Aug
11
Sun
2013
POSTER SESSION : Neurobiology and Computational Neuroscience @ Poster Corridor 2: First Floor Lobby Area
Aug 11 @ 4:30 pm – 6:30 pm
Aug
12
Mon
2013
Introducing the Track, Neurobiology & Computational Neuroscience @ Amriteshwari Hall
Aug 12 @ 9:00 am – 9:26 am

9083583257_671719d5edShyam Diwakar, Ph.D.
Assistant Professor, Amrita School of Biotechnology

Plenary Talk: Watching the network change during the formation of associative memory @ Amriteshwari Hall
Aug 12 @ 9:27 am – 9:58 am

UpinderUpinder S. Bhalla, Ph.D.
Professor & Dean, NCBS, Bengaluru, India


Watching the network change during the formation of associative memory

The process of learning is measured through behavioural changes, but it is of enormous interest to understand its cellular and network basis. We used 2-photon imaging of hippocampal CA1 pyramidal neuron activity in mice to monitor such changes during the acquisition of a trace conditioning task. One of the questions in such learning is how the network retains a trace of a brief conditioned stimulus (a sound), until the arrival of a delayed unconditioned stimulus (a puff of air to the eye). During learning, the mice learn to blink when the tone is presented, well before the arrival of the air puff.

The mice learnt this task in 20-50 trials. We observed that in this time-frame the cells in the network changed the time of their peak activity, such that their firing times tiled the interval between sound and air puff. Thus the cells seem to form a relay of activity. We also observed an evolution in functional connectivity in the network, as measured by groupings of correlated cells. These groupings were stable till the learning protocol commenced, and then changed. Thus we have been able to observe two aspects of network learning: changes in activity (relay firing), and changes in connectivity (correlation groups).

Upi Bhalla Upi

Invited Talk: Can we compute what we think? @ Amriteshwari Hall
Aug 12 @ 10:20 am – 10:51 am

gauteGaute 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).

Gaute

Invited Talk: Diagnostic Biomarker for Alzheimer’s disease from Noninvasive and State-of-the-art Magnetic Resonance Spectroscopic Technique @ Amriteshwari Hall
Aug 12 @ 10:53 am – 11:24 am

PravatPravat Mandal, Ph.D.
Professor, Neurospectroscopy & Neuroimaging, National Brain Research Center, India


Pravat Mandal (1)
Pravat Mandal (3)
Invited Talk: A Far- Western Clinical Proteomics Approach to Detect Molecules of Clinical and Pathological Significance in the Neurodegenerative Disease Multiple Sclerosis @ Amriteshwari Hall
Aug 12 @ 11:27 am – 11:50 am

krishnakumarKrishnakumar Menon, Ph.D.
Associate Professor, Centre for Nanosciences & Molecular Medicine, Amrita University, Kochi, India


A Far-Western Clinical Proteomics Approach to Detect Molecules of Clinical and Pathological Significance in the Neurodegenerative Disease Multiple Sclerosis.

Multiple Sclerosis (MS), an autoimmune neurodegenerative disorder of the central nervous system. The disease affects young adults at their prime age leading to severe debilitation over several years.  Despite advances in MS research, the cause of the disease remains elusive. Thus, our objective is to identify novel molecules of pathological and diagnostic significance important in the understanding, early diagnosis and treatment of MS. Biological fluids such as cerebrospinal fluid (CSF), that bathe the brain serve as a potential source for the identification of pathologically significant autoantibody reactivity in MS.  In this regard, we report the development of an unbiased clinical proteomics approach for the detection of reactive CSF molecules that target brain proteins from patients with MS. Proteins of myelin and myelin-axolemmal complexes were separated by two-dimensional gel electrophoresis, blotted onto membranes and probed separately with biotinylated unprocessed CSF samples. Protein spots that reacted specifically to MS-CSF’s were further analyzed by matrix assisted laser desorption ionization-time-of-flight time-of-flight mass spectrometry. In addition to previously reported proteins found in MS, we have identified several additional molecules involved in mitochondrial and energy metabolism, myelin gene expression and/or cytoskeletal organization. Among these identified molecules, the cellular expression pattern of collapsin response mediator protein-2 and ubiquitin carboxy-terminal hydrolase L1 were investigated in human chronic-active MS lesions by immunohistochemistry. The observation that in multiple sclerosis lesions phosphorylated collapsin response mediator protein-2 was increased, whereas Ubiquitin carboxy-terminal hydrolase L1 was down-regulated, not only highlights the importance of these molecules in the pathology of this disease, but also illustrates the use of our approach in attempting to decipher the complex pathological processes leading to multiple sclerosis and other neurodegenerative diseases.  Further, we show that in clinicaly isolated syndrome (CIS), we could identify important molecules that could serve as an early diagnostic biomarker in MS.

Krishnakumar

Invited Talk: Functional MR Imaging of the brain: An Overview
Aug 12 @ 11:51 am – 12:17 pm

claudiaClaudia AM Wheeler-Kingshott, Ph.D.
University Reader in Magnetic Resonance Physics, Department of Neuroinflammation, UCL Institute of Neurology, London, UK


Abstract

Detecting neuronal activity in vivo non-invasively is possible with a number of techniques. Amongst these, in 1990 functional magnetic resonance imaging (fMRI) was proposed as a technique that has a great ability to spatially map brain activity by exploiting the blood oxygenation level dependent (BOLD) contrast mechanism [1, 2]. In fact, neuronal activation triggers a demand for oxygen and induces a localised increase in blood flow and blood volume, which actually exceeds the metabolic needs. This in turns causes an increase of oxyhaemoglobin in the venous compartment, which is a transient phenomenon and is accompanied by a transient change (decrease) in the concentration of deoxyhaemoglobin. Due to its paramagnetic properties, the amount of deoxyhaemoglobin present in the venous blood affects the local magnetic field seen by the spins (protons) and determines the local properties of the MR signal. A decrease in deoxyhaemoglobin during neuronal activity, therefore, induces local variations of this magnetic field that increases the average transverse relaxation time of tissue, measured via the T2* parameter [3]. This means that there is an increase of the MR signal (of the order of a few %, typically <5%) linked to metabolic changes happening during brain function. Activation can be inferred at different brain locations by performing tasks while acquiring the MR signal and comparing periods of rest to periods of activity.

The macroscopic changes of the BOLD signal are well characterised, while the reason for the increased blood supply, exceeding demands, needs further thoughts. Here we will discuss two approaches for explaining the BOLD phenomenon, one that links it to adenosine triphosphate production [4] and enzyme saturation, the other that relates it to the very slow diffusion of oxygen through the blood-brain-barrier with a consequent compensatory high demand of oxygen [5]. Some evidence of restricted oxygen diffusion has been shown by means of hypercapnia [6], although it is not excluded that both mechanisms may be present.

Overall, the BOLD signal changes theory and its physiological basis will be presented and discussed.

References

  1. Ogawa, S., et al., Brain magnetic resonance imaging with contrast dependent on blood oxygenation. Proc Natl Acad Sci U S A, 1990. 87(24): p. 9868-72.
  2. Kwong, K.K., et al., Dynamic magnetic resonance imaging of human brain activity during primary sensory stimulation. Proc Natl Acad Sci U S A, 1992. 89(12): p. 5675-9.
  3. Bandettini PA, et al. Spin-echo and gradient-echo EPI of human brain activation using BOLD contrast: a comparative study at 1.5 T. NMR Biomed. 1994 Mar;7(1-2):12-20
  4.  Fox, P.T., et al., Nonoxidative glucose consumption during focal physiologic neural activity. Science, 1988. 241(4864): p. 462-4.
  5. Gjedde, A., et al. Reduction of functional capillary density in human brain after stroke. J Cereb Blood Flow Metab, 1990. 10(3): p. 317-26.
  6. Hoge, R.D., et al., Linear coupling between cerebral blood flow and oxygen consumption in activated human cortex. Proc Natl Acad Sci U S A, 1999. 96(16): p. 9403-8.

Invited Talk: Modelling the syncytial organization and neural control of smooth muscle: insights into autonomic physiology and pharmacology @ Amriteshwari Hall
Aug 12 @ 12:20 pm – 12:43 pm

RohitRohit Manchanda, Ph.D.
Professor, Biomedical Engineering Group, IIT-Bombay, India


Modelling the syncytial organization and neural control of smooth muscle: insights into autonomic physiology and pharmacology

We have been studying computationally the syncytial organization and neural control of smooth muscle in order to help explain certain puzzling findings thrown up by experimental work. This relates in particular to electrical signals generated in smooth muscles, such as synaptic potentials and spikes, and how these are explicable only if three-dimensional syncytial biophysics are taken fully into account.  In this talk, I shall provide an illustration of outcomes and insights gleaned from such an approach. I shall first describe our work on the mammalian vas deferens, in which an analysis of the effects of syncytial coupling led us to conclude that the experimental effects of a presumptive gap junction uncoupler, heptanol, on synaptic potentials were incompatible with gap junctional block and could best be explained by a heptanol-induced inhibition of neurotransmitter release, thus compelling a reinterpretation of the mechanism of action of this agent.  I shall outline the various lines of evidence, based on indices of syncytial function, that we adduced in order to reach this conclusion. We have now moved on to our current focus on urinary bladder biophysics, where the questions we aim to address are to do with mechanisms of spike generation. Smooth muscle cells in the bladder exhibit spontaneous spiking and spikes occur in a variety of distinct shapes, making their generation problematic to explain. We believe that the variety in shapes may owe less to intrinsic differences in spike mechanism (i.e., in the complement of ion channels participating in spike production) and more to features imposed by syncytial biophysics. We focus especially on the modulation of spike shape in a 3-D coupled network by such factors as innervation pattern, propagation in a syncytium, electrically finite bundles within and between which the spikes spread, and some degree of pacemaker activity by a sub-population of the cells. I shall report two streams of work that we have done, and the tentative conclusions these have enabled us to reach: (a) using the NEURON environment, to construct the smooth muscle syncytium and endow it with synaptic drive, and (b) using signal-processing approaches, towards sorting and classifying the experimentally recorded spikes.

Rohit (1) Rohit (2)

Plenary Talk: Realistic modeling-new insight into the functions of the cerebellar network @ Amriteshwari Hall
Aug 12 @ 1:37 pm – 2:24 pm

egidioEgidio D’Angelo, MD, Ph.D.
Full Professor of Physiology & Director, Brain Connectivity Center, University of Pavia, Italy


Realistic modeling: new insight into the functions of the cerebellar network

Realistic modeling is an approach based on the careful reconstruction of neurons synapses starting from biological details at the molecular and cellular level. This technique, combined with the connection topologies derived from histological measurements, allows the reconstruction of precise neuronal networks. Finally, the advent of specific software platforms (PYTHON-NEURON) and of super-computers allows large-scale network simulation to be performed in reasonable time. This approach inverts the logics of older theoretical models, which anticipated an intuition on how the network might work.  In realistic modeling, network properties “emerge” from the numerous biological properties embedded into the model.

This approach is illustrated here through an outstanding application of realistic modeling to the cerebellar cortex network. The neurons (over 105) are reproduced at a high level of detail generating non-linear network effects like population oscillations and resonance, phase-reset, bursting, rebounds, short-term and long-term plasticity, spatiotemporal redistrbution of input patterns. The model is currently being used in the context of he HUMAN BRAIN PROJECT to investigate the cerebellar network function.

Correspondence should be addressed to

Dr. EgidioD’Angelo,
Laboratory of Neurophysiology
Via Forlanini 6, 27100 Pavia, Italy
Phone: 0039 (0) 382 987606
Fax: 0039 (0) 382 987527
dangelo@unipv.it

Acknowledgments

This work was supported by grants from European Union to ED (CEREBNET FP7-ITN238686, REALNET FP7-ICT270434) and by grants from the Italian Ministry of Health to ED (RF-2009-1475845).

Egidio

Invited Talk: Control of sequential movements: insights from the oculomotor system @ Amriteshwari Hall
Aug 12 @ 2:26 pm – 2:54 pm

adityaAditya Murthy, Ph.D.
Associate Professor, Centre For Neuroscience, Indian Institute of Science, Bangalore, India


Since Karl Lashley’s seminal work on the formulation of serial order, numerous models assume simultaneous representation of competitive elements of a sequence, to account for serial order effects in different types of behavior like typing, speech, etc. Such models follow two basic assumptions: (1) more than one plan representation can be simultaneously active in a planning layer; (2) the most active plan is chosen in another layer called the competitive choice layer. Using the oculomotor system I will describe behavioral and neurophysiological experiments that tests the two critical predictions of such queuing models, providing evidence that basal ganglia in monkeys and humans instantiate a form of queuing that transforms parallel movement representations into more serial representations, allowing for the expression of sequential saccadic eye movements.

Aditya Murthy (2)

Invited Talk: Neuroprotective and neurodestructive effects of Ayurvedic drug constituents: Parkinson’s disease @ Amriteshwari Hall
Aug 12 @ 2:55 pm – 3:20 pm

mohanakumarK. 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

MOhan (1) MOhan (2)

Delegate Talk: BrainSurfer- A Novel Neurofeedback Tool for ADHD Training @ Amriteshwari Hall
Aug 12 @ 3:25 pm – 3:35 pm
Delegate Talk: BrainSurfer- A Novel Neurofeedback Tool for ADHD Training @ Amriteshwari Hall | Vallikavu | Kerala | India

David Ibanez, Laura Dubreuil and Alejandro Rier


Neurofeedback (NF) is a type of biofeedback that uses real time display of electroencephalography to illustrate brain activity. EEG features are extracted and displayed allowing the user to, with practice, modulate their temporal evolution. Neurofeedback training has many therapeutic applications such as attention deficit hyperactivity disorder (ADHD), migraine, depression or conduct disorders. This document presents NeuroSurfer, a novel general-purpose tool for neurofeedback training with a use case of attention deficit hyperactivity disorder (ADHD) treatment.

Aug
13
Tue
2013
Introducing the Track: Bioinformatics & Computational Biology @ Sathyam Hall
Aug 13 @ 9:10 am – 9:15 am

9083583257_671719d5edShyam Diwakar, Ph.D.
Assistant Professor, Amrita School of Biotechnology

Plenary Talk: Modeling strategy based on Petri-nets @ Sathyam Hall
Aug 13 @ 9:20 am – 10:00 am

jaapJaap Heringa, Ph.D.
Director & Professor of Bioinformatics, IBIVU VU University Amsterdam, The Netherlands


Modeling strategy based on Petri-nets

In my talk I will introduce a formal modeling strategy based on Petri-nets, which are a convenient means of modeling biological processes. I will illustrate the capabilities of Petri-nets as reasoning vehicles using two examples: Haematopoietic stem cell differentiation in mice, and vulval development in C. elegance. The first system was modeled using a Boolean implementation, and the second using a coarse-grained multi-cellular Petri-net model. Concepts such as the model state space,  attractor states, and reasoning to adapt the model to the biological reality will be discussed.

Invited Talk: Interpretation of Genomic Variation – Identifying Rare Variations Leading to Disease @ Sathyam Hall
Aug 13 @ 10:20 am – 10:40 am

SrinivasanRajgopal Srinivasan, Ph.D.
Principal Scientist & Head Bio IT R&D, TCS Innovation Labs, India


Interpretation of Genomic Variation – Identifying Rare Variations Leading to Disease

Genome sequencing technologies are generating an abundance of data on human genetic variations. A big challenge lies in interpreting the functional relevance of such variations, especially in clinical settings. A first step in understanding the clinical relevance of genetic variations is to annotate the variants for region of occurrence, degree of conservation both within and across species, pattern of variation across related individuals, novelty of the variation and know effects of related variations.  Several tools already exist for this purpose. However, these tools have their strengths and weaknesses. A second issue is the development of algorithms, which, given a rich annotation of variants are able to prioritize the variants as being relevant to the phenotype under investigation.

In my talk I will detail work that has been done in our labs to address both of the above problems. I will also illustrate the application of these tools that helped identify a rare mutation in the ATM gene leading to a diagnosis of AT in two infants.

 

 

Invited Talk @ Sathyam Hall
Aug 13 @ 10:45 am – 11:15 am

ajayAjay Shah, Ph.D.
Director, Research Informatics, City of Hope , CA, USA


 

Invited Talk: A cost-effective approach to Protein Structure-guided Drug Discovery: Aided by Bioinformatics, Chemoinformatics and computational chemistry @ Sathyam Hall
Aug 13 @ 11:15 am – 11:40 am

kalKal 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.

 

Invited Talk: Rare disease diagnostic platform @ Sathyam Hall
Aug 13 @ 11:40 am – 12:20 pm

PrashantPrashanth Athri, Ph.D.
Senior Specialist, Strand Life Sciences, Bengaluru, India


Rare disease diagnostic platform

At Strand, genomic sequencing combined with bioinformatic analysis have provided discriminative diagnosis in the case of rare genetic disorders. Inspired by these cases, we are building an integrated software that combines curated literature content and bioinformatics databases with a clinically oriented user interface to substantially compress time taken to determine likely candidate genetic variants in a Diagnostic Odyssey. At the back end we employ various algorithms that systematically query our diverse knowledgebase to provide the clinicians a comprehensive, and possibly multidimensional, annotation of the variant in the context of disease.

 

Plenary Talk: Interspike Interval Distribution of Neuronal Model with distributed delay: Emergence of unimodal, bimodal and Power law @ Sathyam Hall
Aug 13 @ 1:20 pm – 2:00 pm

karmeshuKarmeshu, Ph.D.
Dean & Professor, School of Computer & Systems Sciences & School of Computational & Integrative Sciences, Jawaharlal Nehru University, India.


Interspike Interval Distribution of Neuronal Model with distributed delay: Emergence of unimodal, bimodal and Power law

The study of interspike interval distribution of spiking neurons is a key issue in the field of computational neuroscience. A wide range of spiking patterns display unimodal, bimodal  ISI patterns including power law behavior. A challenging problem is to understand the biophysical mechanism which can generate  the empirically observed patterns. A neuronal model with distributed delay (NMDD) is proposed and is formulated as an integro-stochastic differential equation which corresponds to a non-markovian process. The widely studied IF and LIF models become special cases of this model. The NMDD brings out some interesting features when excitatory rates are close to inhibitory  rates rendering the drift close to zero. It is interesting that NMDD model with gamma type memory kernel can also account for bimodal ISI pattern. The mean delay of the memory kernels plays a significant role in bringing out the transition from unimodal to bimodal  ISI distribution. It is interesting to note that when a collection of neurons group together and fire together, the ISI distribution exhibits  power law.

 

Invited Talk: Nanoscale Simulations – Tackling Form and Formulation Challenges in Drug Development and Drug Delivery @ Sathyam Hall
Aug 13 @ 2:15 pm – 2:40 pm

lalithaLalitha Subramanian, Ph.D.
Chief Scientific Officer & VP, Services at Scienomics, USA


Nanoscale Simulations – Tackling Form and Formulation Challenges in Drug Development and Drug Delivery

Lalitha Subramanian, Dora Spyriouni, Andreas Bick, Sabine Schweizer, and Xenophon Krokidis Scienomics

The discovery of a compound which is potent in activity against a target is a major milestone in Pharmaceutical and Biotech industry. However, a potent compound is only effective as a therapeutic agent when it can be administered such that the optimal quantity is transported to the site of action at an optimal rate. The active pharmaceutical ingredient (API) has to be tested for its physicochemical properties before the appropriate dosage form and formulation can be designed. Some of the commonly evaluated parameters are crystal forms and polymorphs, solubility, dissolution behavior, stability, partition coefficient, water sorption behavior, surface properties, particle size and shape, etc. Pharmaceutical development teams face the challenge of quickly and efficiently determining a number of properties with small quantities of the expensive candidate compounds. Recently the trend has been to screen these properties as early as possible and often the candidate compounds are not available in sufficient quantities. Increasingly, these teams are leveraging nanoscale simulations similar to those employed by drug discovery teams for several decades. Nanoscale simulations are used to predict the behavior using very little experimental data and only if this is promising further experiments are done. Another aspect where nanoscale simulations are being used in drug development and drug delivery is to get insights into the behavior of the system so that process failures can be remediated and formulation performance can be improved. Thus, the predictive screening and the in-depth understanding leads to experimental efficiency resulting in far-reaching business impacts.

With specific examples, this talk will focus on the different types of nanoscale simulations used to predict properties of the API in excipients and also provide insight into system behavior as a function of shelf life, temperature, mechanical stress, etc.

Invited Talk: Applying Machine learning for Automated Identification of Patient Cohorts @ Sathyam Hall
Aug 13 @ 2:40 pm – 3:05 pm

SriSairamSrisairam Achuthan, Ph.D.
Senior Scientific Programmer, Research Informatics Division, Department of Information Sciences, City of Hope, CA, USA


Applying Machine learning for Automated Identification of Patient Cohorts

Srisairam Achuthan, Mike Chang, Ajay Shah, Joyce Niland

Patient cohorts for a clinical study are typically identified based on specific selection criteria. In most cases considerable time and effort are spent in finding the most relevant criteria that could potentially lead to a successful study. For complex diseases, this process can be more difficult and error prone since relevant features may not be easily identifiable. Additionally, the information captured in clinical notes is in non-coded text format. Our goal is to discover patterns within the coded and non-coded fields and thereby reveal complex relationships between clinical characteristics across different patients that would be difficult to accomplish manually. Towards this, we have applied machine learning techniques such as artificial neural networks and decision trees to determine patients sharing similar characteristics from available medical records. For this proof of concept study, we used coded and non-coded (i.e., clinical notes) patient data from a clinical database. Coded clinical information such as diagnoses, labs, medications and demographics recorded within the database were pooled together with non-coded information from clinical notes including, smoking status, life style (active / inactive) status derived from clinical notes. The non-coded textual information was identified and interpreted using a Natural Language Processing (NLP) tool I2E from Linguamatics.

Delegate Talk: VARANT: The Variant Annotation Tool @ Sathyam Hall
Aug 13 @ 3:05 pm – 3:20 pm
Delegate Talk: VARANT: The Variant Annotation Tool @ Sathyam Hall | Vallikavu | Kerala | India

Kunal Kundu, Sushma Motamarri, Uma Sunderam, Steven E. Brenner and Rajgopal Srinivasan.


VARANT: The Variant Annotation Tool

Genome sequencing technologies are generating an abundance of data on human genetic variations. A big challenge lies in interpreting the functional relevance of such variations, especially in clinical settings. A first step in understanding the clinical relevance of genetic variations is to annotate the variants for region of occurrence, degree of conservation both within and across species, pattern of variation across related individuals, novelty of the variation and know effects of related variations. Several tools already exist for this purpose. However, these tools have their strengths and weaknesses. We will present an open-source tool, VARANT, written in the python programming language, that is easily extended to incorporate newer annotations.

A detailed variant annotation places variants in context, highlights significant findings and prioritizes candidates for further analysis. With this outlook we developed VARANT to annotate, prioritize and visualize variants. VARANT has 5 levels of annotation – genomic position based, gene based, untranslated region (UTR) based, mutation effect prediction and gene level disease association. The databases used for annotations have been compiled from several sources. The genomic position based annotation comprises of tagging variants present in dbSNP and 1000 Genomes projects, GWAS variants, variants in functionally constrained region and variants overlapping epigenetic signals. The gene-based annotation includes, the distance from splice sites for intronic variants; gene, transcript, amino acid change and splicing silencer and enhancers information for exonic variants. UTR based annotations comprise of UTR functional sites like miRNA binding site, internal ribosomal entry site, variations and deletions in UTR5-Coding Sequence(CDS) boundary, exon-intron boundary and CDS-UTR3 boundary.Mutation effect predictions are incorporated from PolyPhen2 and SIFT. Thus, a detailed annotation with VARANT captures multiple biological aspects of a variant and helps in filtering variants based on disease context. The input and output of VARANT is the universal Variant Call Format, with facilities to export the annotations to popular formats such as comma/tab separated values and MS Excel. Using a desktop computer with single core and 4GB RAM VARANT annotates over 50,000 variants/minute and can be readily parallelized. Being an exhaustive annotator with good performance using modest computational hardware, VARANT is a useful annotation tool for analyzing genomic variants. Furthermore, the tool includes facilities to update the underlying data sources in an automated fashion, and is easily extended to add additional annotations. VARANT also provides an interface to visualize variants in an annotated VCF file and to filter variants interactively based on annotation features like – region, mutation effect etc, and inheritance models. In addition to annotation, there are ongoing efforts to incorporate a variant prioritization module using the annotated features as well as inheritance information.

Delegate Talk: Efficient gene prioritization @ Sathyam Hall
Aug 13 @ 3:25 pm – 3:35 pm
Delegate Talk: Efficient gene prioritization @ Sathyam Hall | Vallikavu | Kerala | India

Bhadrachalam Chitturi, Balaji Raghavachari and Donghyun Kim


Efficient gene prioritization

The gene prioritization, GP, problem seeks to identify the most promising genes among several candidate genes. In genetics, gene related conditions are typically associated with chromosomal regions, say with GWAS. These associations yield lists of candidate genes. A priori, some genes i.e. seed genes, are associated with a specific disease D; additional genes that are implicated via associations constitute the potential candidates. Thus, most promising novel candidates for D are sought. In network based approach, a protein protein interaction network, i.e. NP , and a set S of seed genes constitute the prior knowledge. We treat a gene and the protein that it encodes identically. Various GP algorithms based on guilt by association are run on the NP to predict novel candidates [1–6]. They rank a new candidate gene by its estimated association to D.

Distance between a pair of genes is the shortest path measured in the number of edges. Diameter of a set of genes is the longest distance between any pair of genes in terms of the number of edges. The density of a set X of genes is defined as e(X)/|X| where e(X) denotes the number of edges among genes of X and |X| denotes the number of genes of X. The set S: (i) can be of minimal size (say one), (ii) is tightly coupled in NP , i.e. has low-diameter/high-density, or (iii) is loosely coupled, i.e. has high-diameter/low-density. Similarly, the GP algorithms can be partitioned into: Type-1 that ignore the edge weights and Type-2 that employ the edge weights. However, currently, the prioritization process neither exploits the character of S nor the type of GP algorithm that is run. Given S, we compute two core networks of NP which we call NC1 and NC2 that are subnetworks of NP . The idea is to execute GP algorithms of Type-1 and Type-2 on NC1 and NC2 respectively instead of NP . Typically, NC1 and NC2 are much smaller than NP . Also, one runs several algorithms of Type-1 and Type-2 [2–4, 6] and takes consensus [6].

In general, the time to run a GP algorithm say AP on NP i.e. t1 or to compute NC1 and NC2 i.e. t2 is proportional to e(NP ) where e(NP ) e(NC1) and e(NP ) e(NC2). However, executing AP on NC1/NC2 (a much smaller network) is much more efficient than executing AP on NP . We run several GP algorithms onNC1/NC2 [6] but computeNC1/NC2 only once. So, overall our method is more efficient. Preliminary implementation results show that for several GP algorithms, the candidates identified by our method match the topmost prioritized candidates identified by the direct execution of the algorithm on NP . Overall, our method was more efficient. Based on the number of candidates that we seek and the nature of S, we can generate variants of NCx, x ∈ {1, 2}. In some cases, AP determines the appropriate variant of NCx.

Delegate Talk: Insilico Analysis of hypothetical proteins from Leishmania donovani: A Case study of a membrane protein of the MFS class reveals their plausible roles in drug resistance @ Sathyam Hall
Aug 13 @ 3:35 pm – 3:50 pm
Delegate Talk: Insilico Analysis of hypothetical proteins from Leishmania donovani: A Case study of a membrane protein of the MFS class reveals their plausible roles in drug resistance @ Sathyam Hall | Vallikavu | Kerala | India

Nitish Sathyanrayanan, Sandesh Ganji and Holenarsipur Gundurao Nagendra.


Insilico Analysis of hypothetical proteins from Leishmania donovani: A Case study of a membrane protein of the MFS class reveals their plausible roles in drug resistance

Kala-azar or visceral leishmaniais (VL), caused by protozoan parasite Leishmania donovani, is one of the leading causes of morbidity and mortality in Bihar, India (Guerin et al. 2002; Mubayi et al. 2010). The disease is transmitted to the humans mainly by the vector, Phlebotmus argentipes, commonly known as Sand fly. The majority of VL (> 90%) occurs in only six countries: Bangladesh, India, Nepal, Sudan, Ethiopia and Brazil (Chappuis et al. 2007). In the Indian subcontinent, about 200 million people are estimated to be at risk of developing VL and this region harbors an estimated 67% of the global VL disease burden. The Bihar state only has captured almost 50% cases out of total cases in Indian sub-continent (Bhunia et al. 2013). ‘Conserved hypothetical’ proteins pose a challenge not just to functional genomics, but also to biology in general (Galperin and Koonin 2004). Leishmania donovani (strain BPK282A1) genome consists of a staggering ∼65% of hypothetical proteins. These uncharacterized proteins may enable better appreciation of signalling pathways, general metabolism, stress response and even drug resistance.

Delegate Talk: Pharmacophore modeling, atom-based 3D-QSAR and molecular docking studies on Pyrimido[5,4-e][1,2,4]triazine derivatives as PLK 1 inhibitors @ Sathyam Hall
Aug 13 @ 3:55 pm – 4:10 pm
Delegate Talk: Pharmacophore modeling, atom-based 3D-QSAR and molecular docking studies on Pyrimido[5,4-e][1,2,4]triazine derivatives as PLK 1 inhibitors @ Sathyam Hall | Vallikavu | Kerala | India

Rajasekhar Chekkara, Venkata Reddy Gorla and Sobha Rani Tenkayala


Pharmacophore modeling, atom-based 3D-QSAR and molecular docking studies on Pyrimido[5,4-e][1,2,4]triazine derivatives as PLK 1 inhibitors

Polo-like kinase 1 (PLK1) is a significant enzyme with diverse biological actions in cell cycle progression, specifically mitosis. Suppression of PLK1 activity by small molecule inhibitors has been shown to inhibit cancer, being BI 2536 one of the most potent active inhibitor of PLK1 mechanism. Pharmacophore modeling, atom-based 3D-QSAR and molecular docking studies were carried out for a set of 54 compounds belonging to Pyrimido[5,4-e][1,2,4]triazine derivatives as PLK1 inhibitors. A six-point pharmacophoremodel AAADDR, with three hydrogen bond acceptors (A), two hydrogen bond donors (D) and one aromatic ring (R) was developed by Phase module of Schrdinger suite Maestro 9. The generated pharmacophore model was used to derive a predictive atom-based 3D quantitative structure-activity relationship analysis (3D-QSAR) model for the training set (r2 = 0.88, SD = 0.21, F = 57.7, N = 44) and for test set (Q2 = 0.51, RMSE = 0.41, PearsonR = 0.79, N = 10). The original set of compounds were docked into the binding site of PLK1 using Glide and the active residues of the binding site were analyzed. The most active compound H18 interacted with active residues Leu 59, Cys133 (glide score = −10.07) and in comparison of BI 2536, which interacted with active residues Leu 59, Cys133 (glide score = −10.02). The 3D-QSAR model suggests that hydrophobic and electron-withdrawing groups are essential for PLK1 inhibitory activity. The docking results describes the hydrogen bond interactions with active residues of these compounds. These results which may support in the design and development of novel PLK1 inhibitors.

POSTER SESSION: Bioinformatics and Computational Biology @ Poster Corridor 1: First Floor Lobby Area
Aug 13 @ 4:30 pm – 6:30 pm