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

Since joining USC


67. Ahmidouch, M., Tangella, N.‡, and Finley, S.D. (2023) “Agent-based modeling of tumor-immune interactions reveals determinants of final tumor states”. in revision. [bioRxiv]

66. Gelbach, P.E.† and Finley, S.D.# (2023) “Flux sampling in genome-scale metabolic modeling of microbial communities”. in revision. [bioRxiv]

65. Tserunyan, V.† and Finley, S.D.# (2023) “Information-theoretic analysis of a model of CAR-4-1BB-mediated NFκB activation”. Bulletin of Mathematical Biology. 86: 5. [journal]

64. 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]

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. 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]

61. 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]

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]  


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]


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]


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]


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]


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]


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]


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]


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]


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]