A case for the reuse and adaptation of mechanistic computational models to study transplant immunology

Abstract

| INTRODUC TI ONWhen deciding whether to use computational models to study transplant immunology mechanisms, researchers are faced with two main questions: (a) what are computational models useful for? and (b) should I build my own model or use one that already exists and adapt it? This review aims to provide answers to these questions by explaining the strengths of the use of computational models, reviewing examples of how the use of computational models has successfully furthered our understanding of alloimmune responses, and presenting the advantages of reusing of publicly available immune mechanistic computational models to study key questions immune questions in transplant. | S TRENG TH S OF MECHANIS TI C COMPUTATIONAL MODEL SComputational models are mathematical descriptions of biological processes that are useful tools (a) to summarize knowledge in quantitative terms, (b) to provide mechanistic understanding of biological processes, and (c) to generate new hypotheses. 1-3 | To summarize knowledge in quantitative termsBuilding computational models is indeed not conceptually different from performing a literature search of the biological system under study (eg, immune system, lungs, and cells) and constructing a diagram with all the relevant players (eg, cell types and molecular entities) and processes (eg chemical reactions, cell-to-cell interactions) that influence its response or behavior. Creating this type of diagram or working model is a task commonly used by most researchers to help guide the design of experiments. However, some key biological features are Computational mechanistic models constitute powerful tools for summarizing our knowledge in quantitative terms, providing mechanistic understanding, and generating new hypotheses. The present review emphasizes the advantages of reusing publicly available computational models as a way to capitalize on existing knowledge, reduce the number of parameters that need to be adjusted to experimental data, and facilitate hypothesis generation. Finally, it includes a step-by-step example of the reuse and adaptation of an existing model of immune responses to tuberculosis, tumor growth, and blood pathogens, to study donor-specific antibody (DSA) responses. This review aims to illustrate the benefit of leveraging the currently available computational models in immunology to accelerate the study of alloimmune responses, and to encourage modelers to share their models to further advance our understanding of transplant immunology. K E Y W O R D Salloantibody, alloantigen, basic (laboratory) research/science, cellular biology, immune regulation, immunobiology, molecular biology, signaling/signaling pathways, T cell biology, translational research/science

In‐depth analysis of patterns in selection of different physiologically‐based pharmacokinetic modeling tools: Part II — Assessment of model reusability and comparison between open and non‐open source‐code software

“…According to the findings Reusability, the central notion for this study, has not been investigated extensively. A report published by Fribourg (2020) focused on the reusability of models for immunology, while another by Keating et al (2020) was centred around the suitability of coding languages for reusability. The reproducibility of mechanistic models in quantitative systems pharmacology (QSP) and their links to OSC nature have been the subject of some previous reports (Cucurull-Sanchez et al, 2019;Kirouac et al, 2019).…”

“…Reusability, the central notion for this study, has not been investigated extensively. A report published by Fribourg (2020) focused on the reusability of models for immunology, while another by Keating et al. (2020) was centred around the suitability of coding languages for reusability.…”

Multiscale modeling in the framework of biological systems and its potential for spaceflight biology studies

“…Terrestrial models should be tested for spaceflight simulation efficacy as repurposing existing models would be faster and less resource-intensive than creating new space-specific models. 143 Such model-scale-coupling, and indeed wider systems modeling, could be facilitated by further development of multiscale modeling software programs, like ENISIMSM, tailored to each type of biological process, or perhaps a more generic software capable of working with various types of systems. However, given that there is not yet a generic mathematical model for systems biology, this will take some time to develop and implement.…”

Multidimensional digital biomarker of cognitive health: unobtrusive and continuous monitoring of cognitive changes using smartphones

“…Importantly, no modeling approach is universally better than others, and the mechanistic-computational approach is no exception. Instead, it is often advised to, when appropriate, combine mechanistic modeling with machine learning and statistical modeling as such a hybrid approach allows one to take advantage of the benefits of each modeling methodology while balancing their pros and cons [131][132][133][134][135][136][137] .…”