Software package DEBtum
DEBtum has been written by Fleur Kelping (coding),
Ingeborg van Leeuwen
(modelling) and Bas
Kooijman (theoretical biology).
DEBtum is a userfriendly software package designed to analyze the
results from longterm carcinogenicity studies.
What is DEBtum ?
The aim of the DEBtum software package is to provide a tool for the analysis of data from longterm carcinogenicity tests. The package can be used not only to study tumor incidence data but also, for instance, body growth data. Moreover, the package allows the user to analyze several kinds of data simultaneously. That is, for instance, tumor incidence data can be evaluated taking information on food consumption and body weight into account. Some important features of the package are:
 The package includes models describing normal physiological processes such as body growth, food consumption, and aging. Data on food consumption, body growth, and mortality can thus be analyzed with aid of the DEBtum package.
 The package also includes models describing one or more steps of the carcinogenic process. Therefore, tumor incidence data as well as data concerning intermediate phases of the carcinogenic process can be studied with the DEBtum package.
 The main feature of the DEBtum package is that it allows the user to create a combined model that optimally takes advantage of all available data.
 The data fitting application includes procedures to estimate parameter values and test the goodness of fit. The graphics application displays the model curves corresponding to the estimated parameter values together with the KaplanMeier corrections of the tumor incidence data.
 The package calculates cancer risk estimates.
Experimental Data
Input
The main purpose of the DEBtum package is to facilitate the analysis of results from longterm singlecompound carcinogenicity studies. End points of interest in such carcinogenicity studies are primarily preneoplastic and neoplastic changes, but also include degree of malignancy, time to tumor appearance, multiplicity of (pre)neoplasia, and occurrence of metastasis. In addition to these endpoints, measurements of body weight and food consumption are usually recorded. Accurate interpretation of a dataset by the DEBtum package requires definition of the following details:
 Doses
 Food levels (relevant in caloric restriction studies)
 Initial number of animals in each group
 Species (e.g., B6C3F_{1} mice)
 Average age at study initiation (e.g., 4 weeks)
 Exposure route (inhalation, oral, ...)
 Time unit (days, weeks, months, ...)
 Additional info on columns in Excel file

Some of the entries above, as experimental species, are only required if the user wants to use the available sets of default parameter values.
Types of data that can be analyzed
Data concerning physiological aspects of the experimental animals:
 Food consumption/Body weight
 Body weights
 Metabolic rates (Respiration)
 Mortality (Time to death)
Data concerning processes involved in chemical carcinogenesis:
 Internal concentration of (pro)carcinogen
 Time to detectable tumor
 Time to death with tumor
 Tumor multiplicity
 Tumor growth
Data selection
The DEBtum package allows the user to select a subgroup from a dataset. A subgroup is defined as a set of restrains on dosegroup, sex, age, food level (dietgroup), etc. The package's applications can then be applied to the selected subgroup only.
Model Definition
As mentioned above, the DEBtum package includes two categories of mathematical models:
 Category A: models describing physiological processes. Among them are models for food consumption (Fmodels), body growth (Vmodels) and aging (Smodels)
 Category B: models describing processes involved in chemical carcinogenesis. Among them are models for kinetics (Kmodels), tumor induction (Imodels), tumor growth (Tmodels) and effects (Emodels).
In the context of the DEBtum package, a model is composed of one or more modules. The number of modules depends on the data to analyze. For instance, a model for body growth is constituted by a minimum of one module (a Vmodel) and a maximum of two modules (Vmodel and a Fmodel). Notice that the first model ignores the influence of food ingestion on body weight. For each possible dataset, the table below shows the modulecomposition of a model that can be fitted to the data. Italics means that the indicated category or module/process may be ignored. The user should be aware, however, that relevant processes may not be taken into account when a certain part/module is ignored.
Data to Analyze  Modules 
Body weights  Fmodel + Vmodel 
Food Intake/Body weight  Fmodel + Vmodel 
Respiration rates  Fmodel + Vmodel 
Mortality  Fmodel + Vmodel + Smodel 
Internal concentration  category A + Kmodel 
Time to detectable tumor  category A + Kmodel + Imodel + Tmodel 
Time to death with tumor  category A + Kmodel + Imodel + Tmodel + Emodel 
DEBtum Documentation
 I.M.M. van Leeuwen and C. Zonneveld (2001) ``From exposure to effect: a comparison of modeling approaches to chemical carcinogenesis,'' Mutation Research  Reviews 489(1), 17  45. Abstract.
 I.M.M. van Leeuwen, F.D.L. Kelpin, and S.A.L.M. Kooijman (2002) ``A mathematical model that accounts for the effects of caloric restriction on body weight and longevity,'' Biogerontology 3(6):
373  381. Abstract.
 I.M.M. van Leeuwen (2003) ``Mathematical Models in Cancer Risk Assessment,'' PhDThesis, Vrije Universiteit, Amsterdam. Samenvatting.
 I.M.M. van Leeuwen, C. Zonneveld and S.A.L.M. Kooijman (2003) ``The embedded tumor: host physiology is important for the evaluation of tumor growth,'' British Journal of Cancer. Revised Manuscript Submitted August 2003. Abstract.
 S.A.L.M. Kooijman (2000). ``Dynamic Energy and Mass Budgets in biological systems. Theory and applications.'' Cambridge University Press, Cambridge. Abstract.
 I.M.M. van Leeuwen, F.D.L. Kelpin, and S.A.L.M. Kooijman (In preparation). ``DEBtum User's Guide''.
Other Relevant References
 Abramowitz and Stegun, editors (1972). ``Handbook of Mathematical Functions,'' chapter 25.5, Dover Publ., New York.
 B.N. Ames and L.S. Gold (1990). ``Too many rodent carcinogens: Mitogenesis increases mutagenesis,'' Science 249, 970971.
 P. Armitage and R.Doll (1954) ``The age distribution of cancer and a multistage theory of carcinogenesis.'' British Journal of Cancer, 8(1):112.
 W. ten Berge (1999). ``KaplanMeier tumour probability as starting point for doseresponse modelling provides accurate lifetime risk estimates from rodent carcinogenicity studies,'' Annals New York Academy of Sciences 895: 112124.
 ECETOC (1996). ``Risk Assessment for carcinogens,'' Monograph 24, European Centre for Ecotoxicology and Toxicology of Chemicals, Brussels.
 E.R. Fearon and B. Vogelstein (1990). ``A genetic model for colorectal tumorigenesis,'' Cell 61, 759767.
 G. Gregori, L. Hanin, G. Luebeck, S. Moolgavkar, and A. Yakovlev (2002).
``Testing goodness of fit for stochastic models of carcinogenesis.''
Mathematical Biosciences, 175:1329.
 D. Hanahan and R.A. Weinberg (2000). ``The hallmarks of cancer,'' Cell 100, 5770.
 A.K. Laird (1964). ``Dynamics of tumor growth.'' British Journal of Cancer, 18:490502.
 J.R. Leis and M.A. Kramer (1988). ``The simultaneous solution and sensitivity analysis of systems described by ordinary differential equations.''
Transactions on Mathematical Software, 14(1):4560.
 S.H. Moolgavkar, D. Krewski, L. Zeise, E. Cardis, and H. Möller, editors (1999). ``Quantitative Estimation and Prediction of Human Cancer Risks.'' IARC Scientific Publications, Lyon.
 S.H. Moolgavkar and E.G. Luebeck (1990). ``Twoevent model for carcinogenesis: Biological, mathematical and statistical considerations.''
Risk Analysis, 10:323341.
 J.A. Nelder and R. Mead (1965). ``A simplex method for function minimization.'' Computer Journal, 7:308315.
 C.W. Ueberhuber (1997). ``Numerical Computation,'' vol.2, chapter 14, pages 325335, Springer.
 R. Weindruch, R.L. Walford, S. Fligiel, and D. Guthrie (1986). ``The retardation of aging in mice by dietary restriction: Longevity,
cancer, immunity and lifetime energy intake. Journal of Nutrition, 116:641654.
Disclaimer
The DEBtumpackage was written as part of the research project DEBtum (VBI4692), financially supported by the Netherlands Technology Foundation (STW) and DSM. Although the package has been written with care, authors, the Vrije Universiteit, nor the publisher will accept any responsibility for the results obtained with this package.
Go to the DEBtumproject Information, the Theoretical Biology Home page, the DEB information page or the DEB laboratory