Publications


Note: # indicates corresponding author; Research group members: * Postdoctoral Fellow, † Ph.D. student, ‡ undergraduate student

Since joining USC

2021

  1. Li, D.† and Finley, S.D.# (2021) “Mechanistic insights into the heterogeneous response to anti-VEGF treatment in tumors". Systems and Computational Oncology. e1013.
    2. [journal]
  1. Stevens, K.R.#, Masters, K.S., Imoukhuede, P., Haynes, K.A., Setton, L.A., Cosgriff-Hernandez, E., Bell, M.A.L., Rangamani, P., El-Samad, H., Sakiyama-Elbert, S., Finley, S.D., Willits, R.K., Koppes, A.N., Chesler, N., Christman, K., Allen, J., Wong, J.Y., Desai, T., Eniola-Adefeso# (2021) “Fund Black Scientists". APL Cell. 184(3), P561-565.
    2. [pubmed] [journal]

2020

  1. Makaryan, S.Z.† and Finley, S.D.# (2020) “An optimal control approach for enhancing Natural Killer cells' secretion of cytolytic molecules". APL Bioengineering. 4, 046107.
    2. [pubmed] [journal]
  1. Cess, C.G.† and Finley, S.D.# (2020) “Multi-scale modeling of macrophage – T cell interactions within the tumor microenvironment". PLOS Computational Biology. In press.
    2. [pubmed] [journal]
  1. Mortlock, R.D.‡ and Finley, S.D.# (2020) “Dynamic regulation of JAK-STAT signaling through the prolactin receptor predicted by computational modeling". Cellular and Molecular Bioengineering.
    2. [pubmed] [journal]
  1. Song, M.† and Finley, S.D.# (2020) “ERK and Akt exhibit distinct signaling responses following stimulation by pro- angiogenic factors". Cell Communication and Signaling. 18(1): 114.
    2. [pubmed] [journal]
  1. Makaryan, S.Z..† and Finley, S.D.# (2020) “Enhancing network activation in natural killer cells: predictions from in silico modeling". Integrative Biology. 12(5): 109-121.
    2. [pubmed] [journal]
  1. Wu, Q.† and Finley, S.D.# (2020) “Mathematical model predicts effective strategies to inhibit VEGF-eNOS signaling". Journal of Clinical Medicine. 9(5): 1255.
    2. [pubmed] [journal]
  1. Rohrs, J.A.†, Siegler, E.L., Wang, P. and Finley, S.D.# (2020) “ERK activation in CAR T cells is amplified by CD28-mediated increase in CD3ζ phosphorylation". iScience. 23(4), 101023.
    2. [pubmed] [journal]
  1. Makaryan, S.Z..^†, Cess, C.G.^† and Finley, S.D.# (2020) “Modeling immune cell behavior across scales in cancer". WIREs Systems Biology and Medicine. e1484. ^ Equal contributions
    2. [pubmed] [journal]
  1. Cess, C.G.† and Finley, S.D.# (2020) “Data-driven analysis of a mechanistic model of CAR T cell signaling predicts effects of cell-to-cell heterogeneity". Journal of Theoretical Biology. 489, 110205
    2. [pubmed] [journal]

2019

  1. Wu, Q.† and Finley, S.D.# (2019) “Modeling cell signaling in heterogeneous cancer environments". Current Opinion in Systems Biology. 17, 15-23.
    2. [journal]
  1. Li, D.† and Finley, S.D.# (2019) “Exploring the extracellular regulation of the tumor angiogenic interaction network using a systems biology model". Frontiers in Physiology. 10, 823
    2. [pubmed] [journal]
  1. Szeto, G.Z.^ and Finley, S.D.^ (2019) “Integrative approaches to cancer immunotherapy". Current Opinion in Systems Biology. 5(7), 400-410. ^, Co-corresponding authors.
    2. [pubmed] [journal]
  1. Roy, M.* and Finley, S.D.# (2019) “Metabolic reprogramming dynamics in tumor spheroids: Insights from a multicellular, multiscale model". PLoS Computational Biology. 15(6):e1007053.
    2. [pubmed] [journal]
  1. Finley, S.D.# (2019) “Metabolism in cancer progression”. Physical Biology, as part of The 2019 Mathematical Oncology Roadmap (Rockne, R.C. et al.
    2. [pubmed] [journal]
  1. Rohrs, J.A.†, Wang, P. and Finley, S.D.# (2019) “Understanding the dynamics of T cell activation through the lens of computational modeling”. JCO Clinical Cancer Informatics.
    2. [pubmed] [journal]

2018

  1. Song, M.† and Finley, S.D.# (2018) “Mechanistic insight into activation of MAPK signaling by pro-angiogenic factors”. BMC Systems Biology. 12:145.
    2. [pubmed] [journal]
  1. Rohrs, J.A.†, Zheng, D., Graham, N.A., Wang, P. and Finley, S.D.# (2018) “Computational model of chimeric antigen receptors explains site-specific phosphorylation kinetics”. Biophysical Journal. 115(6): P1116-1129.
    2. [pubmed] [journal]
  1. Wu, Q.†, Arnheim, A.D.‡, and Finley, S.D.# (2018) “In silico mouse study identifies tumor growth kinetics as biomarkers for the outcome of anti-angiogenic treatment”. Journal of the Royal Society Interface. 15(145): 20180243.
    2. [pubmed] [journal]
  1. Rohrs, J.A.†, Makaryan, S.Z.†, and Finley, S.D.# (2018) “Constructing predictive cancer systems biology models”. bioRxiv Mathematical Oncology Channel.
    2. [bioRxiv]
  1. Li, D.† and Finley, S.D.# (2018) “The impact of tumor receptor heterogeneity on the response to anti-angiogenic cancer treatment”. Integrative Biology. 10: 253-269
    2. [pubmed] [journal]

2017

  1. Wu, Q.† and Finley, S.D.# (2017) “Predictive model identifies strategies to enhance TSP1-mediated apoptosis signaling”. Cell Communication and Signaling. 15: 53.
    2. [pubmed] [journal]
  1. Gaddy, T.D.‡, Wu, Q.†, Arnheim, A.D.‡ and Finley, S.D.# (2017) “Mechanistic modeling quantifies the influence of tumor growth kinetics on the response to anti-angiogenic treatment”. PLoS Computational Biology. 13(12): e1005874.
    2. [pubmed] [journal]
  1. Roy, M.* and Finley, S.D.# (2017) “Computational model predicts the effects of targeting cellular metabolism in pancreatic cancer”. Frontiers in Physiology. 8:217.
    2. [pubmed] [journal]
  1. Typpo, K.V., Wong, H.R., Finley, S.D., Daniels, R.C., Seely, J.E., and Lacroix, J. (2017) “Monitoring severity of multiple organ dysfunction syndrome: New technologies”. Pediatric Critical Care Medicine. 18(3 Suppl 1): S24-S31.
    2. [pubmed] [journal]

2016

  1. Chu, L.H., Ganta, V.J., Choi, M., Chen, G., Finley, S.D., Annex, B., and Popel, A.S. (2016) “A multiscale computational model predicts distribution of anti-angiogenic isoform VEGF165b in peripheral arterial disease in human and mouse”. Scientific Reports. 6, 37030.
    2. [pubmed] [journal]
  1. Rohrs, J.A.†, Sulistio, C.D.‡, and Finley, S.D.# (2016) “Predictive model of thrombospondin-1 and vascular endothelial growth factor in breast tumor tissue”. npj Systems Biology and Applications (Nature publishing journal). 2, 16030.
    2. [pubmed] [journal]
  1. Rohrs, J.A.†, Wang, P., and Finley, S.D.# (2016) “Predictive model of lymphocyte-specific protein tyrosine kinase (LCK) autoregulation”. Cellular and Molecular Bioengineering. 9(3), 351-367. **Selected for the 2016 Young Innovators issue of the journal
    2. [pubmed] [journal]
  1. Soto-Ortiz, L.*# and Finley, S.D. (2016) ''A cancer treatment based on synergy between anti-angiogenic and immune cell therapies''. Journal of Theoretical Biology. 394, 197-211.
    2. [pubmed] [journal]

2015

  1. Finley, S.D.#, Angelikopoulos, P., Koumoutsakos, P., and Popel, A.S. (2015) ''Pharmacokinetics of anti-VEGF agent aflibercept in cancer predicted by data driven, molecular-detailed model''. CPT: Pharmacometrics & Systems Pharmacology. 4(11), 641-649.
    2. [pubmed] [journal]
  1. Finley, S.D.#, Chu, L.H., Popel, A.S. (2015) ''Computational systems biology approaches to anti-angiogenic cancer therapeutics''. Drug Discovery Today. 20(2), 187-197.
    2. [pubmed] [journal]

2014

  1. Logsdon, E.A., Finley, S.D., Popel, A.S., and Mac Gabhann, F. (2014) ''A systems biology view of blood vessel growth and remodeling''. Journal of Cellular and Molecular Medicine. 18(8), 1491-1508.
    2. [pubmed] [journal]
  1. Finley, S.D.#, Dhar, M. and Popel, A.S. (2013) ''Compartment model predicts VEGF secretion and investigates the effects of VEGF Trap in tumor-bearing mice''. Frontiers in Oncology. 3, 196.
    2. [pubmed] [journal]
  1. Finley, S.D.# and Popel, A.S. (2013) ''Effect of tumor microenvironment on tumor VEGF during anti- VEGF treatment: systems biology predictions''. Journal of the National Cancer Institute. 105(11), 802-11.
    2. [pubmed] [journal]
  1. Finley, S.D.# and Popel, A.S. (2012) ''Predicting the effects of anti-angiogenic agents targeting specific VEGF isoforms''. The AAPS Journal. 14(3), 500-509.
    2. [pubmed] [journal]
  1. Klinke, D.J. and Finley, S.D. (2012) ''Timescale analysis of rule-based biochemical reaction networks''. Biotechnology Progress. 28(1), 33-44.
    2. [pubmed] [journal]
  1. Finley, S.D.#, Engel-Stefanini, M.O., Imoukhuede, P.I., and Popel, A.S. (2011) ''Pharmacokinetics and pharmacodynamics of VEGF-neutralizing agents''. BMC Systems Biology. 5:193.
    2. [pubmed] [journal]
  1. Yen, P.**, Finley, S.D.**#, Stefanini, M.O., and Popel, A.S. (2011) ''A two-compartment model of VEGF distribution in the mouse''. PLoS ONE. 6:e27514. **Contributed equally.
    2. [pubmed] [journal]
  1. Finley, S.D., Gupta, D., Cheng, N., and Klinke, D.J. (2011) ''Inferring relevant control mechanisms for Interleukin-12 signaling in naive CD4+ T cells''. Immunology and Cell Biology. 89(1), 100-110.
    2. [pubmed] [journal]
  1. Finley, S.D., Broadbelt, L.J., and Hatzimanikatis, V. (2010) ''In silico feasibility of novel biodegradation pathways for 1,2,4-trichlorobenzene''. BMC Systems Biology. 4:7.
    2. [pubmed] [journal]
  1. Finley, S.D., Broadbelt, L.J., and Hatzimanikatis, V. (2009) ''Computational framework for predictive biodegradation''. Biotechnology and Bioengineering. 104(6), 1086-1097. **Awarded Elmer Gaden Jr. Award
    2. [pubmed] [journal]
  1. Finley, S.D., Broadbelt, L.J., and Hatzimanikatis, V. (2009) ''Thermodynamic analysis of biodegradation pathways''. Biotechnology and Bioengineering. 103(3), 532-541.
    2. [pubmed] [journal]

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