Processes 2019, 7,37
98. Wilson, J.T.; van Loon, R.; Wang, W.; Zawieja, D.C.; Moore, J.E. Determining the combined effect of the
lymphatic valve leaflets and sinus on resistance to forward flow. J. Biomech. 2015, 48, 3584–3590. [CrossRef]
[PubMed]
99. Roose, T.; Swartz, M.A. Multiscale modeling of lymphatic drainage from tissues using homogenization
theory. J. Biomech. 2012, 45, 107–115. [CrossRef][PubMed]
100. Jafarnejad, M.; Zawieja, D.C.; Brook, B.S.; Nibbs, R.J.B.; Moore, J.E. A novel computational model predicts
key regulators of chemokine gradient formation in lymph nodes and site-specific roles for CCL19 and
ACKR4. J. Immunol. 2017, 199, ji1700377. [CrossRef]
101. Jafarnejad, M.; Woodruff, M.C.; Zawieja, D.C.; Carroll, M.C.; Moore, J.E. Modeling lymph flow and fluid
exchange with blood vessels in lymph nodes. Lymphat. Res. Biol. 2015, 13, 234–247. [CrossRef]
102. Cooper, L.J.; Heppell, J.P.; Clough, G.F.; Ganapathisubramani, B.; Roose, T. An image-based model of fluid
flow through lymph nodes. Bull. Math. Biol. 2016, 78, 52–71. [CrossRef]
103. Marino, S.; Gideon, H.P.; Gong, C.; Mankad, S.; McCrone, J.T.; Lin, P.L.; Linderman, J.J.; Flynn, J.A.L.;
Kirschner, D.E. Computational and empirical studies predict mycobacterium tuberculosis-specific T cells as
a biomarker for infection outcome. PLoS Comput. Biol. 2016, 12, e1004804. [CrossRef][PubMed]
104. Gong, C.; Linderman, J.J.; Kirschner, D. Harnessing the heterogeneity of T cell differentiation fate to fine-tune
generation of effector and memory T cells. Front. Immunol. 2014, 5, 57. [CrossRef][PubMed]
105. Margaris, K.N.; Black, R.A. Modelling the lymphatic system: Challenges and opportunities. J. R. Soc. Interface
2012, 9, 601–612. [CrossRef][PubMed]
106. Meyer-Hermann, M. A mathematical model for the germinal center morphology and affinity maturation.
J. Theor. Biol. 2002, 216, 273–300. [CrossRef][PubMed]
107. Meyer-Hermann, M.E.; Maini, P.K. Cutting edge: Back to “one-way” germinal centers. J. Immunol. 2005, 174,
2489–2493. [CrossRef][PubMed]
108. Meyer-Hermann, M.E.; Maini, P.K.; Iber, D. An analysis of B cell selection mechanisms in germinal centers.
Math. Med. Biol. A J. IMA 2006, 23, 255–277. [CrossRef][PubMed]
109. Bogle, G.; Dunbar, P.R. T cell responses in lymph nodes. Wiley Interdiscip. Rev. Syst. Biol. Med. 2010, 2,
107–116. [CrossRef]
110. Bogle, G.; Dunbar, P.R. Agent-based simulation of T-cell activation and proliferation within a lymph node.
Immunol. Cell Biol. 2010, 88, 172–179. [CrossRef]
111. Bogle, G.; Dunbar, P.R. On-lattice simulation of T cell motility, chemotaxis, and trafficking in the lymph node
paracortex. PLoS ONE 2012, 7, e45258. [CrossRef]
112. Bogle, G.; Dunbar, P.R. Simulating T-cell motility in the lymph node paracortex with a packed lattice
geometry. Immunol. Cell Biol. 2008, 86, 676–687. [CrossRef]
113. Moreau, H.D.; Bogle, G.; Bousso, P. A virtual lymph node model to dissect the requirements for T-cell
activation by synapses and kinapses. Immunol. Cell Biol. 2016, 94, 680–688. [CrossRef][PubMed]
114. Folcik, V.A.; An, G.C.; Orosz, C.G. The Basic Immune Simulator: An agent-based model to study the interactions
between innate and adaptive immunity. Theor. Biol. Med. Model. 2007, 4, 39. [CrossRef][PubMed]
115. Kim, P.S.; Levy, D.; Lee, P.P. Modeling and simulation of the immune system as a self-regulating network.
Methods Enzymol. 2009, 467, 79–109. [PubMed]
116. Jacob, C.; Sarpe, V.; Gingras, C.; Feyt, R.P. Swarm-based simulations for immunobiology: What can
agent-based models teach us about the immune system? In Intelligent Systems Reference Library; Springer:
Berlin/Heidelberg, Germany, 2011; Volume 11, pp. 29–64.
117. Marino, S.; El-Kebir, M.; Kirschner, D. A hybrid multi-compartment model of granuloma formation and T
cell priming in Tuberculosis. J. Theor. Biol. 2011, 280, 50–62. [CrossRef][PubMed]
118. Marino, S.; Kirschner, D. A multi-compartment hybrid computational model predicts key roles for dendritic
cells in Tuberculosis infection. Computation 2016, 4, 39. [CrossRef][PubMed]
119. Dréau, D.; Stanimirov, D.; Carmichael, T.; Hadzikadic, M. An agent-based model of solid tumor progression.
In Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes
in Bioinformatics); Springer: Berlin/Heidelberg, Germany, 2009; Volume 5462, pp. 187–198.
120. Pappalardo, F.; Forero, I.M.; Pennisi, M.; Palazon, A.; Melero, I.; Motta, S. Simb16: Modeling induced
immune system response against B16-melanoma. PLoS ONE 2011, 6, e26523. [CrossRef][PubMed]
60
98. Wilson, J.T.; van Loon, R.; Wang, W.; Zawieja, D.C.; Moore, J.E. Determining the combined effect of the
lymphatic valve leaflets and sinus on resistance to forward flow. J. Biomech. 2015, 48, 3584–3590. [CrossRef]
[PubMed]
99. Roose, T.; Swartz, M.A. Multiscale modeling of lymphatic drainage from tissues using homogenization
theory. J. Biomech. 2012, 45, 107–115. [CrossRef][PubMed]
100. Jafarnejad, M.; Zawieja, D.C.; Brook, B.S.; Nibbs, R.J.B.; Moore, J.E. A novel computational model predicts
key regulators of chemokine gradient formation in lymph nodes and site-specific roles for CCL19 and
ACKR4. J. Immunol. 2017, 199, ji1700377. [CrossRef]
101. Jafarnejad, M.; Woodruff, M.C.; Zawieja, D.C.; Carroll, M.C.; Moore, J.E. Modeling lymph flow and fluid
exchange with blood vessels in lymph nodes. Lymphat. Res. Biol. 2015, 13, 234–247. [CrossRef]
102. Cooper, L.J.; Heppell, J.P.; Clough, G.F.; Ganapathisubramani, B.; Roose, T. An image-based model of fluid
flow through lymph nodes. Bull. Math. Biol. 2016, 78, 52–71. [CrossRef]
103. Marino, S.; Gideon, H.P.; Gong, C.; Mankad, S.; McCrone, J.T.; Lin, P.L.; Linderman, J.J.; Flynn, J.A.L.;
Kirschner, D.E. Computational and empirical studies predict mycobacterium tuberculosis-specific T cells as
a biomarker for infection outcome. PLoS Comput. Biol. 2016, 12, e1004804. [CrossRef][PubMed]
104. Gong, C.; Linderman, J.J.; Kirschner, D. Harnessing the heterogeneity of T cell differentiation fate to fine-tune
generation of effector and memory T cells. Front. Immunol. 2014, 5, 57. [CrossRef][PubMed]
105. Margaris, K.N.; Black, R.A. Modelling the lymphatic system: Challenges and opportunities. J. R. Soc. Interface
2012, 9, 601–612. [CrossRef][PubMed]
106. Meyer-Hermann, M. A mathematical model for the germinal center morphology and affinity maturation.
J. Theor. Biol. 2002, 216, 273–300. [CrossRef][PubMed]
107. Meyer-Hermann, M.E.; Maini, P.K. Cutting edge: Back to “one-way” germinal centers. J. Immunol. 2005, 174,
2489–2493. [CrossRef][PubMed]
108. Meyer-Hermann, M.E.; Maini, P.K.; Iber, D. An analysis of B cell selection mechanisms in germinal centers.
Math. Med. Biol. A J. IMA 2006, 23, 255–277. [CrossRef][PubMed]
109. Bogle, G.; Dunbar, P.R. T cell responses in lymph nodes. Wiley Interdiscip. Rev. Syst. Biol. Med. 2010, 2,
107–116. [CrossRef]
110. Bogle, G.; Dunbar, P.R. Agent-based simulation of T-cell activation and proliferation within a lymph node.
Immunol. Cell Biol. 2010, 88, 172–179. [CrossRef]
111. Bogle, G.; Dunbar, P.R. On-lattice simulation of T cell motility, chemotaxis, and trafficking in the lymph node
paracortex. PLoS ONE 2012, 7, e45258. [CrossRef]
112. Bogle, G.; Dunbar, P.R. Simulating T-cell motility in the lymph node paracortex with a packed lattice
geometry. Immunol. Cell Biol. 2008, 86, 676–687. [CrossRef]
113. Moreau, H.D.; Bogle, G.; Bousso, P. A virtual lymph node model to dissect the requirements for T-cell
activation by synapses and kinapses. Immunol. Cell Biol. 2016, 94, 680–688. [CrossRef][PubMed]
114. Folcik, V.A.; An, G.C.; Orosz, C.G. The Basic Immune Simulator: An agent-based model to study the interactions
between innate and adaptive immunity. Theor. Biol. Med. Model. 2007, 4, 39. [CrossRef][PubMed]
115. Kim, P.S.; Levy, D.; Lee, P.P. Modeling and simulation of the immune system as a self-regulating network.
Methods Enzymol. 2009, 467, 79–109. [PubMed]
116. Jacob, C.; Sarpe, V.; Gingras, C.; Feyt, R.P. Swarm-based simulations for immunobiology: What can
agent-based models teach us about the immune system? In Intelligent Systems Reference Library; Springer:
Berlin/Heidelberg, Germany, 2011; Volume 11, pp. 29–64.
117. Marino, S.; El-Kebir, M.; Kirschner, D. A hybrid multi-compartment model of granuloma formation and T
cell priming in Tuberculosis. J. Theor. Biol. 2011, 280, 50–62. [CrossRef][PubMed]
118. Marino, S.; Kirschner, D. A multi-compartment hybrid computational model predicts key roles for dendritic
cells in Tuberculosis infection. Computation 2016, 4, 39. [CrossRef][PubMed]
119. Dréau, D.; Stanimirov, D.; Carmichael, T.; Hadzikadic, M. An agent-based model of solid tumor progression.
In Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes
in Bioinformatics); Springer: Berlin/Heidelberg, Germany, 2009; Volume 5462, pp. 187–198.
120. Pappalardo, F.; Forero, I.M.; Pennisi, M.; Palazon, A.; Melero, I.; Motta, S. Simb16: Modeling induced
immune system response against B16-melanoma. PLoS ONE 2011, 6, e26523. [CrossRef][PubMed]
60