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. 2019 Mar 26:10:287.
doi: 10.3389/fphar.2019.00287. eCollection 2019.

A Computational Model of Tumor Growth and Anakoinosis

Pan Pantziarka  1   2 , Lina Ghibelli  3 , Albrecht Reichle  4
Affiliations

A Computational Model of Tumor Growth and Anakoinosis

Pan Pantziarka et al. Front Pharmacol. .

Abstract

Anakoinosis is a new cancer treatment paradigm that posits a key role for communicative reprogramming within tumor systems. To date no mathematical or computational models of anakoinosis have been developed. Here we outline the NEATG_A system, a first computational model of communicative reprogramming. The model recapitulates key features of real tumor systems and responses to both traditional cytotoxic treatments and biomodulatory/anakoinotic treatments. Results are presented and discussed, particularly with respect to the implications for future cancer treatment protocols.

Keywords: anakoinosis; biomodulatory therapy; cancer; computational model; drug repurposing.

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Figures

FIGURE 1
FIGURE 1
Handshake protocol.
FIGURE 2
FIGURE 2
Cell fate program – note the same cycle is used for both Malignant and Normal cells. At cell division Malignant cells may probabilistically undergo a mutational event.
FIGURE 3
FIGURE 3
Cell division and apoptosis.
FIGURE 4
FIGURE 4
Impact of Tolerance on rate of tumor growth. (A) Change in Malignant cell counts over time for different values of Tolerance. (B) Change in number of Grid Elements invaded by tumor over time for different values of Tolerance.
FIGURE 5
FIGURE 5
Relationship between tumor growth and Tolerance value. (A) Number of Malignant cell counts at 3000 iterations for different values of Tolerance (mean and standard deviation). (B) Number of Grid Elements invaded by tumor at 3000 iterations for different values of Tolerance (mean and standard deviation).
FIGURE 6
FIGURE 6
Genetic heterogeneity and Tolerance value. (A) Number of mutations per malignant cell at 3000 iterations for different values of Tolerance (mean and standard deviation). (B) Number of clonal subpopulations at 3000 iterations for different values of Tolerance (mean and standard deviation).
FIGURE 7
FIGURE 7
Genomic evolution and Tolerance. (A) Number of distinct genomes at 3000 iterations for different values of Tolerance (mean and standard deviation). (B) Evolution of distinct genomes for Tol = 1 and Tol = 9 (mean and standard deviation).
FIGURE 8
FIGURE 8
(A) Spatial distribution of tumor growth over time. Note the emergence of necrotic areas in the tumor mass (denoted in black). (B) The corresponding growth curve showing increase in Malignant cell counts.
FIGURE 9
FIGURE 9
Tumor response to cytotoxic treatment. (A) Impact of cytotoxic treatment on Malignant cell counts for different values of Tolerance. (B) Impact of cytotoxic treatment on tumor invasion for different values of Tolerance.
FIGURE 10
FIGURE 10
Tumor kill and recovery rates. (A) The tumor kill rate for different values of Tolerance. (B) The tumor regrowth rate for different values of Tolerance.
FIGURE 11
FIGURE 11
Tumor response to anakoinosis. (A) Impact of anakoinosis treatment on Malignant cell counts for different values of Tolerance. (B) Impact of anakoinosis treatment on tumor invasion for different values of Tolerance.
FIGURE 12
FIGURE 12
Anakoinosis treatment for 50 runs. (A) Mean pre-treatment growth rate for 50 runs. (B) Mean and standard deviation of Malignant cell counts after 50 runs at 1000, 1500, and 2000 iterations.
FIGURE 13
FIGURE 13
Tumor kill and recovery rates following anakoinosis. (A) The tumor kill rate for different values of Tolerance after anakoinosis treatment. (B) The tumor regrowth rate for different values of Tolerance after anakoinosis treatment.
FIGURE 14
FIGURE 14
Tumor responses to combination treatment. (A) Impact of combination treatment on Malignant cell counts for different treatment duration. (B) Impact of combination treatment on tumor invasion for different treatment duration.
FIGURE 15
FIGURE 15
Tumor kill and recovery rates after combination treatment. (A) The tumor kill rate for different duration of combination treatment. (B) The overall Malignant cell growth rate for different duration of combination treatment.
FIGURE 16
FIGURE 16
NEATG_A data comparison to real tumor growth. (A) Long-term growth, for l5000 iterations, of Malignant cells and concomitant reduction in non-tumor grid elements (mean and standard deviation). (B) Growth of monolayer cells from human cancer cell lines (reproduced from Castro et al., 2003), superimposed in red Malignant cell growth from (A) (red line) scaled and transformed to compare to human cancer cell lines.
FIGURE 17
FIGURE 17
Growth at iteration = 400 following combination treatment.
See this image and copyright information in PMC

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