Full Schedule

Gaute Einevoll, Ph.D.
Professor of Physics, Department of Mathematical Sciences & Technology, Norwegian University of Life Sciences (UMB)

Multiscale modeling of cortical network activity: Key challenges

Gaute T. Einevoll Computational Neuroscience Group, Norwegian University of Life Sciences, 1432 Ås, Norway; Norwegian National Node of the International Neuroinformatics Coordinating Facility (INCF)

Several challenges must be met in the development of multiscale models of cortical network activity. In the presentation I will, based on ongoing work in our group (http://compneuro.umb.no/ ) on multiscale modeling of cortical columns, outline some of them. In particular,

1. Combined modeling schemes for neuronal, glial and vascular dynamics [1,2],

2. Development of sets of interconnected models describing system at different levels of biophysical detail [3-5],

3. Multimodal modeling, i.e., how to model what you can measure [6-12],

4. How to model when you don’t know all the parameters, and

5. Development of neuroinformatics tools and routines to do simulations efficiently and accurately [13,14].

References:

1. L. Øyehaug, I. Østby, C. Lloyd, S.W. Omholt, and G.T. Einevoll: Dependence of spontaneous neuronal firing and depolarisation block on astroglial membrane transport mechanisms, J Comput Neurosci 32, 147-165 (2012)
2. I. Østby, L. Øyehaug, G.T. Einevoll, E. Nagelhus, E. Plahte, T. Zeuthen, C. Lloyd, O.P. Ottersen, and S.W. Omholt: Astrocytic mechanisms explaining neural-activity-induced shrinkage of extraneuronal space, PLoS Comp Biol 5, e1000272 (2009)
3. T. Heiberg, B. Kriener, T. Tetzlaff, A. Casti, G.T. Einevoll, and H.E. Plesser: Firing-rate models can describe the dynamics of the retina-LGN connection, J Comput Neurosci (2013)
4. T. Tetzlaff, M. Helias, G.T. Einevoll, and M. Diesmann: Decorrelation of neural-network activity by inhibitory feedback, PLoS Comp Biol 8, e10002596 (2012).
5. E. Nordlie, T. Tetzlaff, and G.T. Einevoll: Rate dynamics of leaky integrate-and-fire neurons with strong synapses, Frontiers in Comput Neurosci 4, 149 (2010)
6. G.T. Einevoll, F. Franke, E. Hagen, C. Pouzat, K.D. Harris: Towards reliable spike-train recording from thousands of neurons with multielectrodes, Current Opinion in Neurobiology 22, 11-17 (2012)
7. H. Linden, T Tetzlaff, TC Potjans, KH Pettersen, S Grun, M Diesmann, GT Einevoll: Modeling the spatial reach of LFP, Neuron 72, 859-872 (2011).
8. H. Linden, K.H. Pettersen, and G.T. Einevoll: Intrinsic dendritic filtering gives low-pass power spectra of local field potentials, J Computational Neurosci 29, 423-444 (2010)
9. K.H. Pettersen and G.T. Einevoll: Amplitude variability and extracellular low-pass filtering of neuronal spikes, Biophysical Journal 94, 784-802 (2008).
10. K.H. Pettersen, E. Hagen, and G.T. Einevoll: Estimation of population firing rates and current source densities from laminar electrode recordings, J Comput Neurosci 24, 291-313 (2008).
11. K. Pettersen, A. Devor, I. Ulbert, A.M. Dale and G.T. Einevoll. Current-source density estimation based on inversion of electrostatic forward solution: Effects of finite extent of neuronal activity and conductivity discontinuities, Journal of Neuroscience Methods 154, 116-133 (2006).
12. G.T. Einevoll, K. Pettersen, A. Devor, I. Ulbert, E. Halgren and A.M. Dale: Laminar Population Analysis: Estimating firing rates and evoked synaptic activity from multielectrode recordings in rat barrel cortex, Journal of Neurophysiology 97, 2174-2190 (2007).
13. LFPy: A tool for simulation of extracellular potentials (http://compneuro.umb.no)
14. E. Nordlie, M.-O. Gewaltig, H. E. Plesser: Towards reproducible descriptions of neuronal network models, PLoS Comp Biol 5, e1000456 (2009).

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

1. 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
2. Handbook of Plant-Based Biofuels, Editor- Ashok Pandey, CRC Press, Francis & Taylors, Boca Raton, USA, p 297 (2008) ISBN 978-q-5602-2175-3
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
4. Biofuels, Special issue of Journal of Scientific & Industrial Research, Guest Editors- C Larroche and Ashok Pandey, 64(11), 797-988 (2005) ISSN: 0022-4456

Sunilkumar Sukumaran, Ayyappan Nair, Madhuri Subbiah, Gunja Gupta, Lakshmi Rajakrishna, Pradeep Savanoor Raghavendra, Subbulakshmi Karthikeyan, Salini Krishnan Unni and Ganesh Sambasivam

Genotoxicity is defined as DNA damage that leads to gene mutations which can become tumorigenic. Genotoxicity testing is important to ensure drug safety and is mandatory prior to Phase I/II clinical trials of new drugs. The results from genetic toxicology studies help to identify hazardous drugs and environmental genotoxins. Currently, among others there are four tests recommended by regulatory authorities (Ames test-bacterial, chromosome aberrations; in vitro gene mutation-eukaryotic cells and in vivo test). These assays are laborious, time consuming, require large quantities of test compounds and limited by throughput challenges. The site and mechanism of genotoxicity are not revealed by these assays and data obtained from bacterial tests might not translate the same in mammals. To address these we have developed a novel, versatile, human cell based, high throughput, reporter based genotoxicity screen (Anthem’s Genotox screen). This screen is performed on genetically engineered human cell lines that express 3 reporter genes under transcriptional control of ‘early DNA damage sensors’ (p53, p21 and GADD153). These genes are involved in DNA repair, cell cycle arrest and/or apoptosis. p21 and GADD are also known to be induced in a p53 independent manner. p53 blocks G1/S transition of cell cycle while the p53 independent DNA damage block G2/M transition. Identification of the mechanism of genotoxicity helps in rational drug designing. Additionally, the platform can be used to screen other potential genotoxins from cosmetics, food and environment. Initial validation studies of the Genotox screen was performed with over 60 compounds chosen from a variety of chemical classes. The genotoxic potential of metabolites was tested using rat liver S9 fractions. The results demonstrated a sensitivity of 86.7–92.3% and a specificity of 70–78.6% when compared with currently available in vitro genotoxicity assays. This Genotox screen would prove to be an invaluable human cell based tool to weed out potential genotoxins in various industries.

DOWNLOAD THE ABSTRACT