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Multicellular Logic Circuits, Part III: A Model

September 26, 2007

In Part I and Part II of this series, I discussed genetic algorithms and why we might want to create artificial machines that begin life as a single cell, and develop into networks of identical communicating cells. In this post, I want to begin describing a model that works along these lines.

The model is a highly stylized and simplified cartoon of biological multicellular organisms. It my attempt to make the simplest model possible that captures the essence of what is happening in biology. So understand that biology is more complicated than this model; but the goal is a model stripped down to those essential elements that cannot be taken away if one wants something that looks like life. Thus, the model is proposed in the spirit of the Ising model of magnets in statistical physics; the simplest model that captures the general behavior we are looking for.

The first question is what do we want our machine (or “circuit” or “network” or “organism”; I will use these terms interchangeably) to do? As is quite conventional in hardware design, I will presume the organism receives some input signals from the world, and it is supposed to produce some desired output signal, which depends on the inputs it has received at the current and previous times. Thus, the circuit should in general be capable of creating memories, that lets it store something about previous inputs.

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The organism begins its life as a single cell, and then has two phases in its life, a dynamic “embryonic” phase and a static “adult” phase. During the embryonic phase, the cells in the organism can undergo developmental events, primarily cell duplication, but also perhaps cell death or cell relocation, to sculpt out the final network of communicating cells. After the embryonic phase is complete (say after a fixed amount of time has passed, or some signal is generated by the circuit) the adult phase is entered. The network is static in structure during the adult phase. It is during the adult phase that the network can be tested to see whether it properly computes the desired input-output function. The figure above is a pictorial representation of the model that hopefully makes clear what I have in mind.

Each of the cells in the network will have an internal structure, defined primarily by “logic units” which send signals to each other. The computations performed by the organism will simply be the computations performed by the logic units inside of its cells. The details of what the logic units do, and how they are connected to each other, is specified by a “genome” or “program” for the organism.

Look at the figure below for a peek inside an individual cell in the model. Each cell will have an identical set of logic units, with identical connections between the logic units.

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The logic units compute an output according to some fixed function of their inputs. They transmit that output after some delay, which is also part of their fixed function. The output of one logic unit will be the input of another; they send “signals” to each other.

These signals are of various types (see the above figure). The first type of signal, called a “factor signal,” will always go from a logic unit to another logic unit in the same cell. The second type of signal, called an “inter-cellular signal,” will always go from a logic unit to a logic unit in a different cell. The third type of signal, called a “developmental output signal,” will not actually go to another logic unit, but will be a signal to the cell development apparatus to perform some important development event, such as duplication or programmed cell death. Finally, the fourth type of signal, called a “developmental input signal,” will be used by the cell development apparatus to signal that some type of cell development event has occurred, and will serve as an input to logic units.

Remember that initial cell (the “fertilized egg”) will need to have a set of logic units that enable it to automatically create the adult network, so it must effectively contain the instructions for development as well as for the adult circuit. It might seem hard to imagine that this can work, but it can. In the next post in this series, I will discuss in more detail the process of development in this model, and then we will be in position to look at some interesting multicellular circuits that I have designed.

If you don’t want to wait, you can visit this page to find a PDF and a PowerPoint version of a talk I gave on the subject at a conference in Santa Fe in May 2007, although unfortunately, it might be hard to decipher without my explanation…

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Cynthia Kenyon’s Long-lived Worms

September 19, 2007

Professor Cynthia Kenyon is a pioneering researcher in the biology of aging. A couple years ago, she presented a Harvey Lecture at Rockefeller University on her work; that lecture was similar to the one I heard her give at last year’s Woods Hole summer school course on aging. I think that it’s worth highlighting some of the things she has to say.

“We began our studies in the early 1990s. At that time, and for years before, many people assumed that aging was a haphazard process, not subject to regulation. Our tissues just break down, and we die. But the more I thought about it, the more I started to question this view. A mouse lives two years, whereas a bat can live 30 years or more. A rat lives three years; a squirrel, 25. These animals differ by their genes, so there must be genes that affect aging. Also, nothing in biology seems to “just happen”; everything seems to be regulated, often in quite an extraordinary way.

My experience as a developmental biologist sharpened my thoughts about aging. People were once very skeptical about looking for developmental genes. Treating frog embryos with acid can produce a second head, and inhibiting pyrimidine synthesis in flies produces small wings, so many people thought that genes affecting development would also affect things like the Krebs cycle, or pH. They were wrong. There is a dedicated regulatory circuitry for pattern formation. In addition, many people thought that developmental mechanisms would differ completely in different kinds of animals, but again they were wrong. In fact, the degree of evolutionary conservation is striking. So it seemed to me that something as fundamental as aging might also be subject to regulation. Maybe there would be a molecular longevity “dial,” like a thermostat, that is universal but set to run at different rates in different kinds of animals. The dial would be turned up in mice (which age quickly) and down in bats (which age slowly). I wrote extensively about this in the 1990’s (Kenyon, 1996, 1997), suggesting, for example, that aging might be regulated by something like the heterochronic genes of C. elegans, which control the timing of developmental events.”

…

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Click on figure to expand

“Since we obtained such a long lifespan when we killed the gonads of daf-2 mutants, we wondered what would happen if we reduced daf-2 activity even more in these animals. Using a stronger daf-2 allele would run the risk of triggering dauer formation, but we found that we could dodge dauer formation if we subjected long-lived daf-2(e1368) mutants to daf-2 RNAi soon after hatching. When we did this, and killed the gonads as well, the animals lived six times as long as normal (Fig. 2.16). Incredibly, the animals remained healthy and vigorous for a very long time. In fact, when Nuno Arantes-Oliveira, the graduate student doing this work, showed two 144-day-old animals, still moving around, to other lab members and asked them to guess the age of the animals, they reckoned five days! [For a movie of these two spunky animals, see Arantes-Oliveira et al. (2003).] It is remarkable that with just a few minor changes, it is possible to produce such an enormous lifespan extension (the equivalent of 500 years in humans) with no obvious effect on the vitality of the animals.”

…

“If we really could live longer, remaining youthful and disease-free, why haven’t scientists been working on this already? First, as I said, they didn’t think it was possible, since aging was thought to be unruly and random. Second, and even more important, we haven’t had any role models to emulate, primates that shoot rockets to the moon, go to the opera, and live for 300 years. If we did, we might already know how to stay young and live much longer than we do. We invented airplanes because we could see birds could fly. Now that we know that animals can live longer than they do, perhaps soon we will learn how to extend our own youthfulness and lifespan. It may not be that difficult. Since there are short-lived and long-lived insects, birds, and mammals, longevity must have evolved not just once but many times. Maybe the path to increased longevity is in us already, in the form of a network of genes and proteins, waiting to be nudged in just the right way.”

I recommend you read the whole thing–it’s quite readable, and the scientific results are breathtaking.

And if you’re interested, here is a video from earlier this year with Charlie Rose interviewing a panel of biologists about the remarkable progress that has been made in aging research recently. Members of the panel include Kenyon and Lenny Guarente, another leader in the field whose book I previously reviewed.

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Multicellular Logic Circuits, Part II: Cells

September 18, 2007

In my post “Multicellular Logic Circuits, Part I: Evolution,” I discussed evolution and genetic algorithms; I want to continue that discussion here.

There are two salient facts of biology that are completely inescapable. The first is that all organisms are shaped by the process of evolution. The second is that all organisms are constructed from cells.

Furthermore, all complex multicellular organisms begin life as a single cell, and undergo a process of development through cell division to mature into an adult. And no matter how different any two organisms may be on the gross macroscopic level that we are used to, inside their cells the chemical processes of life are fundamentally very similar.

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Thus it is no accident that the titles of the two leading textbooks in molecular biology are The Molecular Biology of the Gene by Watson, et. al. and The Molecular Biology of the Cell by Alberts et. al. [These are both great books. This link to the first chapter of MBOC is an excellent entry point into modern biology. And if you are serious about learning biology, I also strongly recommend the companion Molecular Biology of the Cell: A Problems Approach, by Wilson and Hunt, which will force you to think more actively about the material.]

It therefore seems reasonable that if we want to construct artificial systems that achieve the performance of natural ones, we should consider artificially evolving a system constructed from cells.

Although there are typically many different cell types in a mature multi-cellular organism, all the different cells of the organism, with the exception of sperm and egg cells, share an identical genetic specification in their DNA. The different behavior of cells with identical genetic specifications is the result of the cells having different histories and being subjected to different environments.

More specifically, the behavior of a biological cell is controlled by complex genetic regulatory mechanisms that determine which genes are transcribed into messenger RNA and then translated into proteins. One very important regulatory mechanism is provided by the proteins called “transcription factors” that bind to DNA regulatory regions upstream of the protein coding regions of genes, and participate in the promotion or inhibition of the transcription of DNA into RNA. The different histories of two cells might lead to one having a large concentration of a particular transcription factor, and the other having a low concentration, and thus the two cells would express different genes, even though they had identical DNA.

Another important mechanism that controls the differential development of different types of cells in a multi-cellular organism is the biochemical signaling sent between cells. Signals such as hormones have the effect of directing a cell down a particular developmental pathway.

In general, the transcription factors, hormones, and multitude of other control mechanisms used in biological cells are organized into a network which can be represented as a “circuit” where the state of the system is characterized by the concentrations of the different biochemical ingredients. In fact, biologists are now using wiring diagrams to help summarize biological circuits; see for example, the “Biotapestry editor” developed by Eric Davidson’s lab at Caltech.

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[I strongly recommend Davidson’s recent book The Regulatory Genome: Gene Regulatory Networks in Development and Evolution for an exciting introduction to the burgeoning “evo-devo” field; if you don’t have any background in biology, you may prefer The Coiled Spring, by Ethan Bier for a somewhat more popular account.]

Turning to the problem of designing artifical systems, a natural question is what theoretical advantages exist, from the point of view of designing with evolution, to using an identical genetic specification for all the cells in a multi-cellular organism.

One potential advantage is that relatively small changes to the genetic specification of the organism can concurrently alter the behavior of many different kinds of cells at many different times during the development of the organism. Therefore, if there is the possibility of an advantageous change to the circuitry controlling a cell, then it can be found once and used many times instead of needing to find the same advantageous mutation repeatedly for each of the cells in the organism.

Another related potential advantage is that a highly complicated organism can be specified in a relatively compact way. If each of the trillions of cells in a complex organism like a human had to be separately specified, then the overall amount of information required to describe the human genome would be multiplied more than a trillion-fold. Clearly, it is much more efficient to re-use the identical circuitry in many different types of cells.

In other words, biology uses a strategy of specifying a complex multi-cellular organism by just specifying a single cell–all the other cells in the mature organism are grown organically out of the developmental process. This seems like a strategy worth imitating.

On the other hand, the constraint that each cell in an organism should share an identical genetic specification clearly causes complications from the point of view of design. For example, it is important that genes that are designed to function in one type of cell at one point in development not cause problems for different type of cell at a different point in development. Clearly, good design of the control logic that turns genes on and off is essential to the proper functioning of a multi-cellular organism.

In the next post in this series, I will turn to the construction of a concrete model for multi-cellular circuits that tries to capture, as simply as possible, the essence of what is happening in biology. 

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The Life of the Lab Biologist

September 1, 2007

As I mentioned in a previous post, I was lucky to be able to attend, as a student, the 2006 Molecular Biology of Aging summer course at the Woods Hole Marine Biology Laboratory. This three week course was intensive; part the time was spent in lectures, where many of the world’s leading experts on aging explained their research in detail (and the students were able to ask lots of questions), and the rest was spent in the lab. There was also often time to attend some of the many other stimulating talks in molecular biology or neuroscience being held elsewhere at Woods Hole. Because the subject was so far from my normal research, I took vacation time to attend; I suppose it doesn’t seem like much of a vacation, but in fact Woods Hole is incredibly stimulating, and it was one of the most memorable and refreshing vacations I’ve ever had.

We performed real experiments in the lab, as the other students were all biology post-docs and grad students who were there to learn cutting edge techniques. I was also assigned to a group, led by Dr. Meng Wang, a post-doc in Gary Ruvkun’s lab, and we learned how to perform RNA interference (RNAi) screens on the nematode worm C. Elegans.

The RNAi technique lets you suppress the transcription of any single gene in the worm’s genome. An “RNAi screen” means that you divide the population of worms that into groups organized so that each group has a different gene suppressed, and you make sure that you have a group for each gene in the genome. For each group of worms, you check whether it has some phenotype that you’re interested in (in our case it was the ability to breed at a later age than usual). That way, you can quickly find genes that are involved in the phenotype.

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The picture above is from the lab at Woods Hole. From left to right are Michael Morissette, Andrew Midzak, Serkalem Tadesse, myself, and John Cumbers. The others have finished their work, but I was slower than everybody else, so I’m guessing that I was still counting worms or something.

Biologists work incredibly long hours at the lab, often doing work that is exciting in terms of its implications, but sometimes pretty dull and repetitive in the doing; biologists are dedicated people! On the other hand, lab life seems much more social compared to the life of a computer scientist or physicist. (Although there is much more social interaction in those fields than in fields like history, as I know by observing my historian wife. I always find it ironic that humanists, who tend to be outgoing people, usually find themselves working in a much more solitary way than scientists.)

One thing I learned was that lab biology is largely a matter of learning and using “protocols,” which are basically like scientific recipes. Take a look at this amusing video, which features the highly talented John Cumbers (who was one of my lab-mates) and produced by the Brown iGEM team:

Another protocol was for “picking” worms (moving them from one petri dish to another). An adult C. Elegans is only about 1 millimeter long, so picking them up is not easy. You do it under a microscope with a special thin wire (a “picker”). You sort of try to scoop them up, but the worms run away! It’s like a video game, except not nearly as fun, really. Here’s a video showing the technique in action.

You should notice by the way that the picked worm is glowing. That’s because the worm is a mutant: a gene for a fluorescent protein has been spliced into its genome attached to another gene (daf-12) of interest. That way you can know where daf-12 is expressed in its body. (This video was submitted by user a99xel to YouTube).

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The Teaching Company

August 24, 2007

My wife and I are both big fans of the college courses produced by the Teaching Company. The courses cover a wide variety of subjects, and come in a range of video and/or audio formats.

I personally find that the audio format usually works somewhat better. The lecturers are very good, but watching a professor lecture on TV is inevitably somewhat dull. On the other hand, when I’m driving or riding in a car or train, I find that an audio lecture fills an ideal amount of mental bandwidth. (Every so often, the lecture gets complicated at the same time as the driving, but one can always rewind).

My favorite course so far was Robert Greenberg’s course on How to Listen to and Understand Great Music. Music courses are naturally a great fit for an audio course!

Otherwise, there are unfortunately not that many science and mathematics courses that go beyond the beginning undergraduate level, although I did enjoy Stephen Nowicki’s course on Biology. If you are interested in history or philosophy, or other subjects in the humanities (like my wife, who is a historian) there are many more interesting options.

The Teaching Company has an unusual pricing policy. The courses are very expensive, except when they go on sale, when they cost roughly one quarter the normal price and are very reasonable. All the courses go on sale on a regular rotation, so unless you are in a tremendous hurry, you should definitely wait until the course you are interested in goes on sale. A lot of the courses are available at libraries too, so you can borrow one to see if you like it first.

Here’s another blog post endorsing the Teaching Company, with some reader comments on their favorite courses.

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Mitochondria

August 22, 2007

As I mentioned in my previous post about Lenny Guerente’s book “Ageless Quest,” aging research has, within the last 15 years, gone from being a scientific backwater to a mainstream field of scientific research, with new discoveries now regularly featured on the cover of Nature or Science (as in the Nature issue from June 2007 below.)

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Although we now are capable of manipulating the aging process, including significantly extending the lifespan of many laboratory animals, it is still a frustrating fact that there is no consensus about the ultimate cause or causes of aging.

One viewpoint, which is probably only held by a significant minority of scientists in the field, is that the aging process is strongly connected to mitochondria, which are the power plants or batteries of our cell, converting nutrients into useful packets of energy in the form of ATP. We’re used to the idea that electronic equipment fails when the batteries go dead, so it’s not such a stretch to take a close look at the mitochondria.

What’s more, mitochondria produce much of the “pollution” in the cell in the form of the free radicals that are a by-product of the oxidative phosphorylation process (the process that turns nutrients into energy). Those free-radicals can damage proteins or DNA, particularly the mitochondrial DNA (this is special DNA, inherited from the mother, that resides in the mitochondria rather than the nucleus) that codes for a few essential mitochondrial proteins.

So one theory says that there is a kind of vicious circle, whereby old mitochondria start emitting more free radicals, which further damages the mitochondria, until the mitochondria are so damaged that they don’t produce sufficient energy and start damaging the rest of the cell. Right now, the consensus view on whether the experimental facts really fit that theory is “Maybe.”

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If you want to learn more about mitochondria, I highly recommend “Power, Sex, Suicide: Mitochondria and the Meaning of Life (you’ve just got to love that title), by Nick Lane. Lane’s book is popular science, but it’s a very deep book, and actually proposes theories, including theories of aging, that you won’t see elsewhere in the literature. It’s not an easy book to read, but it’s very worthwhile.

Alternatively, you might enjoy this video of Douglas Wallace lecturing on the role of mitochondria in diseases and aging. Wallace, a professor from UC Irvine, delivers highly entertaining and persuasive lectures.

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Lectures on Disordered Systems

August 12, 2007

Many physicists study “disordered systems,” such as materials like glasses where the molecules making up the material are arranged randomly in space, in contrast to crystals, where all the particles are arranged in beautiful repeating patterns.

The symmetries of crystals make them much easier to analyze than glasses, and new theoretical methods had to be invented before physicists could make any headway in computing the properties of disordered systems. Those methods have turned out to be closely connected to approaches, such as the “belief propagation” algorithm, that are widely used in computer science, artificial intelligence, and communications theory, with the result that physicists and computer scientists today regularly exchange new ideas and results across their disciplines.

Returning to the physics of disordered systems, physicists began working on the problem in the 1970’s by considering the problem of disordered magnets (also called “spin glasses”). My Ph.D. thesis advisor, Philip W. Anderson summarized the history as follows:

“In 1975 S.F. (now Sir Sam) Edwards and I wrote down the “replica” theory of the phenomenon I had earlier named “spin glass”, followed up in ’77 by a paper of D.J. Thouless, my student Richard Palmer, and myself. A brilliant further breakthrough by G. Toulouse and G. Parisi led to a full solution of the problem, which turned out to entail a new form of statistical mechanics of wide applicability in fields as far apart as computer science, protein folding, neural networks, and evolutionary modelling, to all of which directions my students and/or I contributed.”

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In 1992, I presented five lectures on “Quenched Disorder: Understanding Glasses Using a Variational Principle and the Replica Method” at a Santa Fe Institute summer school on complex systems. The lectures were published in a book edited by Lynn Nadel and Daniel Stein, but that book is very hard to find, and I think that these lectures are still relevant, so I’m posting them here. As I say in the introduction, “I will discuss technical subjects, but I will try my best to introduce all the technical material in as gentle and comprehensible a way as possible, assuming no previous exposure to the subject of these lectures at all.”

The first lecture is an introduction to the basics of statistical mechanics. It introduces magnetic systems and particle systems, and describes how to exactly solve non-interacting magnetic systems and particle systems where the particles are connected by springs.

The second lecture introduces the idea of variational approaches. Roughly speaking, the idea of a variational approach is to construct an approximate but exactly soluble system that is as close as possible to the system you are interested in. The grandly titled “Gaussian variational method” is the variational method that tries to find the set of particles and springs that best approximates an interacting particle system. I describe in this second lecture how the Gaussian variational method can be applied to heteropolymers like proteins.

The next three lectures cover the replica method, and combine it with the variational approach. The replica method is highly intricate mathematically. I learned it at the feet of the masters during my two years at the Ecole Normale Superieure (ENS) in Paris. In particular, I was lucky to work with Jean-Philippe Bouchaud, Antoine Georges, and Marc Mezard, who taught me what I knew. I thought it unfortunate that there wasn’t a written tutorial on the replica method, so the result were these lectures. Marc told me that for years afterwards they were given to new students of the replica method at the ENS.

Nowadays, the replica method is a little less popular than it used to be, mostly because it is all about computing averages of quantities over many samples of systems that are disordered according to some probability distribution. While those averages are very useful in physics, they are somewhat less important in computer science, where you usually just want an algorithm to deal with the one disordered system in front of you, rather than an average over all the possible disordered systems.

Santa Fe Institute Lectures

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Multicellular Logic Circuits, Part I: Evolution

August 7, 2007

If we want to construct artificial machines that rival the capabilities of biological organisms, we should try to understand the principles by which complex natural “machines” such as plants and animals are created.

It is generally agreed, at least by scientists, that all natural organisms have been “designed” by the completely blind and random process of evolution. Through evolution, a population of organisms tends to become progressively better adapted to its environment via the mutation of genomes of individuals in the population, and the selection and more rapid reproduction of the fittest organisms in that population.

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Harvard professor Martin Nowak a has written a lovely and elegant book describing the mathematics of evolutionary dynamics, using the ideas of evolutionary game theory; here is a video of Nowak describing evolutionary game theory at Harvard in 2004.

My own interest is not so much in analyzing evolution, but in exploiting it. If we understand evolution so well, shouldn’t we be able to use it to design useful machines?

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Of course, humans have already for many centuries exploited evolution, using artificial selection to breed domesticated animals or cultivate useful plants.

But I am looking for something else: the design of artificial machines through artificial selection. Although it has never been a mainstream idea, computer scientists have pursued such dreams since the 1950’s. When I was in graduate school in the 1980’s, I loved reading John Holland’s seminal 1975 book “Adaptation in Natural and Artificial Systems.”

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Holland and his students were deeply influential in popularizing the whole field of genetic algorithms.

Another important figure in the field is John Koza, who has advocated for many years one of the most important variants of genetic algorithms, which he calls “genetic programming.” In genetic programming, computer programs, typically written in Lisp, are evolved through a process that involves mutating the programs by altering or swapping branches of the computation tree representing the program.

Genetic programming and genetic algorithms more generally, have had considerable success creating interesting and useful systems and programs. Nevertheless, I think it is fair to say that these ideas are still considered “fringe” ideas in the scientific and engineering community, and they have not widely replaced more conventional software and hardware design strategies.

So what might be missing? I will begin discussing that in Part II.


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“Ageless Quest” by Lenny Guarente

August 5, 2007


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MIT professor Lenny Guarente is a pioneer and leader in the study of the molecular biology of aging. This book is a popularized account of some of the early research that he and his students and post-docs conducted; research that helped move the study of aging from being a kind of slightly disreputable scientific backwater to one of the most dynamic and exciting fields of modern molecular biology. Guarente’s research focused on sirtuins, which are proteins that are now understood to retard aging in a wide variety of organisms, with mechanisms that vary depending on the organism.”

Ageless Quest” is an easy read and a great introduction to the field. It had a surprising amount of impact on me; after reading this book I decided that I wanted to learn more about what was happening in this very important field, so I audited an MIT reading course on the molecular biology of aging taught by Angeiszka Czopik and Danica Chen, two post-docs in Prof. Guarente’s lab, and then I attended the 2006 Summer School Course on the molecular biology of aging at Woods Hole’s famous Marine Biological Laboratory, organized by Gary Ruvkun and Steve Austad.

This book probably won’t have that big an impact on you! It’s a pretty light book weighing in at only 154 pages; but you can learn a lot whether or not you have a background in biology.

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