able to sufficiently grasp in our mind its workings, anticipate its behavior, and
prepare our actions. We simply cannot hold the many aspects of a dynamic process
at once in mind.
The history of modern science, including the life sciences, is one of reduction—
an effort to concentrate on ever-smaller parts of the overall system in hopes of
advancing knowledge about its constituent parts. Being able to assemble the
knowledge about the pieces so something new and meaningful can be said about
overall system behavior, however, requires a different kind of knowledge—systems
knowledge—and new concepts and tools. Or, as Freeman Dyson (2008) observed:
I’m 84, so I’m definitely over the hill. If I were starting today as a scientist, I’d certainly
study biology. I’d probably be much better at doing biology today than I used to be, because
it is now much more of a theoretical subject. Now you can do biology pretty well with
computers.
. . ..
The future of biology is exciting and unknown. The main thing is that the era of
molecules is over and the era of organisms is here. The reductionist model was the basis
of biology in the 20
th century, and it was enormously successful – we reduced everything to
molecules. We found out wonderful things. The problem in the next century is putting it
together. We know pretty much what the building blocks are. The question is, how do they
actually function? How does the system work as a system? [1]
We need to be able to capture our knowledge—and possibly that of others—in a
consistent and transparent way so that we can better understand, and act in, a
changing world. There are by now general rules and computer programs available
that have been found useful in letting the modelers quickly get down to the business
of capturing their experience inside a computer. Such knowledge capturing is
essential to both learning and understanding. But just as we needed microscopes
and telescopes to extend the reach of the eye, we need dynamic simulation to extend
the reach of our mind. In this process, the computer becomes a facilitator, but it
does not substitute for our ability to develop and understand complex dynamic
systems, rather, it requires the process and skill of modeling that we called for
above. The computer is a means by which we can enlarge our reach into as of yet
unexplored territory, and we need to accustom ourselves to the possibilities it opens
for us. In this sense, the computer is not unlike other great technologies that
required from us that we familiarized ourselves with them and got to understand
their powers and limitations.
We have long been accustomed to machinery which easily out-performs us in physical
ways. That causes us no distress. On the contrary, we are only too pleased to have devices
which regularly propel us at great speed across the ground - a good five times as fast as the
swiftest human athlete - or that can dig holes or demolish unwanted structures at rates
which would put teams of dozens of men to shame. We are even more delighted to have
machines that can enable us physically to do things we have never been able to do before:
they can lift us into the sky and deposit us at the other side of an ocean in a matter of hours.
These achievements do not worry our pride. But to be able to think - that has been a very
human prerogative [2].
Dynamic modeling is a process of extending our knowledge, and the computer is
the only means toward this end. The history of dynamic modeling is traced back to
World War II and the immense technical effort mounted by the scientists involved
4
1 Modeling Dynamic Biological Systems
prepare our actions. We simply cannot hold the many aspects of a dynamic process
at once in mind.
The history of modern science, including the life sciences, is one of reduction—
an effort to concentrate on ever-smaller parts of the overall system in hopes of
advancing knowledge about its constituent parts. Being able to assemble the
knowledge about the pieces so something new and meaningful can be said about
overall system behavior, however, requires a different kind of knowledge—systems
knowledge—and new concepts and tools. Or, as Freeman Dyson (2008) observed:
I’m 84, so I’m definitely over the hill. If I were starting today as a scientist, I’d certainly
study biology. I’d probably be much better at doing biology today than I used to be, because
it is now much more of a theoretical subject. Now you can do biology pretty well with
computers.
. . ..
The future of biology is exciting and unknown. The main thing is that the era of
molecules is over and the era of organisms is here. The reductionist model was the basis
of biology in the 20
th century, and it was enormously successful – we reduced everything to
molecules. We found out wonderful things. The problem in the next century is putting it
together. We know pretty much what the building blocks are. The question is, how do they
actually function? How does the system work as a system? [1]
We need to be able to capture our knowledge—and possibly that of others—in a
consistent and transparent way so that we can better understand, and act in, a
changing world. There are by now general rules and computer programs available
that have been found useful in letting the modelers quickly get down to the business
of capturing their experience inside a computer. Such knowledge capturing is
essential to both learning and understanding. But just as we needed microscopes
and telescopes to extend the reach of the eye, we need dynamic simulation to extend
the reach of our mind. In this process, the computer becomes a facilitator, but it
does not substitute for our ability to develop and understand complex dynamic
systems, rather, it requires the process and skill of modeling that we called for
above. The computer is a means by which we can enlarge our reach into as of yet
unexplored territory, and we need to accustom ourselves to the possibilities it opens
for us. In this sense, the computer is not unlike other great technologies that
required from us that we familiarized ourselves with them and got to understand
their powers and limitations.
We have long been accustomed to machinery which easily out-performs us in physical
ways. That causes us no distress. On the contrary, we are only too pleased to have devices
which regularly propel us at great speed across the ground - a good five times as fast as the
swiftest human athlete - or that can dig holes or demolish unwanted structures at rates
which would put teams of dozens of men to shame. We are even more delighted to have
machines that can enable us physically to do things we have never been able to do before:
they can lift us into the sky and deposit us at the other side of an ocean in a matter of hours.
These achievements do not worry our pride. But to be able to think - that has been a very
human prerogative [2].
Dynamic modeling is a process of extending our knowledge, and the computer is
the only means toward this end. The history of dynamic modeling is traced back to
World War II and the immense technical effort mounted by the scientists involved
4
1 Modeling Dynamic Biological Systems