We consider an iterated model of agents playing a two-player game on a graph. The agents change their strategies as the game progresses based on anticipated payoffs. Using only the time series of the agents’ strategies, we determine the pairwise mutual information between all agents in the graph, and use these values as a predictors of the graph’s topology. From this, we assess the influence of various model parameters on the effectiveness of mutual information at recovering the actual causal structure. It is found that the degree to which the functional connectivity reflects the actual causal structure of the graph strongly depends on which game is being played and how the agents are changing their strategies. Further, there is evidence that the edge density of the graph may also have some impact on the accuracy of the inferred network. This approach allows us to better connect the dynamics of the systems under study with the difference in their functional and actual connectivity, and has broad implications for the interpretation and application of information-based network inference. The methods and analyses described can be generalized and applied to other types of network models.

Biochemical reactions underlie all living processes. Like many systems, their web of interactions is difficult to fully capture and quantify with simple mathematical objects. Nonetheless, a huge volume of research has suggested many real-world systems–including biochemical systems–can be described simply as ‘scale-free’ networks, characterized by power-law degree distributions. More recently, rigorous statistical analyses upended this view, suggesting truly scalefree networks may be rare. We provide a first application of these newer methods across two distinct levels of biological organization: analyzing an ensemble of biochemical reaction networks generated from 785 ecosystem-level metagenomes and 1082 individual-level genomes (representing all domains of life). Our results confirm only a few percent of biochemical networks meet the criteria necessary to be more than super-weakly scale-free. We perform distinguishability tests across individual and ecosystem-level biochemical networks and find there is no sharp transition in the organization of biochemistry across distinct levels of the biological hierarchy–a result that holds across network projections. This suggests the existence of common organizing principles operating across different levels of biology, which can best be elucidated by analyzing all possible coarse-grained projections of biochemistry in tandem across scales.

Many organisms such as planaria, axolotls and deer exhibit prodigious regenerative abilities, being capable of regenerating complex organs or entire body plans. An understanding of how these organisms store and modify their morphological patterning information is necessary to identify modes of control and intervention. Insight into this process is key to the development of novel biomedical applications. In this work, we present the CANN( k ) model: an abstract computational model of pattern regeneration which couples an artificial neural network (ANN) with a k -color cellular automaton (CA). The ANN provides a global information processing system which generates state-dependent update rules for the CA. The CANN( k ) models are constructed to generate target patterns which are stable under perturbations of the pattern. We generate ensembles of CANN(4) models for each of the 4-color patterns, assess their sensitivity to changes of the ANN structure. This provides a novel model for understanding the important biological phenomenon of neural control of cellular morphogenesis in development or regeneration.