Computational Approaches to Modeling the Brain and the Cell



Computational Approaches to Modeling the Brain and the Cell

Theoretical computer science can provide powerful tools to help understand how complex biological systems, such as brains and cells, process and exchange information.


The scientific understanding of complex systems typically evolves by a process of iterative refinement in which simple models are replaced by more complex ones. Historically this process of refinement has usually proceeded in a rather ad hoc way. Now, however, a systematic approach is called for as today’s scientists strive for a comprehensive understanding of highly complex information processing systems such as the human brain or the intricate network of regulatory signals that are transmitted within and between cells. Insights from theoretical computer science (TCS) can help guide scientific investigation of these complex systems, similar to how TCS insights and algorithms contributed to the Human Genome Project.

As one example of how TCS may aid scientific investigation in the future, insights from the field of computational learning theory could help guide the adaptive design of experiments to uncover the structure of biological information-processing systems that are currently not well understood. A large body of research in this area studies the issue of what are the “right” questions to ask (i.e. experiments to perform) in order to maximize the useful knowledge that can be obtained about a unknown information-processing system. Collaboration between theoretical computer scientists who study adaptive learning techniques and biologists has the potential to greatly improve the efficiency of scientific investigation.

Contributors and Credits

Scott Aaronson, Bhaskar DasGupta, Richard Karp, Rocco Servedio, Diane Souvaine


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