Dynamic systems modeling is a method to study systematic properties of a complex system. The basic principles, procedures, and tools available to develop a dynamic systems model of complex metabolic processes are detailed. Here, a photosynthetic carbon metabolism model, which includes the Calvin Benson cycle, photorespiration, and starch and sucrose synthesis pathways, is used as an example to illustrate the whole process of model development.
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Understanding the dynamics driving obesity in socioeconomically deprived urban neighbourhoods: an expert-based systems map.
BMC Med
January 2025
Department of Public Health, Erasmus MC University Medical Center, Rotterdam, the Netherlands.
Background: Over the past decades, the prevalence of obesity among adults has rapidly increased, particularly in socioeconomically deprived urban neighbourhoods. To better understand the complex mechanisms behind this trend, we created a system map exposing the underlying system driving obesity prevalence in socioeconomically deprived urban neighbourhoods over the last three decades in the Netherlands.
Methods: We conducted Group Model Building (GMB) sessions with a group of thirteen interdisciplinary experts to develop a Causal Loop Diagram (CLD) of the obesogenic system.
Theoretical and Practical Aspects of the Nonlinear Dynamics' Methods of Heart Rate Variability Analyses in Tachyarrhythmia Patients Underwent Radiofrequency Catheter Ablation.
Cardiovasc Eng Technol
January 2025
Department of Biomedical Engineering, Northwestern University, Chicago, IL, USA.
Purpose: This study explores the use of heart rate variability (HRV) analysis, a noninvasive technique for assessing the autonomic nervous system, by applying nonlinear dynamics and chaos theory to detect chaotic behavior in RR intervals and assess cardiovascular health.
Methods: Employing the "System Analysis of Heart Rate Dynamics" (SADR) program, this research combines chaos analysis with the short-time Fourier transform to assess nonlinear dynamic parameters in HRV. It includes constructing phase portraits in Takens space and calculating measures of chaos to identify deterministic chaos indicators.
Pediatric Cardiovascular Multiscale Modeling using a Functional Mock-up Interface.
Cardiovasc Eng Technol
January 2025
School of Biomedical Engineering, Science and Health Systems, Drexel University, 3141 Chestnut Street, Rm. 718, Philadelphia, PA, 19104, USA.
Purpose: Computational models of the cardiovascular system continue to increase in complexity. As more elements of the physiology are captured in multiscale models, there is a need to efficiently integrate subsystems. The objective of this study is to demonstrate the effectiveness of a coupling methodology, called functional mock-up interface (FMI), as applied to multiscale cardiovascular modeling.
View Article and Find Full Text PDFApplication of a high-throughput swarm-based deep neural network Algorithm reveals SPAG5 downregulation as a potential therapeutic target in adult AML.
Funct Integr Genomics
January 2025
Intelligent OMICS Limited, Nottingham, United Kingdom.
Gene‒gene interactions play pivotal roles in disease pathogenesis and are fundamental in the development of targeted therapeutics, particularly through the elucidation of oncogenic gene drivers in cancer. The systematic analysis of pathways and gene interactions is critical in the drug discovery process for various cancer subtypes. SPAG5, known for its role in spindle formation during cell division, has been identified as an oncogene in several cancers, although its specific impact on AML remains underexplored.
View Article and Find Full Text PDFCollective dynamical regimes predict invasion success and impacts in microbial communities.
Nat Ecol Evol
January 2025
Physics of Living Systems, Department of Physics, Massachusetts Institute of Technology, Cambridge, MA, USA.
The outcomes of ecological invasions may depend on either characteristics of the invading species or attributes of the resident community. Here we use a combination of experiments and theory to show that the interplay between dynamics, interaction strength and diversity determine the invasion outcome in microbial communities. We find that the communities with fluctuating species abundances are more invasible and diverse than stable communities, leading to a positive diversity-invasibility relationship among communities assembled in the same environment.
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