anisms of life, coupled with engineered
environments for synthetic biological
design, can lead us toward the use of
ad hoc bacteria to repair environmental
damages as well as to produce energy.
Another major technological impact
will be computer scientists’ ability to
properly address the challenges posed
by the hardware revolution—
increasingly stressing parallelism in place of speed
of processors—through new integrated
programming environments amenable
to concurrency and complexity.
Quantitative algorithmic descriptions of
biological processes add causal, spatial,
and temporal dimensions to molecular machinery’s behavior that is usually
hidden in the equations. Algorithmic
systems biology allows us to take a step
forward in our understanding of life by
transforming collections of pictures
(cartoons) into spectacular films (the
mechanistic dynamics of life). In fact,
the languages and algorithms emerging
from quantitative computing can be instrumental not only to systems biology
but also to the scientific understanding
of interactions in general.
Unraveling the basic mechanisms
adopted by living organisms for manipulating information goes to the heart
of computer science: computability.
Life underwent billions of years of tests
and was optimized during this very long
time; we can learn new computational
paradigms from it that will enhance
our field. The same arguments apply to
hardware architectures as well. Starting
from the basics, we can use these new
computational paradigms to strengthen resource management and hence
operating systems, to develop primitives to instruct highly parallel systems
and hence (concurrent) programming
languages, and to develop software environments that ensure higher quality
and better properties than current software applications.
Algorithmic systems biology can
contribute to the future both of life
sciences and natural sciences through
interconnecting models and experiments. New conceptual and computational tools, integrated in a user-friend-ly environment, can be employed by
life scientists to predict the behavior
of multilevel and multiscale biological
systems, as well as of other kinds of sys-
tems, in a modular, composable, scalable, and executable manner.
Algorithmic systems biology can also
contribute to the future of computer science by developing a new generation of
operating systems and programming
languages. They will enable advanced
simulation-based research, within a
quantitative framework that connects
in-silico replicas and actual systems, and
enabled by biologically inspired tools.
The author thanks the CoSBi team for
the numerous inspiring discussions.
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Corrado Priami (Priami@cosbi.eu) is president and Ceo
of the Microsoft research-university of trento Centre
for Computational and systems biology and professor of
Computer science at the department of engineering and
Information sciences of the university of trento.
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