R-3 Repository :: Browsing by Author "Qutub, Amina A."

Browsing by Author "Qutub, Amina A."

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A Crowdsourcing Approach to Developing and Assessing Prediction Algorithms for AML Prognosis
(Public Library of Science, 2016) Noren, David P.; Long, Byron L.; Norel, Raquel; Rrhissorrakrai, Kahn; Hess, Kenneth; Hu, Chenyue Wendy; Bisberg, Alex J.; Schultz, Andre; Engquist, Erik; Liu, Li; Lin, Xihui; Chen, Gregory M.; Xie, Honglei; Hunter, Geoffrey A.M.; Boutros, Paul C.; Stepanov, Oleg; DREAM 9 AML-OPC Consortium; Norman, Thea; Friend, Stephen H.; Stolovitzky, Gustavo; Kornblau, Steven; Qutub, Amina A.
Acute Myeloid Leukemia (AML) is a fatal hematological cancer. The genetic abnormalities underlying AML are extremely heterogeneous among patients, making prognosis and treatment selection very difficult. While clinical proteomics data has the potential to improve prognosis accuracy, thus far, the quantitative means to do so have yet to be developed. Here we report the results and insights gained from the DREAM 9 Acute Myeloid Prediction Outcome Prediction Challenge (AML-OPC), a crowdsourcing effort designed to promote the development of quantitative methods for AML prognosis prediction. We identify the most accurate and robust models in predicting patient response to therapy, remission duration, and overall survival. We further investigate patient response to therapy, a clinically actionable prediction, and find that patients that are classified as resistant to therapy are harder to predict than responsive patients across the 31 models submitted to the challenge. The top two performing models, which held a high sensitivity to these patients, substantially utilized the proteomics data to make predictions. Using these models, we also identify which signaling proteins were useful in predicting patient therapeutic response.
Advances in Glioblastoma Multiforme Treatment: New Models for Nanoparticle Therapy
(Frontiers, 2018) Ozdemir-Kaynak, Elif; Qutub, Amina A.; Yesil-Celiktas, Ozlem
The most lethal form of brain cancer, glioblastoma multiforme, is characterized by rapid growth and invasion facilitated by cell migration and degradation of the extracellular matrix. Despite technological advances in surgery and radio-chemotherapy, glioblastoma remains largely resistant to treatment. New approaches to study glioblastoma and to design optimized therapies are greatly needed. One such approach harnesses computational modeling to support the design and delivery of glioblastoma treatment. In this paper, we critically summarize current glioblastoma therapy, with a focus on emerging nanomedicine and therapies that capitalize on cell-specific signaling in glioblastoma. We follow this summary by discussing computational modeling approaches focused on optimizing these emerging nanotherapeutics for brain cancer. We conclude by illustrating how mathematical analysis can be used to compare the delivery of a high potential anticancer molecule, delphinidin, in both free and nanoparticle loaded forms across the blood-brain barrier for glioblastoma.
Decoupling Lineage-Associated Genes in Acute Myeloid Leukemia Reveals Inflammatory and Metabolic Signatures Associated With Outcomes
(Frontiers, 2021) Abbas, Hussein A.; Mohanty, Vakul; Wang, Ruiping; Huang, Yuefan; Liang, Shaoheng; Wang, Feng; Zhang, Jianhua; Qiu, Yihua; Hu, Chenyue W.; Qutub, Amina A.; Dail, Monique; Bolen, Christopher R.; Daver, Naval; Konopleva, Marina; Futreal, Andrew; Chen, Ken; Wang, Linghua; Kornblau, Steven M.
Acute myeloid leukemia (AML) is a heterogeneous disease with variable responses to therapy. Cytogenetic and genomic features are used to classify AML patients into prognostic and treatment groups. However, these molecular characteristics harbor significant patient-to-patient variability and do not fully account for AML heterogeneity. RNA-based classifications have also been applied in AML as an alternative approach, but transcriptomic grouping is strongly associated with AML morphologic lineages. We used a training cohort of newly diagnosed AML patients and conducted unsupervised RNA-based classification after excluding lineage-associated genes. We identified three AML patient groups that have distinct biological pathways associated with outcomes. Enrichment of inflammatory pathways and downregulation of HOX pathways were associated with improved outcomes, and this was validated in 2 independent cohorts. We also identified a group of AML patients who harbored high metabolic and mTOR pathway activity, and this was associated with worse clinical outcomes. Using a comprehensive reverse phase protein array, we identified higher mTOR protein expression in the highly metabolic group. We also identified a positive correlation between degree of resistance to venetoclax and mTOR activation in myeloid and lymphoid cell lines. Our approach of integrating RNA, protein, and genomic data uncovered lineage-independent AML patient groups that share biologic mechanisms and can inform outcomes independent of commonly used clinical and demographic variables; these groups could be used to guide therapeutic strategies.
Development of Dynamic DNA Probes for High-Content in situ Proteomic Analyses
(2012-09-05) Schweller, Ryan; Diehl, Michael R.; Qutub, Amina A.; Farach-Carson, Cindy
Dynamic DNA complexes are able to undergo multiple hybridization and dissociation events through a process called strand displacement. This unique property has facilitated the creation of programmable molecular detection systems and chemical logic gates encoded by nucleotide sequence. This work examines whether the ability to selective exchange oligonucleotides among different thermodynamically-stable DNA complexes can be harnessed to create a new class of imaging probes that permit fluorescent reporters to be sequentially activated (“turned on”) and erased (“turned off”). Here, dynamic DNA complexes detect a specific DNA-conjugated antibody and undergo strand displacement to liberate a quencher strand and activate a fluorescent reporter. Subsequently, incubation with an erasing complex allows the fluorophore to be stripped from the target strand, quenched, and washed away. This simple capability therefore allows the same fluorescent dyes to be used multiple times to detect different markers within the same sample via sequential rounds of fluorescence imaging. We evaluated and optimized several DNA complex designs to function efficiently for in situ molecular analyses. We also applied our DNA probes to immunofluorescence imaging using DNA-conjugated antibodies and demonstrated the ability to at least double the number of detectable markers on a single sample. Finally, the probe complexes were reconfigured to act as AND-gates for the detection of co-localized proteins. Given the ability to visualize large numbers of cellular markers using dynamic DNA probe complexes, high-content proteomic analyses can be performed on a single sample, enhancing the power of fluorescence imaging techniques. Furthermore, dynamic DNA complexes offer new avenues to incorporate DNA-based computations and logic for in situ molecular imaging and analyses.
Endothelial cells decode VEGF-mediated Ca2+ signaling patterns to produce distinct functional responses
(American Association for the Advancement of Science, 2016) Noren, David P.; Chou, Wesley H.; Lee, Sung Hoon; Qutub, Amina A.; Warmflash, Aryeh; Wagner, Daniel S.; Popel, Aleksander S.; Levchenko, Andre
A single extracellular stimulus can promote diverse behaviors among isogenic cells by differentially regulated signaling networks. We examined Ca2+ signaling in response to VEGF (vascular endothelial growth factor), a growth factor that can stimulate different behaviors in endothelial cells. We found that altering the amount of VEGF signaling in endothelial cells by stimulating them with different VEGF concentrations triggered distinct and mutually exclusive dynamic Ca2+ signaling responses that correlated with different cellular behaviors. These behaviors were cell proliferation involving the transcription factor NFAT (nuclear factor of activated T cells) and cell migration involving MLCK (myosin light chain kinase). Further analysis suggested that this signal decoding was robust to the noisy nature of the signal input. Using probabilistic modeling, we captured both the stochastic and deterministic aspects of Ca2+ signal decoding and accurately predicted cell responses in VEGF gradients, which we used to simulate different amounts of VEGF signaling. Ca2+ signaling patterns associated with proliferation and migration were detected during angiogenesis in developing zebrafish.
FGFR4 and β-Klotho in Metastatic Prostate Cancer
(2013-07-24) Shenefelt, Derek; Lwigale, Peter Yunju; Farach-Carson, Cindy; Carson, Daniel D.; Wagner, Daniel S.; Qutub, Amina A.
FGFR4 and β-Klotho in Metastatic Prostate Cancer by Derek LaMar Shenefelt Fibroblast growth factors and fibroblast growth factor receptors have been associated with the aggressiveness and progression of Prostate Cancer (PCa). Also, β-Klotho is a known co-receptor with FGFR4 for FGF19 in the liver however, the role of this co-receptor pair remains unclear in the setting of PCa. I demonstrated that FGFR4 and KLB mRNA and protein are highly expressed in PCa cells when compared to bone marrow stromal cells, a common site of metastasis. I also provide support for the association of FGFR4 and KLB in PCa, suggesting a functional co-receptor pair capable of altering cellular signaling. FGFR4-KLb may also provide some level of protection to PCa cells from chemotherapeutics. This analysis of FGFR4 and KLB expression and signaling in PCa has provided novel insights into phenotypic alterations during PCa progression while also providing new avenues of study to further explore the role and importance of this exciting co-receptor complex.
Identifying Image Derived Features of Radiation Therapy Response: Tumor and Normal Tissue
(2018-08-01) Tang, Tien T; Qutub, Amina A.; Gaber, M. Waleed
Brain tumors constitutes the second most common malignancy in children. Management of these tumors with surgical resection, radiation therapy and chemotherapy presents significant challenges, with cure rates lagging compared to other pediatric cancers. While the introduction of radiation therapy (RT) has significantly improved patient outcome, survivors are never the less prone to cognitive impairment and other radiation-induced side effects. Therefore early detection of treatment resistance and treatment side effects are important for treatment planning and patient prognosis. Monitoring of brain tumor’s response is commonly done using medical imaging techniques such as magnetic resonance (MR) and positron emission tomography (PET). In addition to the clinical value of providing information regarding tumor location, size, and metabolism, these images can also be further analyzed to extract quantitative imaging features which can provide additional information for tumor characterization that preserves the spatial and temporal heterogeneity of the tumor. In this work, texture analysis will be utilized to establish quantitative image features that will assist in understanding and predicting RT response of tumors and detection of radiation-induced normal tissue injury. Using preclinical models, quantitative image features will be mined from MR and PET scans in radioresponsive and radioresistant tumors to establish universal and tumor-specific imaging markers of treatment response. Furthermore we will establish imaging markers that will provide immediate readout of normal tissue injury and map out the long term changes caused by RT. The outcome of our research will provide clinicians with a toolset to predict, detect, and understand RT response in both tumor and normal tissue for the personalization of treatment for affected children.In this work, texture analysis will be utilized to establish quantitative image features that will assist in understanding and predicting RT response of tumors and detection of radiation-induced normal tissue injury. Using preclinical models, quantitative image features will be mined from MR and PET scans in radioresponsive and radioresistant tumors to establish universal and tumor-specific imaging markers of treatment response. Furthermore we will establish imaging markers that will provide immediate readout of normal tissue injury and map out the long term changes caused by RT. The outcome of our research will provide clinicians with a toolset to predict, detect, and understand RT response in both tumor and normal tissue for the personalization of treatment for affected children.
Image-based Classification of Tumor Type and Growth Rate using Machine Learning: a preclinical study
(Springer Nature, 2019) Tang, Tien T.; Zawaski, Janice A.; Francis, Kathleen N.; Qutub, Amina A.; Gaber, M. Waleed
Medical images such as magnetic resonance (MR) imaging provide valuable information for cancer detection, diagnosis, and prognosis. In addition to the anatomical information these images provide, machine learning can identify texture features from these images to further personalize treatment. This study aims to evaluate the use of texture features derived from T1-weighted post contrast scans to classify different types of brain tumors and predict tumor growth rate in a preclinical mouse model. To optimize prediction models this study uses varying gray-level co-occurrence matrix (GLCM) sizes, tumor region selection and different machine learning models. Using a random forest classification model with a GLCM of size 512 resulted in 92%, 91%, and 92% specificity, and 89%, 85%, and 73% sensitivity for GL261 (mouse glioma), U87 (human glioma) and Daoy (human medulloblastoma), respectively. A tenfold cross-validation of the classifier resulted in 84% accuracy when using the entire tumor volume for feature extraction and 74% accuracy for the central tumor region. A two-layer feedforward neural network using the same features is able to predict tumor growth with 16% mean squared error. Broadly applicable, these predictive models can use standard medical images to classify tumor type and predict tumor growth, with model performance, varying as a function of GLCM size, tumor region, and tumor type.
Inferring causal molecular networks: empirical assessment through a community-based effort
(Springer Nature, 2016) Hill, Steven M.; Heiser, Laura M.; Cokelaer, Thomas; Unger, Michael; Nesser, Nicole K.; Carlin, Daniel E.; Zhang, Yang; Sokolov, Artem; Paull, Evan O.; Wong, Chris K.; Graim, Kiley; Bivol, Adrian; Wang, Haizhou; Zhu, Fan; Afsari, Bahman; Danilova, Ludmila V.; Favorov, Alexander V.; Lee, Wai Shing; Taylor, Dane; Hu, Chenyue W.; Long, Byron L.; Noren, David P.; Bisberg, Alexander J.; HPN-DREAM Consortium; Mills, Gordon B.; Gray, Joe W.; Kellen, Michael; Norman, Thea; Friend, Stephen; Qutub, Amina A.; Fertig, Elana J.; Guan, Yuanfang; Song, Mingzhou; Stuart, Joshua M.; Spellman, Paul T.; Koeppl, Heinz; Stolovitzky, Gustavo; Saez-Rodriguez, Julio; Mukherjee, Sach
It remains unclear whether causal, rather than merely correlational, relationships in molecular networks can be inferred in complex biological settings. Here we describe the HPN-DREAM network inference challenge, which focused on learning causal influences in signaling networks. We used phosphoprotein data from cancer cell lines as well asﾠin silicoﾠdata from a nonlinear dynamical model. Using the phosphoprotein data, we scored more than 2,000 networks submitted by challenge participants. The networks spanned 32 biological contexts and were scored in terms of causal validity with respect to unseen interventional data. A number of approaches were effective, and incorporating known biology was generally advantageous. Additional sub-challenges considered time-course prediction and visualization. Our results suggest that learning causal relationships may be feasible in complex settings such as disease states. Furthermore, our scoring approach provides a practical way to empirically assess inferred molecular networks in a causal sense.
Multiplexed In Situ Immunofluorescence Using Dynamic DNA Complexes
(Wiley, 2012) Schweller, Ryan M.; Zimak, Jan; Duose, Dzifa Y.; Qutub, Amina A.; Hittelman, Walter N.; Diehl, Michael R.
Mycoplasma contamination of leukemic cell lines alters protein expression determined by reverse phase protein arrays
(Springer, 2018) Hoff, Fieke W.; Hu, Chenyue W.; Qutub, Amina A.; Qiu, Yihua; Graver, Elizabeth; Hoang, Giang; Chauhan, Manasi; de Bont, Eveline S.J.M.; Kornblau, Steven M.
Mycoplasma contamination is a major problem in cell culturing, potentially altering the results of cell line-based experiments in largely uncharacterized ways. To define the consequences of mycoplasma infection at the level of protein expression we utilized the reverse phase protein array technology to analyze the expression of 235 proteins in mycoplasma infected, uninfected post treatment, and never-infected leukemic cell lines. Overall, protein profiles of cultured cells remained relatively stable after mycoplasma infection. However, paired comparisons for individual proteins identified that 18.7% of the proteins significantly changed between the infected and the never-infected cell line samples, and that 14.0% of the proteins significantly altered between the infected and the post treatment samples. Six percent of the proteins were affected in the post treatment samples compared to the never-infected samples, and 7.2% compared to treated cells that had never had mycoplasma infection before. Proteins that were significantly altered in the infected cells were enriched for apoptotic signaling processes and auto-phosphorylation, suggesting an increased cellular stress and a decreased growth rate. In conclusion, this study shows that mycoplasma infection of leukemic cell lines alters the proteins expression levels, potentially confounding experimental results. This reinforces the need for regular testing of mycoplasma.
Predicting internal cell fluxes at sub-optimal growth
(BioMed Central Ltd, 2015) Schultz, André; Qutub, Amina A.
Background: Flux Balance Analysis (FBA) is a widely used tool to model metabolic behavior and cellular function. Applications of FBA span a breadth of research from synthetic engineering of biofuels to understanding evolutionary adaptations. FBA predicts metabolic reaction fluxes that optimize a given objective. This objective is generally defined for unicellular organisms by a theoretical reaction which simulates biomass production. FBA has been extremely successful at predicting in E. coli growth rates under different media and gene essentiality, amongst other things. In order to improve predictions, additional constraints are coupled with optimization of the biomass function. Studies have suggested, however, that unicellular organisms - like multicellular organisms - do not grow at optimal rates. To further improve FBA predictions, particularly of internal cell fluxes, new techniques to explore the sub-optimal solution space need to be developed. Results: We present an innovative FBA method called corsoFBA based on the optimization of protein cost at sub-optimal objective levels. Our method shows good agreement with experimental data of E. coli grown at different dilution rates. Maintaining the objective function close to its maximum value predicts metabolic states that closely resemble low dilution rates; while higher dilution rates can be mirrored by lowering the biomass production value. By using a modified version of Extreme Pathways, we are also able to quantify the energy production and overall protein cost for all possible pathways in the central carbon metabolism. Conclusion: Metabolic flux distributions at the optimal objective can be substantially different from the near-optimal distributions. Importantly, the behavior of E. coli central carbon metabolism can be better predicted by exploring the sub-optimal FBA solution space. The corsoFBA method presented here is able to predict the behavior of PEP Carboxylase, the glyoxylate shunt and the Entner-Doudoroff pathway at different glucose levels, a behavior not predicted by the minimization of metabolic steps and FBA alone. This technique can be used to better predict internal cell fluxes under different conditions, and corsoFBA will be of great help for the study of cells from multicellular organisms using Flux Balance Analysis.
Progeny Clustering: A Method to Identify Biological Phenotypes
(Nature Publishing Group, 2015) Hu, Chenyue W.; Kornblau, Steven M.; Slater, John H.; Qutub, Amina A.
Estimating the optimal number of clusters is a major challenge in applying cluster analysis to any type of dataset, especially to biomedical datasets, which are high-dimensional and complex. Here, we introduce an improved method, Progeny Clustering, which is stability-based and exceptionally efficient in computing, to find the ideal number of clusters. The algorithm employs a novel Progeny Sampling method to reconstruct cluster identity, a co-occurrence probability matrix to assess the clustering stability, and a set of reference datasets to overcome inherent biases in the algorithm and data space. Our method was shown successful and robust when applied to two synthetic datasets (datasets of two-dimensions and ten-dimensions containing eight dimensions of pure noise), two standard biological datasets (the Iris dataset and Rat CNS dataset) and two biological datasets (a cell phenotype dataset and an acute myeloid leukemia (AML) reverse phase protein array (RPPA) dataset). Progeny Clustering outperformed some popular clustering evaluation methods in the ten-dimensional synthetic dataset as well as in the cell phenotype dataset, and it was the only method that successfully discovered clinically meaningful patient groupings in the AML RPPA dataset.
progenyClust: an R package for Progeny Clustering
(The R Foundation, 2016) Hu, Chenyue W.; Qutub, Amina A.
Identifying the optimal number of clusters is a common problem faced by data scientists in various research fields and industry applications. Though many clustering evaluation techniques have been developed to solve this problem, the recently developed algorithm Progeny Clustering is a much faster alternative and one that is relevant to biomedical applications. In this paper, we introduce an R package progenyClust that implements and extends the original Progeny Clustering algorithm for evaluating clustering stability and identifying the optimal cluster number. We illustrate its applicability using two examples: a simulated test dataset for proof-of-concept, and a cell imaging dataset for demonstrating its application potential in biomedical research. The progenyClust package is versatile in that it offers great flexibility for picking methods and tuning parameters. In addition, the default parameter setting as well as the plot and summary methods offered in the package make the application of Progeny Clustering straightforward and coherent.
Proteomic Profiling of Acute Promyelocytic Leukemia Identifies Two Protein Signatures Associated with Relapse
(Wiley, 2019) Hoff, Fieke W.; Hu, Chenyue W.; Qutub, Amina A.; Qiu, Yihua; Hornbaker, Marisa J.; Bueso‐Ramos, Carlos; Abbas, Hussein A.; Post, Sean M.; Bont, Eveline S.J.M. de; Kornblau, Steven M.
Purpose: Acute promyelocytic leukemia (APL) is the most prognostically favorable subtype of Acute myeloid leukemia (AML). Defining the features that allow identification of APL patients likely to relapse after therapy remains challenging. Experimental Design: Proteomic profiling is performed on 20 newly diagnosed APL, 205 non‐APL AML, and 10 normal CD34+ samples using Reverse Phase Protein Arrays probed with 230 antibodies. Results: Comparison between APL and non‐APL AML samples identifies 8.3% of the proteins to be differentially expressed. Proteins higher expressed in APL are involved in the pro‐apoptotic pathways or are linked to higher proliferation. The “MetaGalaxy” approach that considers proteins in relation to other assayed proteins stratifies the APL patients into two protein signatures. All of the relapse patients (n = 4/4) are in protein signature 2 (S2). Comparison of proteins between the signatures shows significant differences in relative expression for 38 proteins. Protein expression summary plots suggest less translational activity in combination with a less proliferative character for S2 compared to signature 1. Conclusions and Clinical Relevance: This study provides a potential proteomic‐based classification of APL patients that may be useful for risk stratification and therapeutic guidance. Validation in a larger independent cohort is required.
Recapitulation and Modulation of the Cellular Architecture of a User-Chosen Cell of Interest Using Cell-Derived, Biomimetic Patterning
(American Chemical Society, 2015) Slater, John H.; Culver, James C.; Long, Byron L.; Hu, Chenyue W.; Hu, Jingzhe; Birk, Taylor F.; Qutub, Amina A.; Dickinson, Mary E.; West, Jennifer L.
Heterogeneity of cell populations can confound population-averaged measurements and obscure important findings or foster inaccurate conclusions. The ability to generate a homogeneous cell population, at least with respect to a chosen trait, could significantly aid basic biological research and development of high-throughput assays. Accordingly, we developed a high-resolution, image-based patterning strategy to produce arrays of single-cell patterns derived from the morphology or adhesion site arrangement of user-chosen cells of interest (COIs). Cells cultured on both cell-derived patterns displayed a cellular architecture defined by their morphology, adhesive state, cytoskeletal organization, and nuclear properties that quantitatively recapitulated the COIs that defined the patterns. Furthermore, slight modifications to pattern design allowed for suppression of specific actin stress fibers and direct modulation of adhesion site dynamics. This approach to patterning provides a strategy to produce a more homogeneous cell population, decouple the influences of cytoskeletal structure, adhesion dynamics, and intracellular tension on mechanotransduction-mediated processes, and a platform for high-throughput cellular assays.
Reconstruction of Tissue-Specific Metabolic Networks Using CORDA
(Public Library of Science, 2016) Schultz, André; Qutub, Amina A.
Human metabolism involves thousands of reactions and metabolites. To interpret this complexity, computational modeling becomes an essential experimental tool. One of the most popular techniques to study human metabolism as a whole is genome scale modeling. A key challenge to applying genome scale modeling is identifying critical metabolic reactions across diverse human tissues. Here we introduce a novel algorithm called Cost Optimization Reaction Dependency Assessment (CORDA) to build genome scale models in a tissue-specific manner. CORDA performs more efficiently computationally, shows better agreement to experimental data, and displays better model functionality and capacity when compared to previous algorithms. CORDA also returns reaction associations that can greatly assist in any manual curation to be performed following the automated reconstruction process. Using CORDA, we developed a library of 76 healthy and 20 cancer tissue-specific reconstructions. These reconstructions identified which metabolic pathways are shared across diverse human tissues. Moreover, we identified changes in reactions and pathways that are differentially included and present different capacity profiles in cancer compared to healthy tissues, including up-regulation of folate metabolism, the down-regulation of thiamine metabolism, and tight regulation of oxidative phosphorylation.
Shrinkage Clustering: a fast and size-constrained clustering algorithm for biomedical applications
(BMC, 2018) Hu, Chenyue W.; Li, Hanyang; Qutub, Amina A.
Background: Many common clustering algorithms require a two-step process that limits their efficiency. The algorithms need to be performed repetitively and need to be implemented together with a model selection criterion. These two steps are needed in order to determine both the number of clusters present in the data and the corresponding cluster memberships. As biomedical datasets increase in size and prevalence, there is a growing need for new methods that are more convenient to implement and are more computationally efficient. In addition, it is often essential to obtain clusters of sufficient sample size to make the clustering result meaningful and interpretable for subsequent analysis. Results: We introduce Shrinkage Clustering, a novel clustering algorithm based on matrix factorization that simultaneously finds the optimal number of clusters while partitioning the data. We report its performances across multiple simulated and actual datasets, and demonstrate its strength in accuracy and speed applied to subtyping cancer and brain tissues. In addition, the algorithm offers a straightforward solution to clustering with cluster size constraints. Conclusions: Given its ease of implementation, computing efficiency and extensible structure, Shrinkage Clustering can be applied broadly to solve biomedical clustering tasks especially when dealing with large datasets.
Simulation Predicts IGFBP2-HIF1α Interaction Drives Glioblastoma Growth
(Public Library of Science, 2015) Lin, Ka Wai; Liao, Angela; Qutub, Amina A.
Tremendous strides have been made in improving patients’ survival from cancer with one glaring exception: brain cancer. Glioblastoma is the most common, aggressive and highly malignant type of primary brain tumor. The average overall survival remains less than 1 year. Notably, cancer patients with obesity and diabetes have worse outcomes and accelerated progression of glioblastoma. The root cause of this accelerated progression has been hypothesized to involve the insulin signaling pathway. However, while the process of invasive glioblastoma progression has been extensively studied macroscopically, it has not yet been well characterized with regards to intracellular insulin signaling. In this study we connect for the first time microscale insulin signaling activity with macroscale glioblastoma growth through the use of computational modeling. Results of the model suggest a novel observation: feedback from IGFBP2 to HIF1α is integral to the sustained growth of glioblastoma. Our study suggests that downstream signaling from IGFI to HIF1α, which has been the target of many insulin signaling drugs in clinical trials, plays a smaller role in overall tumor growth. These predictions strongly suggest redirecting the focus of glioma drug candidates on controlling the feedback between IGFBP2 and HIF1α.
Systems approaches for synthetic biology: a pathway toward mammalian design
(Frontiers Media, 2013) Rekhi, Rahul; Qutub, Amina A.
We review methods of understanding cellular interactions through computation in order to guide the synthetic design of mammalian cells for translational applications, such as regenerative medicine and cancer therapies. In doing so, we argue that the challenges of engineering mammalian cells provide a prime opportunity to leverage advances in computational systems biology. We support this claim systematically, by addressing each of the principal challenges to existing synthetic bioengineering approaches—stochasticity, complexity, and scale—with specific methods and paradigms in systems biology. Moreover, we characterize a key set of diverse computational techniques, including agent-based modeling, Bayesian network analysis, graph theory, and Gillespie simulations, with specific utility toward synthetic biology. Lastly, we examine the mammalian applications of synthetic biology for medicine and health, and how computational systems biology can aid in the continued development of these applications.

Browsing by Author "Qutub, Amina A."

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