An empirical Bayesian approach for model-based inference of cellular signaling networks
© Klinke; licensee BioMed Central Ltd. 2009
Received: 29 June 2009
Accepted: 09 November 2009
Published: 09 November 2009
A common challenge in systems biology is to infer mechanistic descriptions of biological process given limited observations of a biological system. Mathematical models are frequently used to represent a belief about the causal relationships among proteins within a signaling network. Bayesian methods provide an attractive framework for inferring the validity of those beliefs in the context of the available data. However, efficient sampling of high-dimensional parameter space and appropriate convergence criteria provide barriers for implementing an empirical Bayesian approach. The objective of this study was to apply an Adaptive Markov chain Monte Carlo technique to a typical study of cellular signaling pathways.
As an illustrative example, a kinetic model for the early signaling events associated with the epidermal growth factor (EGF) signaling network was calibrated against dynamic measurements observed in primary rat hepatocytes. A convergence criterion, based upon the Gelman-Rubin potential scale reduction factor, was applied to the model predictions. The posterior distributions of the parameters exhibited complicated structure, including significant covariance between specific parameters and a broad range of variance among the parameters. The model predictions, in contrast, were narrowly distributed and were used to identify areas of agreement among a collection of experimental studies.
In summary, an empirical Bayesian approach was developed for inferring the confidence that one can place in a particular model that describes signal transduction mechanisms and for inferring inconsistencies in experimental measurements.
Cellular response to extracellular stimuli is governed by biochemical reactions that allow the transfer of information from the cell membrane to the nucleus and back . Based upon well-crafted bench experiments, many of the molecular players in the various signaling pathways are known. However, the roles that individual proteins play at specific points in time and in particular systems are largely unknown . It is precisely in this situation that mathematical models are most helpful .
Mathematical models are tools used to rationalize about the intracellular signaling mechanisms that underpin biological response . Mathematical models that describe biochemical kinetics are explicit statements about molecular-molecular interactions that are presumed to be important in a system and the corresponding dynamics of these interactions. These interactions give rise to a flow of molecular information in the form of reaction pathways . The primary goal of the analysis of these reaction pathways is to make predictions: what do we expect to happen in a particular reacting mixture under particular reaction conditions, given our current understanding of molecular interactions? Similarities confirm our explicit statements while differences between the expected behaviors and new data highlight areas of uncertainty in our understanding and provide the engine for scientific progress . Analogous to experimental studies, the ability of a particular mathematical model to describe a system of interest must include a statement of belief.
Belief derived from a mathematical model is expressed commonly in terms of a single point estimate for the predictions, obtained from the set of parameters that minimizes the variance between model and data . Given that a model constrains the set of possible states of the system, it would be particularly valuable to provide an estimate of the uncertainty associated with the model predictions given the available data. A Bayesian view of statistics is a mathematical expression of our beliefs . Beliefs are established based upon the observation of data and the interpretation of that data within the context of our prior knowledge . Mathematical models provide a quantitative framework for representing prior knowledge of the detailed biochemical interactions that comprise a signaling network. The unknown parameters of the model can be calibrated against the observed network dynamics. With recent advances in computational power, computationally-intensive Bayesian techniques, such as Markov Chain Monte Carlo (MCMC) algorithms , are an attractive option for assessing the uncertainty in the model parameters given the calibration data and are increasingly applied to biological systems .
MCMC techniques provide random walks in parameter space whereby successive steps are weighted by the likelihood of observing data given the corresponding parameter values. A prior distribution is used to determine the size of proposed steps (i.e., the proposal distribution) within parameter space. The historical challenge with implementing a Bayesian approach is to specify a prior distribution for the values of the unknown parameters . Conventional application of Bayesian techniques to cellular signaling networks focus on using proposal distributions that dynamically scale the prior distributions during the simulation (e.g., [11–13]), whereby the structure of the proposal distribution is unchanged during the simulation, or that is based upon the prior information encoded within the model and the parameter values at local optima (e.g., [14, 15]). Moreover, a common approach in the field is to use small models as a vehicle to demonstrate new Bayesian algorithms (e.g., [11–14, 16]). While these small models, sometimes called toy models, are computationally attractive, larger models foreshadow potential problems that may arise when a Bayesian technique is used in practice, such as computational efficiency. The computational efficiency of a MCMC algorithm depends highly on the structure of the proposal distribution. One of the recent advances in the field has been has been a technique that allows the MCMC algorithm to "learn" a better structure for the proposal distribution "on-the-fly." These algorithms are "empirical" Bayesian techniques in the sense that the structure of the proposal distribution is conditioned on the observed data. Adaptive MCMC (AMCMC) algorithms (e.g., ) dynamically adjust the proposal distribution from a non-informative prior distribution at the start of the simulation to a proposal distribution that reflects the structure in the cumulative Markov chain. The objective of this study was to develop and apply an Adaptive MCMC technique to a typical study of cellular signaling pathways. As an illustrative example, a kinetic model for the early signaling events associated with one receptor of the epidermal growth factor (EGF) signaling network was calibrated against dynamic measurements observed in primary rat hepatocytes.
A model for early ErbB1 signaling
To illustrate the empirical Bayesian approach, a mathematical model was developed for describing the early signaling events via the ErbB1 receptor. Developing this model was divided into three steps: specifying the model topology, calibrating the model, and estimating the uncertainty in the model predictions. In the following sections, each of these aspects is described in greater detail.
The resulting biochemical kinetic reaction network specifies a particular causal connectivity diagram between proteins (i.e., a set of coupled non-linear differential algebraic equations). This connectivity diagram must be coupled with parameters for each reaction to simulate the concentration changes with time. The values for the parameters were determined to be consistent with observed experimental data. In particular, dissociation constants for particular protein-protein interactions were constrained by the recent observations by MacBeath and coworkers [27, 28]. In addition, measurable interaction strengths between peptide motifs were used to estimate reaction path degeneracy (Klinke DJ: Signal Transduction Networks in Cancer: Quantitative Parameters Influence Network Topology, submitted) (see Additional file 1: Table S5 for a list of the dissociation constants and values for reaction path degeneracy). These data helped reduce the number of parameters determined from 43 (38 kinetic parameters and 5 initial concentrations) in  to 34 (28 kinetic parameters and 6 initial concentrations) in this study.
As cellular context may influence the strength of a particular pathway , experimental data was extracted from the literature to calibrate the initial signaling events in a specific system: the response of primary rat hepatocytes to 20 nM of EGF. Parameter values were chosen based on appropriate measurements in rat hepatocytes if available. The changes in phosphorylated ErbB1 [18, 30, 31], Grb2 binding to ErbB1 , Grb2 binding to Shc , phosphorylated Shc [18, 22, 31], phosphorylated PLCγ-1 , Sos binding to ErbB1 , and the active fraction of Ras (RasGTP)  were calibrated to observations in isolated hepatocytes from Harlan Sprague Dawley rats upon exposure to 20 nM EGF. Initial expression of ErbB1 prior to EGF stimulation was observed to be 100 nM. However, in the absence of specific kinetic measurements in rat hepatocytes, data obtained from human cell lines were used. Total ErbB1 receptor expression was calibrated to changes in ErbB1 expression in HMEC 184A1 cells observed following exposure to 20 nM EGF . In addition, the dynamics of Shc phosphorylation was also compared against measurements obtained using 10 nM EGF in the MCF-7 cell line . The model equations were encoded and evaluated in MATLAB V7.0 (The MathWorks, Natick, MA). Summed squared error between experimental and simulated measurements was used to determine goodness-of-fit. Using the set of coupled differential algebraic equations, the maximum likelihood estimates for unknown parameter values were determined from these experimental data using a simulated annealing optimization algorithm . The optimum values were used as a starting point for the Markov Chain.
A Bayesian perspective on model-based inference
In equation 2, P(Θ|M) is the probability of sampling a point in parameter space Θ prior to any knowledge about data Y (i.e., the prior for Θ), P(Y) is called conventionally the evidence for a model, and P(Y|Θ, M) is the conditional probability of simulating data Y given Θ and M (i.e., the likelihood of Y given Θ and M).
Similarly, an approximate Bayesian approach was recently described that uses a likelihood function equal to the determinant of the dispersion matrix alone . In practice, the dispersion matrix can be normalized to a reference quantify to minimize the contribution of round-off error in calculating P(Y|Θ, M). Given the rugged landscape of the dispersion matrix with respect to the parameter space, a closed form solution of the expectation integral (i.e., Equation 1) is intractable. As an alternative, Monte Carlo sampling is an efficient method for integrating such complex integrals . In the following section, a Monte Carlo algorithm is proposed that provides a Bayesian estimate of P(Θ|M, Y).
A Bayesian estimate for P(Θ|M, Y) can be obtained using computer-intensive methods. The Metropolis-Hasting (MH) algorithm [34, 35] is a Monte Carlo technique that creates a sequence (i.e., a chain) of θ s that, in the limit of a long chain are drawn from the target distribution, P(Θ|M, Y). When the proposed steps are drawn from a stationary distribution, the resulting chain is a Markov chain. A proposed new step in the Markov chain, θ N , is accepted based upon the relative conditional probability of the new step in capturing the observations (i.e., P(Y|θ N , M)) relative to the current step. This acceptance criteria enables the collective Markov chain to represent samples drawn from the target distribution P(Θ|M, Y). New proposed steps within the Markov chain are drawn from a proposal distribution ∏(·). As mentioned above, the challenge in implementing a Markov Chain Monte Carlo (MCMC) approach is specifying the proposal distribution, ∏(·). In this study, the initial estimate of scale of proposal distribution for Θ was proportional to 1/P, where P is the row dimension of the vector Θ . Parameter sampling was performed in log space to ensure that all kinetic parameters and species concentrations remain positive. In high dimensional problems with expensive calculations of the likelihood function, efficient sampling of P(Θ|M, Y) is essential and can be obtained using a proposal distribution that reflects the structure of the target distribution (i.e., the covariance of P(Θ|M, Y)).
A prior distribution, P(Θ), is used to provide an initial estimate of the proposal distribution, ∏(Θ). In conventional MCMC algorithms, the proposal distribution can be scaled to achieve a target acceptance fraction but the structure of the proposal distribution remains fixed to that specified by the prior distribution. A recent development in the MCMC field has been the development of adaptive MCMC (AMCMC) algorithms (e.g., ) that dynamically adjust the structure of the proposal distribution based upon the prior steps of an evolving Markov chain. The prior distribution used in this study was non-informative (i.e., the same for all parameters), proper (i.e., a finite integral), normally distributed (i.e., N(0, 1/P)), and used to specify the initial proposal distribution (∏(·)). Non-informative in this context means that no information was used to specify that there was greater uncertainty in some of the parameters relative to others. Following a specified "learning" period, the proposal distribution was adjusted, as described in the next section, to reflect the structure in the cumulative Markov chain.
Adaptive MCMC algorithm
The following adaptive MH algorithm was used to generate a Monte Carlo Markov Chain from multi-response data:
where P is the row dimension of Θ, δ jk is the Kronecker's delta, and s is an adjustable proposal scaling factor used to give an initial acceptance fraction of 0.2. Select a value for s. Note that the Kronecker's delta, δ jk , is equal to 1 when the indices j and k are equal and δ jk is equal to 0 when the indices are not equal.
which cumulatively represent a random walk through parameter space with the step size in a particular parameter direction determined by ∏(·).
where Y j is a vector of observed responses for experiment j, M j (θ N ) is the corresponding vector of predicted responses using a model and the corresponding parameter values θ N , and Nobs, jis the number of responses observed in experiment j.
where q(·) is the jumping probability in the direction given as the argument. Given that, in this case, the proposal distribution is symmetric, the q(·) terms cancel.
5. Accept move θi+1= θ N with probability h, else retain current location θi+1= θ i .
6. Update Cov(θi+1... θ0) and s every x AMCMC steps.
where Chol jk (·) is the jk element of the upper triangular matrix obtained from Cholesky factorization of the covariance matrix, Cov(·), and N L is the number of steps contained in an initial non-adapting "learning" period. As proposal density is symmetric, the condition of a detailed balance among random steps within parameter space is satisfied using h as an acceptance probability.
8. Go to 2.
The algorithm was implemented in Matlab (Natick, MA), numerical integration of the set of differential algebraic equations was obtained using the SUNDIALS IDA solver (Lawrence Livermore National Laboratory, Livermore, CA), and R/Bioconductor was used for the analysis of the Markov chain. In addition, the Markov Chains generated by this AMCMC algorithm converge to a stationary target distribution for P(Θ|Y, M) (i.e., is ergodic) as the adaptation of the proposal distribution exhibits diminishing adaptation (i.e., limi→∞||∏ i - ∏i-1|| = 0) .
An inherent characteristic of a Markov chain generated using a Metropolis-Hasting algorithm is the autocorrelative structure of the chain. The presence of autocorrelation between subsequent steps biases estimating the covariance of the model parameters. Mixing is a term used to characterize the autocorrelative structure of the chain. In well-mixed chains, steps separated by a specified distance within the chain exhibit minimal autocorrelation. To minimize the effect of autocorrelation, independent samples from the posterior distribution can be obtained by selecting values from the Markov Chain at every n th iteration, a technique called "thinning." Thinning was used to improve recursive calculation of the proposal covariance of the Markov Chain during the MCMC run. A thinning value of 40 was used to estimate the covariance recursively from the evolving Markov chain, while a thinning value of 200 was used to obtain P(Θ|Y, M) from the final Markov chains.
Convergence of Markov chain
One of the challenges with implementing a MCMC approach for Bayesian inference is deciding when the cumulative Markov chain is a representative sample drawn from the underlying stationary distribution. A Markov chain represents a random walk within parameter space weighted by the relative conditional probability of each step. Convergence is a criteria used to evaluate how long of a chain is necessary to traverse a representative sample of parameter space. Numerous algorithms have been developed to diagnose the convergence of a Markov chain . Most conventional applications of these convergence criteria have focused on the model parameters. However, models of cellular signal transduction pose significant challenges for model-based inference and require a non-conventional approach.
As the objective of this study is to make statements of belief about the predictions of the model rather than particular values of the parameters, a convergence criterion was developed based upon the predictions of the model. The focus on predictions rather than the parameters was motivated by timescale considerations. When a complex dynamical system experiences an abrupt change in environmental conditions, the response of the system can be simplified into different kinetic manifolds (e.g., ). This phenomenon has been termed the slaving principle . The evolution in the system is constrained by the slow variables (i.e., the slow kinetic manifold) while the fast variables (i.e., the fast kinetic manifold) exist at a pseudo-equilibrium. An implication of the slaving principle on Bayesian inference is that the posterior distributions of parameters may exhibit one-sided distributions. The parameters associated with a variable contained within the fast kinetic manifold may be increased above a threshold value with minimal impact on the dynamic response of the system. Below the threshold value, the dynamics of the corresponding variables impinge upon the slow kinetic manifold altering the dynamic response of the system and fitness of the model. In practice, the upper bounds of the parameters are defined by the tolerance of the ODE solver to solve systems with divergent timescales. To surmount this computational challenge, the Gelman-Rubin method was used for assessing convergence of model predictions derived from a series of three parallel Markov chains [41, 42]. The method is based upon the concept that convergence has been achieved when the variance among chains is less than within single chains.
whereby parallel chains of length N should be increased until is less than 1.2 . These converged parallel chains represent samples drawn from a stationary distribution. In conventional use, the Gelman-Rubin method is applied to the second N iterations drawn from parallel chains, each of length 2N. The first N steps are discarded as they are assumed to be drawn from tails of the stationary distributions. Due to the cost of these calculations, only the "learning" period was discarded. The remainder of the parallel chains were used to estimate the convergence of the predictions to a stationary distribution.
The mathematical model, shown schematically in Figure 1, was calibrated against values obtained from the literature. The values reported in the literature were obtained largely in primary rat hepatocytes in response to 20 nM EGF. Kinetic parameters associated with EGF binding to ErbB1 and affinities for protein-protein interactions were used from the literature to reduce the number of free parameters. Dissociation constants (K D 's) for particular protein-protein interactions, where reported, were also obtained from the literature [27, 28] (see Additional file 1: Table S5). In addition, dissociation constants for reactions that create a cyclic pathway (e.g., reaction classes 8, 11, 14, and 15) were constrained by thermodynamic considerations. In addition, the initial values for Grb2, PLC-γ, Shc, Sos, RasGTP, and RasGDP were determined using simulated annealing. A list of all of the parameters can be found in (see Additional file 1: Table S3). The initial concentration of PTP was specified to be 100 nM, as the initial concentration of PTP was confounded with other parameters (data not shown). The initial values for protein expression are dependent on the assumed dissociation constants for protein-protein interaction. Similar dynamics of the system are observed if systematic reductions in the K D 's of protein-protein interaction are coupled with corresponding decreases in the concentration of the signaling proteins (data not shown). Given the best fit values determined using simulated annealing, an empirical Bayesian approach was used to estimate the uncertainty in the model parameters given the available calibration data.
An empirical Bayesian approach to sensitivity analysis
In this study, an empirical Bayesian approach was developed to quantify the uncertainty in the estimated parameters, given the available data, and to estimate the uncertainty in the model predictions, given the range of plausible parameter values. In contrast to recent efforts that use proposal distributions that have fixed structures (e.g., [11–15, 43]), the empirical Bayesian approach used in this study was based upon an adaptive Markov chain Monte Carlo algorithm that adjusts the structure of the proposal distribution based upon the covariance of the cumulative Markov chain. In another recent publication, we used this algorithm to infer the relative strength of control mechanisms in the Interleukin-12 signaling pathway observed in naïve CD4+ T cells by our own hands (Finley SD, Gupta D, Cheng N, Klinke DJ: Inferring Relevant Control Mechanisms for Interleukin-12 Signaling in Naïve CD4+ T Cells, submitted). In this study, an AMCMC algorithm was used to help interpret measurements of Epidermal Growth Factor signaling observed in rat hepatocytes by others using signaling mechanisms postulated from the literature.
Estimated values of the PSRF applied to the parameters (see Additional file 1: Table S3) are consistent with the pairwise scatter plots where parameters with low variance converge first (e.g., kf 15and kdeg 5) while parameters with high variance require a long chain to converge (e.g., kf 6, kf 23, and kf 38). In general, the model results were sensitive to the initial concentrations of Grb2, Shc, and Sos as the posterior distributions were narrower than many of the other parameters shown in Figure 7. A similar observation was reported by Kholodenko et al.  and highlights how variations in protein expression influence cellular response. The rate of degradation of phosphorylated ErbB1 (kdeg 5) and the rate of PTP binding to phosphorylated ErbB1 (kf 15) exhibited the narrowest distributions of all of the parameters. Despite the uncertainty in the parameter values, the model predictions are confined within a relatively narrow region (see Figure 5). By marginalizing the predicted responses over P(Θ|Y), this range in model predictions provided an estimate of P( |M).
Efficiency of sampling parameter space
Model-based inference of cell signaling pathways
where δ(Y) are systematic differences between the model and data and ϵ is a random error component that reflects the inherent uncertainty of a particular assay to probe biology. In analyzing measured data using mathematical models, it is typically assumed that ϵ is independently and identically distributed with a mean of zero and δ(Y) is negligible. These assumptions provide the basis for likelihood functions, such as Equation 3, and some cost functions, such as root mean squared error, used in optimization algorithms. Optimization focuses on finding the best solution to a problem given a set of constraints, such as finding P( |M, Y) such that ϵ is a minimum. Optimization and other aspect related to fitting a model to data was recently reviewed with respect to systems biology . However, there are two aspects of analyzing data using mathematical models that deserve further clarification with respect to the empirical Bayesian approach described herein.
In practice, marginalizing the predicted response variables over the converged Markov chain provided a numerical integration of Equation 20.
Differences between the expected behaviors and new data identify areas where our understanding of the system is inadequate and reveal novel aspects of biology . In the case of signal transduction models, systematic differences may be used to discriminate between competing mechanistic descriptions, such as between recent bench experiments that report aspects of Ras activation  or of dose-dependent aspects of EGF stimulation  and those represented in current models . While developing an empirical Bayesian approach was the primary focus of this work, two additional points deserve mention in analyzing the EGF signaling data. First, the lack of convergence for the Ras predictions suggests that additional information, such as a more quantitative estimate for Ras expression levels within rat primary hepatocytes, are required before a level of belief can be stated regarding the agreement between the new mechanistic aspect of Ras activation and the observed dynamics. Second, an underlying assumption is this model is that all of the reactants are in the same compartment (i.e., localized nearby the plasma membrane). However, a common theme for a variety of signaling receptors suggests that cellular response to receptor stimulation is dependent on the receptor's subcellular location (e.g., [48–50]). Regarding EGF signaling, Vieira et al. reported that PLCγ exhibited an increase in phosphorylation in cells with defective clathrin-mediated endocytosis . This underlying biology may contribute to the inability for the PLCγ predictions to converge. Similar to the Ras predictions, quantitative estimates for PLCγ expression levels within rat primary hepatocytes may help improve the model predictions for PLCγ. Finally, this work helps set the framework for coupling empirical Bayesian techniques with information theory, such as the Akaike Information Criterion [52–54] or Bayes Factor (e.g., [12, 16]), to establish a level of confidence with distinguishing between competing mechanisms of information flow.
A recent study by Chen and coworkers elevates the standard for developing and testing mathematical models of cell signaling networks . In this paper, the uncertainty associated a mathematical model of the canonical pathways associated with the EGF family of receptors, ErbB1 - ErbB4, was estimated using a ensemble of parameter point estimates obtained by simulated annealing. While stochastic sampling of parameter space is an essential requirement for an algorithm to navigate successfully within a rugged landscape with multiple local minima, using a model to make inferences about cell signaling networks requires that the observed samples are representative samples derived from the posterior distribution.
While the dimensionality of the model described by Chen et al. may be too computationally expensive for a conventional Bayesian approach, AMCMC techniques, as described here, provide an attractive approach to sample high-dimensional parameter space efficiently. Moreover, demonstrating that Markov chain is a representative sample of the posterior distribution with a convergence criterion applied to the parameters can make a conventional Bayesian approach computationally intractable due to the presence of different timescales. As described here, the convergence criterion can be applied to the model predictions thereby simplifying the computational burden of the approach.
A common challenge in systems biology is to infer mechanistic descriptions of biological process given limited observations of a biological system [55, 56]. In the area of signal transduction, mathematical models are frequently used to represent a belief about the causal relationships among proteins within a signaling network. The simulated dynamics of these models are dependent on the topology of the signaling network and the parameters associated with each protein-protein interaction. An outstanding question is whether a specific topology can capture the observed biology given a range of plausible parameter values. By integrating over the range of plausible parameter values, we may establish a level of confidence with a specific topology. If the level of confidence is low, we may learn that we have left out a critical link within the signaling network. A Bayesian perspective may be particularly helpful for determining aspects of the biology that are inconsistent with the stated hypothesis about how information flows within a network. As illustrated by the example developed in this paper, models of signal transduction frequently synthesize information derived from a variety of experimental studies. Tools, such as the AMCMC algorithm and Gelman-Rubin convergence metrics, that facilitate evaluating different sources for this information may also be helpful. In summary, an empirical Bayesian approach was developed for inferring the confidence in a particular model for describing signal transduction mechanisms and for inferring inconsistencies in experimental measurements.
This work was supported by grants from the PhRMA Foundation, the National Cancer Institute R15CA132124, and the National Institute of Allergy and Infectious Diseases R56AI076221. The content is solely the responsibility of the author and does not necessarily represent the official views of the National Cancer Institute, the National Institute of Allergy and Infectious Diseases, or the National Institutes of Health.
- Asthagiri AR, Lauffenburger DA: Bioengineering Models of Cell Signaling. Ann Rev Biomed Eng 2000, 2: 31–53. 10.1146/annurev.bioeng.2.1.31View ArticleGoogle Scholar
- Lazebnik Y: Can a biologist fix a radio?--Or, what I learned while studying apoptosis. Cancer Cell 2002, 2: 179–183. 10.1016/S1535-6108(02)00133-2View ArticlePubMedGoogle Scholar
- Anderson AR, Quaranta V: Integrative mathematical oncology. Nat Rev Cancer 2008, 8(3):227–234. 10.1038/nrc2329View ArticlePubMedGoogle Scholar
- Yaffe MB: Signaling Networks and Mathematics. Sci Signal 2008, 1: eg7. 10.1126/scisignal.143eg7PubMedGoogle Scholar
- Broadbelt LJ, Pfaendtner J: Lexicography of kinetic modeling of complex reaction networks. AIChE J 2005, 51: 2112–2121. 10.1002/aic.10599View ArticleGoogle Scholar
- National Research Council (US) Committee on Learning, Research, Practice and Education: How people learn: brain, mind, experience, and school. Washington, DC: National Academies Press; 2000.Google Scholar
- Banga JR: Optimization in computational systems biology. BMC Sys Bio 2008, 2: 47. 10.1186/1752-0509-2-47View ArticleGoogle Scholar
- Gelman A, Carlin JB, Stern HS, Rubin DB: Bayesian Data Analysis. Texts in Statistical Science, Boca Raton, FL: Chapman and Hall; 2004.Google Scholar
- Gamerman D: Markov Chain Monte Carlo Stochastic simulation for Bayesian inference. New York, NY: Chapman & Hall USA; 1997.Google Scholar
- Wilkinson DJ: Bayesian methods in bioinformatics and computational systems biology. Briefings in Bioinformatics 2007, 8(2):109–116. 10.1093/bib/bbm007View ArticlePubMedGoogle Scholar
- Coleman MC, Block DE: Bayesian parameter estimation with informative priors for nonlinear systems. AIChE J 2006, 52(2):651–667. 10.1002/aic.10667View ArticleGoogle Scholar
- Vyshemirsky V, Girolami MA: Bayesian ranking of biochemical system models. Bioinformatics 2008, 24(6):833–839. 10.1093/bioinformatics/btm607View ArticlePubMedGoogle Scholar
- Rogers S, Khanin R, Girolami M: Bayesian model-based inference of transcription factor activity. BMC Bioinformatics 2007, 8(Suppl 2):S2. 10.1186/1471-2105-8-S2-S2PubMed CentralView ArticlePubMedGoogle Scholar
- Battogtokh D, Asch DK, Case ME, Arnold J, Schuttler HB: An ensemble method for identifying regulatory circuits with special reference to the qa gene cluster of Neurospora crassa. Proc Natl Acad Sci USA 2002, 99(26):16904–16909. 10.1073/pnas.262658899PubMed CentralView ArticlePubMedGoogle Scholar
- Brown KS, Sethna JP: Statistical mechanical approaches to models with many poorly known parameters. Phys Rev E 2003, 68(2):021904. 10.1103/PhysRevE.68.021904View ArticleGoogle Scholar
- Toni T, Welch D, Strelkowa N, Ipsen A, Stumpf MPH: Approximate Bayesian computation scheme for parameter inference and model selection in dynamical systems. Journal of the Royal Society Interface 2009, 6(31):187–202. 10.1098/rsif.2008.0172PubMed CentralView ArticleGoogle Scholar
- Haario H, Saksman E, Tamminen J: An Adaptive Metropolis Algorithm. Bernoulli 2001, 7: 223–242. 10.2307/3318737View ArticleGoogle Scholar
- Kholodenko BN, Demin OV, Moehren G, Hoek JB: Quantification of Short Term Signaling by the Epidermal Growth Factor Receptor. J Biol Chem 1999, 274: 30169–30181. 10.1074/jbc.274.42.30169View ArticlePubMedGoogle Scholar
- Schoeberl B, Eichler-Jonsson C, Gilles ED, Muller G: Computational modeling of the dynamics of the MAP kinase cascade activated by surface and internalized EGF receptors. Nat Biotechnol 2002, 20(4):370–375. 10.1038/nbt0402-370View ArticlePubMedGoogle Scholar
- Resat H, Ewald JA, Dixon DA, Wiley HS: An Integrated Model of Epidermal Growth Factor Receptor Trafficking and Signal Transduction. Biophys J 2003, 85: 730–743. 10.1016/S0006-3495(03)74516-0PubMed CentralView ArticlePubMedGoogle Scholar
- Blinov ML, Faeder JR, Goldstein B, Hlavacek WS: A Network Model of Early Events in Epidermal Growth Factor Receptor Signaling That Accounts for Combinatorial Complexity. Biosystems 2006, 83: 136–151. 10.1016/j.biosystems.2005.06.014View ArticlePubMedGoogle Scholar
- Birtwistle MR, Hatakeyama M, Yumoto N, Ogunnaike BA, Hoek JB, Kholodenko BN: Ligand-dependent responses of the ErbB signaling network: experimental and modeling analyses. Mol Sys Bio 2007, 3: 144. [http://www.biomedcentral.com/pubmed/18004277]Google Scholar
- Pawson T, Nash P: Assembly of Cell Regulatory Systems Through Protein Interaction Domains. Science 2003, 300: 445–452. 10.1126/science.1083653View ArticlePubMedGoogle Scholar
- Klinke DJ, Broadbelt LJ: Mechanism Reduction during Computer Generation of Compact Reaction Models. AIChE J 1997, 43: 1828–1837. 10.1002/aic.690430718View ArticleGoogle Scholar
- Klinke DJ, Broadbelt LJ: Construction of a Mechanistic Model of Fischer-Tropsch Synthesis on Ni(111) and Co(0001) Surfaces. Chem Eng Sci 1999, 54: 3379–3389. 10.1016/S0009-2509(98)00386-8View ArticleGoogle Scholar
- Gureasko J, Galush WJ, Boykevisch S, Sondermann H, Bar-Sagi D, Groves JT, Kuriyan J: Membrane-dependent signal integration by the Ras activator Son of sevenless. Nature Struct Mol Bio 2008, 15(5):452–461. 10.1038/nsmb.1418View ArticleGoogle Scholar
- Jones RB, Gordus A, Krall JA, MacBeath G: A quantitative protein interaction network for the ErbB receptors using protein microarrays. Nature 2006, 439: 168–174. 10.1038/nature04177View ArticlePubMedGoogle Scholar
- Kaushansky A, Gordus A, Chang B, Rush J, MacBeath G: A quantitative study of the recruitment potential of all intracellular tyrosine residues on EGFR, FGFR1 and IGF1R. Molecular Biosystems 2008, 4(6):643–653. 10.1039/b801018hPubMed CentralView ArticlePubMedGoogle Scholar
- Klinke DJ, Ustyugova IV, Brundage K, Barnett JB: Modulating Temporal Control of NF-kappaB Activation: Implications for Therapeutic and Assay Selection. Biophys J 2008, 94(11):4249–4259. 10.1529/biophysj.107.120451PubMed CentralView ArticlePubMedGoogle Scholar
- Markevich NI, Moehren G, Demin OV, Kiyatkin A, Hoek JB, Kholodenko BN: Signal processing at the Ras circuit: what shapes Ras activation patterns? Syst Biol 2004, 1: 104–113. 10.1049/sb:20045003View ArticleGoogle Scholar
- Moehren G, Markevich N, Demin O, Kiyatkin A, Goryanin I, Hoek JB, Kholodenko BN: Temperature dependence of the epidermal growth factor receptor signaling network can be accounted for by a kinetic model. Biochemistry 2002, 41: 306–320. 10.1021/bi011506cView ArticlePubMedGoogle Scholar
- Beers KJ: Numerical Methods for Chemical Engineering. Applications in Matlab. Cambridge: Cambridge University Press; 2007.Google Scholar
- Box GEP, Draper NR: The Bayesian Estimation of Common Parameters from Several Responses. Biometrika 1965, 52: 355–365. 10.1093/biomet/52.3-4.355View ArticleGoogle Scholar
- Metropolis N, Rosenbluth AW, Rosenbluth MN, Teller AH, Teller E: Equation of state calculations by fast computing machine. J Chem Phys 1953, 21: 1087–1091. 10.1063/1.1699114View ArticleGoogle Scholar
- Hastings WK: Monte Carlo sampling methods using Markov chains and their applications. Biometrika 1970, 57: 97–109. 10.1093/biomet/57.1.97View ArticleGoogle Scholar
- Gelman A, Roberts GO, Gilks WR: Efficient Metropolis Jumping Rules. In Bayesian Statistics 5. Edited by: Bernardo BJODAPJM, Smith AFM. Oxford University Press; 1996:599–607.Google Scholar
- Roberts GO, Rosenthal JS: Coupling and ergodicity of adaptive Markov chain Monte Carlo algorithms. J Appl Prob 2007, 44(2):458–475. 10.1239/jap/1183667414View ArticleGoogle Scholar
- Cowles MK, Carlin BP: Markov chain Monte Carlo convergence diagnostics: A comparative review. J Am Stat Assoc 1996, 91(434):883–904. 10.2307/2291683View ArticleGoogle Scholar
- Okino MS, Mavrovouniotis ML: Simplification of chemical reaction systems by time-scale analysis. Chem Eng Comm 1999, 176: 115–131. 10.1080/00986449908912149View ArticleGoogle Scholar
- Haken H: Synergetics Introduction and Advanced Topics. New York, NY: Springer-Verlag; 2004.Google Scholar
- Gelman A, Rubin DB: Inference from Iterative Simulation Using Multiple Sequences. Stat Sci 1992, 7(4):457–472. 10.1214/ss/1177011136View ArticleGoogle Scholar
- Brooks SP, Gelman A: General methods for monitoring convergence of iterative simulations. J Comp Graph Stat 1998, 7(4):434–455. 10.2307/1390675Google Scholar
- Flaherty P, Radhakrishnan ML, Dinh T, Rebres RA, Roach TI, Jordan MI, Arkin AP: A Dual Receptor Crosstalk Model of G-Protein-Coupled Signal Transduction. PLoS Comp Bio 2008, 4(9):e1000185. 10.1371/journal.pcbi.1000185View ArticleGoogle Scholar
- Jaqaman K, Danuser G: Linking data to models: data regression. Nature Rev Mol Cell Bio 2006, 7: 813–819. 10.1038/nrm2030View ArticleGoogle Scholar
- Wilkinson JC, Stein RA, Guyer CA, Beechem JM, Staros JV: Real-time kinetics of ligand/cell surface receptor interactions in living cells: Binding of epidermal growth factor to the epidermal growth factor receptor. Biochemistry 2001, 40(34):10230–10242. 10.1021/bi010705tView ArticlePubMedGoogle Scholar
- Macdonald JL, Pike LJ: Heterogeneity in EGF-binding affinities arises from negative cooperativity in an aggregating system. Proc Natl Acad Sci USA 2008, 105: 112–117. 10.1073/pnas.0707080105PubMed CentralView ArticlePubMedGoogle Scholar
- Chen WW, Schoeberl B, Jasper PJ, Niepel M, Nielsen UB, Lauffenburger DA, Sorger PK: Input-output behavior of ErbB signaling pathways as revealed by a mass action model trained against dynamic data. Mol Sys Bio 2009, 5: 239. [http://www.biomedcentral.com/pubmed/19156131]Google Scholar
- Husebye H, Halaas O, Stenmark H, Tunheim G, Sandanger O, Bogen B, Brech A, Latz E, Espevik T: Endocytic pathways regulate Toll-like receptor 4 signaling and link innate and adaptive immunity. Embo Journal 2006, 25(4):683–692. 10.1038/sj.emboj.7600991PubMed CentralView ArticlePubMedGoogle Scholar
- Lei JT, Martinez-Moczygemba M: Separate endocytic pathways regulate IL-5 receptor internalization and signaling. Journal of Leukocyte Biology 2008, 84(2):499–509. 10.1189/jlb.1207828PubMed CentralView ArticlePubMedGoogle Scholar
- Schutze S, Tchikov V, Schneider-Brachert W: Regulation of TNFR1 and CD95 signalling by receptor compartmentalization. Nature Reviews Molecular Cell Biology 2008, 9(8):655–662. 10.1038/nrm2430View ArticlePubMedGoogle Scholar
- Vieira AV, Lamaze C, Schmid SL: Control of EGF receptor signaling by clathrin-mediated endocytosis. Science 1996, 274(5295):2086–2089. 10.1126/science.274.5295.2086View ArticlePubMedGoogle Scholar
- Akaike H: A New Look at the Statistical Model Identification. IEEE Trans Automat Control 1974, 19: 716–723. 10.1109/TAC.1974.1100705View ArticleGoogle Scholar
- Yamaoka K, Nakagawa T, Uno T: Application of Akaike's information criterion (AIC) in the evaluation of linear pharmacokinetic equations. J Pharmacokinet Biopharm 1978, 6: 165–175. 10.1007/BF01117450View ArticlePubMedGoogle Scholar
- Horn R: Statistical methods for model discrimination. Applications to gating kinetics and permeation of the acetylcholine receptor channel. Biophys J 1987, 51: 255–263. 10.1016/S0006-3495(87)83331-3PubMed CentralView ArticlePubMedGoogle Scholar
- Gardner TS, di Bernardo D, Lorenz D, Collins JJ: Inferring Genetic Networks and Identifying Compound Mode of Action via Expression Profiling. Science 2003, 301: 102–105. 10.1126/science.1081900View ArticlePubMedGoogle Scholar
- Lorenz DR, Cantor CR, Collins JJ: A network biology approach to aging in yeast. Proc Natl Acad Sci USA 2009, 106: 1145–1150. 10.1073/pnas.0812551106PubMed CentralView ArticlePubMedGoogle Scholar
- Le Novere N, Moodie S, Sorokin A, Hucka M, Schreiber F, Demir E, Mi H, Matsuoka Y, Wegner K, Le Novere HKN, Hucka M, Mi H, Moodie S, Schreiber F, Sorokin A, Demir E, Wegner K, Aladjem MI, Wimalaratne SM, Bergman FT, Gauges R, Ghaza P, Kawaji H, Li L, Matsuoka Y, Villeger A, Boyd SE, Calzone L, Courtot M, Dogrusoz U, Freeman TC, Funahashi A, Ghosh S, Jouraku A, Kim S, Kolpakov F, Luna A, Sahle S, Schmidt E, Watterson S, Wu G, Goryanin I, Kell DB, Sander C, Sauro H, Snoep JL, Kohn K, Kitano H: The Systems Biology Graphical Notation. Nature Biotechnology 2009, 27(8):735–741. 10.1038/nbt.1558View ArticlePubMedGoogle Scholar
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