About this Research Topic
Mechanistic modeling is a powerful tool for elucidating and probing the behaviors of complex biological systems. By developing mathematical representations based on a deep understanding of biological mechanisms, mechanistic models can provide valuable insights into cellular processes, disease progression, and ecological interactions. They enable researchers to study the dynamics of biological systems, predict their behaviors under different conditions, and inform decision-making in various biological contexts. Applied to human health and disease, despite the inherent complexities and uncertainties of human biology, mechanistic models have found utility in cancer research, cardiovascular disease, neurological disorders, and infectious diseases. These models facilitate the exploration of disease mechanisms, identification of novel therapeutic targets, and optimization of clinical trial design. They can also be used to simulate and predict the effects of genetic variations and environmental exposures on disease outcomes, and aid in the development of personalized medicine strategies and optimal treatment regimens.
Challenges in the application of mechanistic modeling to human health and disease include the inherent complexity of biological systems, the multiscale nature of many biological processes, the availability of comprehensive data for model calibration and validation, and the integration of individual variability and dynamic environmental factors. Addressing these challenges requires interdisciplinary collaborations, advances in experimental techniques for data acquisition, and the development of standardized frameworks for model sharing and validation. This Research Topic aims to present examples of effective applications of mechanistic modeling in human health and disease that address some, or all, of these challenges. Modeling methodologies employed will vary depending on available data and the specific research question at hand, ranging from ordinary differential equations and stochastic models to logical, agent-based, and multiscale models. Computational simulations will explore dynamical responses under different conditions, including therapeutic interventions and environmental perturbations. Dynamical analyses, such as sensitivity and bifurcation analyses, will provide insights into the stability, steady-state behaviors, and emergent properties of these complex systems. Experimental data will range from high-throughput and time-resolved omics data to imaging and microscopy data and data from perturbation experiments and clinical trials.
Topics of interest include applications of mechanistic modeling to the study of cancer, cardiovascular disease, neurological disorders, and infectious diseases. All these diseases are directly and indirectly linked to nutrition and its effects on our genome. For example, in cancer models incorporating genetic and intracellular factors can simulate tumor growth, invasion, and response to therapies and aid in the identification of critical drivers of cancer development and progression. Models of cardiac electrophysiology and hemodynamics can help elucidate the mechanisms underlying arrhythmias, heart failure, and the effects of interventions, such as dietary interventions and/or drug therapy. Mechanistic models can also enable a better understanding of neurological disorders, such as epilepsy, Parkinson's disease, and Alzheimer's disease by capturing the complex dynamics of neuronal activity and the spread of pathological changes, and aid in the identification of novel biomarkers for early diagnosis, many of which are related to our lifestyle. In infectious disease research, models incorporating host-pathogen interactions, immune responses, and epidemiological factors can aid in understanding disease transmission dynamics, the impact of interventions, such as vaccination and lifestyle changes,, and inform public health strategies.
Challenges in the application of mechanistic modeling to human health and disease include the inherent complexity of biological systems, the multiscale nature of many biological processes, the availability of comprehensive data for model calibration and validation, and the integration of individual variability and dynamic environmental factors. Addressing these challenges requires interdisciplinary collaborations, advances in experimental techniques for data acquisition, and the development of standardized frameworks for model sharing and validation. This Research Topic aims to present examples of effective applications of mechanistic modeling in human health and disease that address some, or all, of these challenges. Modeling methodologies employed will vary depending on available data and the specific research question at hand, ranging from ordinary differential equations and stochastic models to logical, agent-based, and multiscale models. Computational simulations will explore dynamical responses under different conditions, including therapeutic interventions and environmental perturbations. Dynamical analyses, such as sensitivity and bifurcation analyses, will provide insights into the stability, steady-state behaviors, and emergent properties of these complex systems. Experimental data will range from high-throughput and time-resolved omics data to imaging and microscopy data and data from perturbation experiments and clinical trials.
Topics of interest include applications of mechanistic modeling to the study of cancer, cardiovascular disease, neurological disorders, and infectious diseases. All these diseases are directly and indirectly linked to nutrition and its effects on our genome. For example, in cancer models incorporating genetic and intracellular factors can simulate tumor growth, invasion, and response to therapies and aid in the identification of critical drivers of cancer development and progression. Models of cardiac electrophysiology and hemodynamics can help elucidate the mechanisms underlying arrhythmias, heart failure, and the effects of interventions, such as dietary interventions and/or drug therapy. Mechanistic models can also enable a better understanding of neurological disorders, such as epilepsy, Parkinson's disease, and Alzheimer's disease by capturing the complex dynamics of neuronal activity and the spread of pathological changes, and aid in the identification of novel biomarkers for early diagnosis, many of which are related to our lifestyle. In infectious disease research, models incorporating host-pathogen interactions, immune responses, and epidemiological factors can aid in understanding disease transmission dynamics, the impact of interventions, such as vaccination and lifestyle changes,, and inform public health strategies.
Keywords: Mechanistic modelling, biochemical pathways, cell-cell interactions, multiscale models, dynamical systems analysis, cancer, cardiovascular disease, neurological disorders, infectious diseases
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