Tutorial 3

8/30 9:15 - 11:45, Hewlett 103

Robustness in Biochemical Circuits: a Reaction Network Theory Approach

Guy Shinar1, Uri Alon1 and Martin Feinberg2

1.Department of Molecular Cell Biology and Department of Physics of Complex Systems, Weizmann Institute of Science, Rehovot 76100, Israel. 2.William G. Lowrie Department of Chemical & Biomolecular Engineering and Department of Mathematics, Ohio State University, 125 Koffolt Laboratories, 140 W. 19th Avenue, Columbus, OH 43210.

Summary One of the central tenets of systems biology is the principal of robustness, which asserts that biochemical circuits have evolved to maintain their function even in the face of large environmental disruption [1, 2]. Because the function of biochemical circuits often depends on the steady state concentration of certain molecular species, it is important to understand the design principles that underlie concentration robustness.

Much has been learned about concentration robustness from the study of specific examples [3-7]. However, broad design principles that explain the structural sources of concentration robustness in large and important classes of biochemical reaction networks have largely remained elusive. Thanks to recent advances [8] in chemical reaction network theory [9-13], we are now closer to understanding how robustness may arise from reaction network structure.

Organization The proposed tutorial is organized as two one-hour sessions. The purpose of the first session is to present the rudimentary concepts of chemical reaction network theory, in particular, those concepts that are important for deriving general results on robustness. The purpose of the second session is to show how these concepts are applied to obtain two general results: a necessary condition for approximate concentration robustness in the very large class that includes all detailed balanced mass-action systems, and a sufficient condition for absolute robustness in a broad and relevant class of open systems. Examples of robust signaling and metabolic networks will be used throughout.

References

  1. Alon, U., An introduction to systems biology : design principles of biological circuits. 2007, Boca Raton, FL: Chapman & Hall/CRC. xvi, 301, [4] of plates.
  2. Barkai, N. and B.Z. Shilo, Variability and robustness in biomolecular systems. Mol Cell, 2007. 28(5): p. 755-60.
  3. LaPorte, D.C., P.E. Thorsness, and D.E. Koshland, Jr., Compensatory phosphorylation of isocitrate dehydrogenase. A mechanism for adaptation to the intracellular environment. J Biol Chem, 1985. 260(19): p. 10563-8.
  4. Barkai, N. and S. Leibler, Robustness in simple biochemical networks. Nature, 1997. 387(6636): p. 913-7.
  5. Batchelor, E. and M. Goulian, Robustness and the cycle of phosphorylation and dephosphorylation in a two-component regulatory system. Proc Natl Acad Sci U S A, 2003. 100(2): p. 691-6.
  6. Shinar, G., et al., Input output robustness in simple bacterial signaling systems. Proc Natl Acad Sci U S A, 2007. 104(50): p. 19931-5.
  7. Shinar, G., J.D. Rabinowitz, and U. Alon, Robustness in glyoxylate bypass regulation. PLoS Comput Biol, 2009. 5(3): p. e100029.
  8. Shinar, G., U. Alon, and M. Feinberg, Sensitivity and robustness in chemical reaction networks. SIAM J. Appl. Math, 2009. 69(4): p. 977-998.
  9. Feinberg, M., Lectures on Chemical Reaction Networks - written version of lectures delivered at the Mathematics Research Center, University of Wisconsin, Madison. http://www.che.eng.ohio-state.edu/~FEINBERG/LecturesOnReactionNetworks/, 1979.
  10. Feinberg, M., Chemical reaction network structure and the stability of complex isothermal reactors - I. The Deficiency Zero and Deficiency One Theorems. Chem. Engng. Sci., 1987(42): p. 2229-2268.
  11. Feinberg, M., The existence and uniqueness of steady states for a class of chemical reaction networks. Archs Ration. Mech. Analysis, 1995(132): p. 311-370.
  12. Craciun, G., Y. Tang, and M. Feinberg, Understanding bistability in complex enzyme-driven reaction networks. Proc Natl Acad Sci U S A, 2006. 103(23): p. 8697-702.
  13. Conradi, C., et al., Subnetwork analysis reveals dynamic features of complex (bio)chemical networks. Proc Natl Acad Sci U S A, 2007. 104(49): p. 19175-80.


Registration Registered attendees please register for this Tutorial.