This cybertextbook is needed by advanced undergraduate and
graduate students and professionals in the biomedical sciences or
in biomedical engineering who want or need to use modern computer
modeling approaches to test theories against experimental data.
The cybertextbook is focussed on practical application; it is a
handbook, a guidebook, and a map for the process of formulating
and testing biological models. The reader will learn a
step-by-step method, applicable to a wide range of biological
problems. The aim is to add useful and powerful tools to the
reader's repertoire of scientific skills, and to make the reader
self-sufficient in the use of these tools.
I am writing for biologists. This book represents an approach
to systems biology that emerges from more than 40 years at the
interface between experimental and computational biology. It is
not intended to convey the "state of the art," nor is
it a collection of standard practices. I agree with the unnamed
editor who, in a 2010 Nature opinion piece, asserted that
a textbook is "One person's view of a field, accumulated
through personal experience, years of research, and face-to-face
interaction with students." This is the ideal I aim to reach.
Systems biology has its roots in many disciplines. I am a cardiovascular
cell physiologist, but I also have deep roots in electrical engineering,
biomedical engineering, and molecular cell biology. Other phrases
might be used to describe the arena in which this book does battle.
You could describe it as computational cell biology, or computer
modeling, or kinetics, or biological control systems. In talks
and lectures and discussions with students I have used all these
phrases to describe what we do. But each of these phrases means
different things to different scientists. Consequently, I need
to say more explicitly what this book is about.
Let me attempt a description from the perspective of a prospective
reader. This is a book about biology from a systems perspective;
the book could be titled Analysis of Complex Biological Systems
or Practical Kinetic Modeling of Biological Systems or Integrative
Bioinformatics. If you are an experimental biologist working on
a system you feel is complex, this book may be for you. For my
purposes, complexity is defined by how many variables you want
to understand - that is, how many things are changing with time
in the biological system you are studying. If that number is greater
than 10, I am writing for you.
Scientists are always taught the virtues of the controlled experiment
- an experiment in which only one thing is changed so that a rational
comparison can be made between the "control" and the
"experimental" results. This is the foundation of the
reductionist paradigm and underlies most of scientific hypothesis
testing. But reductionism has its limits.
If successful, 21st century biomedical science will make the
transition from reductionism to synthesis or integration. This
is not because the end of reductionist experimentation is in sight.
It is fundamentally because the goal of publically funded scientific
research is improvement of the human condition. An assumption
of this text is that synthesis and integration can only be achieved
if we confront complexity on its own terms. In other words, biomedical
scientists whose goals are synthetic of integrative must have
tools to deal with complexity. The human brain is palpably incapable
of predicting the behavior of any interconnected system that meets
my simple complexity criterion. Most of us cannot predict the
behavior of a system of even five such variables, let alone ten.
In the future there may be an entirely new science of complexity
whose form I cannot imagine, but I think it is reasonable to assume
that any such science will be mathematical and computational.
Mathematics and computation are the right tools for complex problems.
The more complex your problem, the more this is true. But scientific
tools only become widely useful when they are accessible to a
large community. My aim is to make mathematical and computational
modeling accessible to all biomedical scientists.