Aug
12
Mon
2013
Dr. Lee Hartwell Session @ Amriteshwari Hall
Aug 12 @ 8:15 pm – 9:15 pm
LeeHartwellLeland 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.

 

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