Although the importance of a systemic perspective is only recently being widely recognized, the call for a systems-oriented Biology is not new. Already in 1928, von Bertalanfy wrote:
[A system consists of] a dynamic order of parts and processes standing in mutual interaction. [ ] The fundamental task of biology [is] the discovery of the laws of biological systems
This call was most
successfully brought to Biochemistry by Michael Savageau. From the late 60s
on, Savageau and co-workers, seconded by other groups, built a powerful
framework (which became known as Biochemical Systems Theory BST) for
systems-analysis of biochemical processes
(Savageau, 1969b;Savageau, 1969a;Savageau,
1976)
. This framework lies on three pillars.
The first is a mathematical
representation of nonlinear systems based on power laws. This representation
formed the basis of a host of very effective tools (collectively known as the
Power-Law Formalism) for the approximation, modeling, numerical simulation and
analysis of nonlinear systems
(Voit, 2000)
.
Figure 1: Two
alternative designs of an elementary regulatory network |
The second is the realization that the design of at least some biological
regulatory networks is a consequence of functional requirements that can be
inferred from a system-oriented analysis. To understand why this is possible in
spite of evolution being largely driven by random events, consider a population
of organisms that are identical in all respects except for alternative designs
of a given regulatory sub-network. For instance, in some organisms the flux
through an essential unbranched biosynthetic pathway is regulated through an
overall feedback loop, with the final product of the pathway inhibiting the
first enzyme (Figure 1), while other organisms lack this feedback loop. The
former design provides higher robustness of metabolite concentrations and flux
against perturbations, and a better coupling between metabolic demand for the
final product and its supply by the pathway
(Savageau, 1972)
. Organisms with this design have
therefore a selective advantage and, in time, dominate the population. Often, as
result of natural selection, only the best design even where many
alternatives exist becomes widespread in extant organisms. Widespread variant
designs tend to reflect different functions, rather than sub‑optimal
solutions for the same functional requirements. So, by comparing the performance
of variant sub-network designs in various functional contexts, one can sometimes
derive simple and general rules (design principles) about what functional
requirements correlate biologically with each design.
The third pillar of BST is a method for mathematically controlled
comparisons
(Savageau, 1972)
, which allows disentangling irreducible
performance differences between related network designs. This method permitted
identifying design principles of a variety of elementary regulatory circuits,
ranging from feedback patterns in unbranched pathways, to two alternative modes
of gene control, to three patterns of coupling of gene circuits
(Savageau, 2001)
.
von Bertalanfy, L. (1968) "General Systems Theory", George Braziller, New York.
Irvine, D. H. and Savageau, M. A. (1990). Efficient solution of nonlinear ordinary differential-equations expressed in S-system canonical form. Siam Journal on Numerical Analysis 27:704-735.
Savageau, M. A. (1969a). Biochemical systems analysis. I. Some mathematical properties of the rate law for the component enzymatic reactions. J Theor Biol 25:365-369.
Savageau, M. A. (1969b). Biochemical systems analysis. II. The steady-state solutions for an n-pool system using a power-law approximation. J Theor Biol 25:370-379.
Savageau, M. A. (1970). Biochemical systems analysis. 3. Dynamic solutions using a power-law approximation. J Theor Biol 26:215-226.
Savageau, M. A. (1972). The behavior of intact biochemical control systems. Curr Top Cell Regul 6:63-130.
Savageau, M. A. (1976). "Biochemical Systems Analysis: A Study of Function and Design in Molecular Biology," Addison-Wesley, Reading, Mass.
Savageau, M. A. (1993). Finding multiple roots of nonlinear algebraic equations using S-system methodology. Applied Mathematics and Computation 55:187-199.
Savageau, M. A. (2001). Design principles for elementary gene circuits: Elements, methods, and examples. Chaos 11:142-159.
Voit, E. O. (2000). "Computational analysis of biochemical systems. A practical guide for biochemists and molecular biologists," Cambridge University Press, Cambridge.
