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.

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

C.W. Hu, H. Li, A.A. Qutub (2017) Shrinkage Clustering: A Fast and Size-Constrained Algorithm for Biomedical Applications, accepted.

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. bioRxiv 055533

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

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. Hoff, C.W. Hu, Y.H. Qiu, S.Y. Yoo, E. de Bont, H. Mahmud, A.A. Qutub, T. Horton, S.M. Kornblau (2017) Recognition of Recurrent Protein Expression Patterns in Pediatric Acute Myeloid Leukemia (AML) Suggest New Therapeutic Targets, submitted.

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 (2017) Histone Modification Patterns using RPPA-based Profiling Predict Outcome in Acute Myeloid Leukemia Patients, submitted.

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.

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.

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.

A. Mahadevan, N. Grandel, J. Robinson, A.A. Qutub (2017) Living Neural Networks: Dynamic Network Analysis of Developing Neural Progenitor Cells, submitted. bioRxiv 055533

MAP2_NeuralProgenitorCells
A developing human neuronal network stained for MAP2 (red) & nuclei (blue). A. Mahadevan, Qutub Lab, 2016
SemisupervisedClustering
A Biowheel graph displaying results of semi-supervised clustering of protein expression levels in leukemia cells. C.W. Hu, Qutub Lab, 2016

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.

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: 68-76. (PDF)

C.W. Hu, A.A. Qutub (2016) progenyClust: an R package for Progeny Clustering, The R Journal, in press.

AMLStudy2

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

CORDA1
CORDA provides tissue-specific models of metabolism as a function of gene & protein expression profiles. A. Schultz, Qutub Lab, 2016

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

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

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. (Cover & Science Signaling Editor’s Choice; PDF, Faculty 1000)

VisS.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. (Interactive DREAM 8 Breast Cancer Data Portal; Highlights Biowheel)

ProgenyClustering
Progeny clustering identifies the ideal number of four clusters for patterned endothelial cells. C.W. Hu, Qutub Lab, 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)

ACSNano
Patterning an endothelial cell of interest. J. Slater, 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.

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

A. Schultz, A.A. Qutub (2015) Predicting Internal Cell Fluxes at Sub-Optimal Growth, BMC Systems Biology, 9:18. (Highlighted as one of the best papers of 2015).

Method1
Phenotyping endothelial cells, where the orientation angles of actin fibers are displayed by color. B. L. Long, D. Ryan, 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.

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.

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.

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.

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

HighwayMaps_ProteomicsS.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.

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.

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

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)

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)

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)

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.

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.

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.

HIFSignaling
Signaling through a hypoxia-inducible transcription factor (HIF1), Qutub Lab, 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.

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.

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.

A.A. Qutub, A.S. Popel (2009) Cell Elongation, Proliferation & Migration Differentiate Endothelial Cell Phenotypes and Determine Capillary Sprouting, BMC System Biology 3.

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.

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.

A.A. Qutub, A.S. Popel (2007) Three Autocrine Feedback Loops Determine HIF1a Expression in Chronic Hypoxia, BBA – Molecular Cell Research 1773: 1511-1525.

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.

A.A. Qutub, C.A. Hunt (2005) Glucose Transport to the Brain: A Systems Model. Brain Res Rev 49: 595-617.