Emerging Topics in Computational Biology Beyond Gene Sequencing

Emerging Topics in Computational Biology: Beyond Gene Sequencing Avani Wildani Salk Institute for Biological Sciences October 10 th, 2014 ; Phoenix, AZ 2014

2014

The lab tech problem ▪ Historically, the role of CS in Biology has been data analysis and automation of onerous pattern matching. ▪ E. g. ▪ Substring matching (Gene “You do Computers. You must be so smart! Analyze my data for me? ” sequencing) ▪ Object Recognition (Neuron identification) ▪ Unsupervised Learning (Tumour tracking) 2014

Really Big Data ▪ 86 billion Image Credit: Human Connectome Project 2014 neurons ▪ > 7. 3 x 1012 possible connections ▪ Throw in time, and things start getting interesting!

Modern Bioinformatics ▪ Storage ▪ Where does this data go? How do we know what to keep? ▪ Information Theory ▪ Mice are expensive! How do we get the most out of our data? ▪ Visualization / HCI ▪ How do we interact with the data? ▪ Networks ▪ Can we get our sensors to communicate? ▪ So much more! 2014

Network Reconstruction / Maximizing Data Set Reuse ▪ Mostly feed-forward ▪ Neurons farther from the retina select for more specific stimuli, up to individual faces − Halle Berry neurons Image Credit: Wikimedia Commons 2014

Persistant Homology for Vision Hubel and Weisel, 1959 Image Credit: Singh et al. 2014

Topology cont. Complex mean: 2. 0 Simple mean: 6. 7 Complex mean: 4. 4 Simple mean: 7. 7 2014

Neural Networks ▪ Bio -> Computing: ▪ Perceptron model ▪ Massively distributed computation Le et al. , ICML 2012 with simple functions ▪ Computing -> Bio: ▪ Networks for roads, Internet, etc. are carefully designed to maximize throughput and lower costs. ▪ Broad robustness is not yet studied in neuroscience ▪ Recurrent Neural Networks may actually have biological significance 2014

More Algorithms in Nature Location Estimation in the Brain Error Correcting Codes Srinivasan and Fiete, 2011 Flashing fireflies Distributed synchronization Ant foraging Transmission control protocol Pagliari et al. 2011 Prabhakar et al. 2012 2014

Moving into biocomputing ▪ Read papers. Find someone who’s work you respect on either the CS or biology side and read everything they cite. ▪ You don’t need to understand the details to draw a parallel. ▪ Talk to biologists at your school. They have a lot to teach us both about their field and about integrating women into the sciences ▪ The physicists are ahead of us. Learn from them 2014

Takeaways ▪ Bioinformatics covers a lot of ground ▪ We’re not done pulling ideas from Bio into CS ▪ There is a brave new frontier for pulling CS ideas into Bio ▪ These problems are approachable without a Bio Ph. D! 2014