blogfather, Orac, posted something yesterday about animal testing in medical laboratories. I’ve been meaning to write something about that for a while; now seems like a good time. I’m not someone who thinks that being cruel to animals is no big deal. I have known some people like that, but thankfully […]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"jetpack_post_was_ever_published":false,"_jetpack_newsletter_access":"","_jetpack_dont_email_post_to_subs":false,"_jetpack_newsletter_tier_id":0,"_jetpack_memberships_contains_paywalled_content":false,"_jetpack_memberships_contains_paid_content":false,"footnotes":"","jetpack_publicize_message":"","jetpack_publicize_feature_enabled":true,"jetpack_social_post_already_shared":false,"jetpack_social_options":{"image_generator_settings":{"template":"highway","default_image_id":0,"font":"","enabled":false},"version":2}},"categories":[54],"tags":[],"class_list":["post-762","post","type-post","status-publish","format-standard","hentry","category-programming"],"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_shortlink":"https:\/\/wp.me\/p4lzZS-ci","jetpack_sharing_enabled":true,"jetpack_likes_enabled":true,"_links":{"self":[{"href":"http:\/\/www.goodmath.org\/blog\/wp-json\/wp\/v2\/posts\/762","targetHints":{"allow":["GET"]}}],"collection":[{"href":"http:\/\/www.goodmath.org\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"http:\/\/www.goodmath.org\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"http:\/\/www.goodmath.org\/blog\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"http:\/\/www.goodmath.org\/blog\/wp-json\/wp\/v2\/comments?post=762"}],"version-history":[{"count":0,"href":"http:\/\/www.goodmath.org\/blog\/wp-json\/wp\/v2\/posts\/762\/revisions"}],"wp:attachment":[{"href":"http:\/\/www.goodmath.org\/blog\/wp-json\/wp\/v2\/media?parent=762"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"http:\/\/www.goodmath.org\/blog\/wp-json\/wp\/v2\/categories?post=762"},{"taxonomy":"post_tag","embeddable":true,"href":"http:\/\/www.goodmath.org\/blog\/wp-json\/wp\/v2\/tags?post=762"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}