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An Introduction to Systems Biology: Design Principles of Biological Circuits
Thorough and accessible, this book presents the design principles of biological systems, and highlights the recurring circuit elements that make up biological networks. It provides a simple mathematical framework which can be used to understand and even design biological circuits. The textavoids specialist terms, focusing instead on several well-studied biological systems that concisely demonstrate key principles. An Introduction to Systems Design Principles of Biological Circuits builds a solid foundation for the intuitive understanding of general principles. It encourages the reader to ask why a system is designed in a particular way and then proceeds to answer with simplified models.
320 pages, Paperback
First published July 7, 2006
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Displaying 1 - 13 of 13 reviews
August 22, 2022
The book is beautiful~
As a graduate student in biology, I always get lost in millions of details and really have the feeling that we may never make sense of biology. This book has an optimistic sense that we are able to find rules behind biology or truly understand biology if we are trying to find them.
Still, biology is so complicated and I don't think the type of research described in the book will solve all the problems, but it does offers some very good points of view that I would like to keep in mind as doing my daily benchwork.
As a graduate student in biology, I always get lost in millions of details and really have the feeling that we may never make sense of biology. This book has an optimistic sense that we are able to find rules behind biology or truly understand biology if we are trying to find them.
Still, biology is so complicated and I don't think the type of research described in the book will solve all the problems, but it does offers some very good points of view that I would like to keep in mind as doing my daily benchwork.
September 8, 2023
This book was a joy to read. It was concise, easy to follow, yet technically substantive.
March 26, 2025
Es lo que llevo leyendo en el metro todo el mes así que lo pongo 😃
Si el profe no fuera tan terrible no hubiera tenido que llegar al extremo de leerme este libro
Lectura por pura necesidad
No le pongo una estrella porque realmente me ha ayudado
Que largo era x favor
Me he acabado el capitulo que me faltaba para procrastinar un poco? pues si, estoy escribiendo la review con el mismo fin? también
Si el profe no fuera tan terrible no hubiera tenido que llegar al extremo de leerme este libro
Lectura por pura necesidad
No le pongo una estrella porque realmente me ha ayudado
Que largo era x favor
Me he acabado el capitulo que me faltaba para procrastinar un poco? pues si, estoy escribiendo la review con el mismo fin? también
April 8, 2018
"An Introduction to Systems Biology: Design Principles of Biological Circuits" provides a great deal of insight into how the connections of gene and protein interaction networks provide the necessary robustness and control to achieve cellular function in the face of chemical noise.
This is a book that can be read at two levels: either a qualitative understanding of the behavior or at a more detailed mathematical level. I personally found the qualitative insights very interesting, even quite exciting. However, I sometimes found the prose and mathematical exposition not as clear as "Mathematical Modeling in Systems Biology: An Introduction" (https://www.goodreads.com/book/show/1...). For this reason, I have decided to give a detailed summary of some of the qualitative findings to promote a broader appreciation for these ideas outside of their mathematical context. Fortunately, these ideas were extremely well organized, with roughly a chapter for every bullet point below.
How does a cell control the conditions in which a given gene is allowed to express a protein? The work behind this book finds out by first assembling the network of activations and repressions at work in a cell, abstracting away biochemical particulars. Then, it looks to see what motifs (structural patterns) occur much more (and less) frequently in those networks as compared to networks assembled by chance. Each motif is then examined for what function it then provides.
Let us now talk about some common motifs found with this approach. Suppose a->b stands for activation and then a-|b for repression.
* Self-repression (a-|a) is a frequently found motif. This allows for "a" to be activated very quickly, with the confidence that it will regulate its own level.
* The consistent feed-forward loop is a very common motif (a->b, a->c, b->c). This acts to filter out transient changes in "a". If both "a" and "b" are required to activate "c", then "c" will not be expressed if "a" isn't activated long enough to also activate "b". Similarly, if either "a" or "b" is sufficient to activate "c", then temporary gaps in the production of "a" will not affect the production of "c", due to the presence of "b".
* The inconsistent feed-forward loop is also common (a->b, a->c, b-|c). This allows for an initial pulse of "c" upon "a", but that is then brought under control by "b".
* The oscillating triple loop called the "repressilator" (a-|b, b-|c, c-|a) is an anti-motif, almost never found in biology, due to its inherent instability. Instead, biology implements oscillating behaviors with a more stable two-node circuit (x->x, x->y, y-|x, where the y-|x interaction is very fast in the scale of the circuit).
* Motifs for the coordination of multiple stages can be discerned in larger graph motifs. Varying responses to a common input can create a first-in, last-out stack of activation. Different sensitivities to the "a" and "b" of coherent feed-forward loops can create first-in, first-out queues as "a" falls to smaller concentrations with respect to "b".
* Development frequently needs to trigger an irreversible change. As a result, downstream effects of a development signal "a" often have mutual influence (a->b, a->c, b->c, c->b) or inhibition (a->b, x->c, b-|c, c-|b) to hold the effect in place after the development signal.
The second half of the book turns to engineering considerations of robustness and cost optimization. How does the cell send correct signals, transcribe the right proteins, and express the right genes for as little management overhead as possible?
* In development, different parts of the organism are signaled by concentrations of morphogens. Independent diffusion and the corresponding exponential decay would not lead to gradients of sufficient lengths in some applications, which is solved by the morphogen having a quicker decay in interaction with itself, leading to a power-law decay rate.
* In a different development system, in which a morphogen would be bound by an inhibitor into a complex, but that inhibitor would be destroyed by a protease, could also show a power-law decay if only the inhibited complex could diffuse at a significant rate, and the protease could cleave the inhibitor only in that complex, showing the power of bundling where self-interaction was not appropriate.
* Kinetic proofreading is a biological error checking phenomena where a partially accepted item (such as allowed into a membrane) is bound into a complex, and then assessed again. If the item fails the second assessment, and is ejected, the complex is not allowed to be checked again, creating a level of error checking effectively squaring the quality of the assessment.
* Cells will often evolve appropriate regulation and control motifs for the environmental conditions they are subject to. For example, for a gene to be regulated, it requires both a certain discrepancy in fitness value for whether or not the gene is expressed and a variability in the environment. The smaller the discrepancy, or the smaller the variability, the more likely the gene is simply expressed or not, without the fitness overhead of gene regulation. Similarly, for a feed-forward loop to evolve, there has to be a certain likelihood of transient signals in the environment that cause a regulated gene to be expressed causing a sufficient impact in fitness for the cost of the feed-forward loop to be expressed. In this manner, the structure of gene regulation often gives an indication about the environment that regulatory structure is optimized for.
* Regulated genes that need to be expressed most of the time will usually require binding to be expressed, while regulated genes that need to be repressed most of the time will require binding to be repressed. Certainly, given the above discussion about optimization this caught me by surprise: why bear the cost of producing a regulatory protein most of the time? One answer is that an unbound state can be activated accidentally by chemically similar proteins. By keeping the state bound in the state it is usually needed, it is less likely to be deactivated (or activated, respectively) in error.
Overall, as you can see, this book delivers fantastic intuition and understanding about the regulatory dynamics of microbiological activity. I think this book is a wonderful pairing with "An Introduction to Systems Biology: Design Principles of Biological Circuits", but would still reveal substantial insights by itself.
This is a book that can be read at two levels: either a qualitative understanding of the behavior or at a more detailed mathematical level. I personally found the qualitative insights very interesting, even quite exciting. However, I sometimes found the prose and mathematical exposition not as clear as "Mathematical Modeling in Systems Biology: An Introduction" (https://www.goodreads.com/book/show/1...). For this reason, I have decided to give a detailed summary of some of the qualitative findings to promote a broader appreciation for these ideas outside of their mathematical context. Fortunately, these ideas were extremely well organized, with roughly a chapter for every bullet point below.
How does a cell control the conditions in which a given gene is allowed to express a protein? The work behind this book finds out by first assembling the network of activations and repressions at work in a cell, abstracting away biochemical particulars. Then, it looks to see what motifs (structural patterns) occur much more (and less) frequently in those networks as compared to networks assembled by chance. Each motif is then examined for what function it then provides.
Let us now talk about some common motifs found with this approach. Suppose a->b stands for activation and then a-|b for repression.
* Self-repression (a-|a) is a frequently found motif. This allows for "a" to be activated very quickly, with the confidence that it will regulate its own level.
* The consistent feed-forward loop is a very common motif (a->b, a->c, b->c). This acts to filter out transient changes in "a". If both "a" and "b" are required to activate "c", then "c" will not be expressed if "a" isn't activated long enough to also activate "b". Similarly, if either "a" or "b" is sufficient to activate "c", then temporary gaps in the production of "a" will not affect the production of "c", due to the presence of "b".
* The inconsistent feed-forward loop is also common (a->b, a->c, b-|c). This allows for an initial pulse of "c" upon "a", but that is then brought under control by "b".
* The oscillating triple loop called the "repressilator" (a-|b, b-|c, c-|a) is an anti-motif, almost never found in biology, due to its inherent instability. Instead, biology implements oscillating behaviors with a more stable two-node circuit (x->x, x->y, y-|x, where the y-|x interaction is very fast in the scale of the circuit).
* Motifs for the coordination of multiple stages can be discerned in larger graph motifs. Varying responses to a common input can create a first-in, last-out stack of activation. Different sensitivities to the "a" and "b" of coherent feed-forward loops can create first-in, first-out queues as "a" falls to smaller concentrations with respect to "b".
* Development frequently needs to trigger an irreversible change. As a result, downstream effects of a development signal "a" often have mutual influence (a->b, a->c, b->c, c->b) or inhibition (a->b, x->c, b-|c, c-|b) to hold the effect in place after the development signal.
The second half of the book turns to engineering considerations of robustness and cost optimization. How does the cell send correct signals, transcribe the right proteins, and express the right genes for as little management overhead as possible?
* In development, different parts of the organism are signaled by concentrations of morphogens. Independent diffusion and the corresponding exponential decay would not lead to gradients of sufficient lengths in some applications, which is solved by the morphogen having a quicker decay in interaction with itself, leading to a power-law decay rate.
* In a different development system, in which a morphogen would be bound by an inhibitor into a complex, but that inhibitor would be destroyed by a protease, could also show a power-law decay if only the inhibited complex could diffuse at a significant rate, and the protease could cleave the inhibitor only in that complex, showing the power of bundling where self-interaction was not appropriate.
* Kinetic proofreading is a biological error checking phenomena where a partially accepted item (such as allowed into a membrane) is bound into a complex, and then assessed again. If the item fails the second assessment, and is ejected, the complex is not allowed to be checked again, creating a level of error checking effectively squaring the quality of the assessment.
* Cells will often evolve appropriate regulation and control motifs for the environmental conditions they are subject to. For example, for a gene to be regulated, it requires both a certain discrepancy in fitness value for whether or not the gene is expressed and a variability in the environment. The smaller the discrepancy, or the smaller the variability, the more likely the gene is simply expressed or not, without the fitness overhead of gene regulation. Similarly, for a feed-forward loop to evolve, there has to be a certain likelihood of transient signals in the environment that cause a regulated gene to be expressed causing a sufficient impact in fitness for the cost of the feed-forward loop to be expressed. In this manner, the structure of gene regulation often gives an indication about the environment that regulatory structure is optimized for.
* Regulated genes that need to be expressed most of the time will usually require binding to be expressed, while regulated genes that need to be repressed most of the time will require binding to be repressed. Certainly, given the above discussion about optimization this caught me by surprise: why bear the cost of producing a regulatory protein most of the time? One answer is that an unbound state can be activated accidentally by chemically similar proteins. By keeping the state bound in the state it is usually needed, it is less likely to be deactivated (or activated, respectively) in error.
Overall, as you can see, this book delivers fantastic intuition and understanding about the regulatory dynamics of microbiological activity. I think this book is a wonderful pairing with "An Introduction to Systems Biology: Design Principles of Biological Circuits", but would still reveal substantial insights by itself.
August 8, 2015
This book represents one of the first comprehensive treatments of systems biology in the form of a textbook intended for teaching at the college level. Brings basic quantitative chemistry to treat cellular biochemical synthesis and transcriptional regulation. Introduces some cell network concepts and offers some insight into selective principles and evolution.
August 7, 2022
I love it,
I used to study control systems, but this book really gave me a different aspect to look onto how control theories are deployed,
let me explain, in engineering we design a control system to keep the system stable,
in system biology the study biology and try to derive the dynamics (Control System) as if you are an inspector investigating an actual case,
I didn't like biology, but this book changed my mind :)
Thank you Uri Alon,
I used to study control systems, but this book really gave me a different aspect to look onto how control theories are deployed,
let me explain, in engineering we design a control system to keep the system stable,
in system biology the study biology and try to derive the dynamics (Control System) as if you are an inspector investigating an actual case,
I didn't like biology, but this book changed my mind :)
Thank you Uri Alon,
December 28, 2021
Amazing book, describing concepts that connect biology, mathematics, physics, chemistry, and some programming. The author is a reference in this field and writes in a very concise and straightforward way.
May 20, 2022
Fantastic book, really changed my understanding of how cells work, and how understandable they can be.
September 13, 2021
Finally got around to reading this one. Glad I waited for this new version. The first part gives an accessible introduction to genetic circuits from a bio-engineering perspective. The second part discusses how robust circuits can exist in the noisy environment of the cell. The final part relates to evolution and optimality of biosystems.
Biology for engineers!
Biology for engineers!
September 10, 2019
Great book
It is a very good book. Very well written, everything is clearly illustrated and presented. It makes a tough subject easy to follow
It is a very good book. Very well written, everything is clearly illustrated and presented. It makes a tough subject easy to follow
January 9, 2021
Very good book containing simply understandable text.
Displaying 1 - 13 of 13 reviews