- Methodology Article
- Open Access
Rational selection of experimental readout and intervention sites for reducing uncertainties in computational model predictions
- Robert J Flassig^{1}Email author,
- Iryna Migal^{1},
- Esther van der Zalm^{1},
- Liisa Rihko-Struckmann^{1} and
- Kai Sundmacher^{1, 2}
https://doi.org/10.1186/s12859-014-0436-5
© Flassig et al.; licensee BioMed Central. 2015
- Received: 25 July 2014
- Accepted: 17 December 2014
- Published: 16 January 2015
Abstract
Background
Understanding the dynamics of biological processes can substantially be supported by computational models in the form of nonlinear ordinary differential equations (ODE). Typically, this model class contains many unknown parameters, which are estimated from inadequate and noisy data. Depending on the ODE structure, predictions based on unmeasured states and associated parameters are highly uncertain, even undetermined. For given data, profile likelihood analysis has been proven to be one of the most practically relevant approaches for analyzing the identifiability of an ODE structure, and thus model predictions. In case of highly uncertain or non-identifiable parameters, rational experimental design based on various approaches has shown to significantly reduce parameter uncertainties with minimal amount of effort.
Results
In this work we illustrate how to use profile likelihood samples for quantifying the individual contribution of parameter uncertainty to prediction uncertainty. For the uncertainty quantification we introduce the profile likelihood sensitivity (PLS) index. Additionally, for the case of several uncertain parameters, we introduce the PLS entropy to quantify individual contributions to the overall prediction uncertainty. We show how to use these two criteria as an experimental design objective for selecting new, informative readouts in combination with intervention site identification. The characteristics of the proposed multi-criterion objective are illustrated with an in silico example. We further illustrate how an existing practically non-identifiable model for the chlorophyll fluorescence induction in a photosynthetic organism, D. salina, can be rendered identifiable by additional experiments with new readouts.
Conclusions
Having data and profile likelihood samples at hand, the here proposed uncertainty quantification based on prediction samples from the profile likelihood provides a simple way for determining individual contributions of parameter uncertainties to uncertainties in model predictions. The uncertainty quantification of specific model predictions allows identifying regions, where model predictions have to be considered with care. Such uncertain regions can be used for a rational experimental design to render initially highly uncertain model predictions into certainty. Finally, our uncertainty quantification directly accounts for parameter interdependencies and parameter sensitivities of the specific prediction.
Keywords
- Computational modeling
- Identifiability
- Experimental design
- Readout selection
- Intervention site selection
- Profile likelihood
- Chlorophyll fluorescence induction
Background
Advances in technology and biotechnology in particular allow us to look inside biological cells and observe dynamic processes occurring at the molecular level. Still, many of these processes can only partially be observed in experiments hampering the experimental exploration of interaction mechanisms. Here, a computational abstraction of the dynamic biochemical process in the form of an ordinary differential equation system (ODE) with unknown parameters can provide answers to the dynamics of unmeasured states that in turn give information on interaction mechanisms. Furthermore, model-based predictions and optimizations are possible. Such a model-based approach relies on the adequacy of the model, i.e. properly identified structure and parameter values. Often, the amount and quality of the experimental data is insufficient for complete model identification resulting into badly constrained or even non-identiafiable parameters, and thus uncertain dynamic model predictions. Although model predictions on observed data can be extremely certain and useful even in the presence of unidentifiable parameters (e.g. [1,2]), model predictions on unobserved (internal) model states that are related to these unidentifiable parameters can be highly uncertain. If model based predictions on internal states are of interest, experimental design can be used to rationally design new experiments with optimized content of information with respect to a specific model prediction. Several excellent publications have appeared over the last years, which focus on identification of computational models for biochemical systems by applying a variety of methodological optimal experimental design approaches, e.g. [3-8].
Profile likelihood estimation has been proven to be a valuable tool for parameter identifiability analysis [9]. Parameter identifiability analysis investigates whether a model parameter can be uniquely determined for the given data and input-output setting. For non-identifiable parameters, there exists an uncountable set of parameters, which yield the same model input-output behavior. As a result, predictions on internal states - states that are not directly observed in experiments - become highly uncertain [9]. An identifiability analysis can hint at a necessary re-design of an experimental input-output setup, a model re-parameterization or reduction to resolve non-identifiabilities [4].
In the following we briefly describe ODE modeling of biochemical processes, parameter estimation in ODE models and parameter identifiability analysis based on the profile likelihood. We then show how to use profile likelihood samples for quantifying individual contributions of parameter uncertainties to uncertainties in the model predictions by introducing the profile likelihood sensitivity index and profile likelihood sensitivity entropy. We further show, how to use this uncertainty quantification for experimental design by formulating the respective multi-criterion objective. The uncertainty quantification in combination with experimental design is illustrated with (i) an intuitive in silico example and (ii) a dynamic chlorophyll fluorescence induction model of the photosynthetic organism D. salina.
Methods
General model formulation
where x(t,θ _{ x }) denotes the states of the system, u(t) indicates an external input function and θ _{ x } a vector of dynamic system parameters. Experimental readouts y(t) are related to the model via the readout function g, which includes scaling and offset parameters θ _{ y } and an additive white measurement noise model \(\epsilon \propto N\left (0,\sigma _{\text {exp}}^{2}\right)\).
Parameter estimation
Here, y _{exp}(t _{ i }) denotes measured data at time points t _{ i } (i=1…n, with n number of time points) and y _{sim}(t _{ i }) indicates the model output for time points t _{ i }. For the assumed measurement noise model and likelihood L we have χ ^{2}∝−2 logL and \(\hat {\theta }\) corresponds to the maximum likelihood estimate (MLE). In the following we thus use χ ^{2} as a placeholder for the likelihood.
Profile likelihood
which represents a function in θ _{ i } of least increase in the likelihood. The least increase is achieved by adjusting θ _{ j },j=1…n _{ θ }∖i accordingly.
with δ _{ α } being the α quantile of the χ ^{2} distribution with d f=1 (pointwise) or d f=n _{ θ } (simultaneous) degrees of freedom [9]. A confidence interval of parameter θ _{ i } is simply given by the borders of CR.
Uncertainty quantification based on profile likelihood sensitivity indices
There exist many advanced methods to analyze and quantify uncertainty propagation in ODE models. In biochemical systems modeling, efficient sampling strategies, including MCMC, profile likelihood and sigma points have been successfully applied to uncertainty analysis in real systems [2,10,11]. These methodological approaches are - in contrast to approaches based on classical Fisher Information (FI) - especially effective in cases of highly nonlinear models, as the nonlinearity is more adequately accounted for. This also holds for the model-based experimental design: Our presented approach is a sample based approach, whereas FI relies on curvature information of the likelihood. As has been shown by several authors, including [9,12], FI may not be well suited for non-linear models. In contrast, the profile likelihood approach accounts for a possible non-linear character of the model. Further, FI-based approaches operate on the covariance matrix in the parameter space and can be given a geometrical interpretation: FI criteria measure the shape and orientation of an n _{ θ } dimensional ellipsoid (to be more specific, the inverse of FI is used). By optimizing such FI-based criteria (e.g. A-, D-, E-optimality), one tries to reduce and distribute uncertainties and their correlations in the parameter space. In the case of non-linear models, this does not guarantee that model-based predictions other than parameter values become more constrained. Our approach (PLS index and entropy) differs in this that it operates in the prediction space (which can also include predictions on parameter values). In this way, our approach is more general. Additionally, profile likelihood samples are readily available once a practically identifiability analysis based on the profile likelihoods (one of the practically most relevant approaches in systems biology) has been performed by the modeler.
which we refer to as the profile likelihood sensitivity index (PLS index) of parameter θ _{ i } for prediction p at time t _{ k }. Note that expressions max/min({·}) define the maximum and minimum (=extremes) over the set \(\{\cdot \}\subset \mathcal {P}_{i}\), which contains model predictions p _{ i }(t _{ k }) sampled along the profile likelihood of parameter θ _{ i }∈CR_{ i }. In the case where p _{ i }(t _{ k })≡x(t _{ k },θ _{ i }) and finite confidence interval of parameter θ _{ i }, max/min({·}) approximate the confidence band around the MLE state trajectory x ^{MLE}(t). This also holds for an arbitrary prediction p. If model parameters are unidentifiable, their respective confidence interval is unbounded. In this case, we suggest sampling a reasonable large range along the profile likelihood (say 3 orders of magnitudes) around the MLE of the unidentifiable parameters. In this way, the impact of unidentifiable parameters on so far unobserved predictions p _{ i }(t _{ k }) is revealed via s _{ i }(t _{ k }). The denominator \(\langle \hat {p}(t)\rangle _{t}\) in Eq. (6) represents the time average of the prediction at the MLE of the parameters.
Shannon’s entropy measures how homogenous PLS indices s _{ i }(t _{ k }) contribute to s _{tot}.
Experimental design to reduce prediction uncertainties
PLS indices can be used to identify highly uncertain predictions and thus provide guidance in designing new, informative experiments: An optimal experimental design (OED) that maximizing the PLS index for an individual parameter corresponds to an experimental region, where the uncertainty of this parameter induces maximal uncertainty in the model prediction. Therefore, if one is interested in reducing the uncertainty of a specific prediction p(t _{ k }) by an additional experiment, one would simply select an experimental design that maximizes the PLS index of p(t _{ k }). Note however, that if one wants to reduce the overall uncertainty of a model prediction as a result of several uncertain model parameters it is not sufficient to identify an experimental region that maximizes Eq. (7). Similar to A-, D- or E-optimality based on FI, one has to trade off maximal s _{tot} and more or less equal contributions to s _{tot} by all uncertain parameters. Here, the measure in Eq. (8) should be maximized aiming at equal contributions of s _{ i }(t _{ k }) and corresponding parameter uncertainties. Such a design should produce homogenous parameter information in the data with respect to the prediction goal of the model.
An often targeted prediction goal is the analysis of unmeasured model states, thus p _{ ij }(t _{ k })≡x _{ j }(t _{ k },θ _{ i }) with j∈{unmeasured states}. Two design scenarios can be distinguished: if one is to choose a set of new, additional readouts from the set {unmeasured states}, one would select states x _{ j } that maximize the objective O=[s _{ j,tot} J _{ j,t o t }]^{T}. If one cannot select a new readout, other design variables as for instant intervention sites (e.g. inhibition of states or associated reactions), stimulus profiles or selection of measurement time points can also be used to optimize the objective O for a given readout setup. Both design scenarios may also be combined. In the Results and discussion section we illustrate how to select additional readouts and/or inhibition sites.
Cultivation of Dunaliella salina
In this part the experimental procedures are described that have been used to obtain the data for the photosynthetic application. The Dunaliella salina strain CCAP19/18 [13] was used, which has been ordered from CCAP (www.ccap.ac.uk). Bacteria in the medium were killed by 100 μM chloramphenicol; other contaminating organisms were not present (PDA tests for fungal contamination; light microscopy at 1000x with oil immersion, Zeiss Axio Image). Medium composition was used as described in [14], but modified by addition of 40 mM Hepes pH 7.5. 1-3 ml was inoculated in 100 ml sterile medium. Cultures were grown in a shaking incubator (Infors HT) at 100 rpm at 16/8 h light /dark cycle at 26°C and 3.5% CO _{2}. FL tubes Gro-Lux 15 W Fluorescent Lamps Sylvania type F15W /GRO/ were used as light source; intensity was 30-60 μ Em ^{−2} s ^{−1}. For chlorophyll fluorescence (Chl F) measurements 7-14 d old cultures were used.
Chlorophyll fluorescence measurements
The DUAL-PAM-100 (Walz, Effeltrich, Germany) using the DUAL-E emitter (actinic light = 620 nm; measuring light = 460 nm) and DUAL-DB detector was used for Chl F measurements. A cell density of 10^{7} ml ^{−1} was taken (adjusted with help of cell counting with the Cellometer Auto T4 Plus, PEQLAB). Measuring light frequency and intensity were adjusted to 500 Hz, and 3 μ Em ^{−2} s ^{−1}, respectively. Before performing fluorescence measurements, samples were kept in the dark for 10 min. Temperature during pre-incubation and measurement was kept at 23±0.5°C, and cell suspensions were stirred to prevent cell sedimentation. A light pulse of 166 μ Em ^{−2} s ^{−1} and duration of 1 s was applied to the cell suspension; 6 replicate measurements were performed at a sampling of Δ t=10^{−4} s. A light intensity of 166 μ E m ^{−2} s ^{−1} was selected. This intensity produced the typical chlorophyll fluorescence induction curve.
Results and discussion
In silico example
Optimal readout selection
Profile likelihood based identifiability analysis and confidence intervals of the in silico model example in log-space
Parameter | \(\hat {\theta }_{i}\) | y = D | y =[ D , A ] ^{ T } | y = [D, B] ^{ T } | y = [D, C] ^{ T } | ||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Identifiability | Lower CI | Upper CI | Identifiability | Lower CI | Upper CI | Identifiability | Lower CI | Upper CI | Identifiability | Lower CI | Upper CI | ||
log10k _{11} | 0.043 | non-identifiable | −∞ | ∞ | non-identifiable | −∞ | 0.65 | non-identifiable | −1.08 | ∞ | non-identifiable | −∞ | ∞ |
log10k _{12} | 0.301 | non-identifiable | −∞ | ∞ | non-identifiable | −∞ | ∞ | non-identifiable | −∞ | 1.07 | non-identifiable | −∞ | ∞ |
log10k _{21} | 0.398 | non-identifiable | −∞ | ∞ | non-identifiable | −∞ | 0.65 | non-identifiable | −∞ | ∞ | non-identifiable | −0.03 | ∞ |
log10k _{22} | 0.004 | non-identifiable | −∞ | ∞ | non-identifiable | −∞ | ∞ | non-identifiable | −∞ | ∞ | non-identifiable | −∞ | 0.45 |
log10k _{23} | −0.301 | non-identifiable | −∞ | ∞ | non-identifiable | −∞ | ∞ | non-identifiable | −∞ | ∞ | non-identifiable | −∞ | 0.33 |
log10d | 0.004 | non-identifiable | −∞ | 0.42 | non-identifiable | −∞ | 0.42 | non-identifiable | −∞ | 0.42 | non-identifiable | −∞ | 0.4 |
Profile likelihood based identifiability analysis and confidence intervals of the in silico model example in log-space
Parameter | \({\hat {\theta }_{i}}\) | y = [D, A B] ^{ T } | y= [D, A C] ^{ T } | y =[D, B C] ^{ T } | y =[D, A B C] ^{ T } | ||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Identifiability | Lower CI | Upper CI | Identifiability | Lower CI | Upper CI | Identifiability | Lower CI | Upper CI | Identifiability | Lower CI | Upper CI | ||
log10k _{11} | 0.043 | identifiable | −0.72 | 0.44 | non-identifiable | −∞ | 0.41 | identifiable | −0.86 | 1.17 | identifiable | −0.70 | 0.36 |
log10k _{12} | 0.301 | non-identifiable | −∞ | 0.86 | non-identifiable | −∞ | ∞ | non-identifiable | −∞ | 0.82 | non-identifiable | −∞ | 0.81 |
log10k _{21} | 0.398 | identifiable | −0.16 | 0.60 | identifiable | 0.07 | 0.63 | identifiable | −0.01 | 1.45 | identifiable | 0.12 | 0.59 |
log10k _{22} | 0.004 | non-identifiable | −∞ | 3 | non-identifiable | −∞ | 0.44 | non-identifiable | −∞ | 0.39 | non-identifiable | −∞ | 0.39 |
log10k _{23} | −0.301 | non-identifiable | −∞ | ∞ | non-identifiable | −∞ | 0.31 | non-identifiable | −∞ | 0.29 | non-identifiable | −∞ | 0.23 |
log10d | 0.004 | non-identifiable | −∞ | 0.42 | non-identifiable | −∞ | 0.40 | non-identifiable | −∞ | 0.39 | non-identifiable | −∞ | 0.37 |
Optimal readout and inhibition site selection
Profile likelihood based identifiability analysis and confidence intervals of the in silico model example in log-space
Parameter | \(\hat {\theta }_{i}\) | \(y=\left [\text {A B C D, C}_{k_{22}}\right ]^{\text {T}}\) | \(y=\left [\text {A B C D, C}_{k_{21}}\right ]^{\text {T}}\) | \(y=\left [\text {A B C D, D}_{k_{21}}\right ]^{\text {T}}\) | ||||||
---|---|---|---|---|---|---|---|---|---|---|
Identifiability | Lower CI | Upper CI | Identifiability | Lower CI | Upper CI | Identifiability | Lower CI | Upper CI | ||
log10k _{11} | 0.043 | identifiable | −0.62 | 0.29 | identifiable | −0.70 | 0.36 | identifiable | −0.25 | 0.23 |
log10k _{12} | 0.301 | non-identifiable | −∞ | 0.73 | non-identifiable | −∞ | 0.81 | identifiable | −0.02 | 0.63 |
log10k _{21} | 0.398 | identifiable | 0.24 | 0.55 | identifiable | 0.12 | 0.59 | identifiable | 0.23 | 0.54 |
log10k _{22} | 0.004 | identifiable | −0.23 | 0.21 | non-identifiable | −∞ | 0.39 | identifiable | −0.33 | 0.24 |
log10k _{23} | −0.301 | non-identifiable | −∞ | 0.02 | non-identifiable | −∞ | 0.23 | non-identifiable | −∞ | 0.17 |
log10d | 0.004 | non-identifiable | −∞ | 0.28 | non-identifiable | −∞ | 0.37 | non-identifiable | −∞ | 0.32 |
Photosynthetic organism D. salina
Model description
According to [17] there are 290 antennae in one antenna complex and thus we fixed A _{0}=290. A total number of 13 unknown model parameters have to be estimated from the data.
Parameter estimation and identifiability
in order to allow estimating sample mean and standard deviations from the replicates. F denotes the fluorescence value at a given time point t, F _{0} is the ground fluorescence at t=0 and F _{ m } is the maximal measured fluorescence.
Parameter identifiability of the D. salina model and confidence intervals based on original data and in silico experiments in log-space
Parameter | \(\boldsymbol {\hat {\theta }_{i}}\) | Original data: y=Gk _{ 2 } x _{ 1 } | Add in silico y = x _{ 3 } | Add in silico y = x _{ 5 } | ||||||
---|---|---|---|---|---|---|---|---|---|---|
Identifiability | Lower CI | Upper CI | Identifiability | Lower CI | Upper CI | Identifiability | Lower CI | Upper CI | ||
log10k _{1} | -1.9507 | identifiable | -1.9526 | -1.9481 | identifiable | -1.9507 | -1.9507 | identifiable | -1.9512 | -1.9499 |
log10k _{2} | 1.2603 | identifiable | 1.2603 | 1.2663 | identifiable | 1.2624 | 1.2641 | identifiable | 1.2626 | 1.2648 |
log10k _{3} | 3.3192 | identifiable | 3.3160 | 3.3251 | identifiable | 3.3173 | 3.320 | identifiable | 3.3183 | 3.3202 |
log10k _{4} | 2.0231 | identifiable | 2.0192 | 2.0269 | identifiable | 2.0220 | 2.0240 | identifiable | 2.0218 | 2.0239 |
log10k _{5} | 4.5278 | identifiable | 4.5214 | 4.5351 | identifiable | 4.5278 | 4.5278 | identifiable | 4.5264 | 4.5296 |
log10k _{6} | 5.5047 | identifiable | 5.4963 | 5.5128 | identifiable | 5.5030 | 5.5056 | identifiable | 5.5030 | 5.5062 |
log10k _{7} | 4.6765 | identifiable | 4.6683 | 4.6841 | identifiable | 4.6742 | 4.6774 | identifiable | 4.6752 | 4.6786 |
log10k _{8} | 2.8452 | identifiable | 2.8222 | 2.8602 | identifiable | 2.8439 | 2.8464 | identifiable | 2.8406 | 2.8487 |
log10k _{9} | 1.5753 | identifiable | 1.5697 | 1.5839 | identifiable | 1.5741 | 1.5759 | identifiable | 1.5749 | 1.5757 |
log10k _{10} | 0.1611 | non-identifiable | −∞ | 0.5602 | identifiable | 0.1611 | 0.1611 | identifiable | 0.1606 | 0.1616 |
log10P Q _{0} | 1.4930 | identifiable | 1.4907 | 1.4947 | identifiable | 1.4922 | 1.4935 | identifiable | 1.4927 | 1.4931 |
log10r _{2} | -0.3069 | identifiable | -0.3106 | -0.3025 | identifiable | -0.3069 | -0.3069 | identifiable | -0.3073 | -0.3065 |
log10G | -2.8061 | identifiable | -2.8067 | -2.8036 | identifiable | -2.8061 | -2.8061 | identifiable | -2.8068 | -2.8055 |
Optimal experimental design for D. salina: Readout selection
As it turned out, states \(Q_{B}^{-}\) and \(Q_{B}^{2-}\) seem good candidates as additional readouts, having large PLS indices (see Additional file 1: Figure S5 for the criterion space), whereas \(Q_{A}^{-}\) has the largest PLS entropy. PQ seems to equally trade-off the PLS index vs. PLS entropy. Since it was not possible to measure these internal states directly, in silico values for \(Q_{B}^{-}\) and PQ were generated for the MLE parameter set to test their suitability as additional readouts. The in silico data were then used to perform identifiability analysis with the profile likelihood approach. The results are presented in Table 4, the corresponding profile likelihoods can be found in Figure 6. The additional in silico data for \(Q_{B}^{-}\) or PQ allow identifying all parameters at α=0.05 (s. Table 4). Then, it would be possible to perform a conclusive, model-based analysis of the fluorescence induction in D. salina including all unmeasured states.
Conclusions
In this work we illustrate how to analyze and quantify uncertainty propagation in ODE models based on profile likelihood samples. We introduce the profile likelihood sensitivity index, which reflects the individual contribution of an uncertain model parameter to a model prediction. In the case of several parameters, parameter interdependencies are - by definition of the profile likelihood - account for, and the sum of profile likelihood sensitivity indices can be used to quantify the overall effect. However, individual parameter uncertainty contributions are not clear. Here we propose to use Shannon’s entropy on the individual profile likelihood sensitivity indices as an additional measure. The PLS entropy describes the amount of uncertainty contributed by each uncertain parameter to the overall PLS index. In this way, PLS entropy can be used to look for homogenous uncertainty contribution. We further describe in a general way, how profile likelihood sensitivity index and entropy can be used to identify experimental regions, where one has to collect data in order to reduce prediction uncertainties. Such an approach is especially valuable in large biochemical networks, where intuitive analysis is hampered by the complexity of the system. Additionally, PLS index and entropy provide information on prediction domains, where one has to consider model-based predictions with care.
We applied the concept of PLS indices and entropies to an intuitive in silico example to illustrate how one can rationally select additional readout and/or intervention sites in order to reduce prediction uncertainties. Finally, using the chlorophyll fluorescence induction of D. salina as a true life case, we illustrate how an initially non-identifiable model can potentially be rendered identifiable by selection additional readout signals.
Availability of supporting data
The supplementary material contains the ODE system of the in silico example, further details on the fluorescence induction model of D. salina (comparison of profile likelihood and classical sensitivity analysis including time course of PLS indices, prediction samples and criterion space for readout selection). We further provide MATLAB code for the in silico example to explore the presented design approach and also data and MATLAB code for the chlorophyll fluorescence induction model (Additional file 2). Further material and support is available upon request.
Declarations
Authors’ Affiliations
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