Predicting functional associations from metabolism using bi-partite network algorithms

<p>Abstract</p> <p>Background</p> <p>Metabolic reconstructions contain detailed information about metabolic enzymes and their reactants and products. These networks can be used to infer functional associations between metabolic enzymes. Many methods are based on the number of metabolites shared by two enzymes, or the shortest path between two enzymes. Metabolite sharing can miss associations between non-consecutive enzymes in a serial pathway, and shortest-path algorithms are sensitive to high-degree metabolites such as water and ATP that create connections between enzymes with little functional similarity.</p> <p>Results</p> <p>We present new, fast methods to infer functional associations in metabolic networks. A local method, the degree-corrected Poisson score, is based only on the metabolites shared by two enzymes, but uses the known metabolite degree distribution. A global method, based on graph diffusion kernels, predicts associations between enzymes that do not share metabolites. Both methods are robust to high-degree metabolites. They out-perform previous methods in predicting shared Gene Ontology (GO) annotations and in predicting experimentally observed synthetic lethal genetic interactions. Including cellular compartment information improves GO annotation predictions but degrades synthetic lethal interaction prediction. These new methods perform nearly as well as computationally demanding methods based on flux balance analysis.</p> <p>Conclusions</p> <p>We present fast, accurate methods to predict functional associations from metabolic networks. Biological significance is demonstrated by identifying enzymes whose strong metabolic correlations are missed by conventional annotations in GO, most often enzymes involved in transport vs. synthesis of the same metabolite or other enzyme pairs that share a metabolite but are separated by conventional pathway boundaries. More generally, the methods described here may be valuable for analyzing other types of networks with long-tailed degree distributions and high-degree hubs.</p

Decoupling Environment-Dependent and Independent Genetic Robustness across Bacterial Species

The evolutionary origins of genetic robustness are still under debate: it may arise as a consequence of requirements imposed by varying environmental conditions, due to intrinsic factors such as metabolic requirements, or directly due to an adaptive selection in favor of genes that allow a species to endure genetic perturbations. Stratifying the individual effects of each origin requires one to study the pertaining evolutionary forces across many species under diverse conditions. Here we conduct the first large-scale computational study charting the level of robustness of metabolic networks of hundreds of bacterial species across many simulated growth environments. We provide evidence that variations among species in their level of robustness reflect ecological adaptations. We decouple metabolic robustness into two components and quantify the extents of each: the first, environmental-dependent, is responsible for at least 20% of the non-essential reactions and its extent is associated with the species' lifestyle (specialized/generalist); the second, environmental-independent, is associated (correlation = ∼0.6) with the intrinsic metabolic capacities of a species—higher robustness is observed in fast growers or in organisms with an extensive production of secondary metabolites. Finally, we identify reactions that are uniquely susceptible to perturbations in human pathogens, potentially serving as novel drug-targets

Constraint-based Functional Similarity of Metabolic Genes: Going Beyond Network Topology

Motivation: Several recent studies attempted to establish measures for the similarity between genes that are based on the topological properties of metabolic networks. However, these approaches offer only a static description of the properties of interest and offer moderate (albeit significant) correlations with pertinent experimental data. Results: Using a constraint-based large-scale metabolic model, we present two effectively computable measures of functional gene similarity, one based on the response of the metabolic network to gene knock-outs and the other based on the metabolic flux activity across a variety of growth media. We applied these measures to 750 genes comprising the metabolic network of the budding yeast. Comparing the in silico computed functional similarities to Gene Ontology (GO) annotations and gene expression data, we show that our computational method captures functional similarities between metabolic genes that go beyond those obtained by the topological analysis of metabolic networks alone, thus revealing dynamic characteristics of gene function. Interestingly, the measure based on the network response to different growth environments markedly outperforms the measure based on its response to gene knockouts, though both have some added synergistic value in depicting the functional relationships between metabolic genes. Contact

Directing block copolymer self-assembly with permanent magnets: Photopatterning microdomain alignment and generating oriented nanopores

Magnetic fields are useful for directing block copolymer (BCP) self-assembly, but to date such a field alignment has required large fields (>5 T) necessitating the use of superconducting magnets. We report an approach that circumvents this limitation by introducing labile reactive mesogens into a liquid crystalline (LC) BCP based on a norbornene backbone with a poly(lactide) minority block that forms hexagonally packed cylinders. The free mesogens co-assemble with the smectic A mesophase of the BCP and enable alignment at fields as low as 0.5 T. The remarkable field response originates from the combined effects of enhanced mobility and decreased segregation strength, and the presence of large micron-scale grains in the system. We demonstrate a robust alignment of mesogen-blended samples using simple permanent magnets. The etching of poly(lactide) yields nanoporous films, while the spatially selective microdomain immobilization by UV-induced crosslinking through a photomask provides a versatile mechanism for creating alignment patterns. We anticipate that the nanoporous materials as generated here may find application in membrane fabrication or BCP lithography, while the ability to spatially pattern alignment is promising for the design of mechanical metamaterials exploiting the shape memory effect of LC elastomers