Bayesian data integration for quantifying the contribution of diverse measurements to parameter estimates

Abstract Motivation Computational models in biology are frequently underdetermined, due to limits in our capacity to measure biological systems. In particular, mechanistic models often contain parameters whose values are not constrained by a single type of measurement. It may be possible to achieve better model determination by combining the information contained in different types of measurements. Bayesian statistics provides a convenient framework for this, allowing a quantification of the reduction in uncertainty with each additional measurement type. We wished to explore whether such integration is feasible and whether it can allow computational models to be more accurately determined. Results We created an ordinary differential equation model of cell cycle regulation in budding yeast and integrated data from 13 different studies covering different experimental techniques. We found that for some parameters, a single type of measurement, relative time course mRNA expression, is sufficient to constrain them. Other parameters, however, were only constrained when two types of measurements were combined, namely relative time course and absolute transcript concentration. Comparing the estimates to measurements from three additional, independent studies, we found that the degradation and transcription rates indeed matched the model predictions in order of magnitude. The predicted translation rate was incorrect however, thus revealing a deficiency in the model. Since this parameter was not constrained by any of the measurement types separately, it was only possible to falsify the model when integrating multiple types of measurements. In conclusion, this study shows that integrating multiple measurement types can allow models to be more accurately determined. Availability and implementation The models and files required for running the inference are included in the Supplementary information. Supplementary information Supplementary data are available at Bioinformatics online.


Introduction
Computational models in biology are frequently underdetermined (Gutenkunst et al., 2007), which can limit their usefulness. This underdetermination is a result of our limited capacity to measure biological systems. A dynamic model of an intracellular regulatory network, for example, might contain several proteins of interest that carry out important functions in the system. We would then ideally like to know the concentrations of all these proteins in their various states and complexes, inside the cell, over time. But such direct measurements are currently not possible. Instead we are limited to indirect measurements such as relative protein levels compared to a control, reporter-based measurements, or averages over populations of cells. A compounding difficulty is that the measurements are often relatively noisy. It is thus challenging to accurately determine the unknown parameters of computational models of biological systems.
Intuitively, one would expect that multiple types of measurements, obtained using different experimental techniques, provide more information than a single type of measurement. The combined information would then be more likely to constrain the parameters in a computational model compared to using only a single measurement type. However, this need not be the case; perhaps one particular dataset, such as the most detailed measurements, already contains all relevant information, making additional datasets irrelevant.
The quantification of how much information a dataset brings to the parameter estimates can be achieved using Bayesian statistics (Vyshemirsky and Girolami, 2008;Wilkinson, 2007). For all unknowns in a model, a probability distribution is specified which quantifies the uncertainty in the parameters. This probability distribution can then be updated based on each of the different datasets, using Bayes' theorem. This provides a convenient framework for the integration of multiple datasets, as it allows a detailed comparison of the amount of information that can be extracted from each of the datasets.
Bayesian statistics has been applied to mechanistic computational models in biology in various settings and model types, including regulatory network models based on ordinary differential equations (ODEs) (Eydgahi et al., 2013;Hug et al., 2013;Toni and Stumpf, 2010;Vyshemirsky and Girolami, 2008;Xu et al., 2010). These applications have so far been limited to the use of a single dataset consisting of one type of measurement. It is thus unclear whether integration of multiple data types within the Bayesian formalism is feasible in practice and whether it is beneficial for achieving more accurate parameter estimates. The purpose of this study was to test the feasibility of this type of data integration and to explore whether multiple data types can indeed provide more accurate parameter estimates.
We tested this approach using a model of a well-studied system, cell cycle regulation in budding yeast. Figure 1 shows the concept of data integration we used: various measurements are included as prior information, subsequently two types of data are incorporated during the inference, and finally the obtained parameter estimates are compared to measurements of these rates from independent studies.

Constructing an initial model
We will use cell cycle regulation in budding yeast as test case, as this system is well studied and there is a host of data available. A central event in cell cycle regulation is the cyclic expression of the cyclin proteins (Morgan, 2007). We wished to model the cyclic expression pattern of four cyclins in particular: the G 1 -phase cyclin CLN3, the G 1 /S-transition cyclin CLN2, the S-phase cyclin CLB5 and the M-phase cyclin CLB2 ( Fig. 2A).
Although many models have been constructed of this system, for example (Chen et al., 2004;Tyson, 1991), we wished to obtain a simple, sparse model that is sufficient for explaining the cyclic expression of the cyclins. To this end we created a simple model that might be able to do this. The structure of this initial model is shown in Figure 2B and the reasoning behind it is as follows. Since the expression of the cyclins oscillate at the transcriptional level, we need to include the transcription factors that are responsible for regulating the transcription of the cyclins in the model. Thus, based on the overview of the cell cycle provided by Morgan (2007), and especially Figure 3-34 therein, we included the three transcription factor complexes SBF, Mcm-Fkh and Swi/Snf. Each of these complexes is represented by one of their subunits: SBF is represented by the regulatory subunit SWI4, Mcm1-Fkh is represented by the coactivator NDD1 and Swi/Snf is represented by the subunit SWI5. We chose these subunits because they are regulatory factors and are transcriptionally oscillating (Santos et al., 2015). As most data are available at the mRNA level, we explicitly included the mRNA transcripts as well as the proteins as species in the model. The dynamics are modeled by including rates for transcription, translation and degradation of both mRNA and protein. To keep the model manageable, we did not explicitly include processes such as post-translational modifications, complex formation and intracellular localization. While these processes are also clearly important for cell cycle regulation, the goal is not to create a detailed model but rather a simple model that is sufficient for explaining the cyclic expression of the cyclins. For the same reason, the model contains fewer signaling events than the more comprehensive model of Chen et al. (2004). The starting model described here will likely require improvement, which we consider below. Starting from a simple model allows us to find a  Fig. 1. Outline of the approach of data integration using Bayesian statistics. Several initial datasets are assimilated into a prior probability distribution for all parameters in the model. Subsequently, multiple datasets are integrated to update the prior and obtain a posterior probability distribution for all parameters. Finally, this posterior probability distribution is compared to validation data Another important modeling choice is that we specified the model entirely in physical units of concentration (micromolars) and time (seconds), rather than using dimensionless parameters and abundances. The physical units allow a comparison of the parameter estimates to measurements from independent studies. The model is specified in terms of ODEs, and the rate equations are based on mass action kinetics with the addition of a non-linear term for modeling inhibitory effects. The model is described in more detail in Section 3, and SBML versions of all models are included in Supplementary File S1.

Constructing priors from several datasets
The Bayesian analysis required us to specify prior probabilities for the unknown parameters in the model. For each of the parameters, we specified priors based either on biochemical limits or on published datasets providing information for a parameter. The prior probability distributions and how they were established are described in more detail in the Supplementary Methods. All datasets used throughout this study are listed in Table 1.

Fitting time course mRNA measurement data
As we wished to obtain a model for the cyclic expression of the cyclins, we first turned to measurements of mRNA gene expression over time (Granovskaia et al., 2010;Pramila et al., 2006;Spellman et al., 2003) and tested whether the model can fit these datasets.
A complication with these datasets is that the measurements were taken under different growth conditions, with different synchronization methods and with slightly different yeast strains, resulting in different doubling times for the cells, ranging from 60 to 100 min. To make the datasets compatible, we used the time-normalized data provided by Cyclebase (Santos et al., 2015), and scaled the times back to an 80-min cell cycle, which is a typical doubling time for yeast cells in rich yeast extract peptone dextrose (YEPD) medium (Tyson et al., 1979).
We fitted the model to these three gene expression datasets simultaneously. The measurements are all made on synchronized cells relative to unsynchronized controls, and the likelihood function was specified such that it reflects this. Specifically, the likelihood of the observed values was centered on the log ratio of the modeled transcript concentration divided by the average modeled concentration over time (see Section 3). We expected that the model would not exactly match the measurement data, and so a t-distribution was used as error model, such that occasional outlying measurements with respect to the model are not penalized too heavily.
The posterior probabilities were calculated using Markov chain Monte Carlo (MCMC) sampling. The relatively large number of dimensions, with the prior in each dimension spanning many orders of magnitude, makes this a challenging inference task. To be able to run the inference in reasonable time, the Bayesian inference software package BCM was used (Thijssen et al., 2016). The posterior probability distribution contained sub-optimal modes, we therefore used parallel tempering (Geyer, 1991) to have a means of escaping these. MCMC sampling relies on a proposal distribution; a distribution from which new candidate parameter values are drawn. For the efficiency of the sampler it is important that the proposal distribution reflects the shape and scale of the (unknown) posterior distribution. We therefore used automated blocking (Turek et al., 2017) and adaptively scaled the proposal distributions (see Section 3). Traces and autocorrelation plots for the convergence analysis of all models are included in Supplementary File S2.
To test the goodness of fit, we first used graphical posterior predictive checking. The posterior predictive distribution describes a new, predicted dataset given the fitted model. Overlaying this posterior predictive distribution on the observed measurements provides a convenient way of identifying which data can and cannot be explained by the model. Figure 3 (top row) shows the posterior predictive distribution of the mean of the relative transcript levels in the fitted model overlaid on the observed measurements. It is immediately clear that the model cannot adequately explain the expression patterns of the four cyclins. The model can only fit the first peak of CLN3 expression, but not the subsequent oscillations or the activation of the other cyclins.
To further quantify the goodness of fit, coefficients of determination (R 2 ) were calculated for the four cyclins (Fig. 3). We compared these values to the R 2 of a spline fit to the data. The spline fit gives a reference R 2 for the optimal fit that can be achieved. The median R 2 for the model fits range from 0.07 to 0.19, whereas a spline fit gives R 2 values ranging from 0.46 to 0.72, again showing that the initial model is insufficient to explain the expression patterns of the cyclins.
2.4 Iterative model refinement to create a well-fitting model for the time course mRNA measurement data As the simplest model could not adequately fit the transcription data, it was necessary to expand the model with additional explanatory factors. We thus searched the literature to find important mechanisms that were missing from the model. For each addition, we re-fitted the model to the data and compared the posterior predictive distributions and R 2 values for expression of the four cyclins. Note that we could not use the marginal likelihood for model selection here, because when we added additional species to the model we also included the expression data for those new species in the likelihood function. This affects the marginal likelihood; the marginal likelihood of two differing sets of data cannot be compared to each other. The first addition to the model which we considered was the transcription factor HCM1. There is a significant delay between the transcriptional peak of SWI4 and NDD1, especially compared to the peaks of CLN2 and CLB5, which occur more rapidly after the expression of SWI4 (see Supplementary File S2 for the trajectories of all species). The transcription factor HCM1 has been found to be an important part of the transcriptional cell cycle regulation system (Pramila et al., 2006), and the inclusion of this factor could introduce the necessary delay in the model. As shown in Figure 3 (second row), the addition of HCM1 indeed improved the fit of the model, particularly for the induction of the expression of CLN2 and CLB5 after SWI4 expression. However, the model was still not able to explain the oscillatory aspect of the expression of the four cyclins.
The lack of oscillatory behavior of the model suggested that a feedback loop might be required. We therefore considered the addition of the inhibitory transcription factor YOX1 (Pramila et al., 2002). This transcription factor provides a negative feedback loop from SWI4 back to both SWI4 and CLN3. As shown in Figure 3 (third row), with this addition the model could indeed recapitulate the oscillatory aspect of the expression pattern of the four cyclins.
As the magnitude of the oscillations in CLB2 was still greater in the data than could be explained by the model, we considered the addition of another regulatory mechanism, namely the degradation of NDD1 by the anaphase promoting complex (Sajman et al., 2015). This complex is normally active, unless it is inactivated by CLN2 (Morgan, 2007). Thus, NDD1 would be actively degraded until CLN2 signals the start of S-phase. As shown in Figure 3 (bottom row), with the addition of this mechanism the model can indeed better explain the expression pattern of the NDD1-target CLB2.
With these additions to the model, the expression patterns of the four cyclins are adequately explained (R 2 > 0.3 for all cyclins, and at least 65% of the R 2 achieved with a spline fit). Although further additions can be considered, we wished to keep the model as small as possible while achieving a reasonable fit. This was mainly done to keep the computational requirements manageable-to generate 1000 posterior samples for the fourth extended model required approximately 60 h. The structure of the resulting model is similar to the Boolean network model of Orlando et al. (2008) in terms of the transcriptional regulatory network.

Simultaneous fitting of time course and steady-state measurement data
Now that the model is able to explain the relative time course measurements adequately, we can start including additional datasets to test whether the parameters of the model can be more tightly constrained with the integration of additional data. We turned to absolute measurements of the mRNA (Holstege et al., 1998;Velculescu et al., 1997) and protein (Ghaemmaghami et al., 2003;Lu et al., 2007;Newman et al., 2006) concentrations of the species in the model. These measurements were done at steady-state growth conditions in non-synchronized cells. We incorporated this in the likelihood by taking the time average of the modeled trajectories, and setting this time average as the modeled value of the steady-state data, where the time average was taken over two cell cycles.  The addition of absolute concentration data to relative time series data may seem trivial, and it could potentially also be achieved by transforming the kinetic parameters and concentrations accordingly. However, keeping the model specified in physical dimensions (micromolars and seconds) is natural, and more importantly, it allows for a direct comparison of the kinetic rates with measurements of these rates later on. Figure 4 shows the posterior trajectories of the transcripts of one of the cyclins, CLN3, after fitting the relative time course data alone, the absolute steady-state data alone or all data together (trajectories for all other species in the model are included in Supplementary File S2). Several observations can be made. First, it is apparent that the absolute concentrations can be quite high when only time course data is used. When the steady-state data is included however, the concentrations are constrained to be much lower. Second, when only steady-state data is used, the model displays various behaviors including stable expression, decay and oscillations (see the individual trajectories depicted in grey)-each of these behaviors would be consistent with the given average data over a period of two cell cycles. With all measurements types included, the model displays the correct oscillations at concentrations consistent with the steady-state data. Finally, the fit to the relative time course data is not compromised by the inclusion of the absolute steady-state data, and vice versa. The model is thus able to fit both types of data at the same time, and no modifications need to be made to the model structure to accommodate the steady-state data. Figure 5A shows the 90% posterior confidence intervals for several parameters in the model, for the relative time course data alone, the absolute steady-state data alone or all data together (confidence intervals and density plots for all parameters are included in Supplementary  Figs S1 and S2). For several parameters, each data type separately provides some information, but the inclusion of the two types of data together provides significantly tighter confidence intervals, for example for the translation rate. There are also parameters that can already be inferred from the time course data alone; that is, for these parameters the addition of the steady-state data does not reduce the confidence intervals, such as the degradation rate of CLN3. In many cases, the steady-state data by itself provides little information for constraining the parameters, which is not surprising for a dynamic model. However, the addition of the steady-state data to the time course data does reduce the uncertainty compared to the time course data alone. Examples of this are the degradation rate of SWI4 or the transcription rate of CLN2.
In general across all parameters, combining multiple types of measurements reduces the uncertainty in parameter estimates (Fig.  5B). With all data types included, 45 out of 54 parameters have 90% confidence intervals of less than half of the prior range, whereas the steady-state data by itself constrains only 1 parameter to this extent and the time course data 14 parameters. Comparing the added value of the absolute protein and transcript concentrations, we note that it is mainly the transcript concentrations that reduces the uncertainty (columns 4 and 5 in Fig. 5B and see Supplementary Table S1 for the values). Nevertheless, adding the protein concentration data to the time course and transcript concentration data still further reduces the uncertainty for several parameters.

Comparison of parameter estimates with rate measurement data
To test whether the obtained parameter estimates are accurate, we compared them to measurements from three additional, independent  Supplementary Table S1 datasets. In particular, the mRNA degradation rate (Wang et al., 2002), transcription rates (Pelechano et al., 2011) and translation rates (Arava et al., 2003) have been measured for budding yeast. Figure 6A shows the measured values of the parameters compared to the posterior probability distributions of the parameter estimates. For the mRNA degradation rate, the measurements are in close agreement with the predicted rates (Fig. 6A, left panel). We assumed a common rate parameter for all species, while the measurements were done for each gene separately, and there is indeed some variability between the measurements for the genes that were included in the model. Nevertheless, the measured degradation rates of all genes are within the same order of magnitude as the estimated average degradation rate (the difference between the measurements and the maximum a posteriori estimate on log 10 scale is <0.5), so the scale of the average mRNA degradation rate was predicted accurately.
For the transcription rates, these rates in the model are split into two parts: basal transcription and transcription factor induced transcription. The rate measurements are population averages, and as each cell would be in a different stage of the cell cycle, they will be expressing different levels of the transcription factors. To be able to compare the measurements of the transcription rates to the model's estimated rates, it is necessary to calculate the total, average transcription rate. This was obtained by averaging the transcription rate of each gene over time. This rate thus includes the effect of the timevarying expression of the transcription factors. When only time course data was used, the transcription rates were not constrained, but they do have non-zero probability at the measured values. However, when all data are included, the estimated transcription rates closely match the measured values for most genes (Fig. 6A, middle panels; seven of the eight measured values lies within the 90% confidence interval, and the remaining gene is at least within the same order of magnitude). Thus, for the transcription rates, the addition of the absolute concentrations to the relative dynamic data constrained the parameter estimates to values close to or matching the measurements of these rates.
The measured translation rates have been estimated from ribosome densities using polysome profiling, whereby a processing speed of 10 amino acids/s was assumed (Arava et al., 2003). Note that the authors mention that their estimates should be used with caution.
Nevertheless, assuming the estimates from Arava et al. are accurate, then our model estimate using all inference data are two orders of magnitude too high (Fig. 6A, right panel). The model estimate is indeed quite high at around 1 protein/transcript/s. While this is feasible given the prior information, it would require that the transcripts are always essentially fully packed with ribosomes.
To find the reason for this high translation rate estimate, we investigated the trajectories of the transcription factors and their target genes. If we compare the mRNA expression trajectories for the transcription factor SWI4 and its targets CLN2, HCM1 and YOX1 (see Fig. 6B), it makes sense that the model requires a high translation rate. The peaks in transcription of the target genes follow very closely after the peak in transcription of the transcription factor, especially in the first cell cycle. Given that this process of rapid induction of transcription in the model has to occur through the translation of the transcription factor, then there are two ways in which the model might fit the data: either the translation rate must be high, or the concentration of the transcript of the transcription factor must be high. When using only the relative data, it is not possible to distinguish between these scenarios; indeed in this case the translation rate is not constrained: the 90% confidence interval of the translation rate spans almost three orders of magnitude (Fig. 5). However, when including both relative and absolute data, the inference can make use of the information that the concentration of the transcription factor is low. It can thus be inferred that the translation rate must be high, given this model.
It is known that other mechanisms are at play here as well, such as the regulation of SWI4 and the SBF transcription factor complex through phosphorylation by different cyclin/CDKs (Siegmund and Nasmyth, 1996). Indeed it has been shown that induction of G 1phase transcripts can occur in the absence of protein translation (Marini and Reed, 1992). It is likely that a model with additional layers of SWI4 regulation would be able to fit all data with lower translation rates. Unfortunately we were not able to expand the model with such additional effects, as the parameter inference for these expanded models would involve a prohibitive amount of computation time. Regardless, these results show that the model can be identified as being incomplete by using the inference of parameters from multiple datasets. This model deficiency could not be deduced from any of the datasets alone.

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Model equations
The computational model consists of two types of species: the proteins and the mRNA transcripts. The rate equations for these species are based on mass action kinetics, with the addition of a non-linear term for modeling inhibitory effects. For transcripts, the rate equation contains three terms: one for transcription, one for inhibition of transcription and one for degradation. The transcription rate is proportional to the concentration of the activating transcription factor for that gene. This transcription can be inhibited by an inhibitory transcription factor. Each transcript has exactly one activating transcription factor and at most one inhibitory transcription factor. For proteins, the rate equation also contains three terms: one for translation, one for degradation and one for inhibition of degradation. The translation rate is proportional to the concentration of the transcript for that protein, and the degradation rate is proportional to the concentration of the protein itself. See the Supplementary Methods for a more detailed description and the equations.

Prior distributions
For all parameters, we used uniform priors on a log 10 scale. A log scale was chosen as we were interested in the orders of magnitude of the parameters rather than their precise values. The limits of the uniform distributions were chosen based on various data points and biochemical limits as described in the Supplementary Methods.

Likelihood
Firstly, the time average of the concentration of a transcript was calculated by averaging over two full cell cycles. Then, for relative time course data measured using synchronized cells relative to unsynchronized cells, we modeled the relative value by dividing the modeled concentration by the time average and taking the log. As error model we used a t-distribution with three degrees of freedom, as a means of robust inference (Gelman et al., 2014). This distribution was centered on the log ratio of the relative expression. For the absolute concentration data, the time average value is log 10 transformed, and again a t-distribution is used as error model. As for the prior, the likelihood is specified on a log scale as it is sufficient if the model captures the right order of magnitude. See the Supplementary Methods for the equations.

Model inference
The posterior probability distributions were calculated using parallel-tempered MCMC (Geyer, 1991), using the Bayesian inference software package BCM (Thijssen et al., 2016). For the initial model, we used 32 parallel chains with the temperatures of the chains distributed quadratically. The burn-in period was set to 1.25 million samples followed by a sampling period of 5 million posterior samples, which were subsampled at 1 in 2500. At each step, a random choice was made between updating each chain with five Metropolis-Hastings steps and swapping a random adjacent pair of chains. The probability of selecting a swap step was set to 0.99. For the proposal distribution in the Metropolis-Hastings steps, the parameters were blocked automatically (Turek et al., 2017) and we used a multivariate normal distribution for each block of parameters. The proposal covariance matrix for each block was set to the empirical covariance of the preceding samples and adaptively scaled to obtain an acceptance rate of 0.23 within each block. These settings produced sufficiently uncorrelated posterior samples (see Supplementary File S2 for traces and autocorrelation plots) and were sufficient to achieve at least 100 round trips from prior to posterior. The sampling period and subsampling was doubled for model 3 and quadrupled for model 4, such that the resulting posterior samples were still sufficiently uncorrelated and at least 100 round trips from prior to posterior were achieved.
All files required for running the inference in BCM, including the prior and likelihood specification, the models in SBML/CellDesigner format, as well as a NetCDF archive containing the pre-processed data, are included in Supplementary File S1.

Model checking
The model fit was investigated using the posterior predictive distribution and coefficients of determination. The posterior predictive distribution is the probability distribution of a new set of data, given the model and the observed data. This distribution was approximated using the posterior Monte Carlo samples. The coefficients of determination for the time course data were calculated relative to a null model, which has a separate mean for each experiment. A reference R 2 was calculated by fitting a cubic spline to the data with the smoothing parameter selected through cross-validation. See the Supplementary Methods for details and equations.

Discussion
Model determination is an important aspect of computational modeling. Models in systems biology are frequently underdetermined, and as a result it is often not possible to confirm or falsify a particular model. There is thus a need for methods to determine models more accurately. With the increasing amount of data available for many biological systems, the use of multiple datasets to constrain the parameters from different angles is a promising avenue. Bayesian statistics provides a coherent and convenient framework to accomplish this. Here, we have shown that it is feasible to integrate diverse datasets during the Bayesian inference of parameters of an ODE-based model. The process as described here may be useful as a general recipe for integrating diverse measurement types also in other settings. More importantly, we have shown that this integration of diverse data types can provide tighter posterior estimates, at least in obtaining the right order of magnitude, thus achieving more accurate model determination. We noticed that even when a single dataset, taken by itself, provides little information, it can still significantly improve parameter estimates when used in conjunction with other datasets.
There are several challenges when using this type of data integration based on model simulation and Bayesian statistics. The biggest challenge is the scaling of the computational demands with respect to the size of the model. This is due to two reasons. First, the simulation of a computational model typically does not scale well with model size (cubically in the case of direct, implicit ODE solvers). Second, the parameter inference is increasingly challenging when the number of parameters increases. Although in theory Monte Carlo methods scale independently of the dimensionality, this requires that the samples are concentrated in regions of high posterior probability. The efficiency of generating a good set of samples critically depends on the proposal distribution that is used. Given the complex shape of the posterior probability distributions of biological computational models, in particular the presence of multiple modes and ridges (Girolami, 2008;Hug et al., 2013), proposal distributions typically become much less efficient with higher dimensionality. Both of these computational challenges apply more generally to any approach using model simulation and global parameter inference. Increases in computational capabilities, more efficient simulation methods and sampling or optimization methods tailored for the inference of biological computational models may allow larger models in the future.
For budding yeast, and their cell cycle regulation in particular, many more measurements have been performed, such as mRNA quantification by qPCR (Miura et al., 2008) and RNA sequencing (Nagalakshmi et al., 2008), protein-level time course data by mass spectrometry (Flory et al., 2006) and GFP-tagged time lapse microscopy (Ball et al., 2011). In principle, these data can be integrated with the same approach as was done for the data in this study, and it would be interesting to explore the contributions and concordance of these measurements. A challenge for further integration of time course data is the synchronization of the timing, which is not straightforward when using different experimental setups. This synchronization can also directly affect kinetic rates, for example the alignment of transcript and protein time course data can directly influence the estimated translation rate.
To be able to compare the contribution of the different datatypes, it is necessary to quantify the uncertainty in the parameter estimates, which was achieved here using Bayesian statistics. The quantification of uncertainty has previously been achieved with different approaches as well (reviewed by Vanlier et al., 2013), including using the profile likelihood (Raue et al., 2009) and through bootstrapping (Brä nnmark et al., 2010). The incorporation of multiple datasets in the likelihood function can in principle be translated to these formalisms as well. A unique advantage of the Bayesian approach is the ability to explicitly include data as prior information, which we have utilized to incorporate various datasets. Profile likelihoods may be computationally more efficient to calculate than posterior probabilities, although the calculations still involve the most challenging aspect, namely global optimization. The profile likelihood is limited in that it provides uncertainty estimates for each parameter separately rather than for all parameters jointly.
In conclusion, we have shown that diverse types of measurements can be successfully integrated during the inference of parameters of ODE systems using Bayesian statistics. This integration provided more tightly constrained parameter estimates, thereby achieving a more accurate model determination.