Background Improving our understanding of cancer and other complex diseases requires
integrating diverse data sets and algorithms. Intertwining in vivo and in vitro data and in …

Rapid advancements in high-throughput omics technologies and experimental protocols
have led to the generation of vast amounts of scale-specific biomolecular data on cancer …

Applied mathematics and nonlinear sciences have an enormous potential for application in
cancer. Mathematical models can be used to raise novel hypotheses to test, develop …

Combinations of therapies are being actively pursued to expand therapeutic options and
deal with cancer's pervasive resistance to treatment. Research efforts to discover effective …

Cancer results from a complex interplay of different biological, chemical, and physical
phenomena that span a wide range of time and length scales. Computational modeling may …

The development of new antibody–drug conjugates (ADCs) has led to the approval of 7
ADCs by the FDA in 4 years. Given the impact of intratumoral distribution on efficacy of these …

Understanding the mechanisms of interactions within cells, tissues, and organisms is crucial
to driving developments across biology and medicine. Mathematical modeling is an …

Methyl-β-cyclodextrin (MβCD) is an effective agent for the removal of plasma membrane
cholesterol. In this study, we investigated the modulating effects of MβCD on the …

Translating in vitro biological data into actionable information related to human health holds
the potential to improve disease treatment and risk assessment of chemical exposures …

Effective cancer treatment strategy requires an understanding of cancer behavior and
development across multiple temporal and spatial scales. This has resulted into a growing …