Here are 68 books that Systems Biology fans have personally recommended if you like
Systems Biology.
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Ever since I studied control theory as an undergrad chemical engineer, mathematical models of complex phenomena have fascinated me. Mathematical models have the uncanny ability to uncover key aspects of biological systems, whose complexity poses a great challenge for understanding. As a researcher in systems biology for over 15 years, I have enjoyed reading several books that explore the exciting interface between computation and biology, trying to capture the burgeoning literature on this rapidly advancing field. I hope you enjoy these books and will join these authors on an exciting journey into the cartography of molecular networks underlying every living cell, using a variety of mathematical models!
One of the earliest books on this subject, Uri Alon presents an engaging account of biological networks. Focussing on transcriptional networks, and their motifs, the book illustrates the nexus between network structures and functions. The second edition of the book launched a few years ago and has some updated content and new material on interesting functionalities such as fold change detection. Uri Alon is a very accomplished scientist, mentor, and a leader in the field of biological networks/systems biology.
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 Biology: 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.
Ever since I studied control theory as an undergrad chemical engineer, mathematical models of complex phenomena have fascinated me. Mathematical models have the uncanny ability to uncover key aspects of biological systems, whose complexity poses a great challenge for understanding. As a researcher in systems biology for over 15 years, I have enjoyed reading several books that explore the exciting interface between computation and biology, trying to capture the burgeoning literature on this rapidly advancing field. I hope you enjoy these books and will join these authors on an exciting journey into the cartography of molecular networks underlying every living cell, using a variety of mathematical models!
One of the best broad-based textbooks covering a wide gamut of topics, and in-depth coverage of dynamic models. I like this book for a particularly engaging introduction to the practice of mathematical modelling, excellent catchy illustrations, and nice exercise problems/reading material at the end of each chapter. The book chooses to organise the methods by the type of network (gene systems, protein systems, metabolic systems, and so on). Voit is a very accomplished researcher in the area of dynamic systems modelling and is particularly known for his contributions to Biochemical Systems Theory.
A First Course in Systems Biology is a textbook designed for advanced undergraduate and graduate students. Its main focus is the development of computational models and their applications to diverse biological systems.
Because the biological sciences have become so complex that no individual can acquire complete knowledge in any given area of specialization, the education of future systems biologists must instead develop a student's ability to retrieve, reformat, merge, and interpret complex biological information.
This book provides the reader with the background and mastery of methods to execute standard systems biology tasks, understand the modern literature, and launch into specialized…
Ever since I studied control theory as an undergrad chemical engineer, mathematical models of complex phenomena have fascinated me. Mathematical models have the uncanny ability to uncover key aspects of biological systems, whose complexity poses a great challenge for understanding. As a researcher in systems biology for over 15 years, I have enjoyed reading several books that explore the exciting interface between computation and biology, trying to capture the burgeoning literature on this rapidly advancing field. I hope you enjoy these books and will join these authors on an exciting journey into the cartography of molecular networks underlying every living cell, using a variety of mathematical models!
Perhaps the most authoritative text on Networks, especially for the mathematically inclined. Spanning over 700 pages, this book covers the basics of different types of real-world networks, followed by a detailed run-down of network theory fundamentals, a variety of network models, and finally, their applications. Newman is a highly regarded computer scientist and has contributed several seminal papers to the field of networks.
The scientific study of networks, including computer networks, social networks, and biological networks, has received an enormous amount of interest in the last few years. The rise of the Internet and the wide availability of inexpensive computers have made it possible to gather and analyze network data on a large scale, and the development of a variety of new theoretical tools has allowed us to extract new knowledge from many different kinds of networks. The study of networks is broadly interdisciplinary and important developments have occurred in many fields, including mathematics, physics, computer and information sciences, biology, and the social…
Everybody knows that all animals—bats, bears, sharks, ponies, and people—start out as a single cell: the fertilized egg. But how does something no bigger than the period at the end of this sentence give rise to the remarkable complexity of each of these creatures?
Ever since I studied control theory as an undergrad chemical engineer, mathematical models of complex phenomena have fascinated me. Mathematical models have the uncanny ability to uncover key aspects of biological systems, whose complexity poses a great challenge for understanding. As a researcher in systems biology for over 15 years, I have enjoyed reading several books that explore the exciting interface between computation and biology, trying to capture the burgeoning literature on this rapidly advancing field. I hope you enjoy these books and will join these authors on an exciting journey into the cartography of molecular networks underlying every living cell, using a variety of mathematical models!
An outstanding and authoritative reference on metabolic networks. Discusses all the mathematical foundations of constraint-based methods, followed by detailed discussions of various constraint-based modelling methods. Despite the age, this remains a thorough and excellent account of constraint-based modelling. A revised second edition of this book presents a more detailed overview of metabolic networks in different organisms and is up-to-date with several advances in the field. Palsson is one of the leaders in the field of systems biology and metabolic networks, and his lab is home to many of the most important constraint-based modelling methods, such as flux balance analysis.
Recent technological advances have enabled comprehensive determination of the molecular composition of living cells. The chemical interactions between many of these molecules are known, giving rise to genome-scale reconstructed biochemical reaction networks underlying cellular functions. Mathematical descriptions of the totality of these chemical interactions lead to genome-scale models that allow the computation of physiological functions. Reflecting these recent developments, this textbook explains how such quantitative and computable genotype-phenotype relationships are built using a genome-wide basis of information about the gene portfolio of a target organism. It describes how biological knowledge is assembled to reconstruct biochemical reaction networks, the formulation of…
Mathematics and chemistry were my strongest subjects at school, and I started programming computers when I was 16, but life seemed most important. Hence I studied biochemistry in university but moved into molecular biology with programming to assist the data analysis. My track record in successfully predicting new biology through computing led to a pharmaceutical company recruiting me to do bioinformatics for them. However, not content with studying genes and proteins, I pushed for bioinformatics to move up into metabolism, anatomy, and physiology. That’s when I discovered systems biology. My international reputation lies at this interface and includes discoveries in microbial physiology, botany, agriculture, animal biology, and antenatal diseases.
Of the various books available on this subject, I very much prefer this one because it makes it far easier to do systems biology.
First, it shows you how to view biological regulatory processes as a set of interacting components and their effect on each other. This alone can give clues to the behaviour of the system under different circumstances. However, it then goes on to show how these processes can be defined mathematically, which then enables us to get a quantitative view of what is going on.
When the predicted and observed numbers don’t match, we know that there is a gap in our knowledge and, hence, the place to discover new biology. Using this approach, I have.
... superb, beautifully written and organized work that takes an engineering approach to systems biology. Alon provides nicely written appendices to explain the basic mathematical and biological concepts clearly and succinctly without interfering with the main text. He starts with a mathematical description of transcriptional activation and then describes some basic transcription-network motifs (patterns) that can be combined to form larger networks. - Nature
[This text deserves] serious attention from any quantitative scientist who hopes to learn about modern biology ... It assumes no prior knowledge of or even interest in biology ... One final…
Mathematics and chemistry were my strongest subjects at school, and I started programming computers when I was 16, but life seemed most important. Hence I studied biochemistry in university but moved into molecular biology with programming to assist the data analysis. My track record in successfully predicting new biology through computing led to a pharmaceutical company recruiting me to do bioinformatics for them. However, not content with studying genes and proteins, I pushed for bioinformatics to move up into metabolism, anatomy, and physiology. That’s when I discovered systems biology. My international reputation lies at this interface and includes discoveries in microbial physiology, botany, agriculture, animal biology, and antenatal diseases.
This book turns on its head what I was taught about what controls metabolite flow through a pathway. It covers highly remarkable discoveries concerning which steps control changes in metabolite levels: those at the end rather than the start of pathways. This is amazing because it explains why decades of effort by bioengineers to overproduce particular metabolites was unsuccessful.
In response to a request from such a project, I explained how to block the inhibitory regulation by the early pathway step but added that, according to metabolic control theory, this would leave the end-product levels unchanged. I was correct on both counts! When my group later provided results from using a systems biology approach, they achieved their production target levels.
Nature writer Sharman Apt Russell tells stories of her experiences tracking wildlife—mostly mammals, from mountain lions to pocket mice—near her home in New Mexico, with lessons that hold true across North America. She guides readers through the basics of identifying tracks and signs, revealing a landscape filled with the marks…
Mathematics and chemistry were my strongest subjects at school, and I started programming computers when I was 16, but life seemed most important. Hence I studied biochemistry in university but moved into molecular biology with programming to assist the data analysis. My track record in successfully predicting new biology through computing led to a pharmaceutical company recruiting me to do bioinformatics for them. However, not content with studying genes and proteins, I pushed for bioinformatics to move up into metabolism, anatomy, and physiology. That’s when I discovered systems biology. My international reputation lies at this interface and includes discoveries in microbial physiology, botany, agriculture, animal biology, and antenatal diseases.
This book vindicates my long-held view that biological objects do not act in isolation but interact with other things to make a living whole. It confirms my opinion that genes are not the master controllers of living things.
Furthermore, it showed me that systems occur at different physical scales (molecules, cells, organs, organisms, populations), that the systems at these scales influence each other, and that no scale is dominant. To understand biological/medical phenomena, including human consciousness, one must look at the (multi-scale) systems, not their individual components, in isolation.
Finally, I found it a lot of fun to read because it uses hypothetical stories to illustrate points. For example, silicon-based aliens visit Earth but fail to understand why certain things and people behave the way that they do.
What is Life? Decades of research have resulted in the full mapping of the human genome - three billion pairs of code whose functions are only now being understood. The gene's eye view of life, advocated by evolutionary biology, sees living bodies as mere vehicles for the replication of the genetic codes.
But for a physiologist, working with the living organism, the view is a very different one. Denis Noble is a world renowned physiologist, and sets out an alternative view to the question - one that becomes deeply significant in terms of the living, breathing organism. The genome is…
I am Professor of Philosophy of Science at the University of Bristol. I am interested in most areas of contemporary philosophy, in particular the interplay between philosophy and the natural and social sciences. Much of my recent work has focused on evolutionary biology, a science that is replete with implications for traditional philosophical debates about human nature, knowledge, and our place in the world.
The traditional neo-Darwinian view of evolution understands inheritance in genetic terms, as the transmission of DNA from parents to offspring. Jablonka and Lamb argue convincingly that in addition to genetic inheritance, there exist three other inheritance systems in nature – epigenetic, symbolic, and behavioural – all of which play an important role in evolution. The book is not a work of philosophy in the strict sense, but rather a fascinating and conceptually-rich synthesis of a diverse body of empirical findings which, the authors argue, can only be accommodated by going beyond a purely geno-centric view of evolution.
Ideas about heredity and evolution are undergoing a revolutionary change. New findings in molecular biology challenge the gene-centered version of Darwinian theory according to which adaptation occurs only through natural selection of chance DNA variations. In Evolution in Four Dimensions, Eva Jablonka and Marion Lamb argue that there is more to heredity than genes. They trace four "dimensions" in evolution -- four inheritance systems that play a role in evolution: genetic, epigenetic (or non-DNA cellular transmission of traits), behavioral, and symbolic (transmission through language and other forms of symbolic communication). These systems, they argue, can all provide variations on which…
I have long been fascinated by how very complicated things can arise from comparatively simple ones, because it seems counterintuitive that this is even possible. This led me to lead a life in science, researching how a whole human body can come from a simple egg, and trying to apply what we learn to make new body parts for those who need them. Though much of my professional reading consists of detailed research papers, I have always relied on books to make me think and to show me the big picture. I write books myself, to share with others some of the amazing things that science lets us discover.
This is the best scientific novel I have ever read. The story is fiction (not 'science fiction' in the sense of fantasy, but a story that could easily take place in the real world right now), but its portrayal of how science is done, by a bunch of completely believable characters, is really true-to-life. It's a great way for young people considering a research career to taste what they are really like, and a great way for everyone to ask why we do science the way we do, while enjoying a well-paced multi-layer story, that is written with real wit. [Declaration for transparency: I know the author as a scientific collaborator, but this is nothing to do with my recommendation of her fiction].
Sexism, Secrets and Science: Cat Zero by Jennifer Rohn
Scientist Artie Marshall is perpetually underfunded, relegated to a damp basement, and besieged on all sides by sexist colleagues. Added to that, she is immersed in a messy divorce. But she’s never been happier, studying an obscure cat virus that nobody else in the world seems to have heard of – or cares about.
Everything changes when local cats start dropping dead and Artie’s arcane little research problem becomes worryingly relevant. Matters get worse when people start getting infected too.
Working with her right-hand man Mark, her vet friends and her…
Citizen Scientist begins with this extraordinary statement by the Keeper of Entomology at the London Museum of Natural History, “Study any obscure insect for a week and you will then know more than anyone else on the planet.”
As the author chases the obscure Western red-bellied tiger beetle across New…
I studied statistics and data science for years before anyone ever suggested to me that these topics might have an ethical dimension, or that my numerical tools were products of human beings with motivations specific to their time and place. I’ve since written about the history and philosophy of mathematical probability and statistics, and I’ve come to understand just how important that historical background is and how critically important it is that the next generation of data scientists understand where these ideas come from and their potential to do harm. I hope anyone who reads these books avoids getting blinkered by the ideas that data = objectivity and that science is morally neutral.
People need less Dawkins in their lives and more Lewontin, whose thought-provoking, accessible writing about evolutionary biology stands in fierce opposition to the trend toward genetic determinism that seems to be the rage nowadays. We are not simply our genes, Lewontin says, because the effects DNA has on our lives are mediated by social and environmental factors, many of which we can influence. While it’s nominally about biology, I also read this as a critique of causal inference, generally. What we consider a “cause” reveals our ideological commitments to certain aspects of the world being maintained, and we should be careful what causal lessons we draw from data.
Following in the fashion of Stephen Jay Gould and Peter Medawar, one of the world's leading scientists examines how "pure science" is in fact shaped and guided by social and political needs and assumptions.
