Multiscale Systems Biology and Modeling     

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Multiscale Systems Biology and Modeling - overview

 
Diagram of the hierarchical organization of biology at different scales and examples of data that can be collected at these different levels.

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Understanding biology in the context of dynamic and interacting entities

Biological processes are inherently multiscale; critical events are happening at vastly different temporal and spatial levels. The multiscale aspect presents extreme challenges. Neuroscience, for example, sees a pharmaceutical agent acting at a molecular level to modify channel-gating behavior. However, the induced changes must be considered in the context of changes to synaptic transmission, neuron firing, network dynamics, and ultimately in the context of the whole brain and human behaviors - even populations of individuals. Note that channels operate on the order of nano- or picoseconds, allowing synaptic transmission to occur on the order of milliseconds, and brain dynamics can occur on the order of seconds, hours and days. To take this one step further, a disease process may evolve over years, as seen in neurodegenerative diseases such as Parkinson’s disease or Huntington’s disease.

Developing the theoretical and computational frameworks to bridge these vast differences in spatial and temporal scale will remain a challenge for decades to come. High Performance Computing will provide part of the solution, but we also will need to develop the new theoretical and algorithmic approaches. Moreover, one goal of multiscale work is to allow data collected at different spatial and temporal scales to rationally and quantitatively linked into a holistic picture of a complex hierarchical system. Simulation using mechanistic models is one tool to perform this sort of bridging. We also use data-driven approaches such as statistical models and machine learning.

Recent work has involved including wearable sensors and Internet of Things (IoT) as ways to continuously monitor patients. Here, the challenge is to develop methods so that relatively cheap sensors can be used to gather data on the underlying biophysical system, including changes occurring as a result of diseases or therapies.