TY - JOUR
T1 - Conflicting Biomedical Assumptions for Mathematical Modeling: The Case of Cancer Metastasis
A1 - Divoli, Anna
A1 - MendonĂ§a, Eneida A.
A1 - Evans, James A.
A1 - Rzhetsky, Andrey
Y1 - 2011/10/06
N2 - Author Summary Mathematical models and scientific theories fail not only from internal inconsistency, but also from the poor selection of basic assumptions. Assumptions in computational models of biomedicine are typically provided by scientists who interact directly with empirical data. If we seek to model the dynamics of cancer metastasis and ask experts regarding valid assumptions, how widely will they agree and on which assumptions? To answer this question, we queried 28 faculty-level experts about the progression of metastasis. We demonstrate an unexpected diversity of assumptions across experts leading to a striking lack of agreement over the basic stages and sequence of metastasis. We suggest a formal model and framework that builds on this diversity and enables researchers to evaluate divergent hypotheses about metastasis with experimental data. We conclude that modeling biomedical processes could be substantially improved by harvesting scientific assumptions and exposing them for formalization and experiment.
JF - PLOS Computational Biology
JA - PLOS Computational Biology
VL - 7
IS - 10
UR - http://dx.doi.org/10.1371%2Fjournal.pcbi.1002132
SP - e1002132
EP -
PB - Public Library of Science
M3 - doi:10.1371/journal.pcbi.1002132
ER -