Here are 30 books that An Introduction to Systems Biology fans have personally recommended if you like
An Introduction to Systems Biology.
Shepherd is a community of 11,000+ authors and super readers sharing their favorite books with the world.
Shepherd is reader supported. When you buy books, we may earn an affiliate commission.
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…
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…
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!
A very useful reference on systems biology, a sort of handbook, that provides a lot of breadth on systems biology topics. A unique aspect of this book is a set of chapters, introducing basic biology, mathematical techniques, experimental techniques, and a somewhat elaborate collection of databases/tools. Also includes material on stochastic modelling of biochemical reaction systems.
This advanced textbook is tailored to the needs of introductory course in Systems Biology. It has a compagnion website (WWW.WILEY-VCH.DE/HOME/SYSTEMSBIOLOGY) with solutions to questions in the book and several additional extensive working models. The book is related to the very successful previous title 'Systems Biology in Practice' and has incorporated the feedback and suggestions from many lecturers worldwide. The book addresses biologists as well as engineers and computer scientists. The interdisciplinary team of acclaimed authors worked closely together to ensure a comprehensive coverage with no overlaps in a homogenous and compelling style.
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…
I started my career in neuroscience. I wanted to understand brains. That is still proving difficult, and somewhere along the way, I realized my real motivation was to build things, and I wound up working in AI. I love the elegance of mathematical models of the world. Even the simplest machine learning model has complex implications, and exploring them is a joy.
The best parts of this book really represent a gold standard in pedagogical clarity.
Although it’s now twenty years old, there is still much to learn from this rather unconventional book that covers the boundary between machine learning, information theory, and Bayesian methods. There are also odd tangents and curiosities, some of which work better than others but are never dull.
Just writing this review makes me want to go back to it and squeeze more out of it.
Information theory and inference, taught together in this exciting textbook, lie at the heart of many important areas of modern technology - communication, signal processing, data mining, machine learning, pattern recognition, computational neuroscience, bioinformatics and cryptography. The book introduces theory in tandem with applications. Information theory is taught alongside practical communication systems such as arithmetic coding for data compression and sparse-graph codes for error-correction. Inference techniques, including message-passing algorithms, Monte Carlo methods and variational approximations, are developed alongside applications to clustering, convolutional codes, independent component analysis, and neural networks. Uniquely, the book covers state-of-the-art error-correcting codes, including low-density-parity-check codes, turbo…
As a boy, I wanted to play baseball professionally. But, alas, talent was not within me, and I became one of the few people in the world who chose physics as a career because something else was too hard. Part of my career as a scientist is learning new things; another part is teaching and, hopefully, imbuing students with a love of science. The sports science books here all taught me a great deal, and I have recommended them to several of my students. Sports can be an excellent vehicle for learning some science, and such learning about a sport one loves can make watching the sport even more fun.
I confess that I know Trevor Lipscombe, but I would add his book to this list if I did not know him. Like Haché’s book on ice hockey, I was out of my comfort zone while reading a book on rugby. As I write this, I am in the midst of my third sabbatical year. All three of my sabbatical years have been spent researching at universities in Sheffield, England. People are enthusiastic about rugby in England as well as in other parts of the world.
Not only did this book introduce me to a new way to apply physics, but it also taught me so much about rugby that I can cheer with mates in a pub while watching a match! It is the go-to book on rugby science.
What if Einstein played rugby? Surely Time Magazine's "Man of the Century" might offer useful tips and techniques to defeat the opposition? In this book, the world of physics joins forces with the world of rugby, to show you how to tackle harder, pass safer, run faster, and scrum better - all the things you need to do to win. Blending simple physics, the kind you meet in high school, with anecdotes and stories from the world of rugby, Trevor Lipscombe takes us on a journey from scrum ruck and maul, to the running and passing of the offence, the…
I’m an archaeologist, which means that I’ve been lucky enough to travel to many places to dig and survey ancient remains. What I’ve realized in handling those dusty old objects is that all over the world, in both past and present, people are defined by their stuff: what they made, used, broke, and threw away. Most compelling are the things that people cherished despite being worn or flawed, just like we have objects in our house that are broken or old but that we keep anyway.
This looks like it’s the sternest and most boring book ever, but I love Steedman’s cool-and-collected ability to address the implications of the obvious: You can only do one thing at a time. You only have two hands. And when you’re with one set of belongings, you’re neglecting all the other stuff you own.
Standard economic theory of consumer behaviour considers consumers' preferences, their incomes and commodity prices to be the determinants of consumption. However, consumption takes time and no consumer has more - or less - than 168 hours per week. This simple fact is almost invisible in standard theory, and takes the centre stage in this book.
A noted quantitative hedge fund manager and quant finance author, Ernie is the founder of QTS Capital Management and Predictnow.ai. Previously he has applied his expertise in machine learning at IBM T.J. Watson Research Center’s Human Language Technologies group, at Morgan Stanley’s Data Mining and Artificial Intelligence Group, and at Credit Suisse’s Horizon Trading Group. Ernie was quoted by Bloomberg, the Wall Street Journal, New York Times, Forbes, and the CIO magazine, and interviewed on CNBC’s Closing Bell program. He is an adjunct faculty at Northwestern University’s Master’s in Data Science program and supervises student theses there. Ernie holds a Ph.D. in theoretical physics from Cornell University.
By now, you may notice that I like to recommend textbooks. I use this bestseller for my course in Financial Machine Learning at Northwestern University, but really, nobody interested in financial machine learning hasn’t read this book. The topics are highly relevant to every investor or trader – I read it at least 5 times to digest every nugget and have put them to very productive use in my trading as well as in my fintech firm predictnow.ai. It covers basic techniques such as random forest to advanced techniques such as Hierarchical Risk Parity, which is a big improvement over traditional portfolio optimization methods.
Marcos used to be Head of Machine Learning at AQR (AUM=$143B), and now is the Global Head of Quant Research at Abu Dhabi Investment Authority. He is also very approachable to his readers and students. There was seldom an email or message from me to which…
Learn to understand and implement the latest machine learning innovations to improve your investment performance
Machine learning (ML) is changing virtually every aspect of our lives. Today, ML algorithms accomplish tasks that - until recently - only expert humans could perform. And finance is ripe for disruptive innovations that will transform how the following generations understand money and invest.
In the book, readers will learn how to:
Structure big data in a way that is amenable to ML algorithms
Conduct research with ML algorithms on big data
Use supercomputing methods and back test their discoveries while avoiding false positives
A noted quantitative hedge fund manager and quant finance author, Ernie is the founder of QTS Capital Management and Predictnow.ai. Previously he has applied his expertise in machine learning at IBM T.J. Watson Research Center’s Human Language Technologies group, at Morgan Stanley’s Data Mining and Artificial Intelligence Group, and at Credit Suisse’s Horizon Trading Group. Ernie was quoted by Bloomberg, the Wall Street Journal, New York Times, Forbes, and the CIO magazine, and interviewed on CNBC’s Closing Bell program. He is an adjunct faculty at Northwestern University’s Master’s in Data Science program and supervises student theses there. Ernie holds a Ph.D. in theoretical physics from Cornell University.
Disclaimer: I like Euan’s books not because he is a friend and has endorsed my books. Long before we became friends, I have bought his book, and said to myself “Wow! This is the first book about options trading that is not just a bunch of trite statements about payouts from various straddles and spreads positions!” It talks about some unique arbitrage opportunities that only professionals knew about. On the other hand, the amount of mathematics is very manageable, and can largely be skipped without affecting the practical applications of the concepts.
An A to Z options trading guide for the new millennium and the new economy Written by professional trader and quantitative analyst Euan Sinclair, Option Trading is a comprehensive guide to this discipline covering everything from historical background, contract types, and market structure to volatility measurement, forecasting, and hedging techniques. This comprehensive guide presents the detail and practical information that professional option traders need, whether they're using options to hedge, manage money, arbitrage, or engage in structured finance deals. It contains information essential to anyone in this field, including option pricing and price forecasting, the Greeks, implied volatility, volatility measurement…
