Aug
13
Tue
2013
Plenary Talk: Biosensor and Single Cell Manipulation using Nanopipettes @ Amriteshwari Hall
Aug 13 @ 10:06 am – 10:49 am

NaderNader Pourmand, Ph.D.
Director, UCSC Genome Technology Center,University of California, Santa Cruz


Biosensor and Single Cell Manipulation using Nanopipettes

Approaching sub-cellular biological problems from an engineering perspective begs for the incorporation of electronic readouts. With their high sensitivity and low invasiveness, nanotechnology-based tools hold great promise for biochemical sensing and single-cell manipulation. During my talk I will discuss the incorporation of electrical measurements into nanopipette technology and present results showing the rapid and reversible response of these subcellular sensors  to different analytes such as antigens, ions and carbohydrates. In addition, I will present the development of a single-cell manipulation platform that uses a nanopipette in a scanning ion-conductive microscopy technique. We use this newly developed technology to position the nanopipette with nanoscale precision, and to inject and/or aspirate a minute amount of material to and from individual cells or organelle without comprising cell viability. Furthermore, if time permits, I will show our strategy for a new, single-cell DNA/ RNA sequencing technology that will potentially use nanopipette technology to analyze the minute amount of aspirated cellular material.

Invited Talk: Gut microbiome and health- Moving towards the new era of translational medicine @ Acharya Hall
Aug 13 @ 1:30 pm – 1:50 pm

SharmilaSharmila Mande, Ph.D.
Principal Scientist and Head, Bio Sciences R&D, TCS Innovation Labs, Pune


Gut microbiome and health: Moving towards the new era of translational medicine

The microbes inhabiting our body outnumber our own cells by a factor of 10. The genomes of these microbes, called the ‘second genome’ are therefore expected to have great influence on our health and well being. The emerging field of metagenomics is rapidly becoming the method of choice for studying the microbial community (called microbiomes) present in various parts of the human body. Recent studies have implicated the role of gut microbiomes in several diseases and disorders. Studies have indicated gut microbiome’s role in nutrient absorption, immuno-modulation motor-response, and other key physiological processes. However, our understanding of the role of gut microbiota in malnutrition is currently incomplete. Exploration of these aspects are likely to help in understanding the microbial basis for several physiological disorders associated with malnutrition (eg, increased susceptibility to diarrhoeal pathogens) and may finally aid in devising appropriate probiotic strategies addressing this menace. A metagenomic approach was employed for analysing the differences between gut microbial communities obtained from malnourished and healthy children. Results of the analysis using TCS’ ‘Metagenomic Analysis Platform’ were discussed in detail during my talk.

 

Invited Talk: Applying Machine learning for Automated Identification of Patient Cohorts @ Sathyam Hall
Aug 13 @ 2:40 pm – 3:05 pm

SriSairamSrisairam Achuthan, Ph.D.
Senior Scientific Programmer, Research Informatics Division, Department of Information Sciences, City of Hope, CA, USA


Applying Machine learning for Automated Identification of Patient Cohorts

Srisairam Achuthan, Mike Chang, Ajay Shah, Joyce Niland

Patient cohorts for a clinical study are typically identified based on specific selection criteria. In most cases considerable time and effort are spent in finding the most relevant criteria that could potentially lead to a successful study. For complex diseases, this process can be more difficult and error prone since relevant features may not be easily identifiable. Additionally, the information captured in clinical notes is in non-coded text format. Our goal is to discover patterns within the coded and non-coded fields and thereby reveal complex relationships between clinical characteristics across different patients that would be difficult to accomplish manually. Towards this, we have applied machine learning techniques such as artificial neural networks and decision trees to determine patients sharing similar characteristics from available medical records. For this proof of concept study, we used coded and non-coded (i.e., clinical notes) patient data from a clinical database. Coded clinical information such as diagnoses, labs, medications and demographics recorded within the database were pooled together with non-coded information from clinical notes including, smoking status, life style (active / inactive) status derived from clinical notes. The non-coded textual information was identified and interpreted using a Natural Language Processing (NLP) tool I2E from Linguamatics.