Parameter identifiability and redundancy in a general class of stochastic carcinogenesis models. 2009

Mark P Little, and Wolfgang F Heidenreich, and Guangquan Li
Department of Epidemiology and Public Health, Imperial College, London, UK. mark.little@imperial.ac.uk

BACKGROUND Heidenreich et al. (Risk Anal 1997 17 391-399) considered parameter identifiability in the context of the two-mutation cancer model and demonstrated that combinations of all but two of the model parameters are identifiable. We consider the problem of identifiability in the recently developed carcinogenesis models of Little and Wright (Math Biosci 2003 183 111-134) and Little et al. (J Theoret Biol 2008 254 229-238). These models, which incorporate genomic instability, generalize a large number of other quasi-biological cancer models, in particular those of Armitage and Doll (Br J Cancer 1954 8 1-12), the two-mutation model (Moolgavkar et al. Math Biosci 1979 47 55-77), the generalized multistage model of Little (Biometrics 1995 51 1278-1291), and a recently developed cancer model of Nowak et al. (PNAS 2002 99 16226-16231). RESULTS We show that in the simpler model proposed by Little and Wright (Math Biosci 2003 183 111-134) the number of identifiable combinations of parameters is at most two less than the number of biological parameters, thereby generalizing previous results of Heidenreich et al. (Risk Anal 1997 17 391-399) for the two-mutation model. For the more general model of Little et al. (J Theoret Biol 2008 254 229-238) the number of identifiable combinations of parameters is at most less than the number of biological parameters, where is the number of destabilization types, thereby also generalizing all these results. Numerical evaluations suggest that these bounds are sharp. We also identify particular combinations of identifiable parameters. CONCLUSIONS We have shown that the previous results on parameter identifiability can be generalized to much larger classes of quasi-biological carcinogenesis model, and also identify particular combinations of identifiable parameters. These results are of theoretical interest, but also of practical significance to anyone attempting to estimate parameters for this large class of cancer models.

UI MeSH Term Description Entries
D009154 Mutation Any detectable and heritable change in the genetic material that causes a change in the GENOTYPE and which is transmitted to daughter cells and to succeeding generations. Mutations
D009369 Neoplasms New abnormal growth of tissue. Malignant neoplasms show a greater degree of anaplasia and have the properties of invasion and metastasis, compared to benign neoplasms. Benign Neoplasm,Cancer,Malignant Neoplasm,Tumor,Tumors,Benign Neoplasms,Malignancy,Malignant Neoplasms,Neoplasia,Neoplasm,Neoplasms, Benign,Cancers,Malignancies,Neoplasias,Neoplasm, Benign,Neoplasm, Malignant,Neoplasms, Malignant
D002451 Cell Compartmentation A partitioning within cells due to the selectively permeable membranes which enclose each of the separate parts, e.g., mitochondria, lysosomes, etc. Cell Compartmentations,Compartmentation, Cell,Compartmentations, Cell
D006801 Humans Members of the species Homo sapiens. Homo sapiens,Man (Taxonomy),Human,Man, Modern,Modern Man
D000818 Animals Unicellular or multicellular, heterotrophic organisms, that have sensation and the power of voluntary movement. Under the older five kingdom paradigm, Animalia was one of the kingdoms. Under the modern three domain model, Animalia represents one of the many groups in the domain EUKARYOTA. Animal,Metazoa,Animalia
D013269 Stochastic Processes Processes that incorporate some element of randomness, used particularly to refer to a time series of random variables. Process, Stochastic,Stochastic Process,Processes, Stochastic
D015233 Models, Statistical Statistical formulations or analyses which, when applied to data and found to fit the data, are then used to verify the assumptions and parameters used in the analysis. Examples of statistical models are the linear model, binomial model, polynomial model, two-parameter model, etc. Probabilistic Models,Statistical Models,Two-Parameter Models,Model, Statistical,Models, Binomial,Models, Polynomial,Statistical Model,Binomial Model,Binomial Models,Model, Binomial,Model, Polynomial,Model, Probabilistic,Model, Two-Parameter,Models, Probabilistic,Models, Two-Parameter,Polynomial Model,Polynomial Models,Probabilistic Model,Two Parameter Models,Two-Parameter Model
D042822 Genomic Instability An increased tendency of the GENOME to acquire MUTATIONS when various processes involved in maintaining and replicating the genome are dysfunctional. Genome Instability,Genome Stability,Genomic Stability,Genome Instabilities,Genome Stabilities,Genomic Instabilities,Genomic Stabilities,Instabilities, Genome,Instabilities, Genomic,Instability, Genome,Instability, Genomic,Stabilities, Genome,Stabilities, Genomic,Stability, Genome,Stability, Genomic

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