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A. Mahadevan, B.L. Long, C.W. Hu, D.T. Ryan, G.L. Britton, A. Ligeralde, A. Warmflash, J.T. Robinson, A.A. Qutub (2018) cytoNet: Network Analysis of Cell Communities, submitted.
bioRxiv preprint
cytoNet Cloud

NPCs_Dynamic1Quantitative evaluation of cell cycle dynamics in a differentiating neural stem cell community, A. Mahadevan, Qutub Lab, 2017
C.W. Hu, A.J. Bisberg, A.A. Qutub (2018) Visually Guided Clustering in Biowheel: An Integration of Semi-Supervised Clustering with Interactive Visualization, submitted.
Bioinformatics Peer Prize Winner 2017
bioRxiv preprint
video tutorial

A Biowheel graph displaying results of semi-supervised clustering of protein expression levels in leukemia cells. C.W. Hu, Qutub Lab, 2016
A. Mahadevan, N. Grandel, J. Robinson, A.A. Qutub (2018) Living Neural Networks: Dynamic Network Analysis of Developing Neural Progenitor Cells, submitted.
bioRxiv preprint

A developing human neuronal network stained for nestin (green), MAP2 (red), & nuclei (blue). A. Mahadevan, Qutub Lab, 2016
C.W. Hu, Y.H. Qiu, S.Y. Yoo, A. Ligeralde, K.R. Coombes, A.A. Qutub+, S.M. Kornblau+, +co-senior authors (2018) Quantifying Proteomic Heterogeneities and Hallmarks in Acute Myelogenous Leukemia (AML), submitted.

Quantitative hallmarks of acute myeloid leukemia are identified by MetaGalaxy analysis, which defines novel prognostic data structures based on proteomic signatures, C.W. Hu, Qutub Lab, 2017
E.O. Kaynak, A.A. Qutub, O.Y. Celiktas (2018) Advances in Glioblastoma Multiforme Treatment: New Models for Nanoparticle Therapy, Frontiers in Physiology, in press.

BBB_ElifCells of the blood-brain barrier present a challenge to drug delivery for brain cancer, E.O. Kaynak, 2017.
C. Lantos, S.M. Kornblau, A.A. Qutub (2018) Quantitative Morphological and Cytological Analyses in Leukemia. In: Hematology: Latest Research & Clinical Advances, in press.


Emerging methods integrate digital pathology with deep learning to map biopsy data to molecular information, C. Lantos, Qutub Lab, 2018.
F. Hoff, C.W. Hu, Y.H. Qiu, A. Ligeralde, E. de Bont, S.Y. Yoo, A.A. Qutub, T. Horton, S.M. Kornblau (2018) Recurrent Patterns of Protein Expression Signatures in Pediatric Acute Lymphoblastic Leukemia (ALL): Recognition and Therapeutic Guidance, Molecular Cancer Research, in press.

ALL_hypoxia.pngHypoxic response protein signatures in children with acute lymphoblastic leukemia, C.W. Hu, A. Ligeralde, Qutub Lab; F. Hoff, Kornblau & Horton Labs, 2018
A.D. van Dijk,  C.W. Hu, E.S.J.M. de Bont, Y.H. Qiu, F.W. Hoff, S.Y. Yoo, K.R. Coombes, A.A. Qutub, S.M. Kornblau (2018) Histone Modification Patterns using RPPA-based Profiling Predict Outcome in Acute Myeloid Leukemia Patients, Proteomics,  in press.

HistonePathway1Histone modification proteins’ expression patterns predict outcomes for acute myeloid leukemia, C.W. Hu, Qutub Lab, 2018
F.W. Hoff, C.W. Hu, Y.H. Qiu, S.Y. Yoo, H. Mahmud, E. S. J. M. de Bont, A.A. Qutub, T.M. Horton, S.M. Kornblau (2018) Recognition of Recurrent Protein Expression Patterns in Pediatric Acute Myeloid Leukemia Suggests New Therapeutic Targets, Molecular Cancer Research, in press.

AML_hypoxia_2.PNGHypoxic response protein signatures in children with acute myeloid leukemia, C.W. Hu, A. Ligeralde, Qutub Lab; F. Hoff, Kornblau & Horton Labs, 2018


C.W. Hu, A.A. Qutub (2017) Proteomics in Leukemia. In: Myeloid Leukemia, in press.


Analysis techniques & applications of clinical proteomic screening, C.W. Hu, Qutub Lab, 2017.
C.W. Hu, H. Li, A.A. Qutub (2018) Shrinkage Clustering: A Fast and Size-Constrained Algorithm for Biomedical Applications, BMC Bioinformatics 19: 19. (2017 WABI / Leibniz-Zentrum für Informatik Proceedings)

ShrinkageClustering_2017Shrinkage Clustering addresses three common challenges with clustering (top), and converges to the optimal number of clusters (5) regardless of starting cluster number (bottom), C.W. Hu, 2017.
B. Long, H.Q. Li, T. Tang, N.E. Grandel, A. Mahadevan, A. Abrego, K. Balotin, S.Y. Wong, J. Soto, S. Li, A.A. Qutub (2017) GAIN: A Graphical Method to Automatically Analyze Individual Neurite Outgrowths, J Neuroscience Methods 283: 62-71.
GAIN video tutorial

GAIN_NeuronCounting_GUI_LiGraphical interface for GAIN, showing traces of neurites (green) associated with the soma of neurons (white & red), Q. Li, Qutub Lab, 2016.
A. Quintás-Cardama, C.W. Hu, A.A. Qutub, Y.H. Qiu, X. Zhang, S. Post, N. Zhang, K. Coombes, S. M. Kornblau (2017) p53 Pathway Dysfunction is Highly Prevalent in Acute Myeloid Leukemia Independent of TP53 Mutational Status, Leukemia 6: 1296-1305

P53 and MDM signaling pathways in acute myeloid leukemia determine prognosis. C.W. Hu, Qutub Lab, S.M. Kornblau 2016
A. Schultz, S. Mehta, F. Hoff, C.W. Hu, T. Horton, S.M. Kornblau, A.A. Qutub (2017) Identifying Cancer-Specific Metabolic Signatures Using Constraint-Based Models, Pacific Symposium on Biocomputing 22: 485-496.

Pediatric acute leukemias are predicted to have distinct differences  in relative levels of flux through the ornithine decarboxylase (ORNDC) metabolic pathway. AML: acute myeloid leukemia;  ALL: acute lymphocytic leukemias. A. Schultz, Qutub Lab, 2016


D.P. Noren, W.H. Chou, S.H. Lee, A.A. Qutub, A. Warmflash, D.S. Wagner, A.S. Popel, A. Levchenko (2016) VEGF-Mediated Ca2+ Signaling Steers Endothelial Cell Phenotypes by a Combination of Stochastic and Deterministic Decoding, Science Signaling 9: ra20. (PDF)
Cover & Science Signaling Editor’s Choice
Faculty 1000

Brain vasculature of a developing zebrafish. D.P. Noren, Qutub Lab, 2015. Transgenic zebrafish (D. Wagner)

D.P. Noren, B. Long, R. Norel, K. Rhrissorrakrai, K. Hess, C.W. Hu, A.J. Bisberg, A. Schultz, E. Engquist, L. Liu, E. Lin, G. Chen, H. Xie, G. Hunter, P.C. Boutros, O. Stephanov, AML DREAM Consortium, T. Norman, S. Friend, G. Stolovitzky, S.M. Kornblau, A.A. Qutub (2016) A Crowdsourcing Approach to Developing and Assessing Prediction Algorithms for AML Prognosis, PLOS Computational Biology, 12: e1004890. (PDF)
Press Release


S.M. Hill, L. Heiser, T. Cokelear, M. Unger, D. Carlin, Y. Zhang, A. Sokolov, E. Paul, C.K. Wong, K. Graim, A. Bivol, H. Wang, F. Zhu, B. Afsari, L.V. Danilova, A.V. Favorov, W.S. Lee, D. Taylor, C.W. Hu, D.P. Noren, B.L. Long, A.J. Bisberg, HPN-DREAM Consortium, G.B. Mills, J.W. Gray, M. Kellen, T. Norman, S. Friend, A.A. Qutub, E.J. Fertig, Y. Guan, M. Song, J. Stuart, H. Koeppl, P.T. Spellman, G. Stolovitzky, J.S.-Rodriguez, S. Mukherjee (2016) Empirical Assessment of Causal Network Inference through A Community-Based Effort, Nature Methods, 13: 310-318.
DREAM 8 Breast Cancer Interactive Data Portal
Highlights Biowheel

A. Schultz, A.A. Qutub (2016) Reconstruction of Tissue-Specific Metabolic Networks Using CORDA, PLOS Computational Biology, 12 (3).

CORDA provides tissue-specific models of metabolism as a function of gene & protein expression profiles. A. Schultz, Qutub Lab, 2016
C.W. Hu, A.A. Qutub (2016) progenyClust: an R package for Progeny Clustering, The R Journal, in press.

Progeny Clustering is a computationally efficient algorithm to identify the ideal number of groups from high-dimensional data.
C.W. Hu, Qutub Lab, 2016
L. Liu, Y. Chang, T. Yang, D.P. Noren, B.L. Long, S.M. Kornblau, A.A. Qutub, J. Ye (2016) Evolution-Informed Modeling Improves Outcome Prediction for Cancers, Evolutionary Applications, 10 (1), 68-76. (PDF)

Evolutionary-informed modeling prioritizes genes and proteins that have been conserved longer in evolutionary time. L. Liu, 2016


C.W. Hu, S.M. Kornblau, J.H. Slater, A.A. Qutub (2015) Progeny Clustering: A Method to Identify Biological Phenotypes, Scientific Reports 5: 12894.
R package available
Highlighted in Health Data Management


Progeny Clustering identifies the ideal number of four clusters for patterned endothelial cells. C.W. Hu, Qutub Lab, 2015
J.H. Slater, J.C. Culver, B. Long, C.W. Hu, J. Hu, T. Birk, A.A. Qutub, M.E. Dickinson, J.L. West (2015) Recapitulation of the Cellular Architecture of a User-Chosen Cell-of-Interest Using Cell-Derived, Biomimetic Patterning, ACS Nano, 9: 6128–6138.

Patterning an endothelial cell of interest. J. Slater, 2015.
A. Schultz, A.A. Qutub (2015) Predicting Internal Cell Fluxes at Sub-Optimal Growth, BMC Systems Biology, 9: 18.
Highlighted by BMC as one of the best papers of 2015

corsoFBA is an algorithm based on thermodynamic costs which improves predictions of metabolic fluxes in mammalian cells. A. Schultz, Qutub Lab, 2016
K.W. Lin, A. Liao, A.A. Qutub (2015) Simulation Predicts IGFBP2-HIF1 Signaling Drives Glioblastoma Growth, PloS Computational Biology, 11: e1004169.
Highlighted in JAMA, 2015, 313: 2114

Glioblastoma growth depends on crosstalk between insulin and hypoxic response signaling. Image: K. Bucher, JAMA 2015 highlighting work by K.W. Lin, Qutub Lab, 2015
R. Rekhi, D. Ryan, B. Zaunbrecher, C.W. Hu, A.A. Qutub (2015) Computational Cell Phenotyping in the Lab, Plant and Clinic. In: Computational Bioengineering. (Zhang G, ed.), CRC Press, pp. 265-292.
Patent Pending Technology

Phenotyping endothelial cells, where the orientation angles of actin fibers are displayed by color. B. Long, D. Ryan, T. Birk, et al., Qutub Lab, 2015 (b&w cell: J. Slater)
A.A. Qutub, A.S. Popel (2015) Angiogenesis: Mathematical and Computational Modeling, Encyclopedia of Applied and Computational Mathematics. (B. Engquist, ed.), Springer, pp 58-67.

Processes in angiogenesis from intracellular to tissue. A.A. Qutub, 2013


D.P. Noren, R. Rekhi, B.L. Long, A.A. Qutub (2014) Multiscale Models of Angiogenesis. In: Vascularization: Regenerative Medicine and Tissue Engineering. (E. Brey, ed.), CRC Press, pp. 213-234.

A genetic algorithm compares rules-based models of angiogenesis against results of in vitro angiogenesis assays in order to computationally test hypotheses about endothelial cell behaviors. B. Long, Qutub Lab 2013
R. Rekhi, A.A. Qutub (2013) Systems Biology Approaches for Synthetic Biology: A Pathway Towards Mammalian Design, Frontiers in Computational Physiology and Medicine 4: 285.

Advances in systems biology modeling can help address challenges in mammalian cell engineering. R. Rekhi, Qutub Lab 2013
S.M. Kornblau, A.A. Qutub, H. Yao, H. York, Y. Qiu, D. Graber, F. Ravandi, J. Cortes, M. Andreeff, N. Zhang, K.R. Coombes (2013) Proteomic Profiling Identifies Distinct Protein Patterns in Acute Myelogenous Leukemia CD34+CD38- Stem-Like Cells, PLoS One, 8: e78453.

Key hub proteins are differentially expressed in leukemic stem cells compared to bulk cells.
A.A. Qutub, S.M. Kornblau, 2013


D. Ryan, J. Hu, B. Zaunbrecher, B. Long, A.A. Qutub (2013) Predicting Endothelial Cell Phenotypes in Angiogenesis. Proceedings of the ASME 2013: Global Congress on NanoEngineering for Medicine and Biology 93124: 13-20.
Image informatics integrated with high-throughput experiments identifies distinct cytoskeletal groups of endothelial cells as a function of BDNF and VEGF stimulation. D. Ryan, Qutub Lab, 2013
B. Long, R. Rekhi, J. Jung, A. Abrego, A.A. Qutub (2013) Cells as State Machines: Cell Behavior Patterns Arise during Capillary Formation as a Function of BDNF and VEGF, Journal of Theoretical Biology, 326: 43-57. (PDF)

A cellular state machine model of angiogenesis is statistically tested against experiments & predicts new capillary growth as a function of neurotrophic factors. B.L. Long, Qutub Lab, 2013
R. Schweller, J. Zimak, A.A. Qutub, Hittleman W.N., Diehl M.R. (2012) Multiplexed In Situ Immunofluorescence via Dynamic DNA Complexes. Angewandte Chemie 51: 9292-9296. (Link)

Erasable molecular probes allow six cytoskeletal markers to be probed in HeLa cells using a 3-color epifluorescence microscope. R. Schweller, 2012


H. York, S.M. Kornblau, A.A. Qutub (2012) Network Analysis of Reverse Phase Protein Expression Data: Characterizing Protein Signatures in Acute Myeloid Leukemia Cytogenetic Categories t(8;21) and inv(16), Proteomics 12: 2084-2093. (Link), (PDF)

Proteins and phospho-proteins identified as statistically different between leukemia patients with translocation of chromosomes 8 and 21 and healthy controls, and predicted signaling interactions. A.A. Qutub, 2012
M.O. Stefaninni, A.A. Qutub, F. Mac Gabhann, A.S. Popel (2012) Computational Models of VEGF-Associated Angiogenic Processes in Cancer. Math Med Biol 29: 85-94. (Link) (PDF)

Angiogenesis processes which occur in cancer that have been predicted using 3D computational models. A.A. Qutub, 2012
S.M. Kornblau, Y.H. Qiu, N. Zhang, N. Singh, S. Faderl, A. Ferrajoli, H. York, A.A. Qutub, K.R. Coombes, D.K. Watson (2011) Abnormal Expression of Friend Leukemia Virus Integration 1 (FLI1) Protein Is an Adverse Prognostic Factor in Acute Myeloid Leukemia, Blood 118: 5604-5612. (PDF)
Protein-protein interaction network predicted for FlI1 and SMAD4 in acute myeloid leukemia. H. York, Qutub Lab, 2012


G. Liu, A.A. Qutub, P. Vempati, F. Mac Gabhann, A.S. Popel (2011) Module-Based Multiscale Simulation of Angiogenesis in Skeletal Muscle, Theoretical Biology & Medical Modelling 8: 6.

Computational predictions of sprouting angiogenesis in skeletal muscle during exercise, Liu et al., 2010
J.C. Nathan, A.A. Qutub (2010) “Patient-Specific Modeling of Hypoxic Response and Microvasculature Dynamics.” In: “Patient-Specific Modeling of the Cardiovascular System.” Roy Kerckhoffs (ed.). Springer, pp.183-201. (PDF)

Signaling through a hypoxia-inducible transcription factor (HIF1), Qutub Lab, 2010.
F. Mac Gabhann, A.A. Qutub, B.H. Annex, A.S. Popel (2010) “Systems Biology of Proangiogenic Therapeutic Strategies targeting the VEGF system.” Wiley Interdisciplinary Reviews: Systems Biology and Medicine 2: 694-707.

Angiogenic balance, and the role of transcription factors (TFs), Mac Gabhann et al., 2010.


A.A. Qutub, F. Mac Gabhann, E.D. Karagiannis, A.S. Popel (2010) “Modeling Angiogenesis In Silico: From Nanoscale to Organ System.” In: “Multiscale Modeling of Particle Interactions: Applications in Biology and Nanotechnology.” Michael R. King and David J. Gee (eds.). Wiley, pp. 287-320.

Categories of angiogenesis computational models, A.A. Qutub, 2010
A.A. Qutub, A.S. Popel (2009) Cell Elongation, Proliferation & Migration Differentiate Endothelial Cell Phenotypes and Determine Capillary Sprouting, BMC System Biology 3. (PDF)


3D Rule-based model of predicted cell behaviors during sprouting angiogenesis as a function of haploinsufficiency in the delta-like ligand 4 (DLL4) gene, A.A. Qutub, 2009
A.A. Qutub, F. Mac Gabhann, E.D. Karagiannis, P. Vempati, A.S. Popel (2009) Multiscale Molecular-Based Models of Angiogenesis, IEEE Engineering in Medicine & Biology 28: 14-31.

Schematic of the steps in angiogenesis, F. Wu, 2009

2009 & earlier

A.A. Qutub, G. Liu, P. Vempati, A.S. Popel (2009) Integration of Angiogenesis Modules at Multiple Scales: From Molecular to Tissue. Pacific Symposium on Biocomputing 14: 316-327.
A.A. Qutub, A.S. Popel (2008) Reactive Oxygen Species Regulate HIF1a Differentially in Cancer and Ischemia, Molecular and Cellular Biology 28: 5106-5119.
Signaling Models & PDFs
A.A. Qutub, A.S. Popel (2007) Three Autocrine Feedback Loops Determine HIF1a Expression in Chronic Hypoxia, BBA – Molecular Cell Research 1773: 1511-1525.
Signaling Models & PDFs
A.A. Qutub, A.S. Popel (2006) A Computational Model of Intracellular Oxygen Sensing by Hypoxia-Inducible Factor HIF1a, Journal of Cell Science 119: 3467-3480.
Signaling Models & PDFs
A.A. Qutub, C.A. Hunt (2005) Glucose Transport to the Brain: A Systems Model. Brain Res Rev 49: 595-617.