Co-occurrence networks produced from microbial survey sequencing data are frequently used to identify interactions between community members. While this approach has potential to reveal ecological processes, it has been insufficiently validated due to the technical limitations inherent in studying complex microbial ecosystems. Here, we simulate multi-species microbial communities with known interaction patterns using generalized Lotka-Volterra dynamics. We then construct co-occurrence networks and evaluate how well networks reveal the underlying interactions and how experimental and ecological parameters can affect network inference and interpretation. We find that co-occurrence networks can recapitulate interaction networks under certain conditions, but that they lose interpretability when the effects of habitat filtering become significant. We demonstrate that networks suffer from local hot spots of spurious correlation in the neighborhood of hub species that engage in many interactions. We also identify topological features associated with keystone species in co-occurrence networks. This study provides a substantiated framework to guide environmental microbiologists in the construction and interpretation of co-occurrence networks from microbial survey datasets.

Cell types are the basic building blocks of multicellular organisms and are extensively diversified in animals. Despite recent advances in characterizing cell types, classification schemes remain ambiguous. We propose an evolutionary definition of a cell type that allows cell types to be delineated and compared within and between species. Key to cell type identity are evolutionary changes in the 'core regulatory complex' (CoRC) of transcription factors, that make emergent sister cell types distinct, enable their independent evolution and regulate cell type-specific traits termed apomeres. We discuss the distinction between developmental and evolutionary lineages, and present a roadmap for future research.

The importance of microbial communities (MCs) cannot be overstated. MCs underpin the biogeochemical cycles of the earth's soil, oceans and the atmosphere, and perform ecosystem functions that impact plants, animals and humans. Yet our ability to predict and manage the function of these highly complex, dynamically changing communities is limited. Building predictive models that link MC composition to function is a key emerging challenge in microbial ecology. Here, we argue that addressing this challenge requires close coordination of experimental data collection and method development with mathematical model building. We discuss specific examples where model–experiment integration has already resulted in important insights into MC function and structure. We also highlight key research questions that still demand better integration of experiments and models. We argue that such integration is needed to achieve significant progress in our understanding of MC dynamics and function, and we make specific practical suggestions as to how this could be achieved.

Recent studies highlight linkages among the architecture of ecological networks, their persistence facing environmental disturbance, and the related patterns of biodiversity. A hitherto unresolved question is whether the structure of the landscape inhabited by organisms leaves an imprint on their ecological networks. We analyzed, based on pyrosequencing profiling of the biofilm communities in 114 streams, how features inherent to fluvial networks affect the co-occurrence networks that the microorganisms form in these biofilms. Our findings suggest that hydrology and metacommunity dynamics, both changing predictably across fluvial networks, affect the fragmentation of the microbial co-occurrence networks throughout the fluvial network. The loss of taxa from co-occurrence networks demonstrates that the removal of gatekeepers disproportionately contributed to network fragmentation, which has potential implications for the functions biofilms fulfill in stream ecosystems. Our findings are critical because of increased anthropogenic pressures deteriorating stream ecosystem integrity and biodiversity.stream networks | hydrological regime S treams and rivers sculpt the continental surface, forming fluvial networks (1), within which the biodiversity ranks among the highest on Earth (2). The dendritic nature of fluvial networks has been shown to affect the spatial and temporal patterns of microbial, invertebrate, and fish biodiversity (3-8). Ecological theory and observations posit that the local environment governs the dynamics and diversity of ecological communities in headwaters, the smallest and most abundant streams in fluvial networks. In contrast, dispersal ensures that communities further downstream are shaped not only by their immediate environment but also by upstream processes (3-9). Thus, the dynamics of the metacommunity, which comprises all interconnected communities in a landscape (10), are inextricably linked to the organization and hydrology of the fluvial network (5-8). This perception is essential to understand, predict, and manage streams and rivers and their resistance and resilience to human alterations across scales (that is, from patches to the catchment) (11).Ecological interactions are often usefully represented as networks (12). For example, analyses of food webs and mutualistic (e.g., pollination) networks have demonstrated that network organization can be linked to network persistence, to disturbance (12-16), or to species coexistence and richness (17). Microbial communities are so diverse and poorly studied that mapping out the interactions on the basis of biological knowledge is currently impossible for all but the simplest of habitats. Therefore, cooccurrence networks are increasingly used to infer microbial interactions (18,19) in soils (20), oceans (21), lakes (22), and even in global genomic surveys (23).A key question is whether the organization of microbial cooccurrence networks and their response to disturbance reflect physical characteristics inherent in fluvial networks such as geo...

Abstract:The repressilator is a regulatory cycle of n genes where each gene represses its successor in the cycle: 1 ⊣ 2 ⊣ . . . ⊣ n ⊣ 1. The system is modelled by ODEs for an arbitrary number of identical genes and arbitrarily strong repressor binding. A detailed mathematical analysis of the dynamical behavior is provided for two model systems: (i) a repressilator with leaky transcription and single-step cooperative repressor binding, and (ii) a repressilator with auto-activation and cooperative regulator binding. Genes are assumed to be present in constant amounts, transcription and translation are modelled by single-step kinetics, and mRNAs as well as proteins are assumed to be degraded by first order reactions. Several dynamical patterns are observed: Multiple steady states, periodic and aperiodic oscillations corresponding to limit cycles and heteroclinic cycles, respectively. The results of computer simulations are complemented by a detailed and complete stability analysis of all equilibria and of the heteroclinic cycle.