Statistical Model Checking Based Calibration and Analysis of Bio-pathway Models

Palaniappan, Sucheendra K.; Gyori, Benjamin M.; Liu, Bing; Hsu, David; Thiagarajan, P. S.

doi:10.1007/978-3-642-40708-6_10

Cited by 30 publications

(31 citation statements)

References 30 publications

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“…We reserved the Il6, Il12b, Il1a, Csf3 , and Il10 mRNA time-course analysis under the I 24 R, R 8 I, and R 24 I conditions (Fig. 3, B and C) as the test data set and used the remaining data to train or calibrate the model parameters (such as unknown rate constants) with a statistical logic-based framework that we developed previously (see Materials and Methods for details) (26). Initial protein concentrations were allowed to vary 5% about their nominal values to account for cellular heterogeneities (see table S2 for the resulting estimated kinetic parameters).…”

Section: Resultsmentioning

confidence: 99%

“…To identify the components and reactions that were essential to inducing a synergistic cytokine production, we conducted an extensive sensitivity analysis of the initial concentrations and kinetic parameters to cytokine abundance with our statistical model checking (SMC) technique (26). We first performed control coefficient–based sensitivity analysis of the initial concentrations of the major species in the model.…”

Section: Resultsmentioning

confidence: 99%

“…We constructed a computational model containing the key components and determined the model parameters by an optimal fit to 148 data points through a statistical logic-based parameter estimation framework that we had developed previously (26). The calibrated model was verified to reproduce the dependence of the synergistic production of cytokines on the time interval and order of stimuli, as well as 39 additional data points reserved for model validation.…”

Section: Introductionmentioning

confidence: 99%

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Innate immune memory and homeostasis may be conferred through crosstalk between the TLR3 and TLR7 pathways

Liu

Yang

et al. 2016

Sci. Signal.

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Toll-like receptors (TLRs) recognize pathogen-associated molecular patterns (PAMPs) and stimulate the innate immune response through the production of cytokines. The innate immune response depends on the timing of encountering PAMPs, suggesting a short-term “memory.” In particular, activation of TLR3 appears to prime macrophages for the subsequent activation of TLR7, which leads to synergistically increased production of cytokines. By developing a calibrated mathematical model for the kinetics of TLR3 and TLR7 pathway crosstalk and providing experimental validation, we demonstrated the involvement of the Janus-activated kinase (JAK)–signal transducer and activator of transcription (STAT) pathway in controlling the synergistic production of cytokines. Signaling through this pathway played a dual role: It mediated the synergistic production of cytokines, thus boosting the immune response, and it also maintained homeostasis to avoid an excessive inflammatory response. Thus, we propose that the JAK-STAT pathway provides a cytokine rheostat mechanism, which enables macrophages to fine-tune their responses to multiple, temporally separated infection events involving the TLR3 and TLR7 pathways.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Innate immune memory and homeostasis may be conferred through crosstalk between the TLR3 and TLR7 pathways

Liu

Yang

et al. 2016

Sci. Signal.

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“…This problem is hard for formal verification techniques that reason symbolically about the distribution of an output response. In recent years, statistical verification techniques have received increasing attention [24,19,10,22,27,17,13]. They are simulation-based, requiring just the ability to simulate the model efficiently for various values of design and stochastic parameters.…”

Section: Introductionmentioning

confidence: 99%

“…Their approach uses Monte-Carlo sampling over the design parameter values, wherein the number of simulation runs required to resolve the hypothesis testing problem is used as the fitness function for each design parameter. A similar idea is used by Palaniappan et al to fit parameter values for biological models based on experimental observations as well as model specifications [17]. In their work, SMC is used to derive a fitness function that seeks to measure the fraction of the specifications satisfied by a particular choice of model parameters.…”

Section: Introductionmentioning

confidence: 99%

Statistically Sound Verification and Optimization for Complex Systems

Zhang

Sankaranarayanan

Somenzi

2014

Automated Technology for Verification and Analysis

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Abstract. This paper discusses verification and optimization of complex systems with respect to a set of specifications under stochastic parameter variations. We introduce a simulation-based statistically sound model inference approach that considers systems whose responses depend on a few design parameters and many stochastic parameters. The technique iteratively searches over the space of design parameters by alternating between verification and optimization phases. The verification phase uses statistical model checking to check if the model using the current design parameters satisfies the specifications. Failing this, we seek new values of the design parameters for which statistical verification could potentially succeed. This is achieved through repeated simulations for various values of the design and stochastic parameters, and quantile regression to construct a model that predicts the spread of the responses as a function of the design parameters. The resulting model is used to select a new set of values for the design parameters. We evaluate this approach over several benchmark examples. In each case, the performance is improved significantly compared to the nominal design.

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Statistical Model Checking-Based Analysis of Biological Networks

Liu

Gyori

Thiagarajan

2019

Computational Biology

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We introduce a framework for analyzing ordinary differential equation (ODE) models of biological networks using statistical model checking (SMC). A key aspect of our work is the modeling of single-cell variability by assigning a probability distribution to intervals of initial concentration values and kinetic rate constants. We propagate this distribution through the system dynamics to obtain a distribution over the set of trajectories of the ODEs. This in turn opens the door for performing statistical analysis of the ODE system's behavior. To illustrate this we first encode quantitative data and qualitative trends as bounded linear time temporal logic (BLTL) formulas. Based on this we construct a parameter estimation method using an SMC-driven evaluation procedure applied to the stochastic version of the behavior of the ODE system. We then describe how this SMC framework can be generalized to hybrid automata by exploiting the given distribution over the initial states and the-much more sophisticated-system dynamics to associate a Markov chain with the hybrid automaton. We then establish a strong relationship between the behaviors of the hybrid automaton and its associated Markov chain. Consequently, we sample trajectories from the hybrid automaton in a way that mimics the sampling of the trajectories of the Markov chain. This enables us to verify approximately that the Markov chain meets a BLTL specification with high probability. We have applied these methods to ODE based models of Toll-like receptor signaling and the crosstalk between autophagy and apoptosis, as well as to systems exhibiting hybrid dynamics including the circadian clock pathway and cardiac cell physiology. We

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Statistical Model Checking Based Calibration and Analysis of Bio-pathway Models

Cited by 30 publications

References 30 publications

Innate immune memory and homeostasis may be conferred through crosstalk between the TLR3 and TLR7 pathways

Innate immune memory and homeostasis may be conferred through crosstalk between the TLR3 and TLR7 pathways

Statistically Sound Verification and Optimization for Complex Systems

Statistical Model Checking-Based Analysis of Biological Networks

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