 Methodology article
 Open Access
Bayesian inference of biochemical kinetic parameters using the linear noise approximation
 Michał Komorowski^{1, 2}Email author,
 Bärbel Finkenstädt^{1},
 Claire V Harper^{4} and
 David A Rand^{2, 3}
https://doi.org/10.1186/1471210510343
© Komorowski et al; licensee BioMed Central Ltd. 2009
 Received: 29 January 2009
 Accepted: 19 October 2009
 Published: 19 October 2009
Abstract
Background
Fluorescent and luminescent gene reporters allow us to dynamically quantify changes in molecular species concentration over time on the single cell level. The mathematical modeling of their interaction through multivariate dynamical models requires the deveopment of effective statistical methods to calibrate such models against available data. Given the prevalence of stochasticity and noise in biochemical systems inference for stochastic models is of special interest. In this paper we present a simple and computationally efficient algorithm for the estimation of biochemical kinetic parameters from gene reporter data.
Results
We use the linear noise approximation to model biochemical reactions through a stochastic dynamic model which essentially approximates a diffusion model by an ordinary differential equation model with an appropriately defined noise process. An explicit formula for the likelihood function can be derived allowing for computationally efficient parameter estimation. The proposed algorithm is embedded in a Bayesian framework and inference is performed using Markov chain Monte Carlo.
Conclusion
The major advantage of the method is that in contrast to the more established diffusion approximation based methods the computationally costly methods of data augmentation are not necessary. Our approach also allows for unobserved variables and measurement error. The application of the method to both simulated and experimental data shows that the proposed methodology provides a useful alternative to diffusion approximation based methods.
Keywords
 Posterior Distribution
 Transition Density
 Diffusion Approximation
 Stochastic Simulation Algorithm
 Ordinary Differential Equation Model
Background
The estimation of parameters in biokinetic models from experimental data is an important problem in Systems Biology. In general the aim is to calibrate the model so as to reproduce experimental results in the best possible way. The solution of this task plays a key role in interpreting experimental data in the context of dynamic mathematical models and hence in understanding the dynamics and control of complex intracellular chemical networks and the construction of synthetic regulatory circuits [1]. Among biochemical kinetic systems, the dynamics of gene expression and of gene regulatory networks are of particular interest. Recent developments of fluorescent microscopy allow us to quantify changes in protein concentration over time in single cells (e.g. [2, 3]) even with single molecule precision (see [4] for review). Therefore an abundance of data is becoming available to estimate parameters of mathematical models in many important cellular systems.
 1.
The macroscopic rate equation (MRE) approach which describes the thermodynamic limit of the system with ordinary differential equations and does not take into account random fluctuations due to the stochasticity of the reactions.
 2.
The diffusion approximation (DA) which provides stochastic differential equation (SDE) models where the stochastic perturbation is introduced by a state dependant Gaussian noise.
 3.
The linear noise approximation (LNA) which can be seen as a combination because it incorporates the deterministic MRE as a model of the macroscopic system and the SDEs to approximatively describe the fluctuations around the deterministic state.
Statistical methods based on the MRE have been most widely studied [8, 13–15]. They require data based on large populations. The main advantages of this method are its conceptual simplicity and the existence of extensive theory for differential equations. However, single cells experiments and studies of noise in small regulatory networks created the need for statistical tools that are capable to extract information from fluctuations in molecular species. Few methods used CME to address this. Algorithm, proposed by [16], approximated the likelihood function, the other, suggested by [17] simulated it using Monte Carlo methods. Recently, also a method based on the exact likelihood [18] has been developed. Although, substantial progress has been made in numerical methods for solving CME, inference algorithms based on the CME are computationally intensive and difficult to apply to problems of realistic size and complexity [19]. Another group of methods is based on the DA [9, 20]. This uses likelihood approximation methods (e.g. [21]) that are computationally intensive and require sampling from high dimensional posterior distributions. Inference about the volatility process becomes difficult for low frequency data that are not directly measured at the molecular level [10, 20]. The aim of this study is to investigate the use of the LNA as a method for inference about kinetic parameters of stochastic biochemical systems. We find that the LNA approximation provides an explicit Gaussian likelihood for models with hidden variables and measurement error and is therefore simpler to use and computationally efficient. To account for prior information on parameters our methodology is embedded in the Bayesian paradigm. The paper is structured as follows: We first provide a description of the LNA based modeling approach and then formulate the relevant statistical framework. We then study its applicability in four examples, based on both simulated and experimental data, that clarify principles of the method. Additional file 1 contains details of mathematical and statistical modeling, particularly comparison of the proposed method with an algorithm based on the DA.
Methods
The chemical master equation (CME) is the primary tool to model the stochastic behaviour of a reacting chemical system. It describes the evolution of the joint probability distribution of the number of different molecular species in a spatially homogeneous, well stirred and thermally equilibrated chemical system [11].
where ϕ_{ i }= lim_{Ω→∞, X→∞}X_{ i }/Ω, φ = (ϕ_{1},..., ϕ_{ N })^{ T }and .
where W(t) denotes R dimensional Wiener process, and f_{ i }= f_{ i }(φ) (see Additional file 1 for derivation).
The rationale behind the expansion in terms of is that for constant average concentrations relative fluctuations will decrease with the inverse of the square root of volume [22]. Therefore the LNA is accurate when fluctuations are sufficiently small in relation to the mean (large Ω). Hence, the natural measure of adequacy of the LNA is the coefficient of variation i.e. ratio of the standard deviation to the mean (see Additional file 1). Validity of this approximation is also discussed in details in [22, 23]. In addition it can be shown that the process describing the deviation from the deterministic state converges weakly to the diffusion (3) as Ω → ∞ [24]. In order to use the LNA in a likelihood based inference method we need to derive transition densities of the process x.
Transition densities
The properties of the normal distribution allow us to construct a convenient inference framework that is reminiscent of the Kalman filtering methodology (see e.g. [27]).
Inference
It is rarely possible to observe the time evolution of all molecular components participating in the system of interest [28]. Therefore, we partition the process x_{ t }into those components y_{ t }that are observed and those z_{ t }that are unobserved.
Let , and denote the timeseries that comprise the values of processes x, y and z, respectively, at times t_{0},..., t_{ n }. Here and throughout the paper we use the same letter to denote the stochastic process and its realization.
where the covariance matrix Σ = {Σ^{(i, j)}}_{i, j = 0,..., n}is a submatrix of such that and φ_{ y }is the vector consisting of the observed components of φ.
We use the standard MetropolisHastings (MH) algorithm [30] to sample from the posterior distribution in (13).
Results and Discussion
In order to study the use of the LNA method for inference we have selected four examples which are related to commonly used quantitative experimental techniques such as measurements based on reporter gene constructs and reporter assays based on Polymerase Chain Reaction (e.g. RTPCR, QPCR). For expository reasons, all case studies consider a model of single gene expression.
Model of single gene expression
For the general case it is assumed that the transcription rate k_{ R }(t) is timedependent, reflecting changes in the regulatory environment of the gene such as the availability of transcription factors or chromatin structure.
We will refer to the model in (16) and (17) as the simple model of single gene expression.
In order to test our method on a nonlinear system we will also consider the case of an autoregulated network where the transcription rate of the gene is a function of the concentration of the protein that the gene codes for and where the protein is a transcription factor that inhibits the production of its own mRNA. This is parameterized by a Hill function [31] where k_{ R }(t) now describes the maximum rate of transcription, H is a dissociation constant and n_{ H }is a Hill coefficient.
where . We refer to this model as the autoregulatory model of single gene expression. The two models constitute the basis of our inference studies below.
Inference from fluorescent reporter gene data for the simple model of single gene expression
This form of transcription corresponds to an experiment, where transcription increases for t ≤ b_{3} as a result of being induced by an environmental stimulus and for t > b_{3} decreases towards a baseline level b_{4}.
We assume that at time t_{0} (t_{0} <<b_{3}) the system is in a stationary state. Therefore, the initial condition of the MRE is a function of unknown parameters (ϕ_{ R }(t_{0}), ϕ_{ P }(t_{0})) = (b_{4}/γ_{ R }, b_{4}k_{ P }/γ_{ R }γ_{ P }).
using the standard MH algorithm. The distribution P( Θ) is given by (12).
Inference results for (A) the simple model and (B) autoregulatory model of single gene expression
(A) Param.  Prior  Value  Estimate 1  Estimate 2 

γ _{ R }  Γ (0.44,10^{2})  0.44  0.43 (0.270.60)  0.49 (0.380.61) 
γ _{ P }  Γ (0.52,10^{2})  0.52  0.51 (0.350.67)  0.49 (0.380.61) 
k _{ P }  Exp(100)  10.00  21.09 (3.9167.17)  11.41 (7.6416.00) 
λ  Exp(100)  1.00  1.42 (0.602.57)  1.08 (0.761.36) 
b _{0}  Exp(100)  15.00  6.80 (0.9718.43)  12.78 (9.8015.33) 
b _{1}  Exp(1)  0.40  0.79 (0.053.02)  0.29 (0.180.43) 
b _{2}  Exp(1)  0.40  0.77 (0.082.79)  0.77 (0.321.59) 
b _{3}  Exp(10)  7.00  6.13 (4.417.85)  7.25 (6.797.55) 
b _{4}  Exp(100)  3.00  0.94 (0.112.88)  2.87 (2.113.52) 
(B) Param.  Prior  Value  Estimate 1  Estimate 2 
γ _{ R }  Γ (0.44,10^{2})  0.44  0.44 (0.270.60)  0.42 (0.320.54) 
γ _{ P }  Γ (0.52,10^{2})  0.52  0.49 (0.330.65)  0.49 (0.360.61) 
k _{ P }  Exp(100)  10.00  14.86 (3.1847.97)  9.35 (5.8713.15) 
λ  Exp(100)  1.00  1.26 (0.482.30)  1.15 (0.811.50) 
b _{0}  Exp(100)  15.00  5.99 (0.2023.06)  13.47 (9.2417.13) 
b _{1}  Exp(1)  0.40  0.59 (0.012.75)  0.27 (0.140.53) 
b _{2}  Exp(1)  0.40  0.92 (0.052.92)  0.83 (0.213.52) 
b _{3}  Exp(10)  7.00  6.53(0.7414.69)  7.27 (6.447.79) 
b _{4}  Exp(100)  3.00  2.85 (0.357.19)  2.64 (1.823.32) 
Inference for this model required sampling from the 9 dimensional posterior distribution (number of unknown parameters). If instead one used a diffusion approximation based method it would be necessary to sample from a posterior distribution of much higher dimension (see Additional file 1). In addition, incorporation of the measurement error is straightforward here, whereas for other methods it involves a substantial computational cost [20].
Inference from fluorescent reporter gene data for the model of single gene expression with autoregulation
The following example considers the autoregulatory system with only a small number of reacting molecules. Using SSA we generated artificial data from the single gene expression model with autoregulation. The protein time courses were then sampled every 15 minutes at 101 discrete points per trajectory (see Figure 1B). As before we assume that the mRNA time courses are not observed and that the protein data are of the form given in (20), i.e. proportional to the actual amount of protein with additive Gaussian measurement error. As in the previous case study we estimate parameters from two simulated data sets, a single trajectory and an ensemble of 20 independent trajectories. The inference results summarized in Table 1B show that despite the low number of mRNA (015 molecules, see Figure 2 in Additional file 1) and protein (10250 molecules, see Figure B) all parameters can be estimated well with appropriate precision.
Inference for PCR based reporter data
Inference results for PCR based reporter assay simulated data
Parameter  Prior  Value  Estimate 1  Estimate 2 

γ _{ R }  Exp(1)  0.44  0.45 (0.350.60)  0.46 (0.420.50) 
λ  Exp(100)  1.00  1.07 (0.901.22)  1.01 (0.951.05) 
b _{0}  Exp(100)  15.00  13.13 (10.2015.87)  14.91 (13.8615.77) 
b _{1}  Exp(1)  0.40  0.29 (0.140.51)  0.43 (0.320.54) 
b _{2}  Exp(1)  0.40  0.32 (0.120.93)  0.32 (0.210.43) 
b _{3}  Exp(10)  7.00  7.05 (6.397.63)  6.99 (6.767.15) 
b _{4}  Exp(100)  3.00  2.97 (2.004.18)  3.10 (2.763.43) 
μ _{0}  Exp(100)  6.76  6.90 (5.797.69)  6.55 (6.146.85) 
 Exp(100)  6.76  3.52 (0.018.99)  7.59 (5.449.49) 
Estimation of gfp protein degradation rate from cycloheximide experiment
Inference results for CHX experimental data
Param.  Prior  Estimate LNA  Estimate DA 

γ _{ P }  Exp(1)  0.45 (0.310.62)  0.53 (0.390.67) 
k _{ P }  Exp(50)  0.32(0.101.75)  0.43 (0.161.07) 
λ  Exp(50)  22.79(13.7936.92)  23.85(16.3136.54) 

 889.03(831.44945.34)   
Conclusion
The aim of this paper is to suggest the LNA as a useful and novel approach to the inference of biochemical kinetics parameters. Its major advantage is that an explicit formula for the likelihood can be derived even for systems with unobserved variables and data with additional measurement error. In contrast to the more established diffusion approximation based methods [9, 20] the computationally costly methods of data augmentation to approximate transition densities and to integrate out unobserved model variables are not necessary. Furthermore, this method can also accommodate measurement error in a straightforward way.
The suggested procedure here is implemented in a Bayesian framework using MCMC simulation to generate posterior distributions. The LNA has previously been studied in the context of approximating Poisson birth and death processes [22–24, 35] and it was shown that for a large class of models the LNA provides an excellent approximation. Furthermore, in [35] it is shown that for the systems with linear reaction rates the first two moments of the transition densities resulting from the CME and the LNA are equal. Here we propose using the LNA directly for inference and provide evidence that the resulting method can give very good results even if the number of reacting molecules is very small. In our previous study [10] we have presented differences between fitting deterministic and stochastic models, where we used diffusion approximation based method. Our experience from that work and from study [20] is that implementation of diffusion approximation based methods is challenging especially for data that are sparsely sampled in time because the need for imputation of unobserved time points leads to a very high dimensionality of the posterior distribution. This usually results in highly autocorrelated traces affecting the speed of convergence of the Markov chain. Our method considerably reduces the dimension of the posterior distribution to the number of unknown parameters of a model only and is independent of the number of unobserved components (see Additional file 1). Nevertheless it can only be applied to the systems with sufficiently large volume, where fluctuations around a deterministic state are relatively close to the mean.
Declarations
Acknowledgements
This research was funded by BBSRC SABR grant BB/F005814/1 and EU BIOSIM Network Contract 005137. DAR is funded by EPSRC Senior Research Fellowship EP/C544587/1 and MK by studentship, Dept of Statistics, University of Warwick. CVH was funded by Wellcome Trust Programme Grant (067252, to JRED and MRHW) and now is recipient of The Prof. John Glover Memorial Postdoctoral Fellowship.
Authors’ Affiliations
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