An analytical approach to bistable biological circuit discrimination using real algebraic geometry

Biomolecular circuits with two distinct and stable steady states have been identified as essential components in a wide range of biological networks, with a variety of mechanisms and topologies giving rise to their important bistable property. Understanding the differences between circuit implementations is an important question, particularly for the synthetic biologist faced with determining which bistable circuit design out of many is best for their specific application. In this work we explore the applicability of Sturm's theorem—a tool from nineteenth-century real algebraic geometry—to comparing ‘functionally equivalent’ bistable circuits without the need for numerical simulation. We first consider two genetic toggle variants and two different positive feedback circuits, and show how specific topological properties present in each type of circuit can serve to increase the size of the regions of parameter space in which they function as switches. We then demonstrate that a single competitive monomeric activator added to a purely monomeric (and otherwise monostable) mutual repressor circuit is sufficient for bistability. Finally, we compare our approach with the Routh–Hurwitz method and derive consistent, yet more powerful, parametric conditions. The predictive power and ease of use of Sturm's theorem demonstrated in this work suggest that algebraic geometric techniques may be underused in biomolecular circuit analysis.

[1]  J. Collins,et al.  Construction of a genetic toggle switch in Escherichia coli , 2000, Nature.

[2]  Patrick W. Nelson,et al.  Applications of Sturm sequences to bifurcation analysis of delay differential equation models , 2004 .

[3]  Jeffrey C. Lagarias,et al.  Multivariate descartes rule of signs and sturmfels’s challenge problem , 1997 .

[4]  V. Hakim,et al.  Different cell fates from cell-cell interactions: core architectures of two-cell bistable networks. , 2012, Biophysical journal.

[5]  List Price,et al.  Real solutions to equations from geometry , 2013 .

[6]  C. Sturm,et al.  Collected works of Charles François Sturm , 2009 .

[7]  John Canny,et al.  The complexity of robot motion planning , 1988 .

[8]  Pablo Villoslada,et al.  Steady State Detection of Chemical Reaction Networks Using a Simplified Analytical Method , 2010, PloS one.

[9]  Franco Blanchini,et al.  Structurally robust biological networks , 2011, BMC Systems Biology.

[10]  Kenneth L. Ho,et al.  Parameter-free model discrimination criterion based on steady-state coplanarity , 2011, Proceedings of the National Academy of Sciences.

[11]  J. Hale Theory of Functional Differential Equations , 1977 .

[12]  M. Yamamura,et al.  Tunable synthetic phenotypic diversification on Waddington’s landscape through autonomous signaling , 2011, Proceedings of the National Academy of Sciences.

[13]  Eduardo D. Sontag,et al.  Monotone and near-monotone biochemical networks , 2007, Systems and Synthetic Biology.

[14]  W. R. Cluett,et al.  Dynamic metabolic engineering for increasing bioprocess productivity. , 2008, Metabolic engineering.

[15]  O. Igoshin,et al.  Bistable responses in bacterial genetic networks: designs and dynamical consequences. , 2011, Mathematical biosciences.

[16]  Jeff Hasty,et al.  Designer gene networks: Towards fundamental cellular control. , 2001, Chaos.

[17]  Franco Blanchini,et al.  A Structural Classification of Candidate Oscillatory and Multistationary Biochemical Systems , 2014, Bulletin of mathematical biology.

[18]  Gregory D. Smith,et al.  The capacity for multistability in small gene regulatory networks , 2009 .

[19]  Par C. Sturm Mémoire sur la résolution des équations numériques , 2009 .

[20]  Joseph Miller Thomas Sturm's Theorem for Multiple Roots , 1941 .

[21]  A FORTRAN Program for Applying Sturm's Theorem in Counting Internal Rates of Return , 1981 .

[22]  Christopher A. Voigt,et al.  Multi-input CRISPR/Cas genetic circuits that interface host regulatory networks , 2014, Molecular systems biology.

[23]  Markus Kirkilionis,et al.  Bistability and oscillations in chemical reaction networks , 2009, Journal of mathematical biology.

[24]  D. Hinrichsen,et al.  Robust Stability of positive continuous time systems , 1996 .

[25]  Yan Gong,et al.  Automated design of genetic toggle switches with predetermined bistability. , 2012, ACS synthetic biology.

[26]  References , 1971 .

[27]  Andreja Majerle,et al.  A bistable genetic switch based on designable DNA-binding domains , 2014, Nature Communications.

[28]  Ofer Biham,et al.  Genetic toggle switch without cooperative binding. , 2006, Physical review letters.

[29]  Josef Hofbauer,et al.  An index theorem for dissipative semiflows , 1990 .

[30]  Gregory D. Smith,et al.  Emergence of Switch-Like Behavior in a Large Family of Simple Biochemical Networks , 2011, PLoS Comput. Biol..

[31]  Israel Ncube Stability switching and Hopf bifurcation in a multiple-delayed neural network with distributed delay , 2013 .

[32]  Wendell A Lim,et al.  Design principles of regulatory networks: searching for the molecular algorithms of the cell. , 2013, Molecular cell.

[33]  Brian Ingalls,et al.  Mathematical Modeling in Systems Biology: An Introduction , 2013 .

[34]  Michael Eisermann,et al.  The Fundamental Theorem of Algebra Made Effective: An Elementary Real-algebraic Proof via Sturm Chains , 2008, Am. Math. Mon..

[35]  Elisa Franco,et al.  Design of a molecular bistable system with RNA-mediated regulation , 2014, 53rd IEEE Conference on Decision and Control.

[36]  Jeremy Gunawardena,et al.  The geometry of multisite phosphorylation. , 2008, Biophysical journal.

[37]  P. Olver Nonlinear Systems , 2013 .

[38]  J. Gunawardena Models in Systems Biology: The Parameter Problem and the Meanings of Robustness , 2010 .

[39]  M. West,et al.  Origin of bistability underlying mammalian cell cycle entry , 2011, Molecular systems biology.

[40]  W. Hillen,et al.  Single-chain Tet transregulators. , 2003, Nucleic acids research.

[41]  Teruo Fujii,et al.  Bottom-up construction of in vitro switchable memories , 2012, Proceedings of the National Academy of Sciences.

[42]  Alan Edelman,et al.  Sturm Sequences and Random Eigenvalue Distributions , 2009, Found. Comput. Math..

[43]  Kenneth L. Ho,et al.  Bistability in Apoptosis by Receptor Clustering , 2009, PLoS Comput. Biol..

[44]  M. Maurin,et al.  REVIEW ARTICLE doi: 10.1111/j.1472-8206.2008.00633.x The Hill equation: a review of its capabilities in pharmacological modelling , 2008 .

[45]  G. Jameson Counting zeros of generalised polynomials: Descartes’ rule of signs and Laguerre’s extensions , 2006, The Mathematical Gazette.

[46]  Franco Blanchini,et al.  A structural classification of candidate oscillators and multistationary systems , 2013, bioRxiv.

[47]  Paul Pedersen Multivariate Sturm Theory , 1991, AAECC.

[48]  Heino Prinz,et al.  Hill coefficients, dose–response curves and allosteric mechanisms , 2010, Journal of chemical biology.

[49]  James N. Weiss The Hill equation revisited: uses and misuses , 1997, FASEB journal : official publication of the Federation of American Societies for Experimental Biology.

[50]  W. Lim,et al.  Defining Network Topologies that Can Achieve Biochemical Adaptation , 2009, Cell.

[51]  Priscilla E. M. Purnick,et al.  The second wave of synthetic biology: from modules to systems , 2009, Nature Reviews Molecular Cell Biology.

[52]  Chris Cosner,et al.  Book Review: Monotone dynamical systems: An introduction to the theory of competitive and cooperative systems , 1996 .

[53]  S. Rinaldi,et al.  Positive Linear Systems: Theory and Applications , 2000 .