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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).

K. P. Mohanakumar, Ph.D.
Chief Scientist, Cell Biology & Physiology Division, Indian Institute of Chemical Biology, Kolkata

Neuroprotective and neurodestructive effects of Ayurvedic drug constituents: Parkinson’s disease

The present study reports the good and the bad entities in an Indian traditional medicine used for treating Parkinson’s disease (PD). A prospective clinical trial on the effectiveness of Ayurvedic medication in a population of PD patients revealed significant benefits, which has been attributed to L-DOPA present in the herbs [1]. Later studies revealed better benefits with one of the herbs alone, compared to pure L-DOPA in a clinical trial conducted in UK [2], and in several studies conducted on animal models of PD in independent laboratories world over [3-5]. We have adapted strategies to segregate molecules from the herb, and then carefully removed L-DOPA contained therein, and tested each of these sub-fractions for anti-PD activity in 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine, rotenone and 6-hydroxydopamine -induced parkinsonian animal models, and transgenic mitochondrial cybrids. We report here two classes of molecules contained in the herb, one of which possessed severe pro-parkinsonian (phenolic amine derivatives) and the other having excellent anti-parkinsonian potential (substituted tetrahydroisoquinoline derivatives). The former has been shown to cause severe dopamine depletion in the striatum of rodents, when administered acutely or chronically. It also caused significant behavioral aberrations, leading to anxiety and depression [6]. The latter class of molecules administered in PD animal model [7], caused reversal of behavioral dysfunctions and significant attenuation of striatal dopamine loss. These effects were comparable or better than the effects of the anti-PD drugs, selegiline or L-DOPA. The mechanism of action of the molecule has been found to be novel, at the postsynaptic receptor signaling level, as well as cellular α-synuclein oligomerization and specifically at mitochondria. The molecule helped in maintaining mitochondrial ETC complex activity and stabilized cellular respiration, and mitochondrial fusion-fission machinery with specific effect on the dynamin related protein 1. Although there existed significant medical benefits that could be derived to patients due to the synergistic actions of several molecules present in a traditional preparation, accumulated data in our hands suggest complicated mechanisms of actions of Ayurvedic medication. Our results also provide great hope for extracting, synthesizing and optimizing the activity of anti-parkinsonian molecules present in traditional Ayurvedic herbs, and for designing novel drugs with novel mechanisms of action.

1. N, Nagashayana, P Sankarankutty, MRV Nampoothiri, PK Mohan and KP Mohanakumar, J Neurol Sci. 176, 124-7, 2000.
2. Katzenschlager R, Evans A, Manson A, Patsalos PN, Ratnaraj N, Watt H, Timmermann L, Van der Giessen R, Lees AJ. J Neurol Neurosurg Psychiatry.75, 1672-7, 2004.
3. Manyam BV, Dhanasekaran M, Hare TA. Phytother Res. 18, 706-12, 2004.
4. Kasture S, Pontis S, Pinna A, Schintu N, Spina L, Longoni R, Simola N, Ballero M, Morelli M. Neurotox Res. 15, 111-22, 2009.
5. Lieu CA, Kunselman AR, Manyam BV, Venkiteswaran K, Subramanian T. Parkinsonism Relat Disord.16, 458-65, 2010.
6. T Sengupta and KP Mohanakumar, Neurochem Int. 57, 637-46, 2010.
7. T Sengupta, J Vinayagam, N Nagashayana, B Gowda, P Jaisankar and KP Mohanakumar, Neurochem Res 36, 177-86, 2011

Nader Pourmand, Ph.D.
Director, UCSC Genome Technology Center,University of California, Santa Cruz

Biosensor and Single Cell Manipulation using Nanopipettes

Approaching sub-cellular biological problems from an engineering perspective begs for the incorporation of electronic readouts. With their high sensitivity and low invasiveness, nanotechnology-based tools hold great promise for biochemical sensing and single-cell manipulation. During my talk I will discuss the incorporation of electrical measurements into nanopipette technology and present results showing the rapid and reversible response of these subcellular sensors  to different analytes such as antigens, ions and carbohydrates. In addition, I will present the development of a single-cell manipulation platform that uses a nanopipette in a scanning ion-conductive microscopy technique. We use this newly developed technology to position the nanopipette with nanoscale precision, and to inject and/or aspirate a minute amount of material to and from individual cells or organelle without comprising cell viability. Furthermore, if time permits, I will show our strategy for a new, single-cell DNA/ RNA sequencing technology that will potentially use nanopipette technology to analyze the minute amount of aspirated cellular material.

Kal Ramnarayan, Ph.D.
Co-founder President & Chief Scientific Officer, Sapient Discovery, San Diego, CA, USA

A cost-effective approach to Protein Structure-guided Drug Discovery: Aided by Bioinformatics, Chemoinformatics and computational chemistry

With the mapping of the human genome completed almost a decade ago, efforts are still underway to understand the gene products (i.e., proteins) in the human biological and disease pathways.  Deciphering such information is very important for the discovery and development of small molecule drugs as well as protein therapeutics for various human diseases for which no cure exists.  As an example, with more than 500 members, the kinase family of protein targets continues to be an important and attractive class for drug discovery.  While how many of the members in this family are actually druggable is still to be established, there are several ongoing efforts on this class of proteins across a broad spectrum of disease categories.  Even though in general the protein structural topology might looks similar, there are issues with respect selectivity of identified small molecule inhibitors when, the lead molecule discovery is carried out at the ATP binding site.  As an added complexity, allosteric modulators are needed for some of the members, but the actual site for such modulation on the protein target can not resolved with uncertainty.  In this presentation we will describe a bioinformatics and computational based platform for small molecule discovery for protein targets that are involved in protein-protein interactions as well as targets like kinases and phosphatases.  We will describe a computational approach in which we have used an informatics based platform with several hundred kinases to sort through in silico and identify inhibitors that are likely to be highly selective in the lead generation phase.  We will discuss the implication of this approach on the drug discovery of the kinase and phosphatase classes in general and independent of the disease category.