Shiri Freilich

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.

In their natural environments, microorganisms form complex systems of interactions. Understating the structure and organization of bacterial communities is likely to have broad medical and ecological consequences, yet a comprehensive description of the network of environmental interactions is currently lacking. Here, we mine co-occurrences in the scientific literature to construct such a network and demonstrate an expected pattern of association between the species' lifestyle and the recorded number of co-occurring partners. We further focus on the well-annotated gut community and show that most co-occurrence interactions of typical gut bacteria occur within this community. The network is then clustered into species-groups that significantly correspond with natural occurring communities. The relationships between resource competition, metabolic yield and growth rate within the clusters correspond with the r/K selection theory. Overall, these results support the constructed clusters as a first approximation of a bacterial ecosystem model. This comprehensive collection of predicted communities forms a new data resource for further systematic characterization of the ecological design principals shaping communities. Here, we demonstrate its utility for predicting cooperation and inhibition within communities.

Network analysis transcends conventional pairwise approaches to data analysis as the context of components in a network graph can be taken into account. Such approaches are increasingly being applied to genomics data, where functional linkages are used to connect genes or proteins. However, while microarray gene expression datasets are now abundant and of high quality, few approaches have been developed for analysis of such data in a network context. We present a novel approach for 3-D visualisation and analysis of transcriptional networks generated from microarray data. These networks consist of nodes representing transcripts connected by virtue of their expression profile similarity across multiple conditions. Analysing genome-wide gene transcription across 61 mouse tissues, we describe the unusual topography of the large and highly structured networks produced, and demonstrate how they can be used to visualise, cluster, and mine large datasets. This approach is fast, intuitive, and versatile, and allows the identification of biological relationships that may be missed by conventional analysis techniques. This work has been implemented in a freely available open-source application named BioLayout Express(3D).

Biological disease control of soilborne plant diseases has traditionally employed the biopesticide approach whereby single strains or strain mixtures are introduced into production systems through inundative/inoculative release. The approach has significant barriers that have long been recognized, including a generally limited spectrum of target pathogens for any given biocontrol agent and inadequate colonization of the host rhizosphere, which can plague progress in the utilization of this resource in commercial field-based crop production systems. Thus, although potential exists, this model has continued to lag in its application. New omics' tools have enabled more rapid screening of microbial populations allowing for the identification of strains with multiple functional attributes that may contribute to pathogen suppression. Similarly, these technologies also enable the characterization of consortia in natural systems which provide the framework for construction of synthetic microbiomes for disease control. Harnessing the potential of the microbiome indigenous to agricultural soils for disease suppression through application of specific management strategies has long been a goal of plant pathologists. Although this tactic also possesses limitation, our enhanced understanding of functional attributes of suppressive soil systems through application of community and metagenomic analysis methods provide opportunity to devise effective resource management schemes. As these microbial communities in large part are fostered by the resources endemic to soil and the rhizosphere, substrate mediated recruitment of disease-suppressive microbiomes constitutes a practical means to foster their establishment in crop production systems.