And ER (see e.g., Larter and Craig, 2005; Di Garbo et al., 2007; Postnov et al., 2007; Lavrentovich and Hemkin, 2008; Di Garbo, 2009; Zeng et al., 2009; Amiri et al., 2011a; DiNuzzo et al., 2011; Farr and David, 2011; Oschmann et al., 2017; Kenny et al., 2018). In addition ofmodeling Ca2+ fluxes amongst ER and cytosol, Silchenko and Tass (2008) modeled absolutely free diffusion of extracellular glutamate as a flux. It appears that many of the authors implemented their ODE and PDE models applying some programming language, but several times, for instance, XPPAUT (Ermentrout, 2002) was named as the simulation tool utilized. Due to the stochastic nature of cellular processes (see e.g., Rao et al., 2002; Raser and O’Shea, 2005; Ribrault et al., 2011) and oscillations (see e.g., Perc et al., 2008; Skupin et al., 2008), distinctive stochastic solutions have been developed for both reaction and reactiondiffusion systems. These stochastic solutions might be divided into discrete and continuous-state stochastic methods. Some discretestate reaction-diffusion simulation tools can track every molecule individually in a certain volume with Brownian dynamics combined having a Monte Carlo process for reaction events (see e.g., Stiles and Bartol, 2001; Kerr et al., 2008; Andrews et al., 2010). However, the discrete-state Gillespie stochastic simulation algorithm (Gillespie, 1976, 1977) and leap method (Gillespie, 2001) can be used to model both reaction and reaction-diffusion systems. A few simulation tools currently exist for reaction-diffusion Resolvin D3 Cancer systems working with these procedures (see e.g., Wils and De Schutter, 2009; Oliveira et al., 2010; Hepburn et al., 2012). Additionally, continuous-state chemical Langevin equation (Gillespie, 2000) and quite a few other stochastic differential equations (SDEs, see e.g., Shuai and Jung, 2002; Manninen et al., 2006a,b) happen to be created for TAI-1 Biological Activity reactions to ease the computational burden of discrete-state stochastic methods. A few simulation tools giving hybrid approaches also exist and they combine either deterministic and stochastic strategies or distinctive stochastic approaches (see e.g., Salis et al., 2006; Lecca et al., 2017). From the above-named methods, most realistic simulations are offered by the discrete-state stochastic reactiondiffusion techniques, but none of the covered astrocyte models made use of these stochastic techniques or obtainable simulation tools for both reactions and diffusion for the same variable. Having said that, 4 models combined stochastic reactions with deterministic diffusion in the astrocytes. Skupin et al. (2010) and Komin et al. (2015) modeled with all the Gillespie algorithm the detailed IP3 R model by De Young and Keizer (1992), had PDEs for Ca2+ and mobile buffers, and ODEs for immobile buffers. Postnov et al. (2009) modeled diffusion of extracellular glutamate and ATP as fluxes, had an SDE for astrocytic Ca2+ with fluxes amongst ER and cytosol, and ODEs for the rest. MacDonald and Silva (2013) had a PDE for extracellular ATP, an SDE for astrocytic IP3 , and ODEs for the rest. In addition, some studies modeling just reactions and not diffusion applied stochastic solutions (SDEs or Gillespie algorithm) a minimum of for some of the variables (see e.g., Nadkarni et al., 2008; Postnov et al., 2009; Sotero and Mart ezCancino, 2010; Riera et al., 2011a,b; Toivari et al., 2011; Tewari and Majumdar, 2012a,b; Liu and Li, 2013a; Tang et al., 2016; Ding et al., 2018).3. RESULTSPrevious research in experimental and computational cell biology fields have gu.