Posts Tagged ‘development’

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…

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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