T. Bäck and H.-P. Schwefel. An overview of evolutionary algorithms for parameter optimization. Evolutionary Computation. 1. 1–23. 1997.
[Baeck97]
T. Bäck, D.B. Fogel, and Z. Michalewicz. Handbook of evolutionary computation. Oxford: IOP Publishing/Oxford University Press. 1997.
[Bell66]
M. Bell and M.C. Pike. Remark on algorithm 178 direct search. Communications of the Association for Computing Machinery. 9. 684–685. 1966.
[Benettin80]
G. Benettin, L. Galgani, A. Giorgilli and J.-M. Strelcyn. Lyapunov Characteristic Exponents for smooth dynamical systems and for Hamiltonian systems; A method for computing all of them. Part 2: Numerical application. Meccanica. 15. 21–30. 1980.
[Brent72]
P.R. Brent. A new algorithm for minimizing a function of several variables without calculating derivatives. In Algorithms for minimization without derivatives, (Englewood Cliffs, NJ: Prentice-Hall, Inc.). 117–167. 1973.
[Burns85]
J.A. Burns, A. Cornish-Bowden, A.K. Groen, R. Heinrich, H. Kacser, J.W. Porteous, S.M. Rapoport, T.A. Rapoport, J.W. Stucki, J.M. Tager, R.J.A Wanders, and H.V. Westerhoff. Control of metabolic systems. Trends in Biochemical Sciences. 10. 16. 1985.
[Cao07]
Y. Cao, D. T. Gillespie and L. R. Petzold Adaptive explicit-implicit tau-leaping method with automatic tau selection Journal of Chemical Physics. 126. 224101. 2007.
[Corana87]
A. Corana, M. Marchesi, C. Martini, and S. Ridella. Minimizing multimodal functions of continuous variables with the "simulated annealing" algorithm. ACM Transactions on Mathematical Software. 13. 262–280. 1987.
[Dennis81]
J. E. Dennis, D. M. Gay, and R. E. Welsch. An adaptive nonlinear least-squares algorithm. ACM Transactions on Mathematical Software. 7. 348–368. 1981.
[Dennis81b]
J. E. Dennis, D. M. Gay, and R. E. Welsch. Algorithm 573: NL2SOL—An Adaptive Nonlinear Least-Squares Algorithm [E4] ACM Transactions on Mathematical Software. 7. 369–383. 1981.
[Deuflhard96]
P. Deuflhard and J. Heroth. Dynamic dimension reduction in ODE models. In Scientific Computing in Chemical Engineering, (F. Keil et al. Springer). 29–43. 1996.
[Fletcher87]
R. Fletcher. Practical methods of optimization. 2nd Edition. Chichester: John Wiley & Sons. 1987.
[Fogel92]
D.B. Fogel, L.J. Fogel, and J.W. Atmar. Meta-evolutionary programming. 25th Asiloma Conference on Signals, Systems and Computers. IEEE Computer Society, Asilomar . 540–545. 1992.
[Gibson00]
M.A. Gibson and J. Bruck. Efficient Exact Stochastic Simulation of Chemical Systems with Many Species and Many Channels. Journal of Physical Chemistry. A104(9). 1876–1889. 2000.
[Giersch88]
C. Giersch. Control analysis of metabolic networks. 1. Homogeneous functions and the summation theorems for control coefficients. European Journal of Biochemistry. 174. 509–513. 1988.
[Gill81]
P.E. Gill, W. Murray, and M.H. Wright. Practical Optimization. London, Academic Press. 1981.
[Gillespie76]
D.T. Gillespie. A General Method for Numerically Simulating the Stochastic Time Evolution of Coupled Chemical Reactions. Journal of Computational Physics. 22. 403–434. 1976.
[Gillespie01]
D.T. Gillespie. Approximate accelerated stochastic simulation of chemically reacting systems. J. Comp. Phys. 115, 1716. 2001.
[Goldberg89]
D.E. Goldberg. Genetic algorithms in search, optimization and machine learning. Addison-Wesley, Reading, Mass. 1989.
[Golub96]
G.H. Golub and C.F. van Loan. Matrix computations. Baltimore, 3rd Ed. Johns Hopkins Press. 1996.
[Heinrich74]
R. Heinrich and T.A. Rapoport. A linear steady-state treatment of enzymatic chains. General properties, control and effector strength. European Journal of Biochemistry. 42. 89–95. 1974.
[Heinrich75]
R. Heinrich and T.A. Rapoport. Mathematical analysis of multienzyme systems. II. Steady-state and transient control. BioSystems. 7. 130–136. 1975.
[Hindmarsh83]
A.C. Hindmarsh. ODEPACK, A Systematized Collection of ODE Solvers. Scientific Computing, R. S. Stepleman et al. (eds.), North-Holland, Amsterdam, IMACS Transactions on Scientific Computation. 1. 55–64. 1983.
[Hooke61]
R. Hooke and T. A. Jeeves. "Direct search" solution of numerical and statistical problems. Journal of the Association for Computing Machinery. 8. 212–229. 1961.
[Kacser73]
H. Kacser and J.A. Burns. The control of flux. Symp. Soc. Exp. Biol.. 27. 65–104. 1973.
[Kaupe63]
Kaupe. Algorithm 178 direct search. Communications of the Association of Computing Machinery. 6. 313–314. 1963.
[Kennedy95]
J. Kennedy and R. Eberhart. Particle Swarm Optimization. Proceedings of the Fourth IEEE International Conference on Neural Networks, Perth, Australia. 1942–1948. 1995.
[Kirkpatrick83]
S. Kirkpatrick, J., C.D. Gelatt, and M. P. Vecchi. Optimization by simulated annealing. Science. 220. 671–680. 1983.
[Lam93]
H. Lam. Using CSP to Understand Complex Chemical Kinetics. Combustion Science and Technology. 89. 375–404. 1993.
[Levenberg44]
K. Levenberg. A method for the solution of certain nonlinear problems in least squares. Quart. Appl. Math.. 2. 164–168. 1944.
[Maier91]
W.L. Maier. A Fast Pseudo Random Number Generator. Dr. Dobb's Journal. May. 152–157. 1991.
[Marquardt63]
D.W. Marquardt. An algorithm for least squares estimation of nonlinear parameters. SIAM Journal. 11. 431–441. 1963.
[Matsumoto98]
M. Matsumoto and T. Nishimura. Mersenne twister: A 623-dimensionally equidistributed uniform pseudorandom number generator. ACM Transactions on Modeling and Computer Simulations . 8. 3–30. 1998.
[Michalewicz94]
Z. Michalewicz. Genetic algorithms + data structures = evolution programs. 3rd Edition. Springer-Verlag, Berlin. 1994.
[Mitchell95]
M. Mitchell. An Introduction to Genetic Algorithms. MIT Press, Boston. 1995.
[Nash84]
S. G. Nash. Newton-type minimization via the Lanczos method. SIAM Journal of Numerical Analysis. 21. 770–788. 1984.
[Nelder65]
J. A. Nelder and R. Mead. A simplex method for function minimization. Computer Journal. 7. 308–313. 1965.
[Petzold83]
L. Petzold. Automatic selection of methods for solving stiff and nonstiff systems of ordinary differential equations. SIAM J. Sci. Stat. Comput. 4. 136–148. 1983.
[Powel64]
M.J.D. Powell. An efficient method for finding the minimum of a function of several variables without calculating derivatives. Computer Journal. 7. 155–162. 1964.
[Reder88]
C. Reder. Metabolic control theory: a structural approach. Journal of Theoretical Biology. 135. 175–201. 1988.
[Runarsson00]
T. Runarsson and X. Yao. Stochastic ranking for constrained evolutionary optimization. IEEE Transactions on Evolutionary Computation. 4. 284–294. 2000.
[Shimada79]
I. Shimada and T. Nagashima. A numerical approach to ergodic problem of dissipative dynamical systems. Progress of Theoretical Physics. 61. 1605–1616. 1979.
[Surovtsova09]
I. Surovtsova, N. Simus, Th. Lorenz, A. König, S. Sahle and U. Kummer. Accessible Methods for the Dynamic Time-scale Decomposition of Biochemical Systems. Bioinformatics 25. 2816–2823. 2009.
[Swann72]
W.H. Swann. Direct search methods. Numerical methods for unconstrained optimization., W. Murray, ed. (London & New York: Academic Press). 13–28. 1972.
[Vallabhajosyula06]
R. R. Vallabhajosyula, V. Chickarmane, and H. M. Sauro. Conservation analysis of large biochemical networks. Bioinformatics. 22. 346–353. 2006.
[Westerhoff84]
H.V. Westerhoff and Y.-D. Chen. How do enzyme activities control species concentrations? An additional theorem in the theory of metabolic control. European Journal of Biochemistry. 142. 425–430. 1984.
[Wolf85]
A. Wolf, J. B. Swift, H. Swinney, and J. A. Vastano. Determining Lyapunov exponents from a time series. Physica. 16D. 285–317. 1985.
[Zobeley05]
J. Zobeley et al. A new time-dependent complexity reduction method for biochemical systems. In Transactions on Computational Systems (C. Prami et al., Springer). 90–110. 2005.
