Global architectures of biological networks
Prof. John Doyle, California Institute of Technology
This talk will sketch the insights about the fundamental nature of
complex biological networks that can now be drawn from the convergence
of three research themes. Molecular biology has provided a detailed
description of the much of components of biological networks, and the
organizational principles of these networks are becoming increasingly
apparent. It is now clear that much of the complexity in biology is
driven by its control systems, however poorly understood these remain.
In addition, advanced technology is creating engineering examples of
networks with complexity approaching that of biology. While the
components are entirely different, there is striking convergence at
the network level of the architecture and the role of protocols,
layering, control, and feedback in structuring complex system
modularity. Finally, there is a new mathematical framework for the
study of complex networks that suggests that this apparent
network-level evolutionary convergence both within biology and between
biology and technology is not accidental, and follows necessarily from
the requirements that both biology and technology be efficient and
robust. Through combinations of evolution and natural selection or
engineering design, such systems exhibit highly symbiotic interactions
of extremely heterogeneous components to create functional
hierarchies, with massive use of control and feedback throughout.
This talk will draw on concrete examples from systems biology to
illustrate common themes and challenges in developing a scalable
scientific theory and software infrastructure for complex networks.
The larger aim is to build on mathematics of systems engineering to
create a coherent and complete theoretical infrastructure proceeding
from experimental data to modeling, analysis, inference, and with
tight feedback to experimentation and modeling throughout. A crucial
insight is that both technological design and evolution favor high
robustness to uncertain environments and components, yet allows severe
fragility to novel perturbations, and this "robust yet fragile"
feature must be exploited explicitly in scalable theoretical and
algorithmic approaches. This talk will focus on the discussing our new
understanding of the intrinsic features of biological complexity in
terms accessible to biologists, and with a minimum of mathematical
details. The mathematical implications however are important, as a
theoretical and software infrastructure that does not explicitly
exploit the highly structured, evolved and/or designed, and "robust
yet fragile" nature of such systems is hopelessly doomed to be
overwhelmed by their sheer complexity.
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