A Complex Systems Approach to Understanding Cells as Systems and Agents

Abstract

Many natural systems can be categorized as complex systems, with relatively simple components interacting to generate collective behaviors not easily predicted from the individual components themselves, like the flocking of birds or the formation of oceanic currents. Living systems, in particular, are enriched with complexity. In studying complex systems, abstract mathematical models are often used to identify general principles underlying how the interactions between individuals gives rise to observed collective behaviors. This type of approach allows for a focused investigation into the effects of specific lower-level properties (e.g., interaction distance) on higher-level behaviors (e.g., collective motion) in a controlled setting. In this work, I utilize Boolean network (BN) models to investigate cells, the fundamental units of life, as both systems of intracellular components and the agents that interact within cellular populations. Specifically, I simulate cell-like agents composed of networks with binary-valued nodes. Agents can interact with their environment or with each other via external signals in the form of inputs to designated receptor nodes. With this model, I examine two overarching questions: (1) how internal variables influence the flexibility of cells to process external signals to generate different responses; and (2) how cell-cell communication impacts individual and population behavior in cellular populations. Using a BN reservoir computer model of cellular signal processing, I find that flexibility in signal processing is guaranteed if enough cellular resources (e.g., number of nodes) are available; however, fewer resources could attain flexibility, but with lower probability. I also find that the difficulty of accurately responding to signals is heavily dependent on how sensitive the response needs to be to signal variability. Using a 3D lattice-structured population of interdependent BNs as a model of cellular populations, I find that communication alone can induce cells to exhibit completely different sets of behaviors as compared with non-communicating cells. Furthermore, by tuning the distance over which cells can interact (interaction distance) and the amount of signal that activates a receptor (activation threshold), cellular populations exhibit distinct social behaviors, characterized by different cell type distributions and population diversity. Significantly, the maximum effects of cell-cell communication are observed when the interaction distance only includes one or two neighboring cells. Overall, in this work I have identified how key cellular properties relate to biologically relevant phenotypes, namely signal processing and self-organization.