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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.

D. Narasimha Rao, Ph.D.
Professor, Dept of Biochemistry, Indian Institute of Science, Bangalore, India

Genomics of Restriction-Modification Systems

Restriction endonucleases occur ubiquitously among procaryotic organisms. Up to 1% of the genome of procaryotic organisms is taken up by the genes for these enzymes. Their principal biological function is the protection of the host genome against foreign DNA, in particular bacteriophage DNA. Restriction-modification (R-M) systems are composed of pairs of opposing enzyme activities: an endonuclease and a DNA methyltransferase (MTase). The endonucleases recognise specific sequences and catalyse cleavage of double-stranded DNA. The modification MTases catalyse the addition of a methyl group to one nucleotide in each strand of the recognition sequence using S-adenosyl-L-methionine (AdoMet) as the methyl group donor. Based on their molecular structure, sequence recognition, cleavage position and cofactor requirements, R-M systems are generally classified into three groups. In general R-M systems restrict unmodified DNA, but there are other systems that specifically recognise and cut modified DNA. More than 3500 restriction enzymes have been discovered so far. With the identification and sequencing of a number of R-M systems from bacterial genomes, an increasing number of these have been found that do not seem to fit into the conventional classification.

It is well documented that restriction enzyme genes always lie close to their cognate methyltransferase genes. Analysis of the bacterial and archaeal genome sequences shows that MTase genes are more common than one would have expected on the basis of previous biochemical screening. Frequently, they clearly form part of a R-M system, because the adjacent open reading frames (ORFs) show similarity to known restriction enzyme genes. Very often, though, the adjacent ORFs have no homologs in the GenBank and become candidates either for restriction enzymes with novel specificities or for new examples of previously uncloned specificities. Sequence-dependent modification and restriction forms the foundation of defense against foreign DNAs and thus RM systems may serve as a tool of defense for bacterial cells. RM systems however, sometimes behave as discrete units of life, and any threat to their maintenance, such as a challenge by a competing genetic element can lead to cell death through restriction breakage in the genome, thus providing these systems with a competitive advantage. The RM systems can behave as mobile-genetic elements and have undergone extensive horizontal transfer between genomes causing genome rearrangements. The capacity of RM systems to act as selfish, mobile genetic elements may underlie the structure and function of RM enzymes.

The similarities and differences in the different mechanisms used by restriction enzymes will be discussed. Although it is not clear whether the majority of R-M systems are required for the maintenance of the integrity of the genome or whether they are spreading as selfish genetic elements, they are key players in the “genomic metabolism” of procaryotic organisms. As such they deserve the attention of biologists in general. Finally, restriction enzymes are the work horses of molecular biology. Understanding their enzymology will be advantageous to those who use these enzymes, and essential for those who are devoted to the ambitious goal of changing the properties of these enzymes, and thereby make them even more useful.