Note: # indicates corresponding author; Research group members: * Postdoctoral Fellow, † Ph.D. student, ‡ undergraduate student
2024
72. Tavakoli, N.†¶, Fong, E.J.¶, Coleman, A., Huang, Y-K, Bigger, M., Doche, M.E., Kim, S., Lenz, H-J, Graham, N.A., Macklin, P., Finley, S.D.#, and Mumenthaler, S.A.# (2024) “Merging metabolic modeling and imaging for screening therapeutic targets in colorectal cancer”. In revision. ¶co-first authors. [bioRxiv]
71. Nogalska, A., Eerdeng, J., Akre, S., Vegel-Rodriguez, M., Lee, Y., Bramlett, C., Chowdhury, A.Y., Wang, Bowen, Cess, C.G.†, Finley, S.D., Lu, R.# (2024) “Age-associated imbalance of immune cell regeneration varies across individuals and arises from a distinct subset of stem cells”. Cellular and Molecular Immunology. Accepted.
70. Tangella, N.‡, Cess, C.G.†, Ildefonso, G.V.*, and Finley, S.D.# (2024) “Integrating mechanism-based T cell phenotypes into a model of tumor-immune cell interactions”. APL Bioengineering. [pubmed] [journal]
69. Mangrum, D.S.† and Finley, S.D.# (2024) “Modeling the heterogeneous apoptotic response of caspase-mediated signaling in tumor cells”. Journal of Theoretical Biology. 590, 111857. [pubmed] [journal]
68. Sheen, J., Curtin, L., Finley, S.D.#, Konstorum, A., McGee, R.B., and Craig, M.# (2024) “Integrating diversity, equity, and inclusion into preclinical, clinical, and public health mathematical models”. Bulletin of Mathematical Biology. 86, 56. [pubmed] [journal]
67. Gelbach, P.E.† and Finley, S.D.# (2024) “Flux sampling in genome-scale metabolic modeling of microbial communities”. BMC Bioinformatics. 25: 45. [pubmed] [journal]
66. Tserunyan, V.† and Finley, S.D.# (2024) “Information-theoretic analysis of a model of CAR-4-1BB-mediated NFκB activation”. Bulletin of Mathematical Biology. 86: 5. [pubmed] [journal]
2023
65. Ahmidouch, M., Tangella, N.‡, and Finley, S.D. (2023) “Agent-based modeling of tumor-immune interactions reveals determinants of final tumor states”. [bioRxiv]
64. Gelbach, P.E.† and Finley, S.D.# (2023) “Genome-scale modeling predicts metabolic differences between macrophage subtypes in colorectal cancer”. iScience. 26(9): 107569. [pubmed] [journal]
63. Ildefonso, G.V.* and Finley, S.D.# (2023) “A data-driven Boolean model explains memory subsets and evolution in CD8+ T cell exhaustion”. npj Systems Biology and Applications. 9: 36. [pubmed] [journal]
62. Tserunyan, V.† and Finley, S.D.# (2023) “A systems and computational biology perspective on advancing CAR therapy”. Seminars in Cancer Biology. 94: 34-49. [pubmed] [journal]
61. Peyton S.#, Chow L., Finley S.D., Ford Versypt, A., Hill, R., Kemp M., Langer E., McGuigan A., Meyer A., Seidlits S., Roy, K., Mumenthaler, S.A.# (2023) “Living materials to model the tumor microenvironment”. Nature Reviews Bioengineering. [journal]
60. Cess, C.G.† and Finley, S.D.# (2023) “Calibrating agent-based models to tumor images using representation learning”. PLoS Computational Biology. 19(4): e1011070. [pubmed] [journal]
59. Hanafin, P.O., Abdul Rahim, N. Sharma, R., Cess, C.G.†, Finley, S.D., Bergen, P.J., Velkov, T., Li, J., and Rao, G.G.# (2023) “Proof-of-concept to incorporate insights from multi-omics analyses of polymyxin B in combination with chloramphenicol against Klebsiella pneumoniae”. CPT: Pharmacometrics & Systems Pharmacology. 12(3): 387-400. [pubmed] [journal]
2022
58. Cess, C.G.† and Finley, S.D.# (2022) “Representation learning for a generalized, quantitative comparison of complex model outputs”. pre-print. [arXiv]
57. Huber, H.A.†, Georgia, S.K., and Finley, S.D.# (2022) “Systematic Bayesian posterior analysis facilitates hypothesis formation and guides investigation of pancreatic beta cell signaling”. Journal of Theoretical Biology. 558(7), 111341 [pubmed] [journal]
56. Gelbach, P.E.†, Zheng, D., Fraser, S.E., White, K.L., Graham, N.A., and Finley, S.D.# (2022) “Kinetic and data-driven modeling of pancreatic beta-cell central carbon metabolism and insulin secretion”. PLoS Computational Biology 18(10): e1010555 [pubmed] [journal]
55. Tserunyan, V.† and Finley, S.D.# (2022) “Computational analysis of 4-1BB-induced NFκB signaling suggests improvements to CAR cell design”. Cell Communication and Signaling [pubmed] [journal]
54. Tserunyan, V.† and Finley, S.D.# (2022) “Modeling predicts differences in CAR T cell signaling due to biological variability”. Royal Society Open Science [pubmed] [journal]
53. Simoni, A.‡, Huber, H.A.†, Georgia, S.K., and Finley, S.D.# (2022) “Phosphatases are predicted to govern prolactin-mediated JAK-STAT signaling in pancreatic beta cells”. Integrative Biology. zyac004. [pubmed] [journal]
52. Millette, K.#, Rodriguez, K., Sheng, X., Finley, S.D. and Georgia, S.K.# (2022) “Exogenous lactogenic signaling stimulates beta cell replication in vivo and in vitro”. Biomolecules. [pubmed] [journal]
51. Cess, C.G.† and Finley, S.D.# (2022) “Multiscale modeling of tumor adaption and invasion following anti-angiogenic therapy”. Computational and Systems Oncology. [journal]
50. Song, M.† and Finley, S.D.# (2022) “Mechanistic characterization of endothelial sprouting mediated by pro-angiogenic signaling”. Microcirculation. e12744. [pubmed] [journal]
49. Wang, J.*, Delfarah, A., Gelbach, P.†, Fong, E., Macklin, P.T., Mumenthaler, S.M., Graham, N.A., and and Finley, S.D.# (2022) “Elucidating tumor-stromal metabolic crosstalk in colorectal cancer through constraint-based modeling". Metabolic Engineering. 69, 175-187. [pubmed] [journal]
2021
48. Akkari, L., Finley, S.D., Ho, PC., Jenkins, M., Maier, B.B., McGranahan, N., Mutebi, M., Perera, R.M., Robles-Espinoza, C.D., Vardhana, S., Wan, L., Xu, M.M. (2021) “Challenges and opportunities in 2021”. Nature Cancer. 2(12):1278-1283 [pubmed] [journal]
47. Way, G.P., Greene, C.S., Carninci, P. Carvalho, B.S., de Hoon, M., Finley, S.D., Gosline, S.J.C., Lê Cao, K-A., Lee, J.S.H., Marchionni, L., Robine, N., Sindi, S.S., Theis, F.J., Yang, J.Y.H., Carpenter, A.E., Fertig, E.J.# (2021) “A field guide to cultivating computational biology”. PLOS Biology. 19(10): e3001419. [pubmed] [journal]
46. Finley, S.D.# and Hatzimanikatis, V. (2021) “Mathematical modeling: It’s a matter of scale". Current Opinion in Systems Biology. [journal]
45. Song, M.†, Li, D.†, Makaryan, S.Z.†, and Finley, S.D.# (2021) “Quantitative modeling to understand cell signaling in the tumor microenvironment". Current Opinion in Systems Biology. 27, 100345. [journal]
Finley, S.D.# (2021) “Integrating quantitative approaches in cancer research and oncology". Trends in Cancer. TrendsTalk. 7(4), P270-275. [journal]
43. Li, D.† and Finley, S.D.# (2021) “Mechanistic insights into the heterogeneous response to anti-VEGF treatment in tumors". Systems and Computational Oncology. e1013. [journal]
42. 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". Cell. 184(3), P561-565. [pubmed] [journal]
2020
41. 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. [pubmed] [journal]
40. Cess, C.G.† and Finley, S.D.# (2020) “Multi-scale modeling of macrophage – T cell interactions within the tumor microenvironment". PLOS Computational Biology. 16(12): e1008519. [pubmed] [journal]
39. 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. [pubmed] [journal]
38. 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. [pubmed] [journal]
37. 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. [pubmed] [journal]
36. Wu, Q.† and Finley, S.D.# (2020) “Mathematical model predicts effective strategies to inhibit VEGF-eNOS signaling". Journal of Clinical Medicine. 9(5): 1255. [pubmed] [journal]
35. 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. [pubmed] [journal]
34. 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 [pubmed] [journal]
33. 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 [pubmed] [journal]
2019
32. Wu, Q.† and Finley, S.D.# (2019) “Modeling cell signaling in heterogeneous cancer environments". Current Opinion in Systems Biology. 17, 15-23. [journal]
31. 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 [pubmed] [journal]
30. 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. [pubmed] [journal]
29. 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. [pubmed] [journal]
28. Finley, S.D.# (2019) “Metabolism in cancer progression”. Physical Biology, as part of The 2019 Mathematical Oncology Roadmap (Rockne, R.C. et al.) [pubmed] [journal]
27. 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. [pubmed] [journal]
2018
26. Song, M.† and Finley, S.D.# (2018) “Mechanistic insight into activation of MAPK signaling by pro-angiogenic factors”. BMC Systems Biology. 12:145. [pubmed] [journal]
25. 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. [pubmed] [journal]
24. 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. [pubmed] [journal]
23. Rohrs, J.A.†, Makaryan, S.Z.†, and Finley, S.D.# (2018) “Constructing predictive cancer systems biology models”. bioRxiv Mathematical Oncology Channel. [bioRxiv]
22. 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 [pubmed] [journal]
2017
21. Wu, Q.† and Finley, S.D.# (2017) “Predictive model identifies strategies to enhance TSP1-mediated apoptosis signaling”. Cell Communication and Signaling. 15: 53. [pubmed] [journal]
20. 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. [pubmed] [journal]
19. Roy, M.* and Finley, S.D.# (2017) “Computational model predicts the effects of targeting cellular metabolism in pancreatic cancer”. Frontiers in Physiology. 8:217. [pubmed] [journal]
18. 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. [pubmed] [journal]
2016
17. 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. [pubmed] [journal]
16. 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. [pubmed] [journal]
15. 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 [pubmed] [journal]
14. 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. [pubmed] [journal]
2015
13. 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. [pubmed] [journal]
12. 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. [pubmed] [journal]
2014
11. 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. [pubmed] [journal]
Prior to joining USC
10. 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. [pubmed] [journal]
9. 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. [pubmed] [journal]
8. 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. [pubmed] [journal]
7. Klinke, D.J. and Finley, S.D. (2012) ''Timescale analysis of rule-based biochemical reaction networks''. Biotechnology Progress. 28(1), 33-44. [pubmed] [journal]
6. 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. [pubmed] [journal]
5. 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. [pubmed] [journal]
4. 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. [pubmed] [journal]
3. 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. [pubmed] [journal]
2. 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 [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. [pubmed] [journal]