You are here

confidence building exercise in data and identifiability

Title: A confidence building exercise in data and identifiability: Modeling cancer chemotherapy as a case study.
Name(s): Eisenberg, Marisa C, author
Jain, Harsh V, author
Type of Resource: text
Genre: Journal Article
Date Issued: 2017-10-27
Physical Form: computer
online resource
Extent: 1 online resource
Language(s): English
Abstract/Description: Mathematical modeling has a long history in the field of cancer therapeutics, and there is increasing recognition that it can help uncover the mechanisms that underlie tumor response to treatment. However, making quantitative predictions with such models often requires parameter estimation from data, raising questions of parameter identifiability and estimability. Even in the case of structural (theoretical) identifiability, imperfect data and the resulting practical unidentifiability of model parameters can make it difficult to infer the desired information, and in some cases, to yield biologically correct inferences and predictions. Here, we examine parameter identifiability and estimability using a case study of two compartmental, ordinary differential equation models of cancer treatment with drugs that are cell cycle-specific (taxol) as well as non-specific (oxaliplatin). We proceed through model building, structural identifiability analysis, parameter estimation, practical identifiability analysis and its biological implications, as well as alternative data collection protocols and experimental designs that render the model identifiable. We use the differential algebra/input-output relationship approach for structural identifiability, and primarily the profile likelihood approach for practical identifiability. Despite the models being structurally identifiable, we show that without consideration of practical identifiability, incorrect cell cycle distributions can be inferred, that would result in suboptimal therapeutic choices. We illustrate the usefulness of estimating practically identifiable combinations (in addition to the more typically considered structurally identifiable combinations) in generating biologically meaningful insights. We also use simulated data to evaluate how the practical identifiability of the model would change under alternative experimental designs. These results highlight the importance of understanding the underlying mechanisms rather than purely using parsimony or information criteria/goodness-of-fit to decide model selection questions. The overall roadmap for identifiability testing laid out here can be used to help provide mechanistic insight into complex biological phenomena, reduce experimental costs, and optimize model-driven experimentation.
Identifier: FSU_pmch_28733187 (IID), 10.1016/j.jtbi.2017.07.018 (DOI), PMC6007023 (PMCID), 28733187 (RID), 28733187 (EID), S0022-5193(17)30345-4 (PII)
Keywords: Cancer, Chemotherapy model, Compartmental models, Identifiability, Parameter estimation
Grant Number: U01 CA182915
Publication Note: This NIH-funded author manuscript originally appeared in PubMed Central at
Subject(s): Algorithms
Antineoplastic Agents/administration & dosage
Antineoplastic Agents/therapeutic use
Cell Cycle/drug effects
Dose-Response Relationship, Drug
Models, Biological
Neoplasms/drug therapy
Organoplatinum Compounds/administration & dosage
Organoplatinum Compounds/therapeutic use
Paclitaxel/administration & dosage
Paclitaxel/therapeutic use
Persistent Link to This Record:
Owner Institution: FSU
Is Part Of: Journal of theoretical biology.
Issue: vol. 431

Choose the citation style.
Eisenberg, M. C., & Jain, H. V. (2017). A confidence building exercise in data and identifiability: Modeling cancer chemotherapy as a case study. Journal Of Theoretical Biology. Retrieved from