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2017

A. Mahadevan, B.L. Long, C.W. Hu, D.T. Ryan, G.L. Britton, A. Ligeralde, A. Warmflash, J.T. Robinson, A.A. Qutub (2017) cytoNet: Network Analysis of Cell Communities, submitted.

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 (2017) Visually Guided Clustering in Biowheel: An Integration of Semi-Supervised Clustering with Interactive Visualization, submitted.
Bioinformatics Peer Prize Winner 2017
bioRxiv 0099739
video tutorial

SemisupervisedClustering
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 (2017) The Social Networks of Neural Progenitor Cells, submitted.
bioRxiv 055533

NeuralProgCells_NestinMAP2_ArunMahadevan_QutubLab_2016
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 (2017) Quantifying Proteomic Heterogeneities and Hallmarks in Acute Myelogenous Leukemia (AML), submitted.

OverallSchematic
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
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_mdm2_progenyclustering_wendyhu
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.

aml_all_metab_andreschultz2
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
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, in press (uncorrected proof).
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.
E.O. Kaynak, A.A. Qutub, O.Y. Celiktas (2017) Advances in Glioblastoma Multiforme Treatment: New Models for Nanoparticle Therapy, submitted.

BBB_ElifCells of the blood-brain barrier present a challenge to drug delivery for brain cancer, E.O. Kaynak, 2017.
C.W. Hu, H. Li, A.A. Qutub (2017) Shrinkage Clustering: A Fast and Size-Constrained Algorithm for Biomedical Applications, accepted.

F. Hoff, C.W. Hu, Y.H. Qiu, A. Ligeralde, E. de Bont, S.Y. Yoo, A.A. Qutub, T. Horton, S.M. Kornblau (2017) Recurrent Patterns of Protein Expression Signatures in Pediatric Acute Lymphoblastic Leukemia (ALL): Recognition and Therapeutic Guidance, submitted.

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 (2017) Recognition of Recurrent Protein Expression Patterns in Pediatric Acute Myeloid Leukemia Suggests New Therapeutic Targets, submitted.

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

Zebrafish
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

DREAM9_PLOSComp_Noren.png

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).

CORDA1
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.

ProgenyClustering_2016_Hu_Qutub.png
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)

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

2015

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


ProgenyClustering

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.

ACSNano
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

CORSA1
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

JAMA_Glioma
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

Method1
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.

AngioModels1
Processes in angiogenesis from intracellular to tissue. A.A. Qutub, 2013

2014

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.

GeneticAlgorithm_Ang_Exp_Noren_2013
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.

Frontiers_Rekhi_SysSyn2013_Qutub.png
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.

StemCells_PLOSOne_Qutub_2013
Key hub proteins are differentially expressed in leukemic stem cells compared to bulk cells.
A.A. Qutub, S.M. Kornblau, 2013

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.
EC_Phenotypes_BDNF_VEGF_Ryan_2013_Qutub.png
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)

CellsStates_Angio1
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)

MultiplexImaging_Diehl__AgneChem_2012
Erasable molecular probes allow six cytoskeletal markers to be probed in HeLa cells using a 3-color epifluorescence microscope. R. Schweller, 2012

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)

Proteomics_t(8;21)_Qutub_2012.png
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)

AngiogenesisModels_Qutub_2012.png
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)
FLI1_ProteinInteraction_Blood_2012_Kornblau.png
Protein-protein interaction network predicted for FlI1 and SMAD4 in acute myeloid leukemia. H. York, Qutub Lab, 2012

2011

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.

SkeletalMuscle_2011_Liu.png
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)

HIFSignaling
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.

WIRES_AngiogenicBalance_2011
Angiogenic balance, and the role of transcription factors (TFs), Mac Gabhann et al., 2010.

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.

modeling_types_Nathan_Qutub_Angio_2010.png
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)

AngioCell_Model_Qutub_2009

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.

FlorenceWu_AngiogenesisImage_2009.png
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.