Biological systems can be represented as networks of interacting units. Thus, we have gene regulatory networks, protein-protein interaction networks, signaling networks, metabolic networks, neuronal networks, and food webs. There are far-reaching commonality in the organization of these systems. Understanding how they evolve and function is a core question in modern biology and requires approaches that bring together data and scientific approaches from disparate fields. There has been tremendous progress in unraveling the components of the networks that are responsible for the many functions. However, it has proven more difficult to make quantitative predictions about the evolving structure of biological networks. Our main goal is to build a theory that can explain how large biological networks arise and function, and at the same time apply the intricate dynamics and control of such systems in the design of adaptive engineered systems.
Network theory provides key insights into the organization of brain structure and function measured using noninvasive neuroimaging techniques. Distinct areas of the human brain act as discrete processing units that communicate directly through anatomical pathways or indirectly through synchronous oscillatory electromagnetic or blood-oxygenation-level activity. These networks process information and provide the basis of healthy cognitive functions: for example, people whose networks are more efficiently wired have higher IQs. They are also altered in neuropathologies (e.g., Alzheimer's) and psychiatric disease (schizophrenia), and the degree of alteration is correlated with the severity of a person's symptoms, potentially indicating that redirected information flow on the network leads to changes in cognitive processing. The clinical implications of these results are far-reaching, and include the development of diagnostic biomarkers, the monitoring of treatment, and the personalization of rehabilitation interventions. The development of dynamic network models is critical for the accurate representation of brain function, the potential to predict behavior, and the ability to guide brain trajectories towards healthy states during neurorehabilitation. While tools from mathematics, physics, and control and dynamical systems are being developed for these purposes, an appropriate application of such tools to the brain requires multidisciplinary training.
Related Training Modules
- M4: Analyzing the Connectome of C. elegans
- M5: Model Reduction of Biochemical Networks
- M6: Modeling Network Evolution in Metric Spaces
- M8: Structure and Stability of Bacterial Transcription Networks
- M12: Network Stability in Food Webs
- M25: Modeling Gene Network Evolution
- M32: The Dynamics of Functional Brain Networks
- M36: Investigating Dynamic Frontoparietal Network Allegiance Under Cognitive and Perceptual Load
- M37: Plant Fungi Interactions
- M38: Modeling Yeast Evolution through Yeast-Fly Interactions
- M42: Modeling Alzheimer’s Disease State Dynamics