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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).

Leland H. Hartwell Ph.D.
2001 Nobel Laureate, Physiology & Medicine

Dr. Lee Hartwell received the 2001 Nobel Prize in Physiology / Medicine for his discovery of protein molecules that control the division of cells. He was the President and Director of the Fred Hutchinson Cancer Research Center in Seattle, Washington before moving to Arizona State University’s Center for Sustainable Health.

Dr. Hartwell is also adjunct faculty at Amrita University. He spoke to the delegates at Bioquest from his office in the US, over Amrita’s e-learning platform A-View. Given below are excerpts from his address.

I would like to address the young people in the audience. I know that many of you may have come to this meeting wondering, “How can I become a successful scientist? How can I prepare myself to make a contribution in this world?”

These questions are interesting to me also.

Believe it or not, I am still trying to be a successful scientist. That may surprise you since you probably think that a Nobel laureate must have found the answers. But the problem is that the answers to these questions change with time and the answers are different today than what they were when I began my career fifty years ago. The strategy of the 1960’s doesn’t work so well anymore. What is different now?

First, what we know now is much more. For example, by 1970, no genes from any organisms were sequenced. In 2013, we have the complete sequence of the human genome. Second, not only do we know much more today, accessing that knowledge is easy. Third, obtaining new information is much faster today.

Our rich understanding of science and technology is now needed to solve many serious problems. The human population has reached the size where we are utilizing all available resource of the planet. We are utilizing all of the agricultural land, all of the water, all of the forest and fishing resources. We are also polluting the planet that we live on.

We are polluting the land with fertilizers and pesticides; the oceans with acids and the atmosphere with carbon dioxide. We are using up top soil and ground water, thereby reducing our capacity to feed ourselves. We are using up petroleum, the energy source that our entire economy is dependent on. These are problems we were largely unaware of, fifty years ago. But these are problems that must be solved in your life times.

The big question facing your generation is, how can human beings live sustainably on planet earth. Your two broad goals on sustainability are 1) leave the planet as you first found it for your future generations; don’t use up the resources and don’t pollute the planet 2) everyone deserves to have an equal share of the earth’s resources.

Income strongly determines one’s opportunities in life. Many poor people succumb to chronic diseases and unhealthy environments. This inequality undermines our ability to live sustainably. We can’t ask the poor to leave the planet as they found it if they can’t support their families. Education, healthcare, employment are essential to having a sustainable society.

How can we be a successful scientist in 2013?
1. First choose a problem to solve
2. Ask questions to understand why it is not solved
3. Collaborate with those who can help
4. Develop a solution that works in the real world

Chronic diseases are our major burden and this burden will get worse. Heart disease, diabetes, cancer, dementia and other diseases. The good news is that the chronic diseases are largely preventable and more easily curable if detected early. One question that attracts me is how can we detect disease earlier when it can be more easily cured?

Can we use our increasing knowledge in molecular biology to identify biomarkers for early disease detection?

We need to collaborate very closely with clinicians who care for patients to find out exactly where they need help.

I think if we apply our technology to important clinical questions we will actually save medical expenditure and be well on our way to making a great contribution to society.

Shantikumar Nair, Ph.D.
Professor & Director, Amrita Center for Nanosciences & Molecular Medicine, Amrita University, India

Spatially Distributed and Hierarchical Nanomaterials in Biotechnology 

Although nano materials are well investigated in biotechnology in their zero-, one- and two-dimensional forms, three-dimensional nanomaterials are relatively less investigated for their biological applications.  Three dimensional nano materials are much more complex with several structural and hierarchical variables controlling their mechanical, chemical and biological functionality.  In this talk examples are given of some complex three dimensional systems including,  scaffolds, aggregates, fabrics and membranes. Essentially three types of hierarchies are considered: one-dimensional hierarchy, two-dimensional hierarchy and three-dimensional hierarchy each giving rise to unique behaviors.

Satheesh Babu T. G., Ph.D.
Associate Professor, Department of Sciences, School of Engineering, Amrita University, Coimbatore, India

Nanomaterials for ‘enzyme-free’ biosensing

Enzyme based sensors have many draw backs such as poor storage stability, easily affected by the change in pH and temperature and involves complicated enzyme immobilization procedures.  To address this limitation, an alternative approach without the use of enzyme, “non-enzymatic” has been tried recently. Choosing the right catalyst for direct electrochemical oxidation / reduction of a target molecule is the key step in the fabrication of non-enzymatic sensors.

Non-enzymatic sensors for glucose, creatinine, vitamins and cholesterol are fabricated using different nanomaterials, such as nanotubes, nanowires and nanoparticles of copper oxide, titanium dioxide, tantalum oxide, platinum, gold and graphenes. These sensors selectively catalyse the targeted analyte with very high sensitivity. These nanomaterials based sensors combat the drawbacks of enzymatic sensors.

Aswath Balakrishnan, Kapaettu Satyamoorthy and Manjunath B Joshi

Introduction
Insulin resistance is a hall mark of metabolic disorders such as diabetes. Reduced insulin response in vasculature leads to disruption of IR/Akt/eNOS signaling pathway resulting in vasoconstriction and subsequently to cardiovascular diseases. Recent studies have demonstrated that inflammatory regulator interleukin-6 (IL-6), as one of the potential mediators that can link chronic inflammation with insulin resistance. Accumulating evidences suggest a significant role of epigenetic mechanisms such as DNA methylation in progression of metabolic disorders. Hence the present study aimed to understand the role of epigenetic mechanisms involved during IL-6 induced vascular insulin resistance and its consequences in cardiovascular diseases.

Materials and Methods
Human umbilical vein endothelial cells (HUVEC) and Human dermal microvascular endothelial cells (HDMEC) were used for this study. Endothelial cells were treated in presence or absence of IL-6 (20ng/ml) for 36 hours and followed by insulin (100nM) stimulation for 15 minutes. Using immunoblotting, cell lysates were stained for phosphor- and total Akt levels to measure insulin resistance. To investigate changes in DNA methylation, cells were treated with or without neutrophil conditioned medium (NCM) as a physiological source of inflammation or IL-6 (at various concentrations) for 36 hours. Genomic DNA was processed for HPLC analysis for methyl cytosine content and cell lysates were analyzed for DNMT1 (DNA (cytosine-5)-methyltransferase 1) and DNMT3A (DNA (cytosine-5)-methyltransferase 3A) levels using immunoblotting.

Results
Endothelial cells stimulated with insulin exhibited an increase in phosphorylation of Aktser 473 in serum free conditions but such insulin response was not observed in cells treated with IL-6, suggesting chronic exposure of endothelial cells to IL-6 leads to insulin resistance. HPLC analysis for global DNA methylation resulted in decreased levels of 5-methyl cytosine in cells treated with pro-inflammatory molecules (both by NCM and IL-6) as compared to untreated controls. Subsequently, analysis in cells treated with IL-6 showed a significant decrease in DNMT1 levels but not in DNMT3A. Other pro-inflammatory marker such as TNF-α did not exhibit such changes.

Conclusion
Our study suggests: a) Chronic treatment of endothelial cells with IL-6 results in insulin resistance b) Neutrophil conditioned medium and IL-6 decreases methyl cytosine levels c) DNMT1 but not DNMT3a levels are reduced in cells treated with IL-6.

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