DDE Problems
#
SciMLBase.DDEProblem
— Type
Defines a delay differential equation (DDE) problem. Documentation Page: https://docs.sciml.ai/DiffEqDocs/stable/types/dde_types/
Mathematical Specification of a DDE Problem
To define a DDE Problem, you simply need to give the function , the initial condition at time point , and the history function which together define a DDE:
should be specified as f(u, h, p, t)
(or in-place as f(du, u, h, p, t)
), should be an AbstractArray (or number) whose geometry matches the desired geometry of u
, and should be specified as described below. The history function h
is accessed for all delayed values. Note that we are not limited to numbers or vectors for ; one is allowed to provide as arbitrary matrices / higher dimension tensors as well.
Functional Forms of the History Function
The history function h
can be called in the following ways:
-
h(p, t)
: out-of-place calculation -
h(out, p, t)
: in-place calculation -
h(p, t, deriv::Type{Val{i}})
: out-of-place calculation of thei
th derivative -
h(out, p, t, deriv::Type{Val{i}})
: in-place calculation of thei
th derivative -
h(args...; idxs)
: calculation ofh(args...)
for indicesidxs
Note that a dispatch for the supplied history function of matching form is required for whichever function forms are used in the user derivative function f
.
Declaring Lags
Lags are declared separately from their use. One can use any lag by simply using the interpolant of h
at that point. However, one should use caution in order to achieve the best accuracy. When lags are declared, the solvers can be more efficient and accurate, and this is thus recommended.
Neutral and Retarded Delay Differential Equations
Note that the history function specification can be used to specify general retarded arguments, i.e. h(p,α(u,t))
. Neutral delay differential equations can be specified by using the deriv
value in the history interpolation. For example, h(p,t-τ, Val{1})
returns the first derivative of the history values at time t-τ
.
Note that algebraic equations can be specified by using a singular mass matrix.
Problem Type
Constructors
DDEProblem(f[, u0], h, tspan[, p]; <keyword arguments>) DDEProblem{isinplace,specialize}(f[, u0], h, tspan[, p]; <keyword arguments>)
isinplace
optionally sets whether the function is inplace or not. This is determined automatically, but not inferred. specialize
optionally controls the specialization level. See the specialization levels section of the SciMLBase documentation for more details. The default is AutoSpecialize
.
For more details on the in-place and specialization controls, see the ODEFunction documentation.
Parameters are optional, and if not given, then a NullParameters()
singleton will be used which will throw nice errors if you try to index non-existent parameters. Any extra keyword arguments are passed on to the solvers. For example, if you set a callback
in the problem, then that callback
will be added in every solve call.
For specifying Jacobians and mass matrices, see the DiffEqFunctions page.
Arguments
-
f
: The function in the DDE. -
u0
: The initial condition. Defaults to the valueh(p, first(tspan))
of the history function evaluated at the initial time point. -
h
: The history function for the DDE beforet0
. -
tspan
: The timespan for the problem. -
p
: The parameters with which functionf
is called. Defaults toNullParameters
. -
constant_lags
: A collection of constant lags used by the history functionh
. Defaults to()
. -
dependent_lags
A tuple of functions(u, p, t) -> lag
for the state-dependent lags used by the history functionh
. Defaults to()
. -
neutral
: If the DDE is neutral, i.e., if delays appear in derivative terms. -
order_discontinuity_t0
: The order of the discontinuity at the initial time point. Defaults to0
if an initial conditionu0
is provided. Otherwise, it is forced to be greater or equal than1
. -
kwargs
: The keyword arguments passed onto the solves.
Dynamical Delay Differential Equations
Much like Dynamical ODEs, a Dynamical DDE is a Partitioned DDE of the form:
Constructors
DynamicalDDEProblem(f1, f2[, v0, u0], h, tspan[, p]; <keyword arguments>) DynamicalDDEProblem{isinplace}(f1, f2[, v0, u0], h, tspan[, p]; <keyword arguments>)
Parameter isinplace
optionally sets whether the function is inplace or not. This is determined automatically, but not inferred.
Arguments
-
f
: The function in the DDE. -
v0
andu0
: The initial condition. Defaults to the valuesh(p, first(tspan))...
of the history function evaluated at the initial time point. -
h
: The history function for the DDE beforet0
. Must return an object with the indices 1 and 2, with the values ofv
andu
respectively. -
tspan
: The timespan for the problem. -
p
: The parameters with which functionf
is called. Defaults toNullParameters
. -
constant_lags
: A collection of constant lags used by the history functionh
. Defaults to()
. -
dependent_lags
A tuple of functions(v, u, p, t) -> lag
for the state-dependent lags used by the history functionh
. Defaults to()
. -
neutral
: If the DDE is neutral, i.e., if delays appear in derivative terms. -
order_discontinuity_t0
: The order of the discontinuity at the initial time point. Defaults to0
if an initial conditionu0
is provided. Otherwise, it is forced to be greater or equal than1
. -
kwargs
: The keyword arguments passed onto the solves.
For dynamical and second order DDEs, the history function will return an object with the indices 1 and 2 defined, where h(p, t_prev)[1]
is the value of and h(p, t_prev)[2]
is the value of (this is for consistency with the ordering of the initial conditions in the constructor). The supplied history function must also return such a 2-index object, which can be accomplished with a tuple (v,u)
or vector [v,u]
.
2nd Order Delay Differential Equations
To define a 2nd Order DDE Problem, you simply need to give the function and the initial condition which define an DDE:
f
should be specified as f(du,u,p,t)
(or in-place as f(ddu,du,u,p,t)
), and u₀
should be an AbstractArray (or number) whose geometry matches the desired geometry of u
. Note that we are not limited to numbers or vectors for u₀
; one is allowed to provide u₀
as arbitrary matrices / higher dimension tensors as well.
From this form, a dynamical ODE:
Constructors
SecondOrderDDEProblem(f, [, du0, u0], h, tspan[, p]; <keyword arguments>) SecondOrderDDEProblem{isinplace}(f, [, du0, u0], h, tspan[, p]; <keyword arguments>)
Parameter isinplace
optionally sets whether the function is inplace or not. This is determined automatically, but not inferred.
Arguments
-
f
: The function in the DDE. -
du0
andu0
: The initial condition. Defaults to the valuesh(p, first(tspan))...
of the history function evaluated at the initial time point. -
h
: The history function for the DDE beforet0
. Must return an object with the indices 1 and 2, with the values ofv
andu
respectively. -
tspan
: The timespan for the problem. -
p
: The parameters with which functionf
is called. Defaults toNullParameters
. -
constant_lags
: A collection of constant lags used by the history functionh
. Defaults to()
. -
dependent_lags
A tuple of functions(v, u, p, t) -> lag
for the state-dependent lags used by the history functionh
. Defaults to()
. -
neutral
: If the DDE is neutral, i.e., if delays appear in derivative terms. -
order_discontinuity_t0
: The order of the discontinuity at the initial time point. Defaults to0
if an initial conditionu0
is provided. Otherwise, it is forced to be greater or equal than1
. -
kwargs
: The keyword arguments passed onto the solves.
As above, the history function will return an object with indices 1 and 2, with the values of du
and u
respectively. The supplied history function must also match this return type, e.g. by returning a 2-element tuple or vector.
Example Problems
Example problems can be found in DiffEqProblemLibrary.jl.
To use a sample problem, such as prob_dde_constant_1delay_ip
, you can do something like:
#] add DDEProblemLibrary
using DDEProblemLibrary
prob = DDEProblemLibrary.prob_dde_constant_1delay_ip
sol = solve(prob)
#
SciMLBase.DDEFunction
— Type
DDEFunction{iip,F,TMM,Ta,Tt,TJ,JVP,VJP,JP,SP,TW,TWt,TPJ,S.S2,S3,O,TCV} <: AbstractDDEFunction{iip,specialize}
A representation of a DDE function f
, defined by:
and all of its related functions, such as the Jacobian of f
, its gradient with respect to time, and more. For all cases, u0
is the initial condition, p
are the parameters, and t
is the independent variable.
Constructor
DDEFunction{iip,specialize}(f;
mass_matrix = __has_mass_matrix(f) ? f.mass_matrix : I,
analytic = __has_analytic(f) ? f.analytic : nothing,
tgrad= __has_tgrad(f) ? f.tgrad : nothing,
jac = __has_jac(f) ? f.jac : nothing,
jvp = __has_jvp(f) ? f.jvp : nothing,
vjp = __has_vjp(f) ? f.vjp : nothing,
jac_prototype = __has_jac_prototype(f) ? f.jac_prototype : nothing,
sparsity = __has_sparsity(f) ? f.sparsity : jac_prototype,
paramjac = __has_paramjac(f) ? f.paramjac : nothing,
syms = __has_syms(f) ? f.syms : nothing,
indepsym= __has_indepsym(f) ? f.indepsym : nothing,
paramsyms = __has_paramsyms(f) ? f.paramsyms : nothing,
colorvec = __has_colorvec(f) ? f.colorvec : nothing,
sys = __has_sys(f) ? f.sys : nothing)
Note that only the function f
itself is required. This function should be given as f!(du,u,h,p,t)
or du = f(u,h,p,t)
. See the section on iip
for more details on in-place vs out-of-place handling. The history function h
acts as an interpolator over time, i.e. h(t)
with options matching the solution interface, i.e. h(t; save_idxs = 2)
.
All of the remaining functions are optional for improving or accelerating the usage of f
. These include:
-
mass_matrix
: the mass matrixM
represented in the ODE function. Can be used to determine that the equation is actually a differential-algebraic equation (DAE) ifM
is singular. Note that in this case special solvers are required, see the DAE solver page for more details: https://docs.sciml.ai/DiffEqDocs/stable/solvers/dae_solve/. Must be an AbstractArray or an AbstractSciMLOperator. -
analytic(u0,p,t)
: used to pass an analytical solution function for the analytical solution of the ODE. Generally only used for testing and development of the solvers. -
tgrad(dT,u,h,p,t)
or dT=tgrad(u,p,t): returns -
jac(J,u,h,p,t)
orJ=jac(u,p,t)
: returns -
jvp(Jv,v,h,u,p,t)
orJv=jvp(v,u,p,t)
: returns the directional derivative$\frac{df}{du} v$ -
vjp(Jv,v,h,u,p,t)
orJv=vjp(v,u,p,t)
: returns the adjoint derivative$\frac{df}{du}^\ast v$ -
jac_prototype
: a prototype matrix matching the type that matches the Jacobian. For example, if the Jacobian is tridiagonal, then an appropriately sizedTridiagonal
matrix can be used as the prototype and integrators will specialize on this structure where possible. Non-structured sparsity patterns should use aSparseMatrixCSC
with a correct sparsity pattern for the Jacobian. The default isnothing
, which means a dense Jacobian. -
paramjac(pJ,h,u,p,t)
: returns the parameter Jacobian . -
syms
: the symbol names for the elements of the equation. This should matchu0
in size. For example, ifu0 = [0.0,1.0]
andsyms = [:x, :y]
, this will apply a canonical naming to the values, allowingsol[:x]
in the solution and automatically naming values in plots. -
indepsym
: the canonical naming for the independent variable. Defaults to nothing, which internally usest
as the representation in any plots. -
paramsyms
: the symbol names for the parameters of the equation. This should matchp
in size. For example, ifp = [0.0, 1.0]
andparamsyms = [:a, :b]
, this will apply a canonical naming to the values, allowingsol[:a]
in the solution. -
colorvec
: a color vector according to the SparseDiffTools.jl definition for the sparsity pattern of thejac_prototype
. This specializes the Jacobian construction when using finite differences and automatic differentiation to be computed in an accelerated manner based on the sparsity pattern. Defaults tonothing
, which means a color vector will be internally computed on demand when required. The cost of this operation is highly dependent on the sparsity pattern.
iip: In-Place vs Out-Of-Place
For more details on this argument, see the ODEFunction documentation.
specialize: Controlling Compilation and Specialization
For more details on this argument, see the ODEFunction documentation.
Fields
The fields of the DDEFunction type directly match the names of the inputs.
Solution Type
DDEProblem
solutions return an ODESolution
. For more information, see the ODE problem definition page for the ODESolution
docstring.
Example Problems
Example problems can be found in DiffEqProblemLibrary.jl.
To use a sample problem, such as prob_ode_linear
, you can do something like:
#] add DiffEqProblemLibrary
using DiffEqProblemLibrary.ODEProblemLibrary
# load problems
ODEProblemLibrary.importodeproblems()
prob = ODEProblemLibrary.prob_ode_linear
sol = solve(prob)
DDEs with 1 constant delay
#
DDEProblemLibrary.prob_dde_constant_1delay_ip
— Constant
prob_dde_constant_1delay_ip
Delay differential equation
for ] with history function if and .
Solution
The analytical solution for ] can be obtained by the method of steps and is provided in this implementation.
#
DDEProblemLibrary.prob_dde_constant_1delay_oop
— Constant
prob_dde_constant_1delay_oop
Same delay differential equation as prob_dde_constant_1delay_ip
, but purposefully implemented with an out-of-place function.
#
DDEProblemLibrary.prob_dde_constant_1delay_scalar
— Constant
prob_dde_constant_1delay_scalar
Same delay differential equation as prob_dde_constant_1delay_ip
, but purposefully implemented with a scalar function.
#
DDEProblemLibrary.prob_dde_constant_1delay_long_ip
— Constant
prob_dde_constant_1delay_long_ip
Delay differential equation
for ] with history function if and .
#
DDEProblemLibrary.prob_dde_constant_1delay_long_oop
— Constant
prob_dde_constant_1delay_long_oop
Same delay differential equation as prob_dde_constant_1delay_long_ip
, but purposefully implemented with an out-of-place function.
#
DDEProblemLibrary.prob_dde_constant_1delay_long_scalar
— Constant
prob_dde_constant_1delay_long_scalar
Same delay differential equation as prob_dde_constant_1delay_long_ip
, but purposefully implemented with a scalar function.
DDEs with 2 constant delays
#
DDEProblemLibrary.prob_dde_constant_2delays_ip
— Constant
prob_dde_constant_2delays_ip
Delay differential equation
for ] with history function if and .
Solution
The analytical solution for ] can be obtained by the method of steps and is provided in this implementation.
#
DDEProblemLibrary.prob_dde_constant_2delays_oop
— Constant
prob_dde_constant_2delays_oop
Same delay differential equation as prob_dde_constant_2delays_ip
, but purposefully implemented with an out-of-place function.
#
DDEProblemLibrary.prob_dde_constant_2delays_scalar
— Constant
prob_dde_constant_2delays_scalar
Same delay differential equation as prob_dde_constant_2delays_ip
, but purposefully implemented with a scalar function.
#
DDEProblemLibrary.prob_dde_constant_2delays_long_ip
— Constant
prob_dde_constant_2delays_long_ip
Delay differential equation
for ] with history function if and .
#
DDEProblemLibrary.prob_dde_constant_2delays_long_oop
— Constant
prob_dde_constant_2delays_long_oop
Same delay differential equation as prob_dde_constant_2delays_long_ip
, but purposefully implemented with an out-of-place function.
#
DDEProblemLibrary.prob_dde_constant_2delays_long_scalar
— Constant
prob_dde_constant_2delays_long_scalar
Same delay differential equation as prob_dde_constant_2delays_long_ip
, but purposefully implemented with a scalar function.
DDETest Problems
Some details:
# DDEs with time dependent delays prob_dde_DDETST_A1, prob_dde_DDETST_A2, # DDEs with vanishing time dependent delays prob_dde_DDETST_B1, prob_dde_DDETST_B2, # DDEs with state dependent delays prob_dde_DDETST_C1, prob_dde_DDETST_C2, prob_dde_DDETST_C3, prob_dde_DDETST_C4, # DDEs with vanishing state dependent delays prob_dde_DDETST_D1, prob_dde_DDETST_D2, # neutral DDEs with time dependent delays prob_dde_DDETST_E1, prob_dde_DDETST_E2, # neutral DDEs with vanishing time dependent delays prob_dde_DDETST_F1, prob_dde_DDETST_F2, prob_dde_DDETST_F3, prob_dde_DDETST_F4, prob_dde_DDETST_F5, # neutral DDEs with state dependent delays prob_dde_DDETST_G1, prob_dde_DDETST_G2, # neutral DDEs with vanishing state dependent delays prob_dde_DDETST_H1, prob_dde_DDETST_H2, prob_dde_DDETST_H3, prob_dde_DDETST_H4
#
DDEProblemLibrary.prob_dde_DDETST_A1
— Constant
prob_dde_DDETST_A1
Delay differential equation model of blood production, given by
for ] and history function for .
References
Mackey, M. C. and Glass, L. (1977). Oscillation and chaos in physiological control systems, Science (197), pp. 287-289.
#
DDEProblemLibrary.prob_dde_DDETST_A2
— Constant
prob_dde_DDETST_A2
Delay differential equation model of chronic granulocytic leukemia, given by
for ] and history function
for .
References
Wheldon, T., Kirk, J. and Finlay, H. (1974). Cyclical granulopoiesis in chronic granulocytic leukemia: A simulation study., Blood (43), pp. 379-387.
#
DDEProblemLibrary.prob_dde_DDETST_B1
— Constant
prob_dde_DDETST_B1
Delay differential equation
for ] with history function for ].
Solution
The analytical solution for ] is
References
Neves, K. W. (1975). Automatic integration of functional differential equations: An approach, ACM Trans. Math. Soft. (1), pp. 357-368.
#
DDEProblemLibrary.prob_dde_DDETST_B2
— Constant
prob_dde_DDETST_B2
Delay differential equation
for ] with history function .
Solution
The analytical solution for ] is
References
Neves, K. W. and Thompson, S. (1992). Solution of systems of functional differential equations with state dependent delays, Technical Report TR-92-009, Computer Science, Radford University.
#
DDEProblemLibrary.prob_dde_DDETST_C1
— Constant
prob_dde_DDETST_C1
Delay differential equation
for
References
Paul, C. A. H. (1994). A test set of functional differential equations, Technical Report 249, The Department of Mathematics, The University of Manchester, Manchester, England.
#
DDEProblemLibrary.prob_dde_DDETST_C2
— Constant
prob_dde_DDETST_C2
Delay differential equation
for
for
References
Paul, C. A. H. (1994). A test set of functional differential equations, Technical Report 249, The Department of Mathematics, The University of Manchester, Manchester, England.
#
DDEProblemLibrary.prob_dde_DDETST_C3
— Constant
prob_dde_DDETST_C3
Delay differential equation model of hematopoiesis, given by
for
where
References
Mahaffy, J. M., Belair, J. and Mackey, M. C. (1996). Hematopoietic model with moving boundary condition and state dependent delay, Private communication.
#
DDEProblemLibrary.prob_dde_DDETST_C4
— Constant
prob_dde_DDETST_C4
Delay differential equation model of hematopoiesis, given by the same delay differential equation as prob_dde_DDETST_C3
for
References
Mahaffy, J. M., Belair, J. and Mackey, M. C. (1996). Hematopoietic model with moving boundary condition and state dependent delay, Private communication.
#
DDEProblemLibrary.prob_dde_DDETST_D1
— Constant
prob_dde_DDETST_D1
Delay differential equation
for
for
Solution
The analytical solution for
References
Neves, K. W. (1975). Automatic integration of functional differential equations: An approach, ACM Trans. Math. Soft. (1), pp. 357-368.
#
DDEProblemLibrary.prob_dde_DDETST_D2
— Constant
prob_dde_DDETST_D2
Delay differential equation model of antigen antibody dynamics with fading memory, given by
for
for
References
Gatica, J. and Waltman, P. (1982). A threshold model of antigen antibody dynamics with fading memory, in Lakshmikantham (ed.), Nonlinear phenomena in mathematical science, Academic Press, New York, pp. 425-439.
#
DDEProblemLibrary.prob_dde_DDETST_E1
— Constant
prob_dde_DDETST_E1
Delay differential equation model of a food-limited population, given by
for
References
Kuang, Y. and Feldstein, A. (1991). Boundedness of solutions of a nonlinear nonautonomous neutral delay equation, J. Math. Anal. Appl. (156), pp. 293-304.
#
DDEProblemLibrary.prob_dde_DDETST_E2
— Constant
prob_dde_DDETST_E2
Delay differential equation model of a logistic Gauss-type predator-prey system, given by
for
for
References
Kuang, Y. (1991). On neutral delay logistics Gauss-type predator-prey systems, Dyn. Stab. Systems (6), pp. 173-189.
#
DDEProblemLibrary.prob_dde_DDETST_F1
— Constant
prob_dde_DDETST_F1
Delay differential equation
for
Solution
The analytical solution for
References
Jackiewicz, Z. (1981). One step methods for the numerical solution of Volterra functional differential equations of neutral type, Applicable Anal. (12), pp. 1-11.
#
DDEProblemLibrary.prob_dde_DDETST_F2
— Constant
prob_dde_DDETST_F2
Delay differential equation
for
Solution
The analytical solution for
if
and
References
Neves, K. W. and Thompson, S. (1992). Solution of systems of functional differential equations with state dependent delays, Technical Report TR-92-009, Computer Science, Radford University.
#
DDEProblemLibrary.prob_dde_DDETST_F3
— Constant
prob_dde_DDETST_F3
Delay differential equation
for
Solution
The analytical solution for
#
DDEProblemLibrary.prob_dde_DDETST_F4
— Constant
prob_dde_DDETST_F4
Same delay differential equation as prob_dde_DDETST_F3
with
#
DDEProblemLibrary.prob_dde_DDETST_F5
— Constant
prob_dde_DDETST_F5
Same delay differential equation as prob_dde_DDETST_F3
with
#
DDEProblemLibrary.prob_dde_DDETST_G1
— Constant
prob_dde_DDETST_G1
Delay differential equation
for
Solution
The analytical solution for
References
El’sgol’ts, L. E. and Norkin, S. B. (1973). Introduction to the Theory and Application of Differential Equations with Deviating Arguments, Academic Press, New York, p. 44.
#
DDEProblemLibrary.prob_dde_DDETST_G2
— Constant
prob_dde_DDETST_G2
Delay differential equation
for
Solution
The analytical solution for
El’sgol’ts, L. E. and Norkin, S. B. (1973). Introduction to the Theory and Application of Differential Equations with Deviating Arguments, Academic Press, New York, pp. 44-45.
#
DDEProblemLibrary.prob_dde_DDETST_H1
— Constant
prob_dde_DDETST_H1
Delay differential equation
for
Solution
The analytical solution for
References
Castleton, R. N. and Grimm, L. J. (1973). A first order method for differential equations of neutral type, Math. Comput. (27), pp. 571-577.
#
DDEProblemLibrary.prob_dde_DDETST_H2
— Constant
prob_dde_DDETST_H2
Delay differential equation
for
Solution
The analytical solution for
References
Hayashi, H. (1996). Numerical solution of retarded and neutral delay differential equations using continuous Runge-Kutta methods, PhD thesis, Department of Computer Science, University of Toronto, Toronto, Canada.
#
DDEProblemLibrary.prob_dde_DDETST_H3
— Constant
prob_dde_DDETST_H3
Same delay differential equation as prob_dde_DDETST_H2
with
References
Hayashi, H. (1996). Numerical solution of retarded and neutral delay differential equations using continuous Runge-Kutta methods, PhD thesis, Department of Computer Science, University of Toronto, Toronto, Canada.
#
DDEProblemLibrary.prob_dde_DDETST_H4
— Constant
prob_dde_DDETST_H4
Same delay differential equation as prob_dde_DDETST_H2
with
References
Hayashi, H. (1996). Numerical solution of retarded and neutral delay differential equations using continuous Runge-Kutta methods, PhD thesis, Department of Computer Science, University of Toronto, Toronto, Canada.
Radar5 Test Problems
#
DDEProblemLibrary.prob_dde_RADAR5_oregonator
— Constant
prob_dde_RADAR5_oregonator
Delay differential equation model from chemical kinetics, given by
for
for
References
Epstein, I. and Luo, Y. (1991). Differential delay equations in chemical kinetics. Nonlinear models, Journal of Chemical Physics (95), pp. 244-254.
#
DDEProblemLibrary.prob_dde_RADAR5_robertson
— Constant
prob_dde_RADAR5_robertson
Delay differential equation model of a chemical reaction with steady state solution, given by
for
References
Guglielmi, N. and Hairer, E. (2001). Implementing Radau IIA methods for stiff delay differential equations, Computing (67), pp. 1-12.
#
DDEProblemLibrary.prob_dde_RADAR5_waltman
— Constant
prob_dde_RADAR5_waltman
Delay differential equation model of antibody production, given by
for
for
References
Waltman, P. (1978). A threshold model of antigen-stimulated antibody production, Theoretical Immunology (8), pp. 437-453.
QS Example
#
DDEProblemLibrary.prob_dde_qs
— Constant
prob_dde_qs
Delay differential equation model of Quorum Sensing (QS) of Pseudomonas putida IsoF in continuous cultures.
References
Buddrus-Schiemann et al. (2014). Analysis of N-Acylhomoserine Lactone Dynamics in Continuous Cultures of Pseudomonas Putida IsoF By Use of ELISA and UHPLC/qTOF-MS-derived Measurements and Mathematical Models, Analytical and Bioanalytical Chemistry.