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Deciphering Cellular Computation using Learning Machines Cells and Machines Innovatory

Somali Chaterji, Assistant Professor
Ag and Biological Engineering
Purdue University

Large data volumes in tandem with increasing computational power and bandwidth have made it possible to understand the epigenome; think of the epigenome as the layer pervading the genome and giving every cell its identity. Every cell of the human body has the same genome. How then is a brain cell distinct from an immune cell? This is where the cell’s epigenome offers a distinct “symphony” to diverse contexts in which living cells thrive. Driven by the exabytes of sequencing data being generated, there is an increasing need to analyze genomic big data and computations in the living cells and then to translate them to discoveries in precision medicine. The best studied example of a cellular computation was first considered in the seminal paper by Berg and Purcell who showed that the information a cell can acquire about its environment is fundamentally limited by stochastic fluctuations in the occupancy of the membrane-bound receptor proteins that detect the ligand. This was way back in 1977! Today, abetted by exabytes of genomic data, it is known that there are computations within living cells. Overall, my lab’s goal is to understand some of these cellular computations and to reverse engineer them to restore health and vitality.

In the context of my talk today, these computations refer to the gene-gene and gene-RNA regulatory networks (GRN variants). A GRN is a set of genes, or parts thereof, which interact to control cellular functions. GRNs are important in development, differentiation, and cellular response to ambient signals. How can this “genomical” big data enable the decoding of the computation within cells, rapidly, and at scale? What kinds of algorithms can deal with the inherent heterogeneity, noise, and high-dimensionality of the data pertaining to the cellular computations? Can these efforts result in precise data-driven medicine? I will answer these questions in two parts:

Part 1: I will talk about our Avishkar suite of predictive algorithms, where we uncover the non-canonical signatures of small regulatory RNA (e.g., microRNA, miRNA for short) that target genes.

Part 2: I will present our work on federated cyberinfrastructures for genomics. This is in the context of MG-RAST, the largest metagenomics portal and analysis pipeline and operated by the US Department of Energy and is funded by an NIH R01 grant.

Somali (pronounced Shoh-mah-lee) Chaterji is an Assistant Professor in the Department of Ag and Biological Engineering at Purdue University and leads a team of adventurous students and post-docs at the Cells and Machines Innovatory. Her expertise is in the areas of data science and engineering for genomics and digital agriculture and in building cyberinfrastructures for them. She is a part of the WHIN (Wabash Heartland Innovation Network) leadership team working to bring IoT to advance agriculture. Data is power! She believes in leveraging the power of big data by drawing actionable insights from diverse biomedical data to improve human wellbeing.

Somali got her PhD in Biomedical Engineering from Purdue University, winning the Chorafas International Award (2010), College of Engineering Best Dissertation Award (2010), and the Future Faculty Fellowship Award (2009). She did her Post-doctoral Fellowship at the University of Texas at Austin in the Department of Biomedical Engineering, where her work was supported by an American Heart Association (AHA) award. She has won the best paper award at the ACM BCB conference in 2015. She is also a lab-to-bedside commercialization enthusiast and is a scientific advisor to the IC2 Institute at the University of Texas at Austin since 2014. Somali won Purdue’s Seed-for-Success Award in 2016 for winning a research grant of more than $1M (NIH R01).

Thursday, April 18, 2019 at 10:00am to 11:00am

Kelley Engineering Center, 1007
110 SW Park Terrace, Corvallis, OR 97331

Event Type

Lecture or Presentation

Event Topic


Electrical Engineering and Computer Science
Contact Name

Janet Amador

Contact Email

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