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Biological experience and intuition suggest that self-replication is an inherently complex phenomenon, and early cellular automata models support that conception. More recently, simpler computational models of self-directed replication called sheathed loops have been developed. It is shown here that "unsheathing" these structures and altering certain assumptions about the symmetry of their components leads to a family of nontrivial self-replicating structures, some substantially smaller and simpler than those previously reported. The dependence of replication time and transition function complexity on initial structure size, cell state symmetry, and neighborhood are examined. These results support the view that self-replication is not an inherently complex phenomenon but rather an emergent property arising from local interactions in systems that can be much simpler than is generally believed.

Past models of somatosensory cortex have successfully demonstrated map formation and subsequent map reorganization following localized repetitive stimuli or deafferentation. They provide an impressive demonstration that fairly simple assumptions about cortical connectivity and synaptic plasticity can account for several observations concerning cortical maps. However, past models have not successfully demonstrated spontaneous map reorganization following cortical lesions. Recently, an assumption universally used in these and other cortex models, that peristimulus inhibition is due solely to horizontal intracortical inhibitory connections, has been questioned and an additional mechanism, the competitive distribution of activity, has been proposed. We implemented a computational model of somatosensory cortex based on competitive distribution of activity. This model exhibits spontaneous map reorganization in response to a cortical lesion, going through a two-phase reorganization process. These results make a testable prediction that can be used to experimentally support or refute part of the competitive distribution hypothesis, and may lead to practically useful computational models of recovery following stroke.

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BackgroundLarge-scale genetic studies of common human diseases have focused almost exclusively on the independent main effects of single-nucleotide polymorphisms (SNPs) on disease susceptibility. These studies have had some success, but much of the genetic architecture of common disease remains unexplained. Attention is now turning to detecting SNPs that impact disease susceptibility in the context of other genetic factors and environmental exposures. These context-dependent genetic effects can manifest themselves as non-additive interactions, which are more challenging to model using parametric statistical approaches. The dimensionality that results from a multitude of genotype combinations, which results from considering many SNPs simultaneously, renders these approaches underpowered. We previously developed the multifactor dimensionality reduction (MDR) approach as a nonparametric and genetic model-free machine learning alternative. Approaches such as MDR can improve the power to detect gene-gene interactions but are limited in their ability to exhaustively consider SNP combinations in genome-wide association studies (GWAS), due to the combinatorial explosion of the search space. We introduce here a stochastic search algorithm called Crush for the application of MDR to modeling high-order gene-gene interactions in genome-wide data. The Crush-MDR approach uses expert knowledge to guide probabilistic searches within a framework that capitalizes on the use of biological knowledge to filter gene sets prior to analysis. Here we evaluated the ability of Crush-MDR to detect hierarchical sets of interacting SNPs using a biology-based simulation strategy that assumes non-additive interactions within genes and additivity in genetic effects between sets of genes within a biochemical pathway.ResultsWe show that Crush-MDR is able to identify genetic effects at the gene or pathway level significantly better than a baseline random search with the same number of model evaluations. We then applied the same methodology to a GWAS for Alzheimer’s disease and showed base level validation that Crush-MDR was able to identify a set of interacting genes with biological ties to Alzheimer’s disease.ConclusionsWe discuss the role of stochastic search and cloud computing for detecting complex genetic effects in genome-wide data.

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