Feeds:
Posts
Comments

Posts Tagged ‘systems biology’


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

Read Full Post »


 

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.
  5. ^ Fiers W, Contreras R, Duerinck F, Haegeman G, Iserentant D, Merregaert J, Min Jou W, Molemans F, Raeymaekers A, Van den Berghe A, Volckaert G, Ysebaert M (1976). “Complete nucleotide sequence of bacteriophage MS2 RNA: primary and secondary structure of the replicase gene”. Nature 260 (5551): 500–507.Bibcode 1976Natur.260..500Fdoi:10.1038/260500a0.PMID 1264203.
  6. ^ Sanger F, Air GM, Barrell BG, Brown NL, Coulson AR, Fiddes CA, Hutchison CA, Slocombe PM, Smith M (1977). “Nucleotide sequence of bacteriophage phi X174 DNA”. Nature 265 (5596): 687–695. Bibcode 1977Natur.265..687S.doi:10.1038/265687a0PMID 870828.
  7. ^ Fleischmann RD, Adams MD, White O, Clayton RA, Kirkness EF, Kerlavage AR, Bult CJ, Tomb JF, Dougherty BA, Merrick JM, et al. (1995). “Whole-genome random sequencing and assembly of Haemophilus influenzae Rd”. Science 269 (5223): 496–512.Bibcode 1995Sci…269..496Fdoi:10.1126/science.7542800.PMID 7542800.
  8. ^ “Complete genomes: Viruses”NCBI. 2011-11-17. Retrieved 2011-11-18.
  9. ^ “Genome Project Statistics”Entrez Genome Project. 2011-10-07. Retrieved 2011-11-18.
  10. ^ Hugenholtz, Philip (2002). “Exploring prokaryotic diversity in the genomic era”. Genome Biology 3 (2): reviews0003.1-reviews0003.8. ISSN 1465-6906.
  11. ^ BBC article Human gene number slashed from Wednesday, 20 October 2004
  12. ^ CBSE News, Thursday, 16 October 2003
  13. ^ National Human Genome Research Institute (2004-07-14).“Dog Genome Assembled: Canine Genome Now Available to Research Community Worldwide”Genome.gov. Retrieved 2012-01-20.
  14. ^ McGrath S and van Sinderen D, ed. (2007). Bacteriophage: Genetics and Molecular Biology (1st ed.). Caister Academic Press. ISBN 978-1-904455-14-1.
  15. ^ Herrero A and Flores E, ed. (2008). The Cyanobacteria: Molecular Biology, Genomics and Evolution (1st ed.). Caister Academic Press. ISBN 978-1-904455-15-8.
  16. ^ McElheny, Victor (2010). Drawing the map of life : inside the Human Genome Project. New York NY: Basic Books. ISBN 978-0-465-04333-0.
  17. ^ Hugenholz, P; Goebel BM, Pace NR (1 September 1998).“Impact of Culture-Independent Studies on the Emerging Phylogenetic View of Bacterial Diversity”J. Bacteriol 180 (18): 4765–74. PMC 107498PMID 9733676.
  18. ^ Eisen, JA (2007). “Environmental Shotgun Sequencing: Its Potential and Challenges for Studying the Hidden World of Microbes”PLoS Biology 5 (3): e82.doi:10.1371/journal.pbio.0050082PMC 1821061.PMID 17355177.
  19. ^ Marco, D, ed. (2010). Metagenomics: Theory, Methods and Applications. Caister Academic Press. ISBN 978-1-904455-54-7.
  20. ^ Marco, D, ed. (2011). Metagenomics: Current Innovations and Future TrendsCaister Academic PressISBN 978-1-904455-87-5.
  21. ^ Wang L (2010). “Pharmacogenomics: a systems approach”.Wiley Interdiscip Rev Syst Biol Med 2 (1): 3–22.doi:10.1002/wsbm.42PMID 20836007.
  22. ^ Becquemont L (June 2009). “Pharmacogenomics of adverse drug reactions: practical applications and perspectives”.Pharmacogenomics 10 (6): 961–9. doi:10.2217/pgs.09.37.PMID 19530963.
  23. ^ “Guidance for Industry Pharmacogenomic Data Submissions” (PDF). U.S. Food and Drug Administration. March 2005. Retrieved 2008-08-27.
  24. ^ Squassina A, Manchia M, Manolopoulos VG, Artac M, Lappa-Manakou C, Karkabouna S, Mitropoulos K, Del Zompo M, Patrinos GP (August 2010). “Realities and expectations of pharmacogenomics and personalized medicine: impact of translating genetic knowledge into clinical practice”.Pharmacogenomics 11 (8): 1149–67. doi:10.2217/pgs.10.97.PMID 20712531.

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

 

Read Full Post »


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

Read Full Post »

« Newer Posts