What do you think?
Rate this book
Systems Medicine
Why do we get certain diseases, whereas other diseases do not exist? In this book, Alon, one of the founders of systems biology, builds a foundation for systems medicine. Starting from basic laws, the book derives why physiological circuits are built the way they are. The circuits have fragilities that explain specific diseases and offer new strategies to treat them. By the end, the reader will be able to use simple and powerful mathematical models to describe physiological circuits. The book explores, in three parts, hormone circuits, immune circuits, and aging and age-related disease. It culminates in a periodic table of diseases. Alon writes in a style accessible to a broad range of readers - undergraduates, graduates, or researchers from computational or biological backgrounds. The level of math is friendly and the math can even be bypassed altogether. For instructors and readers who want to go deeper, the book includes dozens of exercises that have been rigorously tested in the classroom
270 pages, Paperback
Published December 15, 2023
18 people are currently reading
244 people want to read
Ratings & Reviews
Friends & Following
Create a free account to discover what your friends think of this book!
Community Reviews
Displaying 1 - 3 of 3 reviews
March 1, 2026
A Masterpiece of Hard-won Simplicity
"Systems Medicine" (SM) is a real delight: completely packed with insight, slowly unpacked with clarity, to reveal great inner simplicies in how we're all put together, how it can go wrong, and what we can do about it. I don't know enough about medicine to say if it is as insightful as it seems, but at least in terms of scientific presentation, it is as Philip reviewed it (https://www.goodreads.com/review/show...), a masterpiece.
The word "systems" can mean a lot of things depending on tradition. In SM, it means modeling physiological processes as connected loops of influence driving growth and feedback processes. Here, we're concerned with the kind of system dynamics that can be modeled with relationships between rates of change. For this purpose, differential equations are used, but not without graphs and diagrams that explain their function. Indeed, every mode of explanation is deployed to develop the book slowly and simply from first principles.
As with the author's previous "Systems Biology", what SM does is to show that a handful of dynamic patterns (motifs) show up repeatedly in different biological contexts. Then, different systems have similar dynamics, as though they were "circuits" with common layouts of feedback components. The real achievement of SM, though, is to show that the differences between the physiological dynamics that manifest, and the pathologies they are subject to, emerge from three basic axioms of biological function and considerations of scale.
Summary of the Resulting Dynamics
I don't think you can have spoilers in a book like this, but in case you disagree, this might be your warning to skip this section.
SM starts with a simple controlling feedback loop, between the energy carrier glucose and the level-regulating hormone insulin, and raises a mystery: how is it that insulin resistance, which should alter the ability to keep glucose at an appropriate level, generally doesn't. The answer is that cells produce other cells (axiom 1), and that we have more of the beta cells that produce insulin, in response to changes in glucose level. In control-theory terms, we have a layer of integral control in addition to differential feedback. Said more simply, over a slower scale, the amount of control applied is level-dependent.
However, biological processes saturate (axiom 2), so there are limits to how many beta cells can be produced, so diabetes can emerge. Too much glucose causes beta cells to die back, as over much of history the problem with insulin regulation was not an extreme excess of glucose, but of beta cells mutating and allowing inappropriate glucose levels. The control systems of the body are not perfectly reproduced, but have to respond to cells mutating (axiom 3). A cluster of beta cells with a mutation causing them to detect the level is too high, and therefore growing excessively, would die back to the same signal.
Most biological systems don't have this auto-toxicity, but instead rely on the immune system to respond to inappropriately high concentrations of hormones and other circulated chemicals. From this emerges a second mystery: why is it much more common to have auto-immune disorders for some organs, excess growth disorders in others, and cancer in yet others?
The answer is scale: if an organ is small enough, it's unlikely enough to develop the mutations of mis-control that no toxicity or immune response is generally appropriate. If an organ is large enough, it is likely that it will develop mutations, and an auto-immune response is appropriate, but then auto-immune disorders emerge. If an organ is very large, mutations from typical cell division will be so frequent that auto-immune disorders will be near certain if the immune system attacks them. For those organs, they're produced by maintaining stem cells, which have mechanisms to produce more faithful copies at the risk of cancer and other degeneracies.
As a result of all this, drugs which can clear away senescent cells that give the immune and maintenance systems too much to do can be a major help across a broad-range of pathologies, as they can clear away the mutant cells requiring inflammation and auto-immune responses.
That, in a nutshell, is what is happening in Systems Medicine, but it is totally worth reading to see how these arguments are developed, and all the subtleties in this.
Conclusion
SM takes a different approach in thinking about systems. Instead of trying to explain the body as a single comprehensive system or look for a single universal dynamic principle, it looks to make comparisons between subsystems and see what common causes can be learned. In addition to molecular biology and medicine, it makes one wonder what further motif-driven explanations might be available in ecology, economics, and other yet larger dynamical systems. Seeing these worked examples offers guidance in how to think about those possibilities.
Overall, I'm amazed on how SM's powerfully simple lens unpacks so much medical complication. It's great as an explanation of what's going on in oneself, but it's also great as a paradigmatic case of the power of looking for families of "simple circuits".
"Systems Medicine" (SM) is a real delight: completely packed with insight, slowly unpacked with clarity, to reveal great inner simplicies in how we're all put together, how it can go wrong, and what we can do about it. I don't know enough about medicine to say if it is as insightful as it seems, but at least in terms of scientific presentation, it is as Philip reviewed it (https://www.goodreads.com/review/show...), a masterpiece.
The word "systems" can mean a lot of things depending on tradition. In SM, it means modeling physiological processes as connected loops of influence driving growth and feedback processes. Here, we're concerned with the kind of system dynamics that can be modeled with relationships between rates of change. For this purpose, differential equations are used, but not without graphs and diagrams that explain their function. Indeed, every mode of explanation is deployed to develop the book slowly and simply from first principles.
As with the author's previous "Systems Biology", what SM does is to show that a handful of dynamic patterns (motifs) show up repeatedly in different biological contexts. Then, different systems have similar dynamics, as though they were "circuits" with common layouts of feedback components. The real achievement of SM, though, is to show that the differences between the physiological dynamics that manifest, and the pathologies they are subject to, emerge from three basic axioms of biological function and considerations of scale.
Summary of the Resulting Dynamics
I don't think you can have spoilers in a book like this, but in case you disagree, this might be your warning to skip this section.
SM starts with a simple controlling feedback loop, between the energy carrier glucose and the level-regulating hormone insulin, and raises a mystery: how is it that insulin resistance, which should alter the ability to keep glucose at an appropriate level, generally doesn't. The answer is that cells produce other cells (axiom 1), and that we have more of the beta cells that produce insulin, in response to changes in glucose level. In control-theory terms, we have a layer of integral control in addition to differential feedback. Said more simply, over a slower scale, the amount of control applied is level-dependent.
However, biological processes saturate (axiom 2), so there are limits to how many beta cells can be produced, so diabetes can emerge. Too much glucose causes beta cells to die back, as over much of history the problem with insulin regulation was not an extreme excess of glucose, but of beta cells mutating and allowing inappropriate glucose levels. The control systems of the body are not perfectly reproduced, but have to respond to cells mutating (axiom 3). A cluster of beta cells with a mutation causing them to detect the level is too high, and therefore growing excessively, would die back to the same signal.
Most biological systems don't have this auto-toxicity, but instead rely on the immune system to respond to inappropriately high concentrations of hormones and other circulated chemicals. From this emerges a second mystery: why is it much more common to have auto-immune disorders for some organs, excess growth disorders in others, and cancer in yet others?
The answer is scale: if an organ is small enough, it's unlikely enough to develop the mutations of mis-control that no toxicity or immune response is generally appropriate. If an organ is large enough, it is likely that it will develop mutations, and an auto-immune response is appropriate, but then auto-immune disorders emerge. If an organ is very large, mutations from typical cell division will be so frequent that auto-immune disorders will be near certain if the immune system attacks them. For those organs, they're produced by maintaining stem cells, which have mechanisms to produce more faithful copies at the risk of cancer and other degeneracies.
As a result of all this, drugs which can clear away senescent cells that give the immune and maintenance systems too much to do can be a major help across a broad-range of pathologies, as they can clear away the mutant cells requiring inflammation and auto-immune responses.
That, in a nutshell, is what is happening in Systems Medicine, but it is totally worth reading to see how these arguments are developed, and all the subtleties in this.
Conclusion
SM takes a different approach in thinking about systems. Instead of trying to explain the body as a single comprehensive system or look for a single universal dynamic principle, it looks to make comparisons between subsystems and see what common causes can be learned. In addition to molecular biology and medicine, it makes one wonder what further motif-driven explanations might be available in ecology, economics, and other yet larger dynamical systems. Seeing these worked examples offers guidance in how to think about those possibilities.
Overall, I'm amazed on how SM's powerfully simple lens unpacks so much medical complication. It's great as an explanation of what's going on in oneself, but it's also great as a paradigmatic case of the power of looking for families of "simple circuits".
March 30, 2024
This was so amazing. Strong recommendation. I didn't know any physiology going in.
July 1, 2025
An extraordinary bulletin from the frontier of medicine.
Probably all of us have heard confusing news about the science of aging, metabolic disease, and degenerative disease. Now at last Uri Alon puts the pieces together with simple mathematical models that quantitatively explain a wealth of real phenomena that I, for one, had never grasped could be related. The power of applying simple and well-understood phenomena from the nonliving world is on full display here. Alon also communicates it all beautifully and as simply as possible. I have never read a book remotely like this short masterpiece.
Probably all of us have heard confusing news about the science of aging, metabolic disease, and degenerative disease. Now at last Uri Alon puts the pieces together with simple mathematical models that quantitatively explain a wealth of real phenomena that I, for one, had never grasped could be related. The power of applying simple and well-understood phenomena from the nonliving world is on full display here. Alon also communicates it all beautifully and as simply as possible. I have never read a book remotely like this short masterpiece.
Displaying 1 - 3 of 3 reviews