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A Computer Scientist's Guide to Cell Biology
This book is designed specifically as a guide for Computer Scientists needing an introduction to Cell Biology. The text explores three different facets of biological systems, experimental methods, and language and nomenclature. The author discusses what biologists are trying to determine from their experiments, how various experimental procedures are used and how they relate to accepted concepts in computer science, and the vocabulary necessary to read and understand current literature in biology. The book is an invaluable reference tool and an excellent starting point for a more comprehensive examination of cell biology.
114 pages, Paperback
First published January 1, 2007
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Displaying 1 - 13 of 13 reviews
March 30, 2025
Not very well-written or well-organized --- this is clearly a programmer's notes edited some to look like a book. Nevertheless, a useful whirlwind tour of biology. The sections on biology imaging methods was especially useful. I'd like to explore methods further to see if there's low-hanging engineering fruit in making them faster/easier. It was useful to get a sense for what bioinformatics is: mostly string processing, if the author is to be believed (making sense of high-throughput processing data gets a paragraph-long treatment). I also now finally understand why mRNA is a big deal: the two pages on it were very well-written. I also left the book with a general sense that even cutting-edge technologies are compositions of quite basic biological principles/processes. The 1000-page textbooks betray how easy it is to start making research contributions, especially if you have a strong CS/machine learning background.
February 27, 2021
As promised in the prologue, the book reads like a student’s class notes for an introductory course.
Professor Cohen is an expert in Machine Learning who took an interest in the applicability of his subject to bioinformatics and wrote this short book as a summary of his preliminary reading. It is not accessible to someone lacking a good high-school background in biology and organic chemistry; neither will it adequately refresh the details for one for whom the subjects have lain dormant for long. It will, however, prime the mind beautifully for the rest of the journey which it is presumed one to whom this book appeals intends to undertake.
The first section sketches DNA transcription and replication; the long middle section is a very detailed overview of some modern experimental techniques and the final brief section touches on the applications of computer science to bioinformatics.
I was struck particularly by Prof. Cohen’s metaphor for experimental biology: we are rather like giants smashing up great collections of minute and complicated machines and coarsely examining the aggregate detritus for a rough sense of the composition of the machines. (His metaphor is delightful extended: imagine a pile of personal computers laid out on a shag carpet, crushed by steamrollers, blown by the fan of a jet engine; the lighter parts blow further before the carpet snags them. We are left with bands of material graded from heavier to lighter and might conclude that a computer is composed of two or three sorts of metal and plastic organized in concentric shells.)
An illuminating section on reaction rates discusses molecular motion, the relative viscosities of cellular membrane and plasma (as butter to water), distances covered by diffusion (under random walk motion the time to cover a certain distance is as the square of the velocity, not linear with velocity) and contact probabilities, which may explain why diffusion is adequate in bacteria but that in larger cells reactions often take place along two dimensional membrane surfaces, where constrained motion increases the chances for reaction.
Other highlights: the behaviour of ion channels in nerve cells is nicely explained, protein gated channels a little less so (they are more complicated), discussion of energy transfer is too large a topic; the notes are merely a starting point.
In short; exactly as advertised, an excellent re-orientation and a guide to what to study next.
Professor Cohen is an expert in Machine Learning who took an interest in the applicability of his subject to bioinformatics and wrote this short book as a summary of his preliminary reading. It is not accessible to someone lacking a good high-school background in biology and organic chemistry; neither will it adequately refresh the details for one for whom the subjects have lain dormant for long. It will, however, prime the mind beautifully for the rest of the journey which it is presumed one to whom this book appeals intends to undertake.
The first section sketches DNA transcription and replication; the long middle section is a very detailed overview of some modern experimental techniques and the final brief section touches on the applications of computer science to bioinformatics.
I was struck particularly by Prof. Cohen’s metaphor for experimental biology: we are rather like giants smashing up great collections of minute and complicated machines and coarsely examining the aggregate detritus for a rough sense of the composition of the machines. (His metaphor is delightful extended: imagine a pile of personal computers laid out on a shag carpet, crushed by steamrollers, blown by the fan of a jet engine; the lighter parts blow further before the carpet snags them. We are left with bands of material graded from heavier to lighter and might conclude that a computer is composed of two or three sorts of metal and plastic organized in concentric shells.)
An illuminating section on reaction rates discusses molecular motion, the relative viscosities of cellular membrane and plasma (as butter to water), distances covered by diffusion (under random walk motion the time to cover a certain distance is as the square of the velocity, not linear with velocity) and contact probabilities, which may explain why diffusion is adequate in bacteria but that in larger cells reactions often take place along two dimensional membrane surfaces, where constrained motion increases the chances for reaction.
Other highlights: the behaviour of ion channels in nerve cells is nicely explained, protein gated channels a little less so (they are more complicated), discussion of energy transfer is too large a topic; the notes are merely a starting point.
In short; exactly as advertised, an excellent re-orientation and a guide to what to study next.
October 12, 2024
Off to a good start laying solid foundations, then the author rushes through complicated subjects without diving. In the end it’s a mere list of titles and a small paragraph, just to mention the topic. Would have liked more depth and clarity.
May 21, 2008
Discovered at GT Barnes & Nobles 2008-05-19, amidst much swoonage on the part of Your Humble Reader! *How have I only now found this?* It looks simply awesome; the few pages I read all aflurry stomping home down 3rd Street back up the jacket copy's claim: "I wish this little gem was available when I was 'learning the ropes' -- it would have been my first choice of reading material." Indeed, already I'm thinking this would have saved me some unpleasantness while attacking Biological Sequence Analysis (Durbin et al) last year. I'm severely tempted to put the rest of my reading stack aside and tear through this little (94 pp!) guy. Joy!
OK, so I was viciously tricked by this book. There's hardly an axiomatic principle or approach to be found; it's all example-based. Lame!
OK, so I was viciously tricked by this book. There's hardly an axiomatic principle or approach to be found; it's all example-based. Lame!
January 2, 2022
Fast and tight introduction to cellular biology. Reads like a sparknotes test prep pack. Can skim it in 1-2 hours.
Topics:
- Prokaryotes (no organelles) vs Eukaryotes (organelles)
- Viruses and Plasmids (small packages of dna transfer, can replicate or influence hosts)
- Cell replication and sexual reproduction.
- Gene expression/suppression as a massive additional dimensionality we barely understand
- Key organelles and roles
- Proteins and amino acids
- Energy pathways (ATP/ADP)
- Spectrometry and how to study cell components and dna strands by weight/length/charge
- Manipulating cells by rna injection
Memorable:
- Far fewer CS Themes than I would have hoped. This is much more a generic guide than anything anchored to CS concepts
- Cells by-in-large interact internally via diffusion, so locality plays a huge role in scope and purpose; very few "global" variables. Also, because cells are at a molecular-level, diffusion is *way* more of a factor than you might think. Atoms are moving around crazy fast, hundreds of miles an hour, just randomly in a way that keeps balancing out centered. This means there is extreme diffusion at a micro level even as you can expect very little diffusion at a macro level.
- This diffusion fact is what allows different cells and proteins to interact so productively. A cell with only .2% of its surface covered in certain receptors can be considered "highly receptive" to a given protein due to all the diffusion and bouncing around.
- Cells are mostly water, and therefore mostly transparent. (hence: microscopes working). The reason larger organs and bodies aren't transparent is because they are so thick. Each cell refracts light slightly and once you stack a few hundred the aggregate becomes opaque.
- We don't understand so much of how DNA/RNA works. But thats okay, because when we learn the mechanism/interface points, we can manipulate them as a blackbox.
- Good introduction to splicing, deleting, and selecting for certain gene expressions
Topics:
- Prokaryotes (no organelles) vs Eukaryotes (organelles)
- Viruses and Plasmids (small packages of dna transfer, can replicate or influence hosts)
- Cell replication and sexual reproduction.
- Gene expression/suppression as a massive additional dimensionality we barely understand
- Key organelles and roles
- Proteins and amino acids
- Energy pathways (ATP/ADP)
- Spectrometry and how to study cell components and dna strands by weight/length/charge
- Manipulating cells by rna injection
Memorable:
- Far fewer CS Themes than I would have hoped. This is much more a generic guide than anything anchored to CS concepts
- Cells by-in-large interact internally via diffusion, so locality plays a huge role in scope and purpose; very few "global" variables. Also, because cells are at a molecular-level, diffusion is *way* more of a factor than you might think. Atoms are moving around crazy fast, hundreds of miles an hour, just randomly in a way that keeps balancing out centered. This means there is extreme diffusion at a micro level even as you can expect very little diffusion at a macro level.
- This diffusion fact is what allows different cells and proteins to interact so productively. A cell with only .2% of its surface covered in certain receptors can be considered "highly receptive" to a given protein due to all the diffusion and bouncing around.
- Cells are mostly water, and therefore mostly transparent. (hence: microscopes working). The reason larger organs and bodies aren't transparent is because they are so thick. Each cell refracts light slightly and once you stack a few hundred the aggregate becomes opaque.
- We don't understand so much of how DNA/RNA works. But thats okay, because when we learn the mechanism/interface points, we can manipulate them as a blackbox.
- Good introduction to splicing, deleting, and selecting for certain gene expressions
March 5, 2022
Pretty good introduction to cell biology and helpfully explains some things from a computer scientist perspective. However there was not really as much of that as I expected, nor is there much content in general (although he does provide resources for learning more)
I had three main takeaways, one of which was expected and two of which were not.
The first was the complexity of the cell, which was not a new concept to me but which I always marvel to learn more details.
The second was that a lot of said complexity is actually pretty chaotic; sometimes we are given a metaphorical image of a cell as a highly organized factory, but it seems that a large amount of cellular activity basically involves the whole soup of materials within floating around until matching things inevitably bump into each other.
The third was that our ability to observe cells is a lot more limited than the impression you get from most educational materials. Although technology has improved, we can’t really watch live cells in close detail and a lot of our knowledge has come from long, hard, and really quite clever lab work.
So in sum, cells are super complex, but they’re also pretty chaotic and we also don’t actually know that much about them.
I had three main takeaways, one of which was expected and two of which were not.
The first was the complexity of the cell, which was not a new concept to me but which I always marvel to learn more details.
The second was that a lot of said complexity is actually pretty chaotic; sometimes we are given a metaphorical image of a cell as a highly organized factory, but it seems that a large amount of cellular activity basically involves the whole soup of materials within floating around until matching things inevitably bump into each other.
The third was that our ability to observe cells is a lot more limited than the impression you get from most educational materials. Although technology has improved, we can’t really watch live cells in close detail and a lot of our knowledge has come from long, hard, and really quite clever lab work.
So in sum, cells are super complex, but they’re also pretty chaotic and we also don’t actually know that much about them.
April 28, 2021
Overall, this book is a good introduction to cell biology for computer scientists or people even remotely familiar with CS concepts.
Some analogies were pretty neat and thus helped obtaining an "accurate feeling" for some topic in no time at all, at other points there were no analogies but only a listing of examples from e.g. the methodology of biologists.
Those were rather boring to me.
My favorite chapter was "Modularity & Locality in Biology" (pp. 33-35).
This chapter contains a very interesting theoretical discussion of diffusion speed in different viscosities and in 2D vs. 3D space.
It also discusses the random walk property and the related efficiency of protein receptors on the cell surface.
Not only were those topics of high interest to me, but also their technical discussion was very well done.
I made about 80 Anki cards for this book; contact me if you're interested and I can send them to you.
Some analogies were pretty neat and thus helped obtaining an "accurate feeling" for some topic in no time at all, at other points there were no analogies but only a listing of examples from e.g. the methodology of biologists.
Those were rather boring to me.
My favorite chapter was "Modularity & Locality in Biology" (pp. 33-35).
This chapter contains a very interesting theoretical discussion of diffusion speed in different viscosities and in 2D vs. 3D space.
It also discusses the random walk property and the related efficiency of protein receptors on the cell surface.
Not only were those topics of high interest to me, but also their technical discussion was very well done.
I made about 80 Anki cards for this book; contact me if you're interested and I can send them to you.
March 4, 2021
In a way, I'm the opposite of the target audience for this: Cohen is a computer scientist who wanted to discuss the basics of biology, whereas I primarily studied biology and have about a hobbyist's understanding of computer science. Still, there's a lot of intersection there, and I found this a very interesting walkthrough of things I already knew approached from a different perspective.
I docked off a star because I feel as though this book went very quickly for an introductory text; if I did not have my background in biology, I suspect there are sections that would have lost me. There are als0 – by admission – few oversimplifications, though nothing more egregious than something you would find in a more mainstream introductory biology textbook. If the title interests you, I certainly recommend giving this a look!
I docked off a star because I feel as though this book went very quickly for an introductory text; if I did not have my background in biology, I suspect there are sections that would have lost me. There are als0 – by admission – few oversimplifications, though nothing more egregious than something you would find in a more mainstream introductory biology textbook. If the title interests you, I certainly recommend giving this a look!
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July 6, 2020It makes no sense to put star rating on books on specific topics.
For me this book was a nice introduction to the biological machinery as well as to the machinery used by biologist.
It requires patience to read the book and at times it is a bit boring by trying to be complete.
But in comparison to other books on molecular biology the author manages to find a compromise between brevity and comprehensiveness. There are many good and at times funny ideas on how a computer scientist can understand biological processes; and it will be good to further think in this direction.
For me this book was a nice introduction to the biological machinery as well as to the machinery used by biologist.
It requires patience to read the book and at times it is a bit boring by trying to be complete.
But in comparison to other books on molecular biology the author manages to find a compromise between brevity and comprehensiveness. There are many good and at times funny ideas on how a computer scientist can understand biological processes; and it will be good to further think in this direction.
July 26, 2017
This very short book provides a terse description of cell biology which can be used as a limited reference for a computer scientist needing to work with a biologist in this area. The section on bioinformatics was 7 pages long and included examples of two algorithms.
January 31, 2023
The name's somewhat misleading. There are only a few Computer Science analogies used in the book.
I found the knowledge in the book to be a bit condensed; too much in too few pages.
Overall, if you're looking for an introduction to cell biology, this is a good one.
I found the knowledge in the book to be a bit condensed; too much in too few pages.
Overall, if you're looking for an introduction to cell biology, this is a good one.
February 17, 2022
Decent, though at times it's chock-full of jargon and that's making it a bit hard to follow if you're not already aware of it.
July 21, 2025
Clear explanation but not that much computer science, and not as much cross-book narrative as I was expecting.
Displaying 1 - 13 of 13 reviews