Nader 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.
Srisairam 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.
Nitish Sathyanrayanan, Sandesh Ganji and Holenarsipur Gundurao Nagendra.
Insilico Analysis of hypothetical proteins from Leishmania donovani: A Case study of a membrane protein of the MFS class reveals their plausible roles in drug resistance
Kala-azar or visceral leishmaniais (VL), caused by protozoan parasite Leishmania donovani, is one of the leading causes of morbidity and mortality in Bihar, India (Guerin et al. 2002; Mubayi et al. 2010). The disease is transmitted to the humans mainly by the vector, Phlebotmus argentipes, commonly known as Sand fly. The majority of VL (> 90%) occurs in only six countries: Bangladesh, India, Nepal, Sudan, Ethiopia and Brazil (Chappuis et al. 2007). In the Indian subcontinent, about 200 million people are estimated to be at risk of developing VL and this region harbors an estimated 67% of the global VL disease burden. The Bihar state only has captured almost 50% cases out of total cases in Indian sub-continent (Bhunia et al. 2013). ‘Conserved hypothetical’ proteins pose a challenge not just to functional genomics, but also to biology in general (Galperin and Koonin 2004). Leishmania donovani (strain BPK282A1) genome consists of a staggering ∼65% of hypothetical proteins. These uncharacterized proteins may enable better appreciation of signalling pathways, general metabolism, stress response and even drug resistance.