Background: Predicting and tailoring optimal cancer treatments presents a major challenge.
Methods: A computational model (kinetically tailored treatment, or KITT model) is developed …

Cancer is the name given to a class of diseases characterized by an imbalance in cell
proliferation and apoptosis, or programmed cell death. Upon detection, cancer is assumed …

The complex, constantly evolving and multifaceted nature of cancer has made it difficult to
identify unique molecular and pathophysiological signatures for each disease variant …

Forecasting future trends in observable phenomena is of great interest to many disciplines.
Methods and models that are able to predict future outcomes have a variety of applications …

Using mathematical models to simulate dynamic biological processes has a long history.
Over the past couple of decades or so, quantitative approaches have also made their way …

Cancer is a major health problem with high mortality rates. In the post-genome era,
investigators have access to massive amounts of rapidly accumulating high-throughput data …

Computational models of cancer chemotherapy have the potential to streamline clinical trial
design, contribute to the design of rational, tailored treatments, and facilitate our …

Medical oncology is in need of a mathematical modeling toolkit that can leverage clinically-
available measurements to optimize treatment selection and schedules for patients. Just as …

The TGI model4, 5 accounts for the dynamics of tumor growth, antitumor drug effect, and
resistance to drug effect. The model describes tumor size (ie, sum of the longest diameters of …

The number of publications on mathematical modeling of cancer is growing at an
exponential rate, according to PubMed records, provided by the US National Library of …