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: A cost-effective approach to Protein Structure-guided Drug Discovery: Aided by Bioinformatics, Chemoinformatics and computational chemistry @ Sathyam Hall
Aug 13 @ 11:15 am – 11:40 am

kalKal Ramnarayan, Ph.D.
Co-founder President & Chief Scientific Officer, Sapient Discovery, San Diego, CA, USA


A cost-effective approach to Protein Structure-guided Drug Discovery: Aided by Bioinformatics, Chemoinformatics and computational chemistry

With the mapping of the human genome completed almost a decade ago, efforts are still underway to understand the gene products (i.e., proteins) in the human biological and disease pathways.  Deciphering such information is very important for the discovery and development of small molecule drugs as well as protein therapeutics for various human diseases for which no cure exists.  As an example, with more than 500 members, the kinase family of protein targets continues to be an important and attractive class for drug discovery.  While how many of the members in this family are actually druggable is still to be established, there are several ongoing efforts on this class of proteins across a broad spectrum of disease categories.  Even though in general the protein structural topology might looks similar, there are issues with respect selectivity of identified small molecule inhibitors when, the lead molecule discovery is carried out at the ATP binding site.  As an added complexity, allosteric modulators are needed for some of the members, but the actual site for such modulation on the protein target can not resolved with uncertainty.  In this presentation we will describe a bioinformatics and computational based platform for small molecule discovery for protein targets that are involved in protein-protein interactions as well as targets like kinases and phosphatases.  We will describe a computational approach in which we have used an informatics based platform with several hundred kinases to sort through in silico and identify inhibitors that are likely to be highly selective in the lead generation phase.  We will discuss the implication of this approach on the drug discovery of the kinase and phosphatase classes in general and independent of the disease category.

 

Plenary Talk: Interspike Interval Distribution of Neuronal Model with distributed delay: Emergence of unimodal, bimodal and Power law @ Sathyam Hall
Aug 13 @ 1:20 pm – 2:00 pm

karmeshuKarmeshu, Ph.D.
Dean & Professor, School of Computer & Systems Sciences & School of Computational & Integrative Sciences, Jawaharlal Nehru University, India.


Interspike Interval Distribution of Neuronal Model with distributed delay: Emergence of unimodal, bimodal and Power law

The study of interspike interval distribution of spiking neurons is a key issue in the field of computational neuroscience. A wide range of spiking patterns display unimodal, bimodal  ISI patterns including power law behavior. A challenging problem is to understand the biophysical mechanism which can generate  the empirically observed patterns. A neuronal model with distributed delay (NMDD) is proposed and is formulated as an integro-stochastic differential equation which corresponds to a non-markovian process. The widely studied IF and LIF models become special cases of this model. The NMDD brings out some interesting features when excitatory rates are close to inhibitory  rates rendering the drift close to zero. It is interesting that NMDD model with gamma type memory kernel can also account for bimodal ISI pattern. The mean delay of the memory kernels plays a significant role in bringing out the transition from unimodal to bimodal  ISI distribution. It is interesting to note that when a collection of neurons group together and fire together, the ISI distribution exhibits  power law.