Processes 2019, 7,37
57. Chavali, A.K.; Gianchandani, E.P.; Tung, K.S.; Lawrence, M.B.; Peirce, S.M.; Papin, J.A. Characterizing
emergent properties of immunological systems with multi-cellular rule-based computational modeling.
Trends Immunol. 2008, 29, 589–599. [CrossRef]
58. Shi, Z.Z.; Wu, C.-H.; Ben-Arieh, D. Agent-based model: A surging tool to simulate infectious diseases in the
immune system. Open J. Model. Simul. 2014, 02, 12–22. [CrossRef]
59. Segovia-Juarez, J.L.; Ganguli, S.; Kirschner, D. Identifying control mechanisms of granuloma formation
during M. tuberculosis infection using an agent-based model. J. Theor. Biol. 2004, 231, 357–376. [CrossRef]
60. Tokarski, C.; Hummert, S.; Mech, F.; Figge, M.T.; Germerodt, S.; Schroeter, A.; Schuster, S.; Linde, J.; Hu, G.
Agent-based modeling approach of immune defense against spores of opportunistic human pathogenic
fungi. Front. Microbiol. 2012, 3, 129. [CrossRef]
61. Dong, X.; Foteinou, P.T.; Calvano, S.E.; Lowry, S.F.; Androulakis, I.P. Agent-based modeling of
endotoxin-induced acute inflammatory response in human blood leukocytes. PLoS ONE 2010, 5, e9249.
[CrossRef][PubMed]
62. Solovyev, A.; Mi, Q.; Tzen, Y.T.; Brienza, D.; Vodovotz, Y. Hybrid equation/agent-based model of
ischemia-induced hyperemia and pressure ulcer formation predicts greater propensity to ulcerate in subjects
with spinal cord injury. PLoS Comput. Biol. 2013, 9, e1003070. [CrossRef][PubMed]
63. Santoni, D.; Pedicini, M.; Castiglione, F. Implementation of a regulatory gene network to simulate the TH1/2
differentiation in an agent-based model of hypersensitivity reactions. Bioinformatics 2008, 24, 1374–1380.
[CrossRef][PubMed]
64. Bailey, A.M.; Lawrence, M.B.; Shang, H.; Katz, A.J.; Peirce, S.M. Agent-based model of therapeutic
adipose-derived stromal cell trafficking during ischemia predicts ability to roll on p-selectin. PLoS Comput. Biol.
2009, 5, e1000294. [CrossRef][PubMed]
65. D’Souza, R.M.; Lysenko, M.; Marino, S.; Kirschner, D.; Souza, R.M.D.; Arbor, A. Data-parallel algorithms
for agent-based model simulation of tuberculosis on graphics processing units. In Proceedings of the 2009
Spring Simulation Multiconference, San Diego, CA, USA, 22–27 March 2009.
66. Song, S.O.; Hogg, J.; Peng, Z.Y.; Parker, R.; Kellum, J.A.; Clermont, G. Ensemble models of neutrophil
trafficking in severe sepsis. PLoS Comput. Biol. 2012, 8, e1002422. [CrossRef][PubMed]
67. Mi, Q.; Rivière, B.; Clermont, G.; Steed, D.L.; Vodovotz, Y. Agent-based model of inflammation and
wound healing: Insights into diabetic foot ulcer pathology and the role of transforming growth factor-β1.
Wound Repair Regen. 2007, 15, 671–682. [CrossRef][PubMed]
68. Alarcón, T.; Byrne, H.M.; Maini, P.K. A mathematical model of the effects of hypoxia on the cell-cycle of
normal and cancer cells. J. Theor. Biol. 2004, 229, 395–411. [CrossRef]
69. Hoehme, S.; Bertaux, F.; Weens, W.; Grasl-Kraupp, B.; Hengstler, J.G.; Drasdo, D. Model prediction
and validation of an order mechanism controlling the spatiotemporal phenotype of early hepatocellular
carcinoma. Bull. Math. Biol. 2018, 80, 1134–1171. [CrossRef]
70. Bianca, C.; Pennisi, M. The triplex vaccine effects in mammary carcinoma: A nonlinear model in tune with
SimTriplex. Nonlinear Anal. Real World Appl. 2012, 13, 1913–1940. [CrossRef]
71. Wang, J.; Zhang, L.; Jing, C.; Ye, G.; Wu, H.; Miao, H.; Wu, Y.; Zhou, X. Multi-scale agent-based modeling on
melanoma and its related angiogenesis analysis. Theor. Biol. Med. Model. 2013, 10, 41. [CrossRef]
72. Kather, J.N.; Poleszczuk, J.; Suarez-Carmona, M.; Krisam, J.; Charoentong, P.; Valous, N.A.; Weis, C.A.;
Tavernar, L.; Leiss, F.; Herpel, E.; et al. In silico modeling of immunotherapy and stroma-targeting therapies
in human colorectal cancer. Cancer Res. 2017, 77, 6442–6452. [CrossRef][PubMed]
73. Pennisi, M.; Pappalardo, F.; Motta, S. Agent based modeling of lung metastasis-immune system competition.
In Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes
in Bioinformatics); Springer: Berlin/Heidelberg, Germany, 2009; Volume 5666, pp. 1–3.
74. Jagiella, N.; Müller, B.; Müller, M.; Vignon-Clementel, I.E.; Drasdo, D. Inferring growth control mechanisms
in growing multi-cellular spheroids of NSCLC cells from spatial-temporal image data. PLoS Comput. Biol.
2016, 12, e1004412. [CrossRef][PubMed]
75. Pennisi, M.; Pappalardo, F.; Palladini, A.; Nicoletti, G.; Nanni, P.; Lollini, P.-L.; Motta, S. Modeling the
competition between lung metastases and the immune system using agents. BMC Bioinform. 2010, 11, S13.
[CrossRef][PubMed]
76. Bezzi, M.; Celada, F.; Ruffo, S.; Seiden, P.E. The transition between immune and disease states in a cellular
automaton model of clonal immune response. Phys. A Stat. Mech. Its Appl. 1997, 245, 145–163. [CrossRef]
58
57. Chavali, A.K.; Gianchandani, E.P.; Tung, K.S.; Lawrence, M.B.; Peirce, S.M.; Papin, J.A. Characterizing
emergent properties of immunological systems with multi-cellular rule-based computational modeling.
Trends Immunol. 2008, 29, 589–599. [CrossRef]
58. Shi, Z.Z.; Wu, C.-H.; Ben-Arieh, D. Agent-based model: A surging tool to simulate infectious diseases in the
immune system. Open J. Model. Simul. 2014, 02, 12–22. [CrossRef]
59. Segovia-Juarez, J.L.; Ganguli, S.; Kirschner, D. Identifying control mechanisms of granuloma formation
during M. tuberculosis infection using an agent-based model. J. Theor. Biol. 2004, 231, 357–376. [CrossRef]
60. Tokarski, C.; Hummert, S.; Mech, F.; Figge, M.T.; Germerodt, S.; Schroeter, A.; Schuster, S.; Linde, J.; Hu, G.
Agent-based modeling approach of immune defense against spores of opportunistic human pathogenic
fungi. Front. Microbiol. 2012, 3, 129. [CrossRef]
61. Dong, X.; Foteinou, P.T.; Calvano, S.E.; Lowry, S.F.; Androulakis, I.P. Agent-based modeling of
endotoxin-induced acute inflammatory response in human blood leukocytes. PLoS ONE 2010, 5, e9249.
[CrossRef][PubMed]
62. Solovyev, A.; Mi, Q.; Tzen, Y.T.; Brienza, D.; Vodovotz, Y. Hybrid equation/agent-based model of
ischemia-induced hyperemia and pressure ulcer formation predicts greater propensity to ulcerate in subjects
with spinal cord injury. PLoS Comput. Biol. 2013, 9, e1003070. [CrossRef][PubMed]
63. Santoni, D.; Pedicini, M.; Castiglione, F. Implementation of a regulatory gene network to simulate the TH1/2
differentiation in an agent-based model of hypersensitivity reactions. Bioinformatics 2008, 24, 1374–1380.
[CrossRef][PubMed]
64. Bailey, A.M.; Lawrence, M.B.; Shang, H.; Katz, A.J.; Peirce, S.M. Agent-based model of therapeutic
adipose-derived stromal cell trafficking during ischemia predicts ability to roll on p-selectin. PLoS Comput. Biol.
2009, 5, e1000294. [CrossRef][PubMed]
65. D’Souza, R.M.; Lysenko, M.; Marino, S.; Kirschner, D.; Souza, R.M.D.; Arbor, A. Data-parallel algorithms
for agent-based model simulation of tuberculosis on graphics processing units. In Proceedings of the 2009
Spring Simulation Multiconference, San Diego, CA, USA, 22–27 March 2009.
66. Song, S.O.; Hogg, J.; Peng, Z.Y.; Parker, R.; Kellum, J.A.; Clermont, G. Ensemble models of neutrophil
trafficking in severe sepsis. PLoS Comput. Biol. 2012, 8, e1002422. [CrossRef][PubMed]
67. Mi, Q.; Rivière, B.; Clermont, G.; Steed, D.L.; Vodovotz, Y. Agent-based model of inflammation and
wound healing: Insights into diabetic foot ulcer pathology and the role of transforming growth factor-β1.
Wound Repair Regen. 2007, 15, 671–682. [CrossRef][PubMed]
68. Alarcón, T.; Byrne, H.M.; Maini, P.K. A mathematical model of the effects of hypoxia on the cell-cycle of
normal and cancer cells. J. Theor. Biol. 2004, 229, 395–411. [CrossRef]
69. Hoehme, S.; Bertaux, F.; Weens, W.; Grasl-Kraupp, B.; Hengstler, J.G.; Drasdo, D. Model prediction
and validation of an order mechanism controlling the spatiotemporal phenotype of early hepatocellular
carcinoma. Bull. Math. Biol. 2018, 80, 1134–1171. [CrossRef]
70. Bianca, C.; Pennisi, M. The triplex vaccine effects in mammary carcinoma: A nonlinear model in tune with
SimTriplex. Nonlinear Anal. Real World Appl. 2012, 13, 1913–1940. [CrossRef]
71. Wang, J.; Zhang, L.; Jing, C.; Ye, G.; Wu, H.; Miao, H.; Wu, Y.; Zhou, X. Multi-scale agent-based modeling on
melanoma and its related angiogenesis analysis. Theor. Biol. Med. Model. 2013, 10, 41. [CrossRef]
72. Kather, J.N.; Poleszczuk, J.; Suarez-Carmona, M.; Krisam, J.; Charoentong, P.; Valous, N.A.; Weis, C.A.;
Tavernar, L.; Leiss, F.; Herpel, E.; et al. In silico modeling of immunotherapy and stroma-targeting therapies
in human colorectal cancer. Cancer Res. 2017, 77, 6442–6452. [CrossRef][PubMed]
73. Pennisi, M.; Pappalardo, F.; Motta, S. Agent based modeling of lung metastasis-immune system competition.
In Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes
in Bioinformatics); Springer: Berlin/Heidelberg, Germany, 2009; Volume 5666, pp. 1–3.
74. Jagiella, N.; Müller, B.; Müller, M.; Vignon-Clementel, I.E.; Drasdo, D. Inferring growth control mechanisms
in growing multi-cellular spheroids of NSCLC cells from spatial-temporal image data. PLoS Comput. Biol.
2016, 12, e1004412. [CrossRef][PubMed]
75. Pennisi, M.; Pappalardo, F.; Palladini, A.; Nicoletti, G.; Nanni, P.; Lollini, P.-L.; Motta, S. Modeling the
competition between lung metastases and the immune system using agents. BMC Bioinform. 2010, 11, S13.
[CrossRef][PubMed]
76. Bezzi, M.; Celada, F.; Ruffo, S.; Seiden, P.E. The transition between immune and disease states in a cellular
automaton model of clonal immune response. Phys. A Stat. Mech. Its Appl. 1997, 245, 145–163. [CrossRef]
58