Data-driven model reduction of agent-based systems using the Koopman generator.

The dynamical behavior of social systems can be described by agent-based models. Although single agents follow easily explainable rules, complex time-evolving patterns emerge due to their interaction. The simulation and analysis of such agent-based models, however, is often prohibitively time-consuming if the number of agents is large. In this paper, we show how Koopman operator theory can be used to derive reduced models of agent-based systems using only simulation data. Our goal is to learn coarse-grained models and to represent the reduced dynamics by ordinary or stochastic differential equations. The new variables are, for instance, aggregated state variables of the agent-based model, modeling the collective behavior of larger groups or the entire population. Using benchmark problems with known coarse-grained models, we demonstrate that the obtained reduced systems are in good agreement with the analytical results, provided that the numbers of agents is sufficiently large.

De novo missense variants in the RAP1B gene identified in two patients with syndromic thrombocytopenia.

We present two independent cases of syndromic thrombocytopenia with multiple malformations, microcephaly, learning difficulties, dysmorphism and other features. Exome sequencing identified two novel de novo heterozygous variants in these patients, c.35G>T p.(Gly12Val) and c.178G>C p.(Gly60Arg), in the RAP1B gene (NM_001010942.2). These variants have not been described previously as germline variants, however functional studies in literature strongly suggest a clinical implication of these two activating hot spot positions. We hypothesize that pathogenic missense variants in the RAP1B gene cause congenital syndromic thrombocytopenia with a spectrum of associated malformations and dysmorphism, possibly through a gain of function mechanism.