Theoretical Modeling Techniques and Their Impact on Tumor Immunology

Abstract

Currently, cancer is one of the leading causes of death in industrial nations. While conventional cancer treatment usually results in the patient suffering from severe side effects, immunotherapy is a promising alternative. Nevertheless, some questions remain unanswered with regard to using immunotherapy to treat cancer hindering it from being widely established. To help rectify this deficit in knowledge, experimental data, accumulated from a huge number of different studies, can be integrated into theoretical models of the tumor-immune system interaction. Many complex mechanisms in immunology and oncology cannot be measured in experiments, but can be analyzed by mathematical simulations. Using theoretical modeling techniques, general principles of tumor-immune system interactions can be explored and clinical treatment schedules optimized to lower both tumor burden and side effects. In this paper, we aim to explain the main mathematical and computational modeling techniques used in tumor immunology to experimental researchers and clinicians. In addition, we review relevant published work and provide an overview of its impact to the field.

Cancer systems immunology

“…differential equations) can be aided by computational approaches and bioinformatics tools often rely upon novel or existing mathematical analyses for their implementation. The breadth of modeling approaches and informatics tools in the field of Systems Biology (applied to cancer, the immune system, or both) is too large to review here and has been discussed elsewhere ( Germain et al, 2011 ; Altrock et al, 2015 ; Meyer and Heiser, 2019 ; Machado et al, 2011 ; Norton et al, 2019 ; Woelke et al, 2010 ; Stuart and Satija, 2019 ; Materi and Wishart, 2007 ). Many modeling approaches, such as Petri Nets ( Petri, 1962 ; Sackmann et al, 2006 ; Koch, 2015 ) and Boolean Networks ( Thomas, 1973 ; Saez-Rodriguez et al, 2007 ; Mai and Liu, 2009 ; Stoll et al, 2012 ), have been used extensively to model regulatory and signaling networks within cells ( Vieira and Vera-Licona, 2019 ).…”

Simulating cancer growth with multiscale agent-based modeling

“…This is currently a dynamic research area partly because performing in silico experiments not only helps to relieve the cost in terms of time and money, but also provides a platform to acquire difficult to measure responses in vivo [76]. ABM has been used to study anti-cancer vaccines that would prime the immune system of humans, specifically cytotoxic T lymphocytes (CTL), against newly forming tumors even before a clinical diagnosis has occurred [77].…”

Hybrid Modelling in Biology: a Classification Review

“…Reviews dedicated to some specific applications of hybrid models can also be found, for example in biological networks modelling [12], in cancer modelling [10,21,30,40], and more specifically tumour growth [24,29], tumour immunology [41], brain cancer [42] or angiogenesis [16]. Many special issues or reviews are also dedicated to the more specific field of hybrid systems [2,17,31,39] which can be considered as a subclass of hybrid models mainly (but not only) related to the field of automatic control.…”