With an unprecedented understanding of the biological mechanisms, interactions, and dynamics occurring at various spatiotemporal scales, we are positioned to tackle the clinical challenges of cancer treatment better. However, cancer continues to be the second most common cause of death worldwide, responsible for one in six fatalities. It is often recognized that molecular heterogeneity, cellular plasticity, cellular migration and invasion, tumor-immune interactions, late-stage clinical presentation or diagnosis, therapeutic resistance, and tissue-scale biophysical barriers to drug delivery often thwart the clinical efforts to successfully treat cancer. With technical advances in our ability to generate and manage large amounts of experimental and clinical data, the application of data analytics, mathematical and computational models, and artificial intelligence is becoming increasingly common to extract meaningful information relevant to cancer biology, drug discovery and development, diagnosis, and personalized medicine to improve the status quo in clinical outcomes of cancer.
This Research Topic is aimed to present mathematical and computational studies, preferably integrated with, or based on experimental or clinical data, that:
i) investigate the underlying mechanisms of cancer development and progression
ii) contribute to the cancer drug discovery and development pipeline
iii) establish biomarkers or methodologies for early diagnosis and prognosis of cancer
iv) understand biophysical barriers to drug delivery (of small molecules, macromolecules, and nano-/micro-particles) in tumors
v) explore cancer therapeutic resistance mechanisms
vi) analyze/predict treatment outcomes to support personalized cancer medicine in the clinic
The proposed studies may be hypothesis-driven or exploratory in nature, and the scale of such investigations may range from molecular to whole-body but be geared towards solving current challenges in clinical management of cancer. The quantitative methods may belong to, but not limited to, one or more of the following domains:
- Systems biology/ Pharmacology
- Quantitative pharmacology
- Pharmacometrics,
- Mechanistic modeling
- Game theory
- Agent based modeling
- Network theory
- Statistical mechanics
- Bioinformatics/ Chemoinformatics
- Molecular modeling
- Machine learning/ Deep learning/ AI
The nature of contributions from authors may be original research papers, meta-analyses, review articles, or perspectives.
With an unprecedented understanding of the biological mechanisms, interactions, and dynamics occurring at various spatiotemporal scales, we are positioned to tackle the clinical challenges of cancer treatment better. However, cancer continues to be the second most common cause of death worldwide, responsible for one in six fatalities. It is often recognized that molecular heterogeneity, cellular plasticity, cellular migration and invasion, tumor-immune interactions, late-stage clinical presentation or diagnosis, therapeutic resistance, and tissue-scale biophysical barriers to drug delivery often thwart the clinical efforts to successfully treat cancer. With technical advances in our ability to generate and manage large amounts of experimental and clinical data, the application of data analytics, mathematical and computational models, and artificial intelligence is becoming increasingly common to extract meaningful information relevant to cancer biology, drug discovery and development, diagnosis, and personalized medicine to improve the status quo in clinical outcomes of cancer.
This Research Topic is aimed to present mathematical and computational studies, preferably integrated with, or based on experimental or clinical data, that:
i) investigate the underlying mechanisms of cancer development and progression
ii) contribute to the cancer drug discovery and development pipeline
iii) establish biomarkers or methodologies for early diagnosis and prognosis of cancer
iv) understand biophysical barriers to drug delivery (of small molecules, macromolecules, and nano-/micro-particles) in tumors
v) explore cancer therapeutic resistance mechanisms
vi) analyze/predict treatment outcomes to support personalized cancer medicine in the clinic
The proposed studies may be hypothesis-driven or exploratory in nature, and the scale of such investigations may range from molecular to whole-body but be geared towards solving current challenges in clinical management of cancer. The quantitative methods may belong to, but not limited to, one or more of the following domains:
- Systems biology/ Pharmacology
- Quantitative pharmacology
- Pharmacometrics,
- Mechanistic modeling
- Game theory
- Agent based modeling
- Network theory
- Statistical mechanics
- Bioinformatics/ Chemoinformatics
- Molecular modeling
- Machine learning/ Deep learning/ AI
The nature of contributions from authors may be original research papers, meta-analyses, review articles, or perspectives.