Simulating Viral Replication within the Human Lung Network with the Francis Crick Institute

by Healthcare

COVID-19 continues to impact populations around the globe and researchers are working tirelessly to answer key questions around the virus’ spread. Computational models are vital in understanding the spread of viruses so that governments and clinicians can take the necessary steps as quickly as possible and save lives. 

Aether Engine is being used to create agent based models of COVID-19 virus transmission around the human lung network in partnership with The Francis Crick Institute. By providing an accessible and fully customisable development environment, Aether Engine is enabling clinical researchers to produce at-scale analyses of the current epidemic.

Viral Replication Simulation (Prototype)

We have built a map of the lung network to simulate the virus travelling through the airways and infecting the cell walls. Simulating the viral replication process which will include many thousands or millions of entities, requires a huge amount of computational power.

Aether Engine can run computationally intensive simulations as it scales across different processors and physical machines, utilising more computing power as the simulations grow in complexity and size. It can figure out which parts of the space are more computationally intensive as it recognises which areas of the simulation are doing more work. It splits the world up using a distributed octree and can assign more CPUs to those areas. 

Multi-agent systems consist of autonomous entities known as agents working collaboratively. Agents solve tasks by learning and acting on interactions with one another and the environment they’re in. These entities could represent anything, including different computers in a network, different pieces of software, or even a person.

When multi-agent systems are used to process lots of real-world data from an emerging situation such as an outbreak, it can allow clinicians to model potential scenarios. These simulations reveal non-obvious outcomes, which otherwise may have gone unnoticed leading to better informed decisions. An illustrative example of this can be seen in our blog Contagion Modelling: Applying Spatial Simulation To Track Pathogen Spread.

Multi-agent system simulations could drastically increase a model’s flexibility in comparison to traditional techniques. This initial agent-based model could be scaled upwards, both in regards to the number of agents, but also introducing additional layers of complexity, including drug interventions data to further multi-omic information. 

We’re now working on extending this model to map the millions of cells in the entire lung network and layer in an understanding of how the immune system interplays with the virus. The simulation will then be used to forecast how susceptible an individual is to infection and the likelihood that they can pass on the virus. It will provide the near real-time analysis necessary to help clinicians choose interventions that lead to the best possible outcomes for their patients, ease the burden on healthcare infrastructure and save lives. 

 

Get in touch today to find out how Hadean could help your organisation rapidly prototype massive scale simulations and make better informed decisions.