Controlling cell–cell interactions is central for understanding key cellular processes and bottom‐up tissue assembly from single cells. The challenge is to control cell–cell interactions dynamically and reversibly with high spatiotemporal precision noninvasively and sustainably. In this study, cell–cell interactions are controlled with visible light using an optogenetic approach by expressing the blue light switchable proteins CRY2 or CIBN on the surfaces of cells. CRY2 and CIBN expressing cells form specific heterophilic interactions under blue light providing precise control in space and time. Further, these interactions are reversible in the dark and can be repeatedly and dynamically switched on and off. Unlike previous approaches, these genetically encoded proteins allow for long‐term expression of the interaction domains and respond to nontoxic low intensity blue light. In addition, these interactions are suitable to assemble cells into 3D multicellular architectures. Overall, this approach captures the dynamic and reversible nature of cell–cell interactions and controls them noninvasively and sustainably both in space and time. This provides a new way of studying cell–cell interactions and assembling cellular building blocks into tissues with unmatched flexibility.

Networks are routinely used to represent large data sets, making the comparison of networks a tantalizing research question in many areas. Techniques for such analysis vary from simply comparing network summary statistics to sophisticated but computationally expensive alignment-based approaches. Most existing methods either do not generalize well to different types of networks or do not provide a quantitative similarity score between networks. In contrast, alignment-free topology based network similarity scores empower us to analyse large sets of networks containing different types and sizes of data. Netdis is such a score that defines network similarity through the counts of small sub-graphs in the local neighbourhood of all nodes. Here, we introduce a sub-sampling procedure based on neighbourhoods which links naturally with the framework of network comparisons through local neighbourhood comparisons. Our theoretical arguments justify basing the Netdis statistic on a sample of similar-sized neighbourhoods. Our tests on empirical and synthetic datasets indicate that often only 10% of the neighbourhoods of a network suffice for optimal performance, leading to a drastic reduction in computational requirements. The sampling procedure is applicable even when only a small sample of the network is known, and thus provides a novel tool for network comparison of very large and potentially incomplete datasets.

Many complex systems can be represented as networks, and the problem of network comparison is becoming increasingly relevant. There are many techniques for network comparison, from simply comparing network summary statistics to sophisticated but computationally costly alignment-based approaches. Yet it remains challenging to accurately cluster networks that are of a different size and density, but hypothesized to be structurally similar. In this paper, we address this problem by introducing a new network comparison methodology that is aimed at identifying common organizational principles in networks. The methodology is simple, intuitive and applicable in a wide variety of settings ranging from the functional classification of proteins to tracking the evolution of a world trade network.

Many real world networks contain a statistically surprising number of certain subgraphs, called network motifs. In the prevalent approach to motif analysis, network motifs are detected by comparing subgraph frequencies in the original network with a statistical null model. In this paper we propose an alternative approach to motif analysis where network motifs are defined to be connectivity patterns that occur in a subgraph cover that represents the network using minimal total information. A subgraph cover is defined to be a set of subgraphs such that every edge of the graph is contained in at least one of the subgraphs in the cover. Some recently introduced random graph models that can incorporate significant densities of motifs have natural formulations in terms of subgraph covers and the presented approach can be used to match networks with such models. To prove the practical value of our approach we also present a heuristic for the resulting NP-hard optimization problem and give results for several real world networks.