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The extent of this influence, however, is not completely understood so far. 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Aardema and Azeddine Elhajouji}, keywords = {Threshold, Mutagenesis, Carcinogenesis}, } @article{ oxidative_damage, author = {C G Fraga and M K Shigenaga and J W Park and P Degan and B N Ames }, title = {Oxidative damage to DNA during aging: 8-hydroxy-2'-deoxyguanosine in rat organ DNA and urine.}, journal = {Proceedings of the National Academy of Sciences}, volume = {87}, number = {12}, pages = {4533-4537}, year = {1990}, doi = {10.1073/pnas.87.12.4533}, URL = {https://www.pnas.org/doi/abs/10.1073/pnas.87.12.4533}, eprint = {https://www.pnas.org/doi/pdf/10.1073/pnas.87.12.4533}, } @article{the_Tumor_Suppressor_Gene, author = {SOUSSI, THIERRY}, title = {The p53 Tumor Suppressor Gene: From Molecular Biology to Clinical Investigation}, journal = {Annals of the New York Academy of Sciences}, volume = {910}, number = {1}, pages = {121-139}, doi = {https://doi.org/10.1111/j.1749-6632.2000.tb06705.x}, url = {https://nyaspubs.onlinelibrary.wiley.com/doi/abs/10.1111/j.1749-6632.2000.tb06705.x}, eprint = {https://nyaspubs.onlinelibrary.wiley.com/doi/pdf/10.1111/j.1749-6632.2000.tb06705.x}, year = {2000} } @article{dna_repair, ISSN = {09598278, 14735709}, URL = {http://www.jstor.org/stable/45074324}, author = {H E Poulsen and H Prieme and S Loft}, journal = {European Journal of Cancer Prevention}, number = {1}, pages = {9--16}, publisher = {Lippincott Williams & Wilkins}, title = {Role of oxidative DNA damage in cancer initiation and promotion}, urldate = {2023-09-19}, volume = {7}, year = {1998} } @article{smith1973, title={The logic of animal conflict}, author={Smith, J Maynard and Price, George R}, journal={Nature}, volume={246}, number={5427}, pages={15--18}, year={1973}, publisher={Nature Publishing Group} } @article{nash_1951, title={Non-cooperative games}, author={Nash, John}, journal={Annals of mathematics}, pages={286--295}, year={1951}, publisher={JSTOR} } @book{EvolutionaryGameTheory_Book, title={Evolutionary game theory, natural selection, and Darwinian dynamics}, author={Vincent, Thomas L and Brown, Joel S}, year={2005}, publisher={Cambridge University Press} } @Article{hrt_Gamma_shape, author={Connelly, Maureen T. and Richardson, Marcie and Platt, Richard}, title={Prevalence and duration of postmenopausal hormone replacement therapy use in a managed care organization, 1990--1995}, journal={Journal of General Internal Medicine}, year={2000}, month={8}, day={01}, volume={15}, number={8}, pages={542-550}, abstract={OBJECTIVE: To determine the prevalence and duration of postmenopausal hormone replacement therapy (HRT) use and identify correlates of adherence to therapy.}, issn={1525-1497}, doi={10.1046/j.1525-1497.2000.03499.x}, url={https://doi.org/10.1046/j.1525-1497.2000.03499.x} } @misc{chaitanya_pre, author = {Gokhale, Chaitanya S. and Traulsen, Arne}, title={Higher-order equivalence of Lotka-Volterra and replicator dynamics}, } @article{replicator_2_bomze, title={Lotka-Volterra equation and replicator dynamics: a two-dimensional classification}, author={Bomze, Immanuel M}, journal={Biological cybernetics}, volume={48}, number={3}, pages={201--211}, year={1983}, publisher={Springer} } @book{protter2005stochastic, author={Protter,Philip E.}, year={2004}, title={Stochastic integration and differential equations}, publisher={Springer}, address={New York;Berlin;}, volume={21.}, edition={Second}, keywords={Martingales (Mathematics); Stochastic differential equations; Stochastic integrals}, isbn={3540003134;9783540003137;}, language={English}, } @Article{tumour_heterogeneity_sequencing, author={Bedard, Philippe L. and Hansen, Aaron R. and Ratain, Mark J. and Siu, Lillian L.}, title={Tumour heterogeneity in the clinic}, journal={Nature}, year={2013}, month={Sep}, day={01}, volume={501}, number={7467}, pages={355-364}, abstract={Recent therapeutic advances in oncology have been driven by the identification of tumour genotype variations between patients, called interpatient heterogeneity, that predict the response of patients to targeted treatments. Subpopulations of cancer cells with unique genomes in the same patient may exist across different geographical regions of a tumour or evolve over time, called intratumour heterogeneity. Sequencing technologies can be used to characterize intratumour heterogeneity at diagnosis, monitor clonal dynamics during treatment and identify the emergence of clinical resistance during disease progression. Genetic interpatient and intratumour heterogeneity can pose challenges for the design of clinical trials that use these data.}, issn={1476-4687}, doi={10.1038/nature12627}, url={https://doi.org/10.1038/nature12627} } @article{amitGameTextbook, title={Game theory with applications in operations management}, author={Amit, RK}, journal={Management}, year={2020}, publisher={Springer} } @book{weibull1997evolutionary, title={Evolutionary game theory}, author={Weibull, J{\"o}rgen W}, year={1997}, publisher={MIT press} } @Article{Eftimie2011, author={Eftimie, Raluca and Bramson, Jonathan L. and Earn, David J. D.}, title={Interactions Between the Immune System and Cancer: A Brief Review of Non-spatial Mathematical Models}, journal={Bulletin of Mathematical Biology}, year={2011}, month={1}, day={01}, volume={73}, number={1}, pages={2-32}, abstract={We briefly review spatially homogeneous mechanistic mathematical models describing the interactions between a malignant tumor and the immune system. We begin with the simplest (single equation) models for tumor growth and proceed to consider greater immunological detail (and correspondingly more equations) in steps. This approach allows us to clarify the necessity for expanding the complexity of models in order to capture the biological mechanisms we wish to understand. We conclude by discussing some unsolved problems in the mathematical modeling of cancer-immune system interactions.}, issn={1522-9602}, doi={10.1007/s11538-010-9526-3}, url={https://doi.org/10.1007/s11538-010-9526-3} } @ARTICLE{frontier_boehme, AUTHOR={Buder, Thomas and Deutsch, Andreas and Klink, Barbara and Voss-Böhme, Anja }, TITLE={Patterns of Tumor Progression Predict Small and Tissue-Specific Tumor-Originating Niches}, JOURNAL={Frontiers in Oncology}, VOLUME={8}, YEAR={2019}, URL={https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2018.00668}, DOI={10.3389/fonc.2018.00668}, ISSN={2234-943X}, ABSTRACT={The development of cancer is a multistep process in which cells increase in malignancy through progressive alterations. Such altered cells compete with wild-type cells and have to establish within a tissue in order to induce tumor formation. The range of this competition and the tumor-originating cell type which acquires the first alteration is unknown for most human tissues, mainly because the involved processes are hardly observable, aggravating an understanding of early tumor development. On the tissue scale, one observes different progression types, namely with and without detectable benign precursor stages. Human epidemiological data on the ratios of the two progression types exhibit large differences between cancers. The idea of this study is to utilize data of the ratios of progression types in human cancers to estimate the homeostatic range of competition in human tissues. This homeostatic competition range can be interpreted as necessary numbers of altered cells to induce tumor formation on the tissue scale. For this purpose, we develop a cell-based stochastic model which is calibrated with newly-interpreted human epidemiological data. We find that the number of tumor cells which inevitably leads to later tumor formation is surprisingly small compared to the overall tumor and largely depends on the human tissue type. This result points toward the existence of a tissue-specific tumor-originating niche in which the fate of tumor development is decided early and long before a tumor becomes detectable. Moreover, our results suggest that the fixation of tumor cells in the tumor-originating niche triggers new processes which accelerate tumor growth after normal tissue homeostasis is voided. Our estimate for the human colon agrees well with the size of the stem cell niche in colonic crypts. For other tissues, our results might aid to identify the tumor-originating cell type. For instance, data on primary and secondary glioblastoma suggest that the tumors originate from a cell type competing in a range of 300 – 1,900 cells.}} @article{tCell_model, title={The Impact of T-cell Exhaustion Dynamics on Tumour--Immune Interactions and Tumour Growth}, author={Lai, Nicholas and Farman, Alexis and Byrne, Helen M}, journal={Bulletin of Mathematical Biology}, volume={87}, number={5}, pages={1--47}, year={2025}, publisher={Springer} } @Article{chemo_immune_supression, author={Sharma, Anirudh and Jasrotia, Shivam and Kumar, Ajay}, title={Effects of Chemotherapy on the Immune System: Implications for Cancer Treatment and Patient Outcomes}, journal={Naunyn-Schmiedeberg's Archives of Pharmacology}, year={2024}, month={May}, day={01}, volume={397}, number={5}, pages={2551-2566}, abstract={Chemotherapy is a cornerstone of cancer treatment, but it can also induce immune suppression, which can have significant implications for patient outcomes. This review paper aims to give a general overview of how chemotherapy affects the immune system and how it affects cancer treatment. Chemotherapy can directly affect immune cells, leading to cytotoxic effects, cell differentiation and function alterations, and cell communication and signaling pathways disruptions. Such immune suppression can weaken the anti-tumor immune response and increase the risk of immune-related toxicities. Understanding the mechanisms of chemotherapy-induced immune suppression is crucial for optimizing treatment strategies. Strategies to mitigate immune suppression include immunomodulatory agents as adjuvants to chemotherapy, combination therapies to enhance immune function, and supportive care measures of the immune system. Additionally, identifying potential biomarkers to predict immune suppression and guide treatment decisions holds promise for personalized cancer medicine. Future directions in this field involve further elucidating underlying mechanisms, exploring novel combination therapies, and developing targeted interventions to minimize immune suppression. By understanding and addressing chemotherapy-induced immune suppression, we can optimize cancer treatment strategies, enhance the anti-tumor immune response, and improve patient outcomes.}, issn={1432-1912}, doi={10.1007/s00210-023-02781-2}, url={https://doi.org/10.1007/s00210-023-02781-2} } @Article{combined_therapy, author={Tang, Sanyi and Li, Shuo and Tang, Biao and Wang, Xia and Xiao, Yanni and Cheke, Robert A.}, title={Hormetic and synergistic effects of cancer treatments revealed by modelling combinations of radio - or chemotherapy with immunotherapy}, journal={BMC Cancer}, year={2023}, month={Oct}, day={27}, volume={23}, number={1}, pages={1040}, abstract={Radio/chemotherapy and immune systems provide examples of hormesis, as tumours can be stimulated (or reduced) at low radio/chemical or antibody doses but inhibited (or stimulated) by high doses.}, issn={1471-2407}, doi={10.1186/s12885-023-11542-6}, url={https://doi.org/10.1186/s12885-023-11542-6} } @Article{Smale1976, author={Smale, S.}, title={On the differential equations of species in competition}, journal={Journal of Mathematical Biology}, year={1976}, month={Mar}, day={01}, volume={3}, number={1}, pages={5-7}, abstract={It is shown that the ordinary differential equation commonly used to describe competing species are compatible with any dynamical behavior provided the number of species in very large.}, issn={1432-1416}, doi={10.1007/BF00307854}, url={https://doi.org/10.1007/BF00307854} } @Article{chemo_menstrual_cycle, author={Bornes, Laura and van Winden, Lennart J. and Geurts, Veerle C. M. and de Bruijn, Beaunelle and Azarang, Leyla and Lanfermeijer, Mirthe and Caruso, Marika and Proost, Natalie and Boeije, Manon and Lohuis, Jeroen O. and Belthier, Guillaume and Noguera Delgado, Eul{\`a}lia and de Gruil, Nadia and Kroep, Judith R. and van de Ven, Marieke and Menezes, Renee and Wesseling, Jelle and Kok, Marleen and Linn, Sabine and Broeks, Annegien and van Rossum, Huub H. and Scheele, Colinda L. G. J. and van Rheenen, Jacco}, title={The oestrous cycle stage affects mammary tumour sensitivity to chemotherapy}, journal={Nature}, year={2025}, month={Jan}, day={01}, volume={637}, number={8044}, pages={195-204}, issn={1476-4687}, doi={10.1038/s41586-024-08276-1}, url={https://doi.org/10.1038/s41586-024-08276-1} } @article{complex_hormon_cycle_model, title = {A complex mathematical model of the human menstrual cycle}, journal = {Journal of Theoretical Biology}, volume = {247}, number = {2}, pages = {303-330}, year = {2007}, issn = {0022-5193}, doi = {https://doi.org/10.1016/j.jtbi.2007.03.011}, url = {https://www.sciencedirect.com/science/article/pii/S0022519307001166}, author = {Isabel Reinecke and Peter Deuflhard}, keywords = {GnRH pulse generator, Follicular phase, Steroid concentration, Delay differential equation}, } @article{3eqCycleModel, author = {Chen, C. Y. and Ward, J. P.}, title = {A mathematical model for the human menstrual cycle}, journal = {Mathematical Medicine and Biology: A Journal of the IMA}, volume = {31}, number = {1}, pages = {65-86}, year = {2013}, month = {01}, issn = {1477-8599}, doi = {10.1093/imammb/dqs048}, url = {https://doi.org/10.1093/imammb/dqs048}, eprint = {https://academic.oup.com/imammb/article-pdf/31/1/65/2557670/dqs048.pdf}, } @ARTICLE{endocrineSystemOoverview, title = "The endocrine system: an overview", author = "Hiller-Sturmh{\"o}fel, S and Bartke, A", journal = "Alcohol Health Res World", volume = 22, number = 3, pages = "153--164", year = 1998, address = "United States", language = "en" } @article{PRINGCycle_pharmaIntervent, title = {A mathematical model of the bovine oestrous cycle: Simulating outcomes of dietary and pharmacological interventions}, journal = {Journal of Theoretical Biology}, volume = {313}, pages = {115-126}, year = {2012}, issn = {0022-5193}, doi = {https://doi.org/10.1016/j.jtbi.2012.08.010}, url = {https://www.sciencedirect.com/science/article/pii/S0022519312003967}, author = {Stephen R. Pring and Markus Owen and John R. King and Kevin D. Sinclair and Robert Webb and Anthony P.F. Flint and Philip C. Garnsworthy}, keywords = {Mathematical modelling, Bovine, Oestrous cycle, Nutrition, Reproduction}, } @Article{state_prob_model_cycle, author={de Paula Oliveira, Thiago and Bruinvels, Georgie and Pedlar, Charles R. and Moore, Brian and Newell, John}, title={Modelling menstrual cycle length in athletes using state-space models}, journal={Scientific Reports}, year={2021}, month={Aug}, day={20}, volume={11}, number={1}, pages={16972}, issn={2045-2322}, doi={10.1038/s41598-021-95960-1}, url={https://doi.org/10.1038/s41598-021-95960-1} } @article{HARRIS201411, title = {Modeling endocrine regulation of the menstrual cycle using delay differential equations}, journal = {Mathematical Biosciences}, volume = {257}, pages = {11-22}, year = {2014}, note = {Multiscale models and methods in biomedicine}, issn = {0025-5564}, doi = {https://doi.org/10.1016/j.mbs.2014.08.011}, url = {https://www.sciencedirect.com/science/article/pii/S0025556414001667}, author = {Leona A. Harris and James F. Selgrade}, keywords = {Bifurcation, Estradiol, Follicle, Parameter, Pituitary gland}, } @Article{Smyth_immune_review, author={Smyth, Mark J. and Godfrey, Dale I. and Trapani, Joseph A.}, title={A fresh look at tumor immunosurveillance and immunotherapy}, journal={Nature Immunology}, year={2001}, month={Apr}, day={01}, volume={2}, number={4}, pages={293-299}, abstract={Despite major advances in our understanding of adaptive immunity and dendritic cells, consistent and durable responses to cancer vaccines remain elusive and active immunotherapy is still not an established treatment modality. The key to developing an effective anti-tumor response is understanding why, initially, the immune system is unable to detect transformed cells and is subsequently tolerant of tumor growth and metastasis. Ineffective antigen presentation limits the adaptive immune response; however, we are now learning that the host's innate immune system may first fail to recognize the tumor as posing a danger. Recent descriptions of stress-induced ligands on tumor cells recognized by innate effector cells, new subsets of T cells that regulate tumor tolerance and the development of spontaneous tumors in mice that lack immune effector molecules, beckon a reflection on our current perspectives on the interaction of transformed cells with the immune system and offer new hope of stimulating therapeutic immunity to cancer.}, issn={1529-2916}, doi={10.1038/86297}, url={https://doi.org/10.1038/86297} } @article{Parish_immune_review, author={Parish,Christopher R.}, year={2003}, title={Cancer immunotherapy: The past, the present and the future}, journal={Immunology and cell biology}, volume={81}, number={2}, pages={106-113}, keywords={adaptive immunity; Animals; cancer immunotherapy; History, 19th Century; History, 20th Century; History, 21st Century; Humans; immune evasion; Immunity; Immunologic Surveillance; immunosurveillance; Immunotherapy - history; Immunotherapy - methods; Immunotherapy - trends; innate immunity; Neoplasms - immunology; Neoplasms - pathology; Neoplasms - therapy}, isbn={0818-9641}, language={English}, } @Article{Lange2019follicles, author={Lange, Alexander and Schwieger, Robert and Pl{\"o}ntzke, Julia and Sch{\"a}fer, Stefan and R{\"o}blitz, Susanna}, title={Follicular competition in cows: the selection of dominant follicles as a synergistic effect}, journal={Journal of Mathematical Biology}, year={2019}, month={Feb}, day={01}, volume={78}, number={3}, pages={579-606}, abstract={The reproductive cycle of mono-ovulatory species such as cows or humans is known to show two or more waves of follicular growth and decline between two successive ovulations. Within each wave, there is one dominant follicle escorted by subordinate follicles of varying number. Under the surge of the luteinizing hormone a growing dominant follicle ovulates. Rarely the number of ovulating follicles exceeds one. In the biological literature, the change of hormonal concentrations and individually varying numbers of follicular receptors are made responsible for the selection of exactly one dominant follicle, yet a clear cause has not been identified. In this paper, we suggest a synergistic explanation based on competition, formulated by a parsimoniously defined system of ordinary differential equations (ODEs) that quantifies the time evolution of multiple follicles and their competitive interaction during one wave. Not discriminating between follicles, growth and decline are given by fixed rates. Competition is introduced via a growth-suppressing term, equally supported by all follicles. We prove that the number of dominant follicles is determined exclusively by the ratio of follicular growth and competition. This number turns out to be independent of the number of subordinate follicles. The asymptotic behavior of the corresponding dynamical system is investigated rigorously, where we demonstrate that the {\$}{\$}{\backslash}omega {\$}{\$}-limit set only contains fixed points. When also including follicular decline, our ODEs perfectly resemble ultrasound data of bovine follicles. Implications for the involved but not explicitly modeled hormones are discussed.}, issn={1432-1416}, doi={10.1007/s00285-018-1284-0}, url={https://doi.org/10.1007/s00285-018-1284-0} } @article{FISCHERHOLZHAUSEN_merge, title = {Hormonal regulation of ovarian follicle growth in humans: Model-based exploration of cycle variability and parameter sensitivities}, journal = {Journal of Theoretical Biology}, volume = {547}, pages = {111150}, year = {2022}, issn = {0022-5193}, doi = {https://doi.org/10.1016/j.jtbi.2022.111150}, url = {https://www.sciencedirect.com/science/article/pii/S0022519322001485}, author = {Sophie Fischer-Holzhausen and Susanna Röblitz}, keywords = {HPG axis, Reproductive hormones, Mathematical modelling, Stochastic dynamical system}, abstract = {We present a modelling and simulation framework for the dynamics of ovarian follicles and key hormones along the hypothalamic-pituitary–gonadal axis throughout consecutive human menstrual cycles. All simulation results (hormone concentrations and ovarian follicle sizes) are in biological units and can easily be compared to clinical data. The model takes into account variability in follicles’ response to stimulating hormones, which introduces variability between cycles. The growth of ovarian follicles in waves is an emergent property in our model simulations and further supports the hypothesis that follicular waves are also present in humans. We use Approximate Bayesian Computation and cluster analysis to construct a population of virtual subjects and to study parameter distributions and sensitivities. The model can be used to compare and optimize treatment protocols for ovarian hyperstimulation, thus potentially forming the integral part of a clinical decision support system in reproductive endocrinology.} } @article{follicle_aging, author={Portillo,A. M. and Peláez,C.}, year={2021}, title={Mathematical modelling of ageing acceleration of the human follicle due to oxidative stress and other factors}, journal={Mathematical medicine and biology}, volume={38}, number={3}, pages={273-291}, isbn={1477-8599;1477-8602;}, language={English}, } @article{follicle_aging_inital, author={Thilagam,Alagu}, year={2016}, title={Mathematical modelling of decline in follicle pool during female reproductive ageing}, journal={Mathematical medicine and biology}, volume={33}, number={1}, pages={107-121}, abstract={The factors which govern the subtle links between follicle loss and mammalian female reproductive ageing remain unclear despite extensive studies undertaken to understand the critical physiological and biochemical mechanisms that underly the accelerated decline in follicle numbers in women older than 37 years. It is not certain whether there is a sole control by the ovary or whether other factors which affect ageing also intersect with the ovarian effect. There is convincing experimental evidence for an interplay of several processes that seem to influence the follicle loss-female reproductive ageing links, with specific hormones (follicle-stimulating hormone, anti-Müllerian hormone, dehydroepiandrosterone) noted to play important roles in follicular dynamics and ovarian ageing. In this work, we examine the subtle links between the rate of follicular decline with ageing and the role of hormones via a series of non-autonomous equations. Simulation results based on the time evolution of the number of ovarian follicles and biochemical changes in the ovarian environment influenced by hormone levels is compared with empirical data based on follicle loss-reproductive ageing correlation studies.}, keywords={Adult; Aging - metabolism; Aging - physiology; Anti-Mullerian Hormone - metabolism; Anti-Mullerian Hormone - physiology; Dehydroepiandrosterone - metabolism; Dehydroepiandrosterone - physiology; Female; Humans; Middle Aged; Models, Theoretical; Ovarian Follicle - metabolism; Ovarian Follicle - physiology; Ovarian Reserve - physiology}, isbn={1477-8599}, language={English}, } @article{ZEEMAN_cycle, title = {Resonance in the menstrual cycle: a new model of the LH surge}, journal = {Reproductive BioMedicine Online}, volume = {7}, number = {3}, pages = {295-300}, year = {2003}, issn = {1472-6483}, doi = {https://doi.org/10.1016/S1472-6483(10)61867-6}, url = {https://www.sciencedirect.com/science/article/pii/S1472648310618676}, author = {ML Zeeman and W Weckesser and D Gokhman}, keywords = {frequency interaction, GnRH pulses, LH surge, mathematical model, oestradiol, resonance}, abstract = {In vertebrates, ovulation is triggered by a surge of LH from the pituitary. The precise mechanism by which rising oestradiol concentrations initiate the LH surge in the human menstrual cycle remains a fundamental open question of reproductive biology. It is well known that sampling of serum LH on a time scale of minutes reveals pulsatile release from the pituitary in response to pulses of gonadotrophin releasing hormone from the hypothalamus. The LH pulse frequency and amplitude vary considerably over the cycle, with the highest frequency and amplitude at the midcycle surge. Here a new mathematical model is presented of the pituitary as a damped oscillator (pulse generator) driven by the hypothalamus. The model LH surge is consistent with LH data on the time scales of both minutes and days. The model is used to explain the surprising pulse frequency characteristics required to treat human infertility disorders such as Kallmann's syndrome, and new experimental predictions are made.} } @article{endometriosis_cycle, author = {Miller, Claire M. and Germano, Domenic P. J. and Chenoweth, Alicia M. and Holdsworth-Carson, Sarah }, title = {Mathematical modelling of macrophage and natural killer cell immune response during early stages of peritoneal endometriosis lesion onset}, journal = {Journal of The Royal Society Interface}, volume = {22}, number = {229}, pages = {20250076}, year = {2025}, doi = {10.1098/rsif.2025.0076}, URL = {https://royalsocietypublishing.org/doi/abs/10.1098/rsif.2025.0076}, eprint = {https://royalsocietypublishing.org/doi/pdf/10.1098/rsif.2025.0076} , } @inproceedings{oskouei2010mathematical, title={Mathematical modelling of the interaction of chlamydia trachomatis with the immune system}, author={Oskouei, Masoumeh Bagher and Mallet, Dann G and Amirshahi, Ashkan and Pettet, Graeme J}, booktitle={Proceedings of the World Congress on Engineering and Computer Science 2010 Vol}, volume={2}, year={2010} } @article{overviewImmunesys, author={Parkin,Jacqueline and Cohen,Bryony}, year={2001}, title={An overview of the immune system}, journal={The Lancet (British edition)}, volume={357}, number={9270}, pages={1777-1789}, keywords={Antigens; B-Lymphocytes - physiology; Cell growth; Chemokines; Collagen; Cytokines; Cytokines - physiology; Cytotoxicity; Fibroblasts; Gangrene; Granulocytes; Growth factors; Humans; Immune system; Immune System - physiology; Immunodeficiency; Immunology; Infections; Interactive systems; Lymph nodes; Lymphocytes; Molecular chains; Neutrophils; Organisms; Organs; Pathogenicity; Pathogens; Proteins; T-Lymphocytes - physiology; Tumor necrosis factor-TNF; Tumors; Virulence; Virulence factors}, isbn={0140-6736}, language={English}, } @Article{molecular_agentbased, author={Sneddon, Michael W. and Faeder, James R. and Emonet, Thierry}, title={Efficient modeling, simulation and coarse-graining of biological complexity with NFsim}, journal={Nature Methods}, year={2011}, month={Feb}, day={01}, volume={8}, number={2}, pages={177-183}, abstract={The Network-Free Stochastic Simulator (NFsim) allows the representation of complex biological systems as rule-based models and facilitates coarse-graining of the reaction mechanisms.}, issn={1548-7105}, doi={10.1038/nmeth.1546}, url={https://doi.org/10.1038/nmeth.1546} } @article{pde_macrophages, title={Mathematical modelling of macrophage dynamics in tumours}, author={Owen, Markus R and Sherratt, Jonathan A}, journal={Mathematical Models and Methods in Applied Sciences}, volume={9}, number={04}, pages={513--539}, year={1999}, publisher={World Scientific} } @Article{chimeric1, AUTHOR = {Adhikarla, Vikram and Awuah, Dennis and Brummer, Alexander B. and Caserta, Enrico and Krishnan, Amrita and Pichiorri, Flavia and Minnix, Megan and Shively, John E. and Wong, Jeffrey Y. C. and Wang, Xiuli and Rockne, Russell C.}, TITLE = {A Mathematical Modeling Approach for Targeted Radionuclide and Chimeric Antigen Receptor T Cell Combination Therapy}, JOURNAL = {Cancers}, VOLUME = {13}, YEAR = {2021}, NUMBER = {20}, ARTICLE-NUMBER = {5171}, URL = {https://www.mdpi.com/2072-6694/13/20/5171}, PubMedID = {34680320}, ISSN = {2072-6694}, ABSTRACT = {Targeted radionuclide therapy (TRT) has recently seen a surge in popularity with the use of radionuclides conjugated to small molecules and antibodies. Similarly, immunotherapy also has shown promising results, an example being chimeric antigen receptor T cell (CAR-T) therapy in hematologic malignancies. Moreover, TRT and CAR-T therapies possess unique features that require special consideration when determining how to dose as well as the timing and sequence of combination treatments including the distribution of the TRT dose in the body, the decay rate of the radionuclide, and the proliferation and persistence of the CAR-T cells. These characteristics complicate the additive or synergistic effects of combination therapies and warrant a mathematical treatment that includes these dynamics in relation to the proliferation and clearance rates of the target tumor cells. Here, we combine two previously published mathematical models to explore the effects of dose, timing, and sequencing of TRT and CAR-T cell-based therapies in a multiple myeloma setting. We find that, for a fixed TRT and CAR-T cell dose, the tumor proliferation rate is the most important parameter in determining the best timing of TRT and CAR-T therapies.}, DOI = {10.3390/cancers13205171} } @article{chimeric2, title={A mathematical model to describe the melanoma dynamics under effects of macrophage inhibition and car t-cell therapy}, author={Rodrigues, G and SILVA, JG and Santurio, DS and Mancera, PFA}, journal={Trends in Computational and Applied Mathematics}, volume={25}, pages={e01734}, year={2024}, publisher={SciELO Brasil} } @article{chimeric3, title={Dual CAR-T cell therapy for glioblastoma: strategies to cure tumour diseases based on a mathematical model}, author={Bodnar, Marek and Piotrowska, Monika J and Bodzioch, Mariusz and Belmonte-Beitia, Juan and Fory{\'s}, Urszula}, journal={Nonlinear Dynamics}, volume={113}, number={2}, pages={1637--1666}, year={2025}, publisher={Springer} } @Article{hybrid1, AUTHOR = {Norton, Kerri-Ann and Gong, Chang and Jamalian, Samira and Popel, Aleksander S.}, TITLE = {Multiscale Agent-Based and Hybrid Modeling of the Tumor Immune Microenvironment}, JOURNAL = {Processes}, VOLUME = {7}, YEAR = {2019}, NUMBER = {1}, ARTICLE-NUMBER = {37}, URL = {https://www.mdpi.com/2227-9717/7/1/37}, ISSN = {2227-9717}, ABSTRACT = {Multiscale systems biology and systems pharmacology are powerful methodologies that are playing increasingly important roles in understanding the fundamental mechanisms of biological phenomena and in clinical applications. In this review, we summarize the state of the art in the applications of agent-based models (ABM) and hybrid modeling to the tumor immune microenvironment and cancer immune response, including immunotherapy. Heterogeneity is a hallmark of cancer; tumor heterogeneity at the molecular, cellular, and tissue scales is a major determinant of metastasis, drug resistance, and low response rate to molecular targeted therapies and immunotherapies. Agent-based modeling is an effective methodology to obtain and understand quantitative characteristics of these processes and to propose clinical solutions aimed at overcoming the current obstacles in cancer treatment. We review models focusing on intra-tumor heterogeneity, particularly on interactions between cancer cells and stromal cells, including immune cells, the role of tumor-associated vasculature in the immune response, immune-related tumor mechanobiology, and cancer immunotherapy. We discuss the role of digital pathology in parameterizing and validating spatial computational models and potential applications to therapeutics.}, DOI = {10.3390/pr7010037} } @Article{hyprid2, author={Mortazavi, Sayyed Mohammad Ali and Firoozabadi, Bahar}, title={Towards a framework for predicting immunotherapy outcome: a hybrid multiscale mathematical model of immune response to vascular tumor growth}, journal={Biomechanics and Modeling in Mechanobiology}, year={2024}, month={Dec}, day={01}, volume={23}, number={6}, pages={2243-2264}, abstract={Studying tumor immune microenvironment (TIME) is pivotal to understand the mechanism and predict the outcome of cancer immunotherapy. Systems biology mathematical models can consider and control various factors of TIME and therefore explore the anti-tumor immune response meticulously. However, the role of tumor vasculature in the recruitment of T cells and the mechanism of T cell migration through TIME have not been studied comprehensively. In this work, we developed a hybrid discrete-continuum multi-scale model to study TIME. The mathematical model includes angiogenesis and T cell recruitment via tumor vasculature. Moreover, solid tumor growth, vascular growth and remodeling, interstitial fluid flow, hemodynamics, and blood rheology are all considered in the model. In addition, different aspects of T cells, including their migration, proliferation, subtype conversion, and interaction with tumor cells are thoroughly included. The model reproduces spatiotemporal distribution of tumor infiltrating T cells that mimics histopathological patterns. Furthermore, TIME model robustly recapitulates different phases of tumor immunoediting. We also examined a number of biomarkers to predict the outcome of immune checkpoint blockade (ICB) treatment. The results demonstrated that although tumor mutational burden (TMB) may predict non-responders to ICB, a combination of different biomarkers is essential to predict the majority of the responders. Based on our results, the ICB response rate varies significantly from 28 to 89{\%} depending on the values of different parameters, even in the cases with high TMB.}, issn={1617-7940}, doi={10.1007/s10237-024-01891-x}, url={https://doi.org/10.1007/s10237-024-01891-x} } @article{chimeric4, author = {Sahoo, Prativa and Yang, Xin and Abler, Daniel and Maestrini, Davide and Adhikarla, Vikram and Frankhouser, David and Cho, Heyrim and Machuca, Vanessa and Wang, Dongrui and Barish, Michael and Gutova, Margarita and Branciamore, Sergio and Brown, Christine E. and Rockne, Russell C. }, title = {Mathematical deconvolution of CAR T-cell proliferation and exhaustion from real-time killing assay data}, journal = {Journal of The Royal Society Interface}, volume = {17}, number = {162}, pages = {20190734}, year = {2020}, doi = {10.1098/rsif.2019.0734}, URL = {https://royalsocietypublishing.org/doi/abs/10.1098/rsif.2019.0734}, eprint = {https://royalsocietypublishing.org/doi/pdf/10.1098/rsif.2019.0734} , abstract = { Chimeric antigen receptor (CAR) T-cell therapy has shown promise in the treatment of haematological cancers and is currently being investigated for solid tumours, including high-grade glioma brain tumours. There is a desperate need to quantitatively study the factors that contribute to the efficacy of CAR T-cell therapy in solid tumours. In this work, we use a mathematical model of predator–prey dynamics to explore the kinetics of CAR T-cell killing in glioma: the Chimeric Antigen Receptor T-cell treatment Response in GliOma (CARRGO) model. The model includes rates of cancer cell proliferation, CAR T-cell killing, proliferation, exhaustion, and persistence. We use patient-derived and engineered cancer cell lines with an in vitro real-time cell analyser to parametrize the CARRGO model. We observe that CAR T-cell dose correlates inversely with the killing rate and correlates directly with the net rate of proliferation and exhaustion. This suggests that at a lower dose of CAR T-cells, individual T-cells kill more cancer cells but become more exhausted when compared with higher doses. Furthermore, the exhaustion rate was observed to increase significantly with tumour growth rate and was dependent on level of antigen expression. The CARRGO model highlights nonlinear dynamics involved in CAR T-cell therapy and provides novel insights into the kinetics of CAR T-cell killing. The model suggests that CAR T-cell treatment may be tailored to individual tumour characteristics including tumour growth rate and antigen level to maximize therapeutic benefit. } } @article{hormonal_therapy_breast, author = {Li, Christopher I. and Malone, Kathleen E. and Porter, Peggy L. and Weiss, Noel S. and Tang, Mei-Tzu C. and Cushing-Haugen, Kara L. and Daling, Janet R.}, title = {Relationship Between Long Durations and Different Regimens of Hormone Therapy and Risk of Breast Cancer}, journal = {JAMA}, volume = {289}, number = {24}, pages = {3254-3263}, year = {2003}, month = {06}, abstract = {ContextWomen using combined estrogen and progestin hormone replacement therapy (CHRT) have an increased risk of breast cancer; however, data on use for long durations and on risk associated with patterns of use are lacking.ObjectiveTo evaluate relationships between durations and patterns of CHRT use and risk of breast cancer by histological type and hormone receptor status.DesignPopulation-based case-control study.SettingThree counties in western Washington State.ParticipantsNine hundred seventy-five women 65-79 years of age diagnosed with invasive breast cancer from April 1, 1997, through May 31, 1999 (histology: 196 lobular cases, 656 ductal cases, 114 cases with other histological type, and 9 cases with an unspecified histological type; estrogen receptor (ER)/progesterone receptor (PR) status: 646 ER+/PR+ cases, 147 ER+/PR− cases, and 101 ER−/PR− cases [6 ER−/PR+ cases and 75 cases with unknown ER/PR status were not included in the analyses herein]) and 1007 population controls.Main Outcome MeasuresRisks of invasive lobular, ductal, ER+/PR+, ER+/PR−, and ER−/PR− breast carcinomas.ResultsWomen using unopposed estrogen replacement therapy (ERT) (exclusive ERT use), even for 25 years or longer, had no appreciable increase in risk of breast cancer, although the associated odds ratios were not inconsistent with a possible small effect. Ever users of CHRT (includes CHRT users who also had used ERT) had a 1.7-fold (95\% confidence interval [CI], 1.3-2.2) increased risk of breast cancer, including a 2.7-fold (95\% CI, 1.7-4.3) increased risk of invasive lobular carcinoma, a 1.5-fold (95\% CI, 1.1-2.0) increased risk of invasive ductal carcinoma, and a 2.0-fold (95\% CI, 1.5-2.7) increased risk of ER+/PR+ breast cancers. The increase in risk was greatest in those using CHRT for longer durations (users for 5-14.9 years and ≥15 years had 1.5-fold [95\% CI, 1.0-2.3] and 1.6-fold [95\% CI, 1.0-2.6] increases in risk of invasive ductal carcinoma, respectively, and 3.7-fold [95\% CI, 2.0-6.6] and 2.6-fold [95\% CI, 1.3-5.3] increases in risk of invasive lobular carcinoma, respectively. Associations of similar magnitudes were seen among users of both sequential and continuous CHRT. Risks of ER+/PR− and ER−/PR− tumors were not increased by use of any form of hormone replacement therapy; however, small numbers of these tumors limited power to detect possible associations.ConclusionThese data suggest that use of CHRT is associated with an increased risk of breast cancer, particularly invasive lobular tumors, whether the progestin component was taken in a sequential or in a continuous manner.}, issn = {0098-7484}, doi = {10.1001/jama.289.24.3254}, url = {https://doi.org/10.1001/jama.289.24.3254}, eprint = {https://jamanetwork.com/journals/jama/articlepdf/196803/joc30501.pdf}, } @incollection{men_cycle_pregancy, title={Physiology, menstrual cycle}, author={Thiyagarajan, Dhanalakshmi K and Basit, Hajira and Jeanmonod, Rebecca}, booktitle={StatPearls [Internet]}, year={2024}, publisher={StatPearls Publishing} } @Comment{jabref-meta: databaseType:bibtex;}