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Topobiology: An Introduction To Molecular Embryology
If you had a complete copy of a dinosaur's DNA and the genetic code, you still would not be able to make a dinosaur—or even determine what one looked like. Why? How do animals get their shape and how does shape evolve? In this important book, Nobel laureate Gerald M. Edelman challenges the notion that an understanding of the genetic code and of cell differentiation is sufficient to answer these questions. Rather, he argues, a trio of related issues must also be investigated—the development of form, the evolution of form, and the morphological and functional bases of behavior. Topobiology presents an introduction to molecular embryology and describes a comprehensive hypothesis to account for the evolution and development of animal form.
256 pages, Paperback
First published November 3, 1988
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About the author
Gerald M. Edelman
27 books102 followersGerald Maurice Edelman (born July 1, 1929) is an American biologist who shared the 1972 Nobel Prize in Physiology or Medicine for work with Rodney Robert Porter on the immune system.[1] Edelman's Nobel Prize-winning research concerned discovery of the structure of antibody molecules.[2] In interviews, he has said that the way the components of the immune system evolve over the life of the individual is analogous to the way the components of the brain evolve in a lifetime. There is a continuity in this way between his work on the immune system, for which he won the Nobel Prize, and his later work in neuroscience and in philosophy of mind.
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November 24, 2015
I will start by confessing that I am not equipped to assess this book’s position in today’s scientific milieu. I read it because of an interest in evo-devo and picked it up second-hand. The book is now nearly 30 years old and both evo-devo and epigenetics have been in a state of revolution ever since. I am not a professional in the field, and can only say that it seems to have been prescient, but without following the literature I could be unaware of it being entirely superseded.
I have for years been toying with the idea of running simulations of embryological concepts to generate lifelike generative art images, so far limited to simulating mollusc shells and a simple “biomorph” program. The problem of generating lifelike forms through genetic algorithms confronts one with the problem of how to allow complexity to emerge and lead to novel Baupläne, but without explicitly coding these in advance or squandering prodigious amounts of computing power. The central message of this book, therefore, flashes brightly, in red neon, in the mind of the programmer: Edelmann describes a class of processes in embryology that operate recursively, and feed back to the genetic code to alter its working epigenetically. This is the key to generating arbitrary levels of complexity.
The scientific effort to understand how it actually works, of course, must have been staggering. Edelmann refers to immunology, his own Nobel field, as well as neurology to show how the same class of processes can operate to generate natural selection in somatic time (during the lifetime of an organism) to search problem space and decompress complexity far exceeding the apparent content of the genetic code itself. His broader model unifies these with embryology as a whole via the contention that the cell’s position and history imprint an “interpretation” of its genes upon it. Cells divide, apoptose, migrate, quorum-sense and signal to each other to influence the different ways they express the same genes. In this way, phenotypes of apparently arbitrary but in fact subtly-constrained complexity are generated to be exposed to natural selection.
The middle section is semi-technical and requires a reasonable scientific vocabulary to follow, although the book is written to be accessible to the non-professional. By and large the book is pretty readable, but it makes greater requirements on one’s scientific literacy than anything I can remember reading in the last few years. The effort is, however, worth it, as this is one of the great scientific revolutions of our time.
It may be my history of reading too much Dawkins, whose name is not mentioned in the book, but there is a slight odour of Evolution Wars gunpowder hanging about this work. The author speaks of determinism and epigenetics almost as if they were contrasting halves of a dichotomy. I will stick my neck out a little as a profane member of the software-engineering and Wiki-surfing communities here and suggest that this does not work as an attack on gene-centric models of evolution, if indeed that was intended. It seems to be true that polarity and other information are introduced into the development process by the direction of entry of sperm and by maternal, mechanical influences, but these would never, normally, be absent in nature. Epigenetic modulation begun after this point would still ensue “deterministically” as I understand the term. Even with stochastic processes involved, environmental conditions being uniform, the same genes will yield the same phenotype, on average. This is not to deny temperature-dependent sex determination, for instance, but to point out that even in a probabilistic range of intermediate temperatures, the frequencies of males and females produced correspond consistently, on average, to the species' and individuals' genes.
Ultimately, I think, we are still gene machines. Long is the way and hard, however, that out of base pairs leads up to eyes blinking in the light.
I have for years been toying with the idea of running simulations of embryological concepts to generate lifelike generative art images, so far limited to simulating mollusc shells and a simple “biomorph” program. The problem of generating lifelike forms through genetic algorithms confronts one with the problem of how to allow complexity to emerge and lead to novel Baupläne, but without explicitly coding these in advance or squandering prodigious amounts of computing power. The central message of this book, therefore, flashes brightly, in red neon, in the mind of the programmer: Edelmann describes a class of processes in embryology that operate recursively, and feed back to the genetic code to alter its working epigenetically. This is the key to generating arbitrary levels of complexity.
The scientific effort to understand how it actually works, of course, must have been staggering. Edelmann refers to immunology, his own Nobel field, as well as neurology to show how the same class of processes can operate to generate natural selection in somatic time (during the lifetime of an organism) to search problem space and decompress complexity far exceeding the apparent content of the genetic code itself. His broader model unifies these with embryology as a whole via the contention that the cell’s position and history imprint an “interpretation” of its genes upon it. Cells divide, apoptose, migrate, quorum-sense and signal to each other to influence the different ways they express the same genes. In this way, phenotypes of apparently arbitrary but in fact subtly-constrained complexity are generated to be exposed to natural selection.
The middle section is semi-technical and requires a reasonable scientific vocabulary to follow, although the book is written to be accessible to the non-professional. By and large the book is pretty readable, but it makes greater requirements on one’s scientific literacy than anything I can remember reading in the last few years. The effort is, however, worth it, as this is one of the great scientific revolutions of our time.
It may be my history of reading too much Dawkins, whose name is not mentioned in the book, but there is a slight odour of Evolution Wars gunpowder hanging about this work. The author speaks of determinism and epigenetics almost as if they were contrasting halves of a dichotomy. I will stick my neck out a little as a profane member of the software-engineering and Wiki-surfing communities here and suggest that this does not work as an attack on gene-centric models of evolution, if indeed that was intended. It seems to be true that polarity and other information are introduced into the development process by the direction of entry of sperm and by maternal, mechanical influences, but these would never, normally, be absent in nature. Epigenetic modulation begun after this point would still ensue “deterministically” as I understand the term. Even with stochastic processes involved, environmental conditions being uniform, the same genes will yield the same phenotype, on average. This is not to deny temperature-dependent sex determination, for instance, but to point out that even in a probabilistic range of intermediate temperatures, the frequencies of males and females produced correspond consistently, on average, to the species' and individuals' genes.
Ultimately, I think, we are still gene machines. Long is the way and hard, however, that out of base pairs leads up to eyes blinking in the light.
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