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Giovanni Widmer

At Tufts Veterinary School of Medicine we are using Illumina technology to sequence PCR amplicons obtained from the bacterial 16S rRNA gene. The analysis of millions of short sequences obtained with this method enables us to assess the taxonomic composition of bacterial populations and the impact of experimental interventions. Some of these analyses are computer-intensive and running them on the cluster saves time. Typically, we use Clustal Omega to align sequences. On the cluster, a samples of a few thousand sequence reads can be aligned in a few minutes. We have also installed mothur on the cluster (mothur.org) and are running sequence analysis programs from this collection. These programs are used to de-noise sequence data and to compute pairwise genetic distance matrices. We visualize the genetic diversity of microbial populations using Principal Coordinate Analysis, which is also computer-intensive. We have adapted this approach to analyze populations of the eukaryotic pathogen Cryptosporidium. Several Cryptosporidium species infect the gastro-intestinal tract of human and animals. Using a similar approach as applied to the analysis of bacterial populations, we assess the diversity of Cryptosporidium parasites infecting a host and monitor the impact of various interventions on the genetic diversity of this parasites.

Daniel Lobo

I am a postdoc in the Biology department and work together with Prof. Michael Levin to create novel artificial intelligence methods for the automated discovery of models of development and regeneration. A major challenge in developmental and regenerative biology is the identification of models that specify the steps sufficient for creating specific complex patterns and shapes. Despite the great number of manipulative and molecular experiments described in the literature, no comprehensive, constructive model exists that explains the remarkable ability of many organisms to restore anatomical polarity and organ morphology after amputation. It is now clear that computational tools must be developed to mine this ever-increasing set of functional data to help derive predictive, mechanistic models that can explain regulation of shape and pattern. We use the Tufts Computer Cluster for running our heuristic searches for the discovery of comprehensive models that can explain the great number of poorly-understood regenerative experiments. Our method requires the simulation of millions of tissue-level experiments, comprising the behavior of thousands of cells and their secreted signaling molecules diffusing according to intensive differential equations. Using the compute cluster, we can massively parallelize the simulation of these experiments and the search for models of regeneration. Indeed, the cluster is an indispensable tool for us to apply cutting-edge artificial intelligence to biological science.

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