Quantitative computational models play an increasingly important role in modern biology.
Such models typically involve many free parameters, and assigning their values is often a …

Profiling candidate therapeutics with limited cancer models during preclinical development
hinders predictions of clinical efficacy and identifying factors that underlie heterogeneous …

In a variety of contexts, physicists study complex, nonlinear models with many unknown or
tunable parameters to explain experimental data. We explain why such systems so often are …

We apply the methods of optimal experimental design to a differential equation model for
epidermal growth factor receptor signalling, trafficking and down-regulation. The model …

Circulating tumor DNA (ctDNA) sequencing is being rapidly adopted in precision oncology,
but the accuracy, sensitivity and reproducibility of ctDNA assays is poorly understood. Here …

The phylogeography of cattle genetic variants has been extensively described and has
informed the history of domestication. However, there remains a dearth of demographic models …

Background Mechanistic biosimulation can be used in drug development to form testable
hypotheses, develop predictions of efficacy before clinical trial results are available, and …

Lineage-restricted transcription factors (TFs) are frequently mutated or overexpressed in
cancer and contribute toward malignant behaviors; however, the molecular bases of their …

The ability of docking to predict angiotensin converting enzyme (ACE) inhibitory dipeptide
sequences was assessed using AutoDock Vina. All potential dipeptides and phospho-…

Successful predictions are among the most compelling validations of any model. Extracting
falsifiable predictions from nonlinear multiparameter models is complicated by the fact that …