Global sensitivity analysis and model-based reactive scheduling of targeted cancer immunotherapy

Kiran, Kanchi Lakshmi; Lakshminarayanan, S.

doi:10.1016/j.biosystems.2010.05.004

Cited by 7 publications

(4 citation statements)

References 42 publications

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“…The two variables are assumed uniformly distributed between 0 and 1. If the samples are generated with Latin hypercube sampling method, the sampling points [47] tumour growth and dendritic cell therapy [48] of x and y will be equally distributed between 0 and 1 ( Fig. 1a), which means that x and y will have one and only one sample at each of the 10 intervals (0, 0…”

Section: Parameter Space Sampling: Latin Hypercube Samplingmentioning

confidence: 99%

Sensitivity analysis approaches applied to systems biology models

2011

IET Syst. Biol.

339

263

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With the rising application of systems biology, sensitivity analysis methods have been widely applied to study the biological systems, including metabolic networks, signalling pathways and genetic circuits. Sensitivity analysis can provide valuable insights about how robust the biological responses are with respect to the changes of biological parameters and which model inputs are the key factors that affect the model outputs. In addition, sensitivity analysis is valuable for guiding experimental analysis, model reduction and parameter estimation. Local and global sensitivity analysis approaches are the two types of sensitivity analysis that are commonly applied in systems biology. Local sensitivity analysis is a classic method that studies the impact of small perturbations on the model outputs. On the other hand, global sensitivity analysis approaches have been applied to understand how the model outputs are affected by large variations of the model input parameters. In this review, the author introduces the basic concepts of sensitivity analysis approaches applied to systems biology models. Moreover, the author discusses the advantages and disadvantages of different sensitivity analysis methods, how to choose a proper sensitivity analysis approach, the available sensitivity analysis tools for systems biology models and the caveats in the interpretation of sensitivity analysis results.

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Section: Parameter Space Sampling: Latin Hypercube Samplingmentioning

confidence: 99%

Sensitivity analysis approaches applied to systems biology models

2011

IET Syst. Biol.

339

263

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“…A large variety of models have been developed in an attempt to describe and predict how tumour volume changes over time. Some of these modelling studies were simulation-based, aiming to explore how mathematical models can be used to optimise the treatment dose and dose schedule for better outcome (Dionysiou and Stamatakos 2007, South et al 2009, Harrold and Parker 2009, Kiran et al 2010, Chen et al 2012. Others are more focused on exploring the various biological and treatment-related phenomena/mechanisms that should be included in the model, in order to better describe the observed data.…”

Section: Introductionmentioning

confidence: 99%

“…The present paper follows this line of research to report a modelling study of tumour volume dynamics during stereotactic ablative radiotherapy (SABR) of early-stage lung cancer patients. The developed models could provide valuable information about how individual patients respond to SABR; this information could be used to optimise patient-specific treatments as outlined in in-silico simulation studies (Dionysiou and Stamatakos 2007, South et al 2009, Harrold and Parker 2009, Kiran et al 2010, Chen et al 2012. Gross tumour volume (GTV) was measured on kV cone-beam CT images using the Varian on-board imaging system immediately before each delivered fraction.…”

Section: Introductionmentioning

confidence: 99%

Mathematical modelling of tumour volume dynamics in response to stereotactic ablative radiotherapy for non-small cell lung cancer

et al. 2015

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This paper reports a modelling study of tumour volume dynamics in response to stereotactic ablative radiotherapy (SABR). The main objective was to develop a model that is adequate to describe tumour volume change measured during SABR, and at the same time is not excessively complex as lacking support from clinical data. To this end, various modelling options were explored, and a rigorous statistical method, the Akaike information criterion, was used to help determine a trade-off between model accuracy and complexity. The models were calibrated to the data from 11 non-small cell lung cancer patients treated with SABR. The results showed that it is feasible to model the tumour volume dynamics during SABR, opening up the potential for using such models in a clinical environment in the future.

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“…In England, 410,500 people have been diagnosed with cancer in 2008 [1]. In US, 1.47 million new cancer cases have been found in 2009 [2]. From the impact of cancer, advanced algorithms have been widely studied in order to improve the treatment efficacy [3,4].…”

Section: Introductionmentioning

confidence: 99%

Optimal Dosing of Breast Cancer Chemotherapy using Robust MPC Based on Linear Matrix Inequalities

Bumroongsri

Kheawhom

2015

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In this paper, we consider an application of robust model predictive control to optimal dosing of breast cancer chemotherapy. The model-patient mismatch is handled by computing an ellipsoidal invariant set containing the measured patient's states at each sampling time. An optimal dose of chemotherapeutic agent is obtained by solving a convex optimization problem subject to linear matrix inequalities. In the case study of simulated patients, the results show that the tumor volume can be reduced to a specified target with up to 30% model-patient mismatch. Moreover, the robust model predictive control algorithm can achieve better treatment results as compared with the nonlinear model predictive control algorithm while the on-line computational time is significantly reduced.

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Global sensitivity analysis and model-based reactive scheduling of targeted cancer immunotherapy

Cited by 7 publications

References 42 publications

Sensitivity analysis approaches applied to systems biology models

Sensitivity analysis approaches applied to systems biology models

Mathematical modelling of tumour volume dynamics in response to stereotactic ablative radiotherapy for non-small cell lung cancer

Optimal Dosing of Breast Cancer Chemotherapy using Robust MPC Based on Linear Matrix Inequalities

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