A Continuation Technique for Maximum Likelihood Estimators in Biological Models

被引:4
作者
Cassidy, Tyler [1 ]
机构
[1] Univ Leeds, Sch Math, Leeds LS2 9JT, England
关键词
Parameter estimation; Numerical continuation; Model calibration; Experimental design; PARAMETER IDENTIFIABILITY; SENSITIVITY-ANALYSIS; SYSTEMS; SELECTION; DYNAMICS;
D O I
10.1007/s11538-023-01200-0
中图分类号
Q [生物科学];
学科分类号
07 ; 0710 ; 09 ;
摘要
Estimatingmodel parameters is a crucial step in mathematical modelling and typically involves minimizing the disagreement between model predictions and experimental data. This calibration data can change throughout a study, particularly if modelling is performed simultaneously with the calibration experiments, or during an on-going public health crisis as in the case of the COVID-19 pandemic. Consequently, the optimal parameter set, or maximal likelihood estimator (MLE), is a function of the experimental data set. Here, we develop a numerical technique to predict the evolution of the MLE as a function of the experimental data. We show that, when considering perturbations from an initial data set, our approach is significantly more computationally efficient that re-fitting model parameters while producing acceptable model fits to the updated data. We use the continuation technique to develop an explicit functional relationship between fit model parameters and experimental data that can be used to measure the sensitivity of the MLE to experimental data. We then leverage this technique to select between model fits with similar information criteria, a priori determine the experimental measurements to which the MLE is most sensitive, and suggest additional experiment measurements that can resolve parameter uncertainty.
引用
收藏
页数:27
相关论文
共 51 条
  • [1] Efficient Generation and Selection of Virtual Populations in Quantitative Systems Pharmacology Models
    Allen, R. J.
    Rieger, T. R.
    Musante, C. J.
    [J]. CPT-PHARMACOMETRICS & SYSTEMS PHARMACOLOGY, 2016, 5 (03): : 140 - 146
  • [2] The mathematics of cancer: integrating quantitative models
    Altrock, Philipp M.
    Liu, Lin L.
    Michor, Franziska
    [J]. NATURE REVIEWS CANCER, 2015, 15 (12) : 730 - 745
  • [3] Endogenous Replication Stress in Mother Cells Leads to Quiescence of Daughter Cells
    Arora, Mansi
    Moser, Justin
    Phadke, Harsha
    Basha, Ashik Akbar
    Spencer, Sabrina L.
    [J]. CELL REPORTS, 2017, 19 (07): : 1351 - 1364
  • [4] Mathematical Models of Cancer: When to Predict Novel Therapies, and When Not to
    Brady, Renee
    Enderling, Heiko
    [J]. BULLETIN OF MATHEMATICAL BIOLOGY, 2019, 81 (10) : 3722 - 3731
  • [5] Optimal Experimental Design for a Bistable Gene Regulatory Network
    Braniff, Nathan
    Richards, Addison
    Ingalls, Brian
    [J]. IFAC PAPERSONLINE, 2019, 52 (26): : 255 - 261
  • [6] Component Characterization in a Growth-Dependent Physiological Context: Optimal Experimental Design
    Braniff, Nathan
    Scott, Matthew
    Ingalls, Brian
    [J]. PROCESSES, 2019, 7 (01)
  • [7] Model-informed experimental design recommendations for distinguishing intrinsic and acquired targeted therapeutic resistance in head and neck cancer
    Cardenas, Santiago D.
    Reznik, Constance J.
    Ranaweera, Ruchira
    Song, Feifei
    Chung, Christine H.
    Fertig, Elana J.
    Gevertz, Jana L.
    [J]. NPJ SYSTEMS BIOLOGY AND APPLICATIONS, 2022, 8 (01)
  • [8] The role of memory in non-genetic inheritance and its impact on cancer treatment resistance
    Cassidy T.
    Nichol D.
    Robertson-Tessi M.
    Craig M.
    Anderson A.R.A.
    [J]. PLoS Computational Biology, 2021, 17 (08)
  • [9] Characterizing Chemotherapy-Induced Neutropenia and Monocytopenia Through Mathematical Modelling
    Cassidy, Tyler
    Humphries, Antony R.
    Craig, Morgan
    Mackey, Michael C.
    [J]. BULLETIN OF MATHEMATICAL BIOLOGY, 2020, 82 (08)
  • [10] Determinants of combination GM-CSF immunotherapy and oncolytic virotherapy success identified through in silico treatment personalization
    Cassidy, Tyler
    Craig, Morgan
    [J]. PLOS COMPUTATIONAL BIOLOGY, 2019, 15 (11)