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US 2016/0103971 A1Published application
Multi-scale complex systems transdisciplinary analysis of response to therapy
AssigneesApplied Minds, LLC, University of Southern California
InventorsW. Daniel Hillis, David B. Agus
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Abstract
Described herein are methods and systems to measure dynamics of disease progression, including cancer growth and response, at multiple scales by multiple techniques on the same biologic system. Methods and systems according to the invention permit personalized virtual disease models. Moreover, the invention allows for the integration of previously unconnected data points into an in silico disease model, providing for the prediction of disease progression with and without therapeutic intervention.
Claims15
A method of predicting cancer emergence in a subject in need thereof, said method comprising
- (a) obtaining from the subject at least one of
- (b) obtaining
- (i) a molecular-scale measurement from the sample,
- (ii) a cellular scale measurement from the sample
- (iii) an organ-scale measurement from the subject, and
- (iv) an organism-scale measurement from the subject;
- (c) providing the measurements obtained from step (b) to a computer comprising a computer executable code for running a state-evolution simulation model of cancer emergence, wherein the measurements are used by the computer executable code as an initial parameter of the state-evolution simulation model, wherein the state-evolution model comprises
- (i) a molecular-scale simulation model of the cancer,
- (ii) a cellular-scale simulation model of the cancer,
- (iii) a tissue-scale simulation model of the cancer,
- iv) an organism-scale simulation model of the cancer, and
- (v) instructions for refining the molecular-scale simulation model, cellular-scale simulation model, tissue-scale simulation model, and the organism-scale simulation model based upon output from the molecular-scale simulation model, cellular-scale simulation model, tissue-scale simulation model, and organism-scale simulation model;
- (d) using the computer, running the state-evolution simulation model to produce an output comprising a prediction of cancer state at the molecular-scale, cellular-scale, tissue-scale and organism-scale level;
- (e) comparing the output of the model to outputs obtained for results of at least one of each of (i) molecular-scale measurement, (ii) cellular-scale measurement, (iii) organ-scale measurement, and (iv) organism-scale measurement from a second subject of known progression status;
- (f) based upon the state-evolution simulation model output of (d) and the comparison of (e), generating a prediction of progression of the cancer in the subject;
- (g) selecting a treatment regimen to ameliorate symptoms associated with the prediction of progression of the cancer in the subject at risk of cancer progression;
- and
- (h) administering the treatment to the subject.
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A multi-scale system for modeling cancer emergence comprising
- (a) a processor instructed by computer-readable medium, wherein said processor comprises
- (i) a first module modeling cellular behavior based on molecular-scale measurements of cancerous and cancer free samples;
- (ii) a second module modeling tissue behavior based on cellular measurements of cancerous and cancer-free samples;
- (iii) a third module modeling organ behavior based on organ-scale measurements of cancerous and cancer free samples;
- (iv) a fourth module modeling host behavior based on organism-scale measurements of cancerous and cancer free samples;
- wherein said processor transforms said measurements into a state-evolution model of cancer emergence;
- and
- (b) an interface capable of receiving and forwarding data from and to said modules to further refme said state-evolution model by a stochastic simulation, wherein said stochastic simulation comprises:
- (i) building a state-evolution model of said measurements;
- (ii) subjecting said state-evolution model to a first virtual perturbation;
- (iii) predicting an outcome resulting from said virtual perturbation;
- (iv) comparing said outcome to observed results from a treatment corresponding to said first virtual perturbation;
- (v) revising said state evolution model if said outcome resulting from said virtual permutation does not correspond to said observed results, so that said state evolution model predicts an outcome corresponding to said observed results;
- vi) repeating steps ii)-v) so as to iteratively improve predictive accuracy of said state evolution model.
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Dependencies are inferred from the claim text.