Fig. 1: Schematic illustration of the AGRN model.
From: A neural network-based model framework for cell-fate decisions and development
a Input components of the AGRN framework. An AGRN model takes as model inputs: (i) the cell differentiation topology of a given developmental process, i.e., the order of the subsequent developmental stages corresponding to different cellular identities, and (ii) the stage-specific binary representations of individual gene expression states in the form of developmental stage vectors. In the developmental stage vectors, colored cells represent on and empty cells represent off gene expression states. The input components are then used to construct a regulatory program matrix M according to simple, modular algebraic rules described in Eqs. (4–6). The M matrix then governs the dynamics of the model by regulating the expression levels of individual genes (pi) as described by the system of differential equations in Eq. 1. (b). c Output components of the AGRN framework. The attractor dynamics is realized by a series of elementary transitions encoded by the associative memory of the matrix. At fork and conditional transitions, in concert with cell-intrinsic machinery (represented by the regulatory program matrix), instructive external signals (triggers) determine the behavior of the system. Pink marbles represent the system’s current state, continuous green- and dashed red arrows represent default- and trigger-induced differentiation pathways, respectively. Gray arrows with blurred end represent previous transitions. The elementary transitions correspond to time series of gene expression level changes (here, for simplicity, only the expression of one key gene per stage is shown). To measure the performance of the model over time, we calculated the Pearson correlation coefficients (r) between the state of the gene expression vector (p(t)) and each developmental stage vector at all t + ∆t time points (d).
