Computational Biomedical Systems Analysis is concerned with the design, analysis, and application of mathematical and computational models of complex systems in biology and medicine. The field uses methods from dynamical systems and control theory, mathematics, informatics, statistics, and computer science to integrate experimental and clinical data into models that can be used to explain biomedical phenomena, manipulate and optimize system responses, and to make predictions and create hypotheses regarding new, untested situations.
Typical applications range from bacteria and viruses to cells, organs, and humans, and from small systems of a few components to very large systems. As a particularly important application, computational models of health and disease can be based on clinical data and aid in disease diagnostics, support clinical decision, and provide valuable screening tools in the development of new drugs. In the near future, computational models will also be able to use the health state and history of an individual to predict specific risks and likely future health trajectories and to design custom-tailored treatments. With this type of personalization, patient care can be improved, costs can be reduced, and training and expertise can be provided to students and doctors through disease simulators.
- Network and systems biology
- Genomics, transcriptomics, proteomics, lipidomics, metabolic pathways, signal transduction
- Complex biomedical data integration
- Cellular systems physiology
- Molecular dynamics simulations in immunology
- Computational modeling of vascular adhesion and the cardiovascular system
- Computational neuroscience and neural control
- Neural recording, modulation, imaging and stimulation; electrophysiology
- Neural interfacing
- Motor pattern generation and sensory processing
- Neuromechanics, optimization, and control of movement and rehabilitation
- Disease models; predictive and personalized medicine
- Microbial metapopulations