Defining rules for cancer cell proliferation in TRAIL stimulation

Abstract

Owing to their self-organizing evolutionary plasticity, cancers remain evasive to modern treatment strategies. Previously, for sensitizing tumor necrosis factor-related apoptosis-inducing ligand (TRAIL)-resistant human fibrosarcoma (HT1080), we developed and validated a dynamic computational model that showed the inhibition of protein kinase (PK)C, using bisindolylmaleimide (BIS) I, enhances apoptosis with 95% cell death. Although promising, the long-term effect of remaining ~ 5% cells is a mystery. Will they remain unchanged or are they able to proliferate? To address this question, here we adopted a discrete spatiotemporal cellular automata model utilizing simple rules modified from the famous “Conway’s game of life”. Based on three experimental initializations: cell numbers obtained from untreated (high), treatment with TRAIL only (moderate), and treatment with TRAIL and BIS I (low), the simulations show cell proliferation in time and space. Notably, when all cells are fixed in their initial space, the proliferation is rapid for high and moderate cell numbers, however, slow and steady for low number of cells. However, when mesenchymal-like random movement was introduced, the proliferation becomes significant even for low cell numbers. Experimental verification showed high proportion of mesenchymal cells in TRAIL and BIS I treatment compared with untreated or TRAIL only treatment. In agreement with the model with cell movement, we observed rapid proliferation of the remnant cells in TRAIL and BIS I treatment over time. Hence, our work highlights the importance of mesenchymal-like cellular movement for cancer proliferation. Nevertheless, re-treatment of TRAIL and BIS I on proliferating cancers is still largely effective.

Searching for simple rules in Pseudomonas aeruginosa biofilm formation

“…Basically, we were required to fit X 1 to X 6 separately for the WT and WT + AZM condition. We performed hundreds of simulations, using the aid of genetic algorithm to fit the data [15]. Notably, the model parameters remained the same as Conway’s game of life.…”

“…Previously, to study cancer cell proliferation in control and drug treated conditions, we developed a discrete spatiotemporal CA model based on simple rules modified from Conway’s game of life [14, 15]. The model’s rules were iteratively guessed and modified until the simulations matched experimental observations [15]. The resultant rules were used to infer the proliferation properties of the control and treated cancer cells.…”

Searching for Simple Rules in Pseudomonas aeruginosa Biofilm Formation

“…Basically, we were required to fit X1 to X6 separately for the WT and WT+AZM condition. We performed hundreds of simulations, using the aid of genetic algorithm to fit the data [13]. Notably, the main difference between the 2 models pointed to only one key 'parameter': the percentage of moving cells (Table 1).…”

“…Previously, to study cancer cell proliferation in control and drug treated conditions, we developed a discrete spatiotemporal CA model based on simple rules modified from Conway's game of life [12,13]. The model's rules were iteratively guessed and modified until the simulations matched experimental observations [13]. The resultant rules were used to infer the proliferation properties of the control and treated cancer cells.…”

On the Complexity of Asynchronous Freezing Cellular Automata

“…Additionally, it is assumed that all these interactions take place simultaneously, so we say that the states are updated in a synchronous scheme. In the last 70 years, CA have been broadly studied [1,2,3,4,5] and there are several applications of these models in Biology [6,7,8], Sociology [9,10,11], Computer Science [12,13], [14] etc. In this context, the latter assumption on the interactions between all the cells being perfectly synchronized is very useful as it turns the model into a massive parallel computing environment [15].…”