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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).

Deepthy Menon, Ph.D.
Associate Professor, Centre for Nanosciences & Molecular Medicine, Health Sciences Campus, Amrita University, Kochi, India

Nanobioengineering of implant materials for improved cellular response and activity

Deepthy Menon, Divyarani V V, Chandini C Mohan, Manitha B Nair, Krishnaprasad C & Shantikumar V Nair

Abstract

Current trends in biomaterials research and development include the use of surfaces with topographical features at the nanoscale (dimensions < 100 nm), which influence biomolecular or cellular level reactions in vitro and in vivo. Progress in nanotechnology now makes it possible to precisely design and modulate the surface properties of materials used for various applications in medicine at the nanoscale. Nanoengineered surfaces, owing to their close resemblance with extracellular matrix, possess the unique capacity to directly affect protein adsorption that ultimately modulates the cellular adhesion and proliferation at the site of implantation. Taking advantage of this exceptional ability, we have nanoengineered metallic surfaces of Titanium (Ti) and its alloys (Nitinol -NiTi), as well as Stainless Steel (SS) by a simple hydrothermal method for generating non-periodic, homogeneous nanostructures. The bio- and hemocompatibility of these nanotextured metallic surfaces suggest their potential use for orthopedic, dental or vascular implants. The applicability of nanotextured Ti implants for orthopedic use was demonstrated in vivo in rat models, wherein early-stage bone formation at the tissue-implant interface without any fibrous tissue intervention was achieved. This nanoscale topography also was found to critically influence bacterial adhesion in vitro, with decreased adherence of staphylococcus aureus. The same surface nanotopography also was found to provide enhanced proliferation and functionality of vascular endothelial cells, suggesting its prospective use for developing an antithrombotic stent surface for coronary applications. Clinical SS & NiTi stents were also modified based on this strategy, which would offer a suitable solution to reduce the probability of late stent thrombosis associated with bare metallic stents. Thus, we demonstrate that nanotopography on implant surfaces has a critical influence on the fate of cells, which in turn dictates the long term success of the implant.

Acknowledgement: Authors gratefully acknowledge the financial support from Department of Biotechnology, Government of India through the Bioengineering program.

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.

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