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.


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


An overarching approach to several disciplines:

  • Other Genomics related subdisciplines:
  • The Biomedical Computing Space

An illustration of the systems approach to 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

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




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

go to



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.




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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.



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