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
Egidio D’Angelo, MD, Ph.D.
Full Professor of Physiology & Director, Brain Connectivity Center, University of Pavia, Italy
Realistic modeling: new insight into the functions of the cerebellar network
Realistic modeling is an approach based on the careful reconstruction of neurons synapses starting from biological details at the molecular and cellular level. This technique, combined with the connection topologies derived from histological measurements, allows the reconstruction of precise neuronal networks. Finally, the advent of specific software platforms (PYTHON-NEURON) and of super-computers allows large-scale network simulation to be performed in reasonable time. This approach inverts the logics of older theoretical models, which anticipated an intuition on how the network might work. In realistic modeling, network properties “emerge” from the numerous biological properties embedded into the model.
This approach is illustrated here through an outstanding application of realistic modeling to the cerebellar cortex network. The neurons (over 105) are reproduced at a high level of detail generating non-linear network effects like population oscillations and resonance, phase-reset, bursting, rebounds, short-term and long-term plasticity, spatiotemporal redistrbution of input patterns. The model is currently being used in the context of he HUMAN BRAIN PROJECT to investigate the cerebellar network function.
Correspondence should be addressed to
Dr. EgidioD’Angelo,
Laboratory of Neurophysiology
Via Forlanini 6, 27100 Pavia, Italy
Phone: 0039 (0) 382 987606
Fax: 0039 (0) 382 987527
dangelo@unipv.it
Acknowledgments
This work was supported by grants from European Union to ED (CEREBNET FP7-ITN238686, REALNET FP7-ICT270434) and by grants from the Italian Ministry of Health to ED (RF-2009-1475845).
Karmeshu, 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.