Agent-Based Models Predict Emergent Behavior of Heterogeneous Cell Populations in Dynamic Microenvironments

Abstract

Computational models are most impactful when they explain and characterize biological phenomena that are non-intuitive, unexpected, or difficult to study experimentally. Countless equation-based models have been built for these purposes, but we have yet to realize the extent to which rules-based models offer an intuitive framework that encourages computational and experimental collaboration. We develop ARCADE, a multi-scale agent-based model to interrogate emergent behavior of heterogeneous cell agents within dynamic microenvironments and demonstrate how complexity of intracellular metabolism and signaling modules impacts emergent dynamics. We perform in silico case studies on context, competition, and heterogeneity to demonstrate the utility of our model for gaining computational and experimental insight. Notably, there exist (i) differences in emergent behavior between colony and tissue contexts, (ii) linear, non-linear, and multimodal consequences of parameter variation on competition in simulated co-cultures, and (iii) variable impact of cell and population heterogeneity on emergent outcomes. Our extensible framework is easily modified to explore numerous biological systems, from tumor microenvironments to microbiomes.

Mapping CAR T-cell design space using agent-based models

“…Cells change state according to the rule set and to their current state ( Supplementary Figures 1 and 2, Supplementary Methods Details ). All new model parameters are listed in Supplementary Table 1 (Kuse et al, 1985; Lauffenburger and Linderman, 1993; Robertson et al, 1996; Frauwirth et al, 2002; De Boer et al, 2003; Deenick et al, 2003; Iwashima, 2003; Jacobs et al, 2008; Busse et al, 2010; Yoon et al, 2010; Wang et al, 2011; Altman and Dang, 2012; Robertson-Tessi et al, 2012; Stone et al, 2012; Cheng et al, 2013; Hegde et al, 2013; Heskamp et al, 2015; Kinjyo et al, 2015; Liu et al, 2015; Obst, 2015; Harris and Kranz, 2016; Hegde et al, 2016; Arcangeli et al, 2017; Borghans and Ribeiro, 2017; Gong et al, 2017; Gherbi et al, 2018; Guedan et al, 2018; Salter et al, 2018; Yu and Bagheri, 2020; 2021).…”

“…Deenick et al, 2003; Iwashima, 2003; Schwartz, 2003; Chmielewski et al, 2004; Macian et al, 2004; Janas et al, 2005; Jacobs et al, 2008; Busse et al, 2010; Malek and Castro, 2010; Pearce, 2010; Yoon et al, 2010; Akbar and Henson, 2011; Wang et al, 2011; Wherry, 2011; Altman and Dang, 2012; Gerriets and Rathmell, 2012; Robertson-Tessi et al, 2012; Stone et al, 2012; Cheng et al, 2013; Crespo et al, 2013; Hegde et al, 2013; Liao et al, 2013; MacIver et al, 2013; Rosenberg, 2014; Buck et al, 2015; Heskamp et al, 2015; Kinjyo et al, 2015; Liadi et al, 2015; Liu et al, 2015; Long et al, 2015; Obst, 2015; Wherry and Kurachi, 2015; Chang and Pearce, 2016; Cherkassky et al, 2016; Golubovskaya and Wu, 2016; Harris and Kranz, 2016; Hegde et al, 2016; Liu et al, 2016; Maus and June, 2016; Sommermeyer et al, 2016; Verbist et al, 2016; Arcangeli et al, 2017; Borghans and Ribeiro, 2017; Gong et al, 2017; Mehta et al, 2017; Gherbi et al, 2018; Guedan et al, 2018; Huang et al, 2018; Kasakovski et al, 2018; Rafiq et al, 2018; Ross and Cantrell, 2018; Salter et al, 2018; Watanabe et al, 2018; Yost et al, 2019; Yu and Bagheri, 2020; Hernandez-Lopez et al, 2021; Yu and Bagheri, 2021).…”

“…There are two subtypes of CAR T-cell agents: CD8 + T-cells that primarily provide cytotoxic functions and CD4 + T-cells that primarily provide stimulatory functions (Liadi et al, 2015;Sommermeyer et al, 2016). Although both T-cell subtypes can provide cytotoxic and stimulatory functions, for simplicity, we specified that each of these T-cell subtypes would perform only their primary function (Kuse et al, 1985;Lauffenburger and Linderman, 1993;Robertson et al, 1996;Frauwirth et al, 2002;De Boer et al, 2003;Deenick et al, 2003;Iwashima, 2003;Jacobs et al, 2008;Busse et al, 2010;Yoon et al, 2010;Wang et al, 2011;Altman and Dang, 2012;Robertson-Tessi et al, 2012;Stone et al, 2012;Cheng et al, 2013;Hegde et al, 2013;Heskamp et al, 2015;Kinjyo et al, 2015;Liu et al, 2015;Obst, 2015;Harris and Kranz, 2016;Hegde et al, 2016;Arcangeli et al, 2017;Borghans and Ribeiro, 2017;Gong et al, 2017;Gherbi et al, 2018;Guedan et al, 2018;Salter et al, 2018;Yu and Bagheri, 2020;2021).…”

A Novel Hybrid Logic-ODE Modeling Approach to Overcome Knowledge Gaps

“…Both Varela et al and Marnellos et al approaches allow to simulate pattern formation at the tissue level without in depth knowledge of the kinetic parameters or species concentrations. Also, agentbased models (ABM) have been employed to address how complex behaviors arise from the cell-cell interactions (Reynolds et al, 2019) or cell-environment interaction (Yu and Bagheri, 2020). In particular, Reynolds et al (2019) developed an ABM that recreates Delta-Notch patterns using for each agent a set of rules that define the increment of each species, thus providing a more abstract view.…”

Whole-Colony Modeling of Escherichia coli

“…Agent-based modeling is an approach that simulates population behaviors that emerge from the actions and interactions of individual agents [19]. Agent-based models (ABMs) have been applied to blood vessel assembly [20], inflammation and fibrosis in human lungs [21], and human cancer tumors [22]. They have also been applied to bacterial populations, for example to study quorum sensing [23], chemotaxis [24], and colony growth [25].…”