System dynamics of cancer in erythropoiesis with multiple <scp>EPO</scp> feedbacks

Sajid, Zamra; Andersen, Morten; Ottesen, Johnny T.

doi:10.1002/sdr.1670

Cited by 3 publications

(1 citation statement)

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“…System science tools such as causal loop diagrams (CLDs) offer a powerful framework to understand the complex interplay of factors influencing a phenomenon of interest [ 1 ]. While they have been widely used in medicine and public health [ 2 – 5 ], their applications in studying quality of life has thus far been limited, e.g., [ 6 ]. We demonstrate the usefulness of CLDs in a case study of applying the framework to the system surrounding health-related quality of life in cancer immunotherapy, particularly by integrating findings from prior research with insights from experts.…”

Section: Introductionmentioning

confidence: 99%

Health-related quality of life in cancer immunotherapy: a systematic perspective, using causal loop diagrams

et al. 2022

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Purpose System science offers a unique set of tools, including causal loop diagrams (CLDs), for stakeholders to better grasp the complexity of factors surrounding quality of life. Because the health-related quality of life (HRQoL) of cancer immunotherapy patients exists within an intricate system affected by and affecting many factors across multiple dimensions, the development of a systems-level model can provide a powerful framework to aid the understanding of this complexity. We developed a CLD for HRQoL of cancer immunotherapy patients. Methods We first applied a literature-based approach to construct a CLD for patients following immunotherapy. We then iteratively reviewed and enhanced the CLD through interviews with subject matter experts. Results Based on the reviewed literature and subject matter expert input, we produced a CLD representing the system surrounding cancer immunotherapy patients’ HRQoL. Several feedback loops are identified that span clinical experiences, oncology teams’ perceptions about immunotherapy, social support structures, and further research and development in cancer immunotherapy, in addition to other components. The CLD enables visualization of thought experiments regarding how a change anywhere in the system can ultimately worsen or improve patients’ HRQoL. Conclusion The CLD illustrates the valuable contribution of a systems perspective to quality-of-life research. This systems-based qualitative representation gives insight on strategies to inhibit harmful effects, enhance beneficial effects, and inherent tradeoffs within the system. The CLD identifies gaps in the literature and offers a communication tool for diverse stakeholders. Our research method provides an example for studying the complexities of quality of life in other health domains.

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Section: Introductionmentioning

confidence: 99%

Health-related quality of life in cancer immunotherapy: a systematic perspective, using causal loop diagrams

et al. 2022

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Understanding Hematopoietic Stem Cell Dynamics—Insights from Mathematical Modelling

et al. 2023

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Purpose of review Hematopoietic stem cells (HSCs) drive blood-cell production (hematopoiesis). Out-competition of HSCs by malignant cells occurs in many hematologic malignancies like acute myeloid leukemia (AML). Through mathematical modelling, HSC dynamics and their impact on healthy blood cell formation can be studied, using mathematical analysis and computer simulations. We review important work within this field and discuss mathematical modelling as a tool for attaining biological insight. Recent findings Various mechanism-based models of HSC dynamics have been proposed in recent years. Key properties of such models agree with observations and medical knowledge and suggest relations between stem cell properties, e.g., rates of division and the temporal evolution of the HSC population. This has made it possible to study how HSC properties shape clinically relevant processes, including engraftment following an HSC transplantation and the response to different treatment. Summary Understanding how properties of HSCs affect hematopoiesis is important for efficient treatment of diseases. Mathematical modelling can contribute significantly to these efforts.

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Aging, Inflammation, and Comorbidity in Cancers—A General In Silico Study Exemplified by Myeloproliferative Malignancies

Ottesen,

Andersen

2023

Cancers

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(1) Background: We consider dormant, pre-cancerous states prevented from developing into cancer by the immune system. Inflammatory morbidity may compromise the immune system and cause the pre-cancer to escape into an actual cancerous development. The immune deficiency described is general, but the results may vary across specific cancers due to different variances (2) Methods: We formulate a general conceptual model to perform rigorous in silico consequence analysis. Relevant existing data for myeloproliferative malignancies from the literature are used to calibrate the in silico computations. (3) Results and conclusions: The hypothesis suggests a common physiological origin for many clinical and epidemiological observations in relation to cancers in general. Examples are the observed age-dependent prevalence for hematopoietic cancers, a general mechanism-based explanation for why the risk of cancer increases with age, and how somatic mutations in general, and specifically seen in screenings of citizens, sometimes are non-increased or even decrease when followed over time. The conceptual model is used to characterize different groups of citizens and patients, describing different treatment responses and development scenarios.

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System dynamics of cancer in erythropoiesis with multiple EPO feedbacks

Cited by 3 publications

References 38 publications

Health-related quality of life in cancer immunotherapy: a systematic perspective, using causal loop diagrams

Health-related quality of life in cancer immunotherapy: a systematic perspective, using causal loop diagrams

Understanding Hematopoietic Stem Cell Dynamics—Insights from Mathematical Modelling

Aging, Inflammation, and Comorbidity in Cancers—A General In Silico Study Exemplified by Myeloproliferative Malignancies

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