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Understanding Cancer from a Systems Biology Point of View: From Observation to Theory and Back starts with a basic question, why do we sometimes observe accelerated metastatic growth after resection of primary tumors? Next, it helps readers understand the systemic nature of cancer and how it affects treatment approaches and decisions. The book puts together aspects of cancer that many readers have most likely never combined, using unfamiliar, novel methods. It is a valuable resource for cancer researchers, cancer biologists, mathematicians and members of the biomedical field who are interested in applying systems biology methodologies for understanding and treating cancer.
List of contents
1. Introduction2. Tumor dormancy3. Cancer immunoediting: a process driven by metabolic competition as a predator-prey-shared resource type model4. Escape from tumor dormancy and time to angiogenic switch as mitigated by tumor- induced stimulation of stroma5. Angiogenesis regulators as a possible key to accelerated growth of secondary tumors following primary tumor resection6. A combination of immune checkpoint inhibition with metronomic chemotherapy as a way of targeting therapy-resistant cancer cells7. Summary and conclusions
About the author
Dr. Irina Kareva is a theoretical biologist, and the primary focus of her research involves using mathematical modeling to study cancer as an evolving ecosystem within the human body, where heterogeneous populations of cancer cells compete for limited resources (i.e., oxygen and glucose), cooperate with each other to fight off predators (the immune system), and disperse and migrate (metastases). In 2017 Dr. Kareva gave a TED talk on using mathematical modeling for biological research. Dr. Kareva's book Understanding cancer from a systems biology point of view: from observation to theory and back was published by Elsevier in 2018. Dr. Kareva is a Senior Scientist in Simulation and Modeling at EMD Serono, Merck KGaA, where she develops quantitative systems pharmacology (QSP) models to help understand and predict dynamics of new therapeutics.
