Ashok Pandey, Ph.D.
Scientist F & Head, Biotechnology Division, National Institute for Interdisciplinary Science and Technology-CSIR), Thiruvananthapuram, India
Alternative renewable resources: Issues and perspectives for India – the case of transport fuels
With the increase in the urbanization way of life and also more and more dependence on materialistic life, there is substantial growing demand for the energy. The science and technological policy of the India has looked several avenues to fulfill this demand through alternative resources such as solar energy, wind energy, tidal energy, bioenergy, etc. The demand for the transport sector is largely met through the import (~70%). Biofuels, in particular bioethanol from lignocellulosic biomass offer attractive possibilities in this regard.
The sugar platform which generates ethanol is considered to be the most valuable solution to the transport fuel demand. Bioethanol can be generated from grains as well as from lignocellulosic plant material by their saccharification to sugars and subsequent fermentation of the sugars to produce ethanol. Bio-ethanol as a transportation fuel is attractive since it is more energy efficient than gasoline and produces less emissions. The benefits of developing biomass to ethanol technology(s) include: increased national energy security, reduction in GHG emissions, use of renewable resources, economic benefits and creation of employment and the foundation of a carbohydrate based chemical industry. However, the utilization of lignocellulosic biomass for fuel generation has not been given the sort of attention it ought to receive. It is known that the technology for ethanol production from biomass has to evolve greatly for an economical commercial scale utilization of the renewable biomass resources. Biomass requires extensive processing involving multiple steps for hydrolysis and fermentation of the raw material for producing ethanol. Feed stock availability, pretreatment, saccharification, fermentation and ethanol recovery are all factors which influence the production of ethanol and which needs R&D efforts for overall improvement of the production economics.
Bioconversion of lignocellulosic biomass (LB) can contribute significantly to the production of organic chemicals also. LB is also considered to be the only foreseeable source of energy. LB is mainly composed of (dry wt basis): cellulose, 40-60; hemicellulose, 20-40; and lignin, 10-25%. Most efficient method of biomass hydrolysis is through enzymatic saccharification5 using cellulases and hemicellulases. Fungal cellulases (FCs) have proved to be a better candidate than other microbial cellulases, with their secreted free cellulase complexes comprising all three components of cellulase [endoglucanases, exoglucanases and cellobiases (glucosidases).
The Centre for Biofuels at NIIST, Trivandrum, India aims ultimately to develop technologies and processes which will address the nation’s need for making fuel ethanol from the renewable resource: biomass. It is proposed to direct R&D activities at the major requirements of a biomass-ethanol technology, which include production of cellulases, hydrolysis of biomass, and ethanol fermentation. Viable technologies for each of these processes will contribute to the overall process development for fuel alcohol production from cheap and renewable biomass resources.
The lecture would present perspectives on bioethanol from lignocellulosic feedstocks.
References
- Biofuels- Alternative Feedstocks and Conversion Processes, Editors- Ashok Pandey, C Larroche, SC Ricke, CG Dussap & E Gnansounou, Academic Press, Elsevier Inc; San Diego, USA, p629 (2011) ISBN: 978-0-12-385099-7
- Handbook of Plant-Based Biofuels, Editor- Ashok Pandey, CRC Press, Francis & Taylors, Boca Raton, USA, p 297 (2008) ISBN 978-q-5602-2175-3
- Biofuels II, Special issue of Journal of Scientific & Industrial Research, Guest Editors- E Gnansounou, C Larroche and Ashok Pandey, 67(11), 837-1040 (2008) ISSN: 0022-4456
- Biofuels, Special issue of Journal of Scientific & Industrial Research, Guest Editors- C Larroche and Ashok Pandey, 64(11), 797-988 (2005) ISSN: 0022-4456
Krishnakumar Menon, Ph.D.
Associate Professor, Centre for Nanosciences & Molecular Medicine, Amrita University, Kochi, India
A Far-Western Clinical Proteomics Approach to Detect Molecules of Clinical and Pathological Significance in the Neurodegenerative Disease Multiple Sclerosis.
Multiple Sclerosis (MS), an autoimmune neurodegenerative disorder of the central nervous system. The disease affects young adults at their prime age leading to severe debilitation over several years. Despite advances in MS research, the cause of the disease remains elusive. Thus, our objective is to identify novel molecules of pathological and diagnostic significance important in the understanding, early diagnosis and treatment of MS. Biological fluids such as cerebrospinal fluid (CSF), that bathe the brain serve as a potential source for the identification of pathologically significant autoantibody reactivity in MS. In this regard, we report the development of an unbiased clinical proteomics approach for the detection of reactive CSF molecules that target brain proteins from patients with MS. Proteins of myelin and myelin-axolemmal complexes were separated by two-dimensional gel electrophoresis, blotted onto membranes and probed separately with biotinylated unprocessed CSF samples. Protein spots that reacted specifically to MS-CSF’s were further analyzed by matrix assisted laser desorption ionization-time-of-flight time-of-flight mass spectrometry. In addition to previously reported proteins found in MS, we have identified several additional molecules involved in mitochondrial and energy metabolism, myelin gene expression and/or cytoskeletal organization. Among these identified molecules, the cellular expression pattern of collapsin response mediator protein-2 and ubiquitin carboxy-terminal hydrolase L1 were investigated in human chronic-active MS lesions by immunohistochemistry. The observation that in multiple sclerosis lesions phosphorylated collapsin response mediator protein-2 was increased, whereas Ubiquitin carboxy-terminal hydrolase L1 was down-regulated, not only highlights the importance of these molecules in the pathology of this disease, but also illustrates the use of our approach in attempting to decipher the complex pathological processes leading to multiple sclerosis and other neurodegenerative diseases. Further, we show that in clinicaly isolated syndrome (CIS), we could identify important molecules that could serve as an early diagnostic biomarker in MS.
Rohit Manchanda, Ph.D.
Professor, Biomedical Engineering Group, IIT-Bombay, India
Modelling the syncytial organization and neural control of smooth muscle: insights into autonomic physiology and pharmacology
We have been studying computationally the syncytial organization and neural control of smooth muscle in order to help explain certain puzzling findings thrown up by experimental work. This relates in particular to electrical signals generated in smooth muscles, such as synaptic potentials and spikes, and how these are explicable only if three-dimensional syncytial biophysics are taken fully into account. In this talk, I shall provide an illustration of outcomes and insights gleaned from such an approach. I shall first describe our work on the mammalian vas deferens, in which an analysis of the effects of syncytial coupling led us to conclude that the experimental effects of a presumptive gap junction uncoupler, heptanol, on synaptic potentials were incompatible with gap junctional block and could best be explained by a heptanol-induced inhibition of neurotransmitter release, thus compelling a reinterpretation of the mechanism of action of this agent. I shall outline the various lines of evidence, based on indices of syncytial function, that we adduced in order to reach this conclusion. We have now moved on to our current focus on urinary bladder biophysics, where the questions we aim to address are to do with mechanisms of spike generation. Smooth muscle cells in the bladder exhibit spontaneous spiking and spikes occur in a variety of distinct shapes, making their generation problematic to explain. We believe that the variety in shapes may owe less to intrinsic differences in spike mechanism (i.e., in the complement of ion channels participating in spike production) and more to features imposed by syncytial biophysics. We focus especially on the modulation of spike shape in a 3-D coupled network by such factors as innervation pattern, propagation in a syncytium, electrically finite bundles within and between which the spikes spread, and some degree of pacemaker activity by a sub-population of the cells. I shall report two streams of work that we have done, and the tentative conclusions these have enabled us to reach: (a) using the NEURON environment, to construct the smooth muscle syncytium and endow it with synaptic drive, and (b) using signal-processing approaches, towards sorting and classifying the experimentally recorded spikes.
Manzoor K, Ph.D.
Professor, Centre for Nanoscience & Molecular Medicine, Amrita University
Targeting aberrant cancer kinome using rationally designed nano-polypharmaceutics
Manzoor Koyakutty, Archana Ratnakumary, Parwathy Chandran, Anusha Ashokan, and Shanti Nair
`War on Cancer’ was declared nearly 40 years ago. Since then, we made significant progress on fundamental understanding of cancer and developed novel therapeutics to deal with the most complex disease human race ever faced with. However, even today, cancer remains to be the unconquered `emperor of all maladies’. It is well accepted that meaningful progress in the fight against cancer is possible only with in-depth understanding on the molecular mechanisms that drives its swift and dynamic progression. During the last decade, emerging new technologies such as nanomedicine could offer refreshing life to the `war on cancer’ by way of providing novel methods for molecular diagnosis and therapy.
In the present talk, we discuss our approaches to target critically aberrant cancer kinases using rationally designed polymer-protein and protein-protein core-shell nanomedicines. We have used both genomic and proteomic approaches to identify many intimately cross-linked and complex aberrant protein kinases behind the drug resistance and uncontrolled proliferation of refractory leukemic cells derived from patients. Small molecule inhibitors targeted against oncogenic pathways in these cells were found ineffective due to the involvement of alternative survival pathways. This demands simultaneous inhibition more than one oncogenic kinases using poly-pharmaceutics approach. For this, we have rationally designed core-shell nanomedicines that can deliver several small molecules together for targeting multiple cancer signalling. We have also used combination of small molecules and siRNA for combined gene silencing together with protein kinase inhibition in refractory cancer cells. Optimized nanomedicines were successfully tested in patient samples and found enhanced cytotoxicity and molecular specificity in drug resistant cases.
Nano-polypharmaceutics represents a new generation of nanomedicines that can tackle multiple cancer mechanisms simultaneously. Considering the complexity of the disease, such therapeutic approaches are not simply an advantage, but indispensable.
Acknowledgements:
We thank Dept. of Biotechnology and Dept. Of Science and Technology,Govt. of India for the financial support through `Thematic unit of Excellence in Medical NanoBiotechnology’ and `Nanomedicine- RNAi programs’.
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.