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Posts Tagged ‘systems biology’


Unraveling Retrograde Signaling Pathways

Reporter: Larry H. Bernstein, MD, FCAP

Unraveling retrograde signaling pathways: finding candidate signaling molecules via metabolomics and systems biology driven approaches
C Caldana, AR Fernie, L Willmitzer and D Steinhauser
Front. Plant Sci. 2012; 3:267.                    http://dx.doi.org/10.3389/fpls.2012.00267

http://fpls.com/Unraveling retrograde signaling pathways: finding candidate signaling molecules via
metabolomics and systems biology driven approaches

signals can be generated within organelles, such as chloroplasts and mitochondria,

  • modulating the nuclear gene expression in a process called
    • retrograde signaling.

Recently, integrative genomics approaches, in which correlation analysis has been applied on transcript and metabolite profiling data
of Arabidopsis thaliana, revealed the identification of metabolites which are

  • putatively acting as mediators of nuclear gene expression.

http://fpls.com/unraveling_retrograde_signaling_pathways:_finding_candidate_signaling_molecules_
via_metabolomics_and_systems_biology_driven_approaches

English: Plant Pathology in Arabidopsis thaliana

English: Plant Pathology in Arabidopsis thaliana (Photo credit: Wikipedia)

B0004313 Gene expression in normal and cancer ...

B0004313 Gene expression in normal and cancer cells (Photo credit: wellcome images)

Related articles

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Reporter: Aviva Lev-Ari, PhD, RN

 

Systems Pharmacology: Pathways to Patient Response @ BioIT World, Boston, MA World Trade Center, April 9-11, 2013

Conference Tracks:

IT Infrastructure – Hardware

Software Development

Cloud Computing

Bioinformatics

Next-Gen Sequencing Informatics

Systems Pharmacology

eClinical Trials Solutions

Data Visualization NEW!

Drug Discovery Informatics

Clinical Omics NEW!

Collaborations and Open

Access Innovations

Cancer Informatics

 

Track 6 focuses on how compounds (drugs) work in the body. How are they influenced by various ‘omics’? How do they vary by tissue? The practical implications of such a compound-centric approach are exciting: new targets, new screens, new markers, new understanding of drug failure mechanisms. The systems computational tool sets including multi-scale modeling, simulation, web-based platforms, etc. will be emphasized.

Final Agenda

 

Download Brochure | Pre-Conference Workshops

 

TUESDAY, APRIL 9

7:00 am Workshop Registration and Morning Coffee

8:00 Pre-Conference Workshops*

 

*Separate Registration Required

2:00 – 7:00 pm Main Conference Registration

4:00 Event Chairperson’s Opening Remarks

Cindy Crowninshield, RD, LDN, Conference Director, Cambridge Healthtech Institute

4:05 Keynote Introduction

Speaker to be Announced, Hitachi Data Systems

 

» 4:15 PLENARY KEYNOTE

Do Network Pharmacologists Need Robot Chemists?

Andrew HopkinsAndrew L. Hopkins, DPhil, FRSC, FSB, Division of Biological Chemistry and Drug Design, College of Life Sciences, University of Dundee

 

5:00 Welcome Reception in the Exhibit Hall with Poster Viewing

Drop off a business card at the CHI Sales booth for a chance to win 1 of 2 iPads® or 1 of 2 Kindle Fires®!*

*Apple ® and Amazon are not sponsors or participants in this program

 

WEDNESDAY, APRIL 10

7:00 am Registration and Morning Coffee

8:00 Chairperson’s Opening Remarks

Phillips Kuhl, Co-Founder and President, Cambridge Healthtech Institute

8:05 Keynote Introduction

Sanjay Joshi, CTO, Life Sciences, EMC Isilon

 

» 8:15 PLENARY KEYNOTE

Atul ButteAtul Butte, M.D., Ph.D., Division Chief and Associate Professor, Stanford University School of Medicine; Director, Center for Pediatric Bioinformatics, Lucile Packard Children’s Hospital; Co-founder, Personalis and Numedii

 

8:55 Benjamin Franklin Award & Laureate Presentation

9:15 Best Practices Award Program

9:45 Coffee Break in the Exhibit Hall with Poster Viewing

 

PHARMACODYNAMIC MODELS

10:50 Chairperson’s Remarks

» Featured Speaker

11:00 Systems Pharmacology in a Post-Genomic Era

Peter Sorger, Ph.D., Professor, Systems Biology, Harvard Medical School; Co-Chair, Harvard Initiative in Systems Pharmacology

I will describe the emergence of “systems pharmacology” as a means to guide the creation of new molecular matter, study cellular networks and their perturbation by drugs, understand pharmaco-kinetics and pharmaco-dynamics in mouse and man and design and analyze clinical trial data. The approach combines mathematical modeling with empirical measurement as a means to tackle basic and clinical problems in pharmacology. Ultimately we aim for models that describe drug responses at multiple temporal and physical scales from molecular mechanism to whole-organism physiology.

11:30 Using Quantitative Systems Pharmacology for De-Risking Projects in CNS R&D

Hugo Geerts, Ph.D., CSO, Computational Neuropharmacology, In Silico Biosciences

Quantitative Systems Pharmacology is a computer based mechanistic modeling approach combining physiology, the functional imaging of genetics with the pharmacology of drug-receptor interaction and parameterized with clinical data and is a possible powerful tool for improving the success rate of CNS R&D projects. The presentation will include failure analyses of unsuccessful clinical trials, correct prospective identification of clinical problems that halted clinical development and estimation of genotype effects on the pharmacodynamics of candidate drugs.

Thomson Reuters logo12:00 pm Systems Pharmacology Approaches to Drug Repositioning

Svetlana Bureeva, Ph.D., Director, Professional Services, Thomson Reuters, IP & Science

Drug repositioning requires advanced computational approaches and comprehensive knowledgebase information to reach success. Thomson Reuters will present on recent advances in drug repositioning approaches, their validation and performance, best practices in using systems biology content, and successful case studies.

12:30 Luncheon Presentation (Sponsorship Opportunity Available) or Lunch on Your Own

 

HIGH CONTENT ANALYSIS: CANCER CELL LINES

1:40 Chairperson’s Remarks

1:45 Systems Pharmacology Using CellMiner and the NCI-60 Cancerous Cell Lines

William Reinhold, Manager, Genomics and Bioinformatics Group, Laboratory of Molecular Pharmacology (LMP), National Cancer Institute (NCI)

CellMiner is a web-based application that allows rapid access to and comparison between 20,503 compound activities and the expression levels of 26,065 genes and 360 microRNAs. Included are 102 FDA-approved drugs as well as 53 in clinical trials. The tool is designed for the non-informatisist, and allows the user wide latitude in defining the question of interest. This opens the door to systems pharmacological studies for physicians, molecular biologists and others without bioinformatics expertise.

2:15 Oncology Drug Combinations at Novartis

Joseph Lehár, Ph.D., Associate Director, Bioinformatics, Oncology Translational Research, Novartis; Adjunct Assistant Professor, Bioinformatics, Boston University

Novartis is undertaking a large-scale effort to comprehensively describe cancer through the lens of cell cultures and tissue samples.  In collaboration with academic and industrial partners, we have generated mutation status, gene copy number, and gene expression data for a library of 1,000 cancer cell lines, representing most cancer lineages and common genetic backgrounds.  Most of these cell lines have been tested for chemosensitivity against ~1,200 cancer-relevant compounds, and we are systematically exploring drug combinations for synergy against ~100 prioritized CCLE lines.  We expect this large-scale campaign to enable efficient patient selection for clinical trials on existing cancer drugs, reveal many therapeutically promising drug synergies or anti-resistance combinations, and provide unprecedented detail on functional interactions between cancer signaling pathways.   I will discuss early highlights of this work and describe our plans to make use of this resource.

2:45 Sponsored Presentations (Opportunities Available)

3:15 Refreshment Break in the Exhibit Hall with Poster Viewing

 

PHARMACODYNAMIC MODELS FOR ONCOLOGY

3:45 Systems Biology in Cancer Immunotherapy: Applications in the Understanding of Mechanism of Action and Therapeutic Response

Debraj Guha Thakurta, Ph.D., Senior Scientist II & Group Leader, Systems Biology, Dendreon Corporation

We are using high-content platforms (DNA and protein microarrays, RNA-seq) in various stages of the development of cellular immunotherapies for cancer. We will provide examples of genomic applications that can aid in the mechanistic understanding and the discovery of molecular markers associated with the efficacy of a cancer immunotherapy..

4:15 Use of Systems Pharmacology to Aid Cancer Clinical Development

Anna Georgieva Kondic, Ph.D., MBA, Senior Principal Scientist, Modeling and Simulation, Merck Research Labs

The last few years have seen an increased use of physiologically-based pharmacokinetics and pharmacodynamics models in Oncology drug development. This is partially due to an improved mechanistic understanding of disease drivers and the collection of better patient-level quantitative data that lends itself to modeling. In this talk, a suite of studies where systems modeling was successfully used to inform either preclinical to clinical transition or clinical study design will be presented. The talk will complete with a potential systems pharmacology framework that can be used systematically in drug development.

4:45 Sponsored Presentations (Opportunities Available)

5:15 Best of Show Awards Reception in the Exhibit Hall

6:15 Exhibit Hall Closes

 

Thursday, April 11

7:00 am Breakfast Presentation (Sponsorship Opportunity Available) or Morning Coffee

 

MODELING AND MINING TARGETS

8:45 Chairperson’s Opening Remarks

8:50 Systems Biology Approach for Identification of New Targets and Biomarkers

I-Ming Wang, Ph.D., Associate Scientific Director, Research Solutions and Bioinformatics, Informatics and Analysis, Merck Research Laboratory

A representative gene signature was identified by an integrated analysis of expression data in twelve rodent inflammatory models/tissues. This “inflammatome” signature is highly enriched in known drug target genes and is significantly overlapped with macrophage-enriched metabolic networks (MEMN) reported previously. A large proportion of genes in this signature are tightly connected in several tissue-specific Bayesian networks built from multiple mouse F2 crosses and human tissue cohorts; furthermore, these tissue networks are very significantly overlapped. This indicates that variable expression in this set of co-regulated genes is the main driver of many disease states. Disease-specific gene sets with the potential of being utilized as biomarkers were also identified with the approach we applied. The identification of this “inflammatome” gene signature extends the coverage of MEMN beyond adipose and liver in the metabolic disease to multiple diseases involving various affected tissues.

9:20 Optimizing Therapeutic Index (TI) by Exploring Co-Dependencies of Target and Therapeutic Properties

Madhu Natarajan, Ph.D., Associate Director, Computational Biology, Discovery Research, Shire HGT

Conventional drug-discovery informatics workflows employ combinations of mechanistic/probabilistic in-silico methods to rank lists of targets; therapeutics are then developed for “optimal” targets. I describe a systems pharmacology approach that instead integrates systematic in-silico therapeutic perturbation with models of target/disease biology to identify conditions for optimal TI; non-intuitively optimal TI is sometimes achieved by pairing sub-optimal targets with therapeutics having appropriate properties.

9:50 Sponsored Presentations (Opportunities Available)

10:20 Coffee Break in the Exhibit Hall and Poster Competition Winners Announced

10:45 Plenary Keynote Panel Chairperson’s Remarks

Kevin Davies, Ph.D., Editor-in-Chief, Bio-IT World

10:50 Plenary Keynote Panel Introduction

Yury Rozenman, Head of BT for Life Sciences, BT Global Services

 

» PLENARY KEYNOTE PANEL

11:05 The Life Sciences CIO Panel

Panelists:
Remy Evard, CIO, Novartis Institutes for BioMedical Research
Martin Leach, Ph.D., Vice President, R&D IT, Biogen Idec
Andrea T. Norris, Director, Center for Information Technology (CIT) and Chief Information Officer, NIH
Gunaretnam Rajagopal, Ph.D., Vice President and CIO, Bioinformatics & External Innovation at Janssen Pharmaceutical Companies of Johnson & Johnson
Cris Ross, Chief Information Officer, Mayo Clinic

 

12:15 pm Luncheon in the Exhibit Hall with Poster Viewing

 

MODELING MOLECULAR AND PATHOPHYSIOLOGICAL DATA

1:55 Chairperson’s Remarks

2:00 Predicting Adverse Side Effects of Drugs Using Systems Pharmacology

Jake Chen, Ph.D., Associate Professor, Indiana University School of Informatics & Purdue University Department of Computer Science; Director, Indiana Center for Systems Biology and Personalized Medicine

A new way of studying drug toxicity is to incorporate biomolecular annotation and network data with clinical observations of drug targets upon drug perturbations. I will describe the development of a novel computational modeling framework, with which we demonstrated the highest drug toxicity prediction accuracies ever reported by far. Adoption of this framework may have profound practical drug discovery implications.

2:30 Holistic Integration of Molecular and Physiological Data and Its Application in Personalized Healthcare

David de Graaf, Ph.D. President and CEO, Selventa

There are multiple industry-wide challenges in aggregating molecular and pathophysiological data for systems pharmacology to transform the process of drug discovery and development. One of the ways to address these challenges is to utilize a common computable biological expression language (BEL) that can provide a comprehensive knowledge network for new discoveries. An application of BEL and its use in identifying clinically relevant predictive biomarkers for patient stratification will be presented.

3:00 The Role of Informatics in ADME Pharmacogenetics

Boyd SteereBoyd Steere, Ph.D., Senior Research Scientist, Lilly Research Laboraories, IT Research Informatics, Eli Lilly

The leveraging of pharmacogenetics to support decisions in early-phase clinical trial design requires informatics methods to integrate, visualize, and analyze heterogeneous data sets from many different discovery platforms.  This presentation describes challenges and solutions in making sense of diverse sets of genetic, protein, and metabolic data in support of ADME pharmacology projects.

3:30 A Systems Pharmacology Approach to Understand and Optimize Functional Selectivity for Non-Selective Drugs

Joshua Apgar, Principal Scientist, Systems Biology, Dept. of Immunology & Inflammation, Boehringer Ingelheim Pharmaceuticals, Inc.

Most commonly the selectivity of a compound is defined in an in vitro or cellular assay, and it is thought of as principally a function of the binding energy of the drug to its on-target and off-target proteins; however, in vivo functional selectivity is much more complicated, and is affected by systems level effects such as multiple feedback processes within and between the various on- and off-target pathways. These systems level processes are often impossible to reconstruct in vitro as they involve many cell types, tissues, and organs systems throughout the body. We show here that through mathematical modeling we were able to identify, in silico, molecular properties that are critical to driving functional selectivity. The models, although simple, capture the key systems pharmacology needed to understand the on- an off- target effects. Surprisingly, in this case, the key driver of functional selectivity is not the affinity of the drugs but rather the pharmacokinetics, with drugs having a short half-life predicted to be the most functionally selective.

 SOURCE:
Final Agenda

 

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Author and Reporter: Anamika Sarkar, Ph.D.

Nitric Oxide (NO) is highly regulated in the blood such that it can be released as vasodilator when needed. The importance and pathway of Nitric Oxide has been nicely reviewed by. “Discovery of NO and its effects of vascular biology”. Other articles which are good readings for the importance of NO are  – a) regulation of glycolysis b) NO in cardiovascular disease c) NO and Immune responses Part I and Part II d) NO signaling pathways. The  effects of NO in diseased states have been reviewed by the articles – “Crucial role of Nitric Oxide in Cancer”, “Nitric Oxide and Sepsis, Hemodynamic Collapse, and the Search for Therapeutic Options”.. (Also, please see Source for more articles on NO and its significance).

Computational models are very efficient tools to understand complex reactions like NO towards physiological conditions. Among them wall shear stress is one of the major factors which is reviewed in the article – “Differential Distribution of Nitric Oxide – A 3-D Mathematical Model”.

Moreover, decrease in availability of NO can lead to many complications like pulmonary hypertension. Some of the causes of decrease in NO have been identified as clinical hypertension, right ventricular overload which can lead to cardiac heart failure, low levels of zinc and high levels of cardiac necrosis.

Sickle Cell disease patients, a hereditary disease, are also known to have decreased levels of NO which can become physiologically challenging. In USA alone, there are 90,000 people who are affected by Sickle cell disease.

Sickle cell disease is breakage of red blood cells (RBC) membrane and resulting release of the hemoglobin (Hb) into blood plasma. This process is also known as Hemolysis. Sickle cell disease is caused by single mutation of Hb which changes RBC from round shape to sickle or crescent shapes (Figure 1).

Image

Figure 1 (A) shows normal red blood cells flowing freely through veins. The inset shows a cross section of a normal red blood cell with normal hemoglobin. Figure 1 (B) shows abnormal, sickled red blood cells The inset image shows a cross-section of a sickle cell with long polymerized HbS strands stretching and distorting the cell shape. Image Source: http://en.wikipedia.org/wiki/Sickle-cell_disease

Sickle Cell RBCs has much shorter life span of 10-20 days when compared with normal RBCs 100-120 days lifespan. Shorter life span of Sickle cell disease RBC’s are compensated by bone marrow generation of new RBCs. However, many times new blood generation cannot cope with the small life span of Sickle cell RBCs and causes pathological condition of Anemia.

RBCs generally breakdown and release Hbs in blood plasma after they reach their end of life span. Thus, in case of Sickle cell disease, there is more cell free Hb than normal. Furthermore, it is known that NO has a very high affinity towards Hbs, which is one of the ways free NO is regulated in blood. As a result presence of larger amounts of cell free Hb in Sickle cell disease lead to less availability of NO.

However, the question remained “what is the quantitative relationship between cell free Hb and depletion of NO. Deonikar and Kavdia (J. Appl. Physiol., 2012) addressed this question by developing a 2 dimensional Mathematical Model of a single idealized arteriole, with different layers of blood vessels diffusing nutrients to tissue layers (Figure 2:  Deonikar and Kavdia Figure 1).

Image

cell free Hb in 2 dimensional representations of blood vessels.

The authors used steady state partial differential equation of circular geometry to represent diffusion of NO in blood and in tissues. They used first and second order biochemical reactions to represent the reactions between NO and RBC and NO autooxidation processes. Some of their reaction model parameters were obtained from literature, rest of them were fitted to experimental results from literature. The model and its parameters are explained in the previously published paper by same authors Deonikar and Kavdia, Annals of Biomed., 2010. The authors found that the reaction rate between NO and RBC is 0.2 x 105, M-1 s-1 than 1.4 x 105, M-1 s-1 as reported before by Butler et.al., Biochim. Biophys. Acta, 1998.

Their results show that even small increase in cell free Hb, 0.5uM, can decrease NO concentrations by 3-7 folds approximately (comparing Fig1(b) and 1(d) of Deonikar and Kavdia, 2012, as shown in Figure 2 of this article). Moreover, their mathematical analysis shows that the increase in diffusion resistance of NO from vascular lumen to cell free zone has no effect on NO distribution and concentration with available levels of cell free Hb.

Deonikar and Kavdia’s mathematical model is a simple representation of actual physiological scenario. However, their model results show that for Sickle cell disease patients, decrease in levels of bioavailable NO is an attribute to cell free Hb, which is in abundant for these patients. Their results show that small increase by 0.5 uM in cell free Hb can cause large decrease in NO concentrations.

These interesting insights from the model can help in further understanding in the context of physiological conditions, by replicating experiments in-vivo and then relating them to other known diseases of Sickle cell disease patients like Anemia, Pulmonary Hypertension. Further, drugs can be targeted towards decreasing free cell Hbs to keep balance in availability of NO, which in turn may help in other related disease like Pulmonary Hypertension of Sickle Cell disease patients.

Sources:

Deonikar and Kavdia (2012) :http://www.ncbi.nlm.nih.gov/pubmed/22223452

Previous model explaining mathematical representation and parameters used in the model :Deonikar and Kavdia, Annals of Biomed., 2010.

Previous paper stating reaction rate of Hb and NO: Butler et.al., Biochim. Biophys. Acta, 1998.

Causes of decrease in NO

Clinical Hypertension : http://www.ncbi.nlm.nih.gov/pubmed/11311074

Right ventricular overload : http://www.ncbi.nlm.nih.gov/pubmed/9559613

Low levels of zinc and high levels of cardiac necrosis : http://www.ncbi.nlm.nih.gov/pubmed/11243421

Sickle Cell Source:

http://en.wikipedia.org/wiki/Sickle-cell_disease

http://www.nhlbi.nih.gov/health/health-topics/topics/sca/

NO Source:

Differential Distribution of Nitric Oxide – A 3-D Mathematical Model:

Discovery of NO and its effects of vascular biology

Nitric Oxide has a ubiquitous role in the regulation of glycolysis -with a concomitant influence on mitochondrial function

Nitric oxide: role in Cardiovascular health and disease

NO signaling pathways

Nitric Oxide and Immune Responses: Part 1

Nitric Oxide and Immune Responses: Part 2

Statins’ Nonlipid Effects on Vascular Endothelium through eNOS Activation

https://pharmaceuticalintelligence.com/2012/10/08/statins-nonlipid-effects-on-vascular-endothelium-through-enos-activation/

Inhibition of ET-1, ETA and ETA-ETB, Induction of NO production, stimulation of eNOS and Treatment Regime with PPAR-gamma agonists (TZD): cEPCs Endogenous Augmentation for Cardiovascular Risk Reduction – A Bibliography

Nitric Oxide, Platelets, Endothelium and Hemostasis

Crucial role of Nitric Oxide in Cancer

The rationale and use of inhaled NO in Pulmonary Artery Hypertension and Right Sided Heart Failure

Nitric Oxide and Sepsis, Hemodynamic Collapse, and the Search for Therapeutic Options

NO Nutritional remedies for hypertension and atherosclerosis. It’s 12 am: do you know where your electrons are?

Clinical Trials Results for Endothelin System: Pathophysiological role in Chronic Heart Failure, Acute Coronary Syndromes and MI – Marker of Disease Severity or Genetic Determination?

Endothelial Function and Cardiovascular Disease

Interaction of Nitric Oxide and Prostacyclin in Vascular Endothelium

Endothelial Dysfunction, Diminished Availability of cEPCs,  Increasing  CVD Risk – Macrovascular Disease – Therapeutic Potential of cEPCs

Cardiovascular Disease (CVD) and the Role of agent alternatives in endothelial Nitric Oxide Synthase (eNOS) Activation and Nitric Oxide Production

 

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Author and Reporter: Anamika Sarkar, Ph.D.

Targeted therapies are proven approaches in Cancer and other complicated diseases. Degrees of activation of measured EGFR and ERB2/HER2 in cancer cells are thought of one of the ways to identify the scale of aggressiveness of cancer in tissues.  There are drugs, mostly for breast cancer, which targets inhibition of these receptors. Lapatinib (Tykerb, GSK – see Source for other targeted drugs) is the first drug which inhibits both EGFR and ERB2/HER2 gave hope to cancer patients, especially advanced ERB2-postive or metastatic breast cancer patients. Despite of proven high efficacy, Lapatinib didn’t show promising results in clinical responses due to acquired resistance.

Komurov et. al. (Mol. Systems.Biol., 2012) used network analysis along with experimental findings on cultured human breast cancer cell lines (SKBR3) and showed that a large part of acquired resistance to Lapatinib is due to  increased levels of activated states of glucose deprivation signaling network. The authors cultured ERB2-positive SKBR3 cells with increasing doses of Lapatinib, to make the control cell lines for analyzing their experimental results in comparison with (SKBR3- R),SKBR3-Resistant cells. Their Western Blot analysis showed that Lapatinib was successful to inhibit down signaling pathways to ERB2 and EGFR in both control and resistant cells however fails to induce apoptotic pathways in resistant cells when compared with the controlled cells.

To identify other factors which can influence the differential effects of Lapatinib on controlled and resistant cell lines, Komurov et. al. used a data biased random walk network analysis method called Netwalk (Komurov et. al. PLOS Comp Biol., 2010). Their method is data driven and based on comparative network analysis of gene expressions at different conditions rather than network analysis at one gene level. Their network analysis identified presence of high levels of genes which act as compensatory mechanisms for glucose deprivation (as shown in Figure 2 of the paper Komurov et. al. (2012) Figure 2). They showed validation of their network analysis findings using Western Blot analysis (as shown in Figure 3 of the paper Komurov et.al. (2012) Figure 3).

 

The authors’ results not only show a nice elegant way of finding new information using network analysis and experimental techniques together, but also points out an important concept which can be future of cancer therapy. Their results show that along with targeting mutated Oncogenes eg., EGFR and ERB2/HER2 as in case of Lapatinib, additional way of controlling the pathway of deprivation of glucose, can achieve better clinical responses for cancer patients with aggressive levels of cancer. Targeting glucose or pathways of glucose can be tricky, because of its ubiquitous links to many physiological functions, including metabolism. However, the levels at which these pathways need to be targeted to achieve certain positive responses at in-vitro, supported by systems biology methods, and then in-vivo studies can be informative.  Moreover, targeting many parts in the network in smaller amounts, along with targeted cancer drugs, may produce interesting results.

Sources:

Komurov et.al. (2012) : http://www.ncbi.nlm.nih.gov/pubmed/22864381

A News and Views on Lapatinib (2005) : http://www.emilywaltz.com/Herceptin.pdf

Komurov et.al. (2010) – Article published on methods of Netwalk : http://www.ncbi.nlm.nih.gov/pubmed/20808879

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Reporter: Aviva Lev-Ari, PhD, RN

NATIONAL CENTERS FOR BIOMEDICAL COMPUTING

An overarching approach to several disciplines:

  • Other Genomics related subdisciplines:
  • The Biomedical Computing Space

An illustration of the systems approach to biology

http://en.wikipedia.org/wiki/Systems_biology

 

The National Centers for Biomedical Computing (NCBCs) are part of the U.S. NIH plan to develop and implement the core of a universal computing infrastructure that is urgently needed to speed progress in biomedical research. Their mission is to create innovative software programs and other tools that will enable the biomedical community to integrate, analyze, model, simulate, and share data on human health and disease.

Biomedical Information Science and Technology Initiative (BISTI): Recognizing the potential benefits to human health that can be realized from applying and advancing the field of biomedical computing, the Biomedical Information Science and Technology Initiative (BISTI) was launched at the NIH in April 2000. This initiative is aimed at making optimal use of computer science and technology to address problems in biology and medicine. The full text of the original BISTI Report (June 1999) is available.

Current Centers

SimBioS
National Center for Simulation of Biological Structures (SimBioS) at Stanford University
MAGNet
National Center for the Multiscale Analysis of Genomic and Cellular Networks (MAGNet) at Columbia University
NA-MIC Logo
National Alliance for Medical Image Computing (NA-MIC) at Brigham and Women’s Hospital, Boston, MA
I2B2
Integrating Biology and the Bedside (I2B2) at Brigham and Women’s Hospital, Boston, MA
NCBO
National Center for Biomedical Ontology (NCBO) at Stanford University
IDASH
Integrate Data for Analysis, Anonymization, and Sharing (IDASH) at the University of California, San Diego

Biositemap is a way for a biomedical research institution of organisation to show how biological information is distributed throughout their Information Technology systems and networks. This information may be shared with other organisations and researchers.

The Biositemap enables web browserscrawlers and robots to easily access and process the information to use in other systems, media and computational formats. Biositemaps protocols provide clues for the Biositemap web harvesters, allowing them to find resources and content across the whole interlink of the Biositemap system. This means that human or machine users can access any relevant information on any topic across all organisations throughout the Biositemap system and bring it to their own systems for assimilation or analysis.

http://en.wikipedia.org/wiki/Biositemaps

http://www.ncbcs.org/

For

Genome and Genetics: Resources @Stanford, @MIT, @NIH’s NCBCS

go to

https://pharmaceuticalintelligence.com/2012/09/18/genome-and-genetics-resources/

 

Biomedical Computation Review (BCR) is a quarterly, open-access magazine funded by the National Institutes of Health and published by Simbios, one of the National Centers for Biomedical Computing located at Stanford University. First published in 2005, BCR covers such topics as molecular dynamicsgenomicsproteomicsphysics-based simulationsystems biology, and other research involvingcomputational biology. BCR’s articles are targeted to those with a general science or biology background, in order to build a community among biomedical computational researchers who come from a variety of disciplines.

http://en.wikipedia.org/wiki/Biomedical_Computation_Review

 

REFERENCES on BIOINFORMATICS

  1. ^ Biositemaps online editor
  2. a b Dinov ID, Rubin D, Lorensen W, et al. (2008). “iTools: A Framework for Classification, Categorization and Integration of Computational Biology Resources”PLoS ONE 3 (5): e2265. doi:10.1371/journal.pone.0002265PMC 2386255PMID 18509477.
  3. ^ M.L. Nelson, J.A. Smith, del Campo, H. Van de Sompel, X. Liu (2006). “Efficient, Automated Web Resource Harvesting”WIDM’06.
  4. ^ Brandman O, Cho J, Garcia-Molina HShivakumar N (2000). “Crawler-friendly Web Servers”ACM SIGMETRICS Performance Evaluation Review 28 (2). doi:10.1145/362883.362894.
  5. ^ Cannata N, Merelli E, Altman RB (December 2005). “Time to organize the bioinformatics resourceome”PLoS Comput. Biol. 1 (7): e76.doi:10.1371/journal.pcbi.0010076PMC 1323464PMID 16738704.
  6. ^ Chen YB, Chattopadhyay A, Bergen P, Gadd C, Tannery N (January 2007). “The Online Bioinformatics Resources Collection at the University of Pittsburgh Health Sciences Library System—a one-stop gateway to online bioinformatics databases and software tools”.Nucleic Acids Res. 35 (Database issue): D780–5. doi:10.1093/nar/gkl781PMC 1669712PMID 17108360.
 REFERENCES on GENOMICS

  1. ^ National Human Genome Research Institute (2010-11-08).“FAQ About Genetic and Genomic Science”Genome.gov. Retrieved 2011-12-03.
  2. ^ EPA Interim Genomics Policy
  3. ^ [1]
  4. ^ Min Jou W, Haegeman G, Ysebaert M, Fiers W (1972). “Nucleotide sequence of the gene coding for the bacteriophage MS2 coat protein”. Nature 237 (5350): 82–88. Bibcode1972Natur.237…82Jdoi:10.1038/237082a0.PMID 4555447.
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http://en.wikipedia.org/wiki/Genomics

 

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Author and Reporter: Anamika Sarkar, Ph.D.

Today, the gold standard treatment for cancer is still chemo therapy or radiation therapy. Drugs are administered to treat patients with different doses, frequencies and combinations. It is recognized that the side effects of all these therapies lead to DNA damage responses (DDR) and their subsequent signaling alterations resulting in cellular functions. Moreover, it is well known that DDR is responsible for complex cross talks and feedback of signaling pathways for progrowth and apoptosis within intracellular as well as extracellular networks (in tissues).

Optimal combinations of drugs in respect of doses or frequencies or order of treatments of different drugs have been recognized as a powerful method of treatment of complex diseases. However, executing experiments of multiple possible combinations of drugs and cell lines can easily lead to very costly proposition. Lee et.al in their paper published in Cell (2012), titled “Sequential Application of Anticancer Drugs Enhances Cell Death by Rewiring Apoptotic Signaling Networks”, reported from experimental results that when triple negative breast cancer (TNBC) cells are treated, with a combination of drugs  – erlotinib, which is an EGFR inhibitor, at least 4 hours before of another drug, doxorubicin – the cells show higher apoptotic (cell death) responses. Other forms of treatments like, single administration of the drugs or treating the cells together with two drugs at same time, did not show any increased levels of apoptosis in TNBC cells.

They complemented their understanding of reason behind such unique behavior of TNBC cells, when exposed to time -stagger treatment of drugs, with systems level modeling. They used quantitative analysis of high throughput reverse-phase protein microarrays and quantitative western blotting of experiments. They chose to measure activation states of 35 signaling proteins at 12 time points following exposure to ertolinib and doxorubicin individually and in combinations. The authors used PLS (Partial Least Square) and PCA (Principle Component Analysis) methods for predictive analysis from data driven model.

They report from their systems level analysis that time – stagger treatment of TNBC with two drugs ertolinib and doxorubicin activate Caspase 8, a key apoptotic signaling component, which remains absent in other combinations of treatments of drugs. They hypothesized that early treatment of ertolinib, inhibits EGFR responses, which increases levels of activated Caspase 8 and gets amplified after getting exposed to the second drug doxorubicin.

Combination therapy in treating complicated diseases like cancer has many importance in making the dose and treatment efficient. However, due to complex nature of signaling pathways, it poses increasing amount of challenges. Lee et. al., address some of those challenges by bringing in synergistic collaborations among different fields – experiments and mathematical modeling, which is the future of drug development.

Sources:

http://www.ncbi.nlm.nih.gov/pubmed/22579283

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