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We demonstrate how a single-celled organism could undertake associative learning. Although to date only one previous study has found experimental evidence for such learning, there is no reason in principle why it should not occur. We propose a gene regulatory network that is capable of associative learning between any pre-specified set of chemical signals, in a Hebbian manner, within a single cell. A mathematical model is developed, and simulations show a clear learned response. A preliminary design for implementing this model using plasmids within Escherichia coli is presented, along with an alternative approach, based on double-phosphorylated protein kinases.

We present BioGraph, a data integration and data mining platform for the exploration and discovery of biomedical information. The platform offers prioritizations of putative disease genes, supported by functional hypotheses. We show that BioGraph can retrospectively confirm recently discovered disease genes and identify potential susceptibility genes, outperforming existing technologies, without requiring prior domain knowledge. Additionally, BioGraph allows for generic biomedical applications beyond gene discovery. BioGraph is accessible at http://www.biograph.be.

In biological organisms, networks of chemical reactions control the processing of information in a cell. A general approach to study the behavior of these networks is to analyze common modules. Instead of this analytical approach to study signaling networks, we construct functional motifs from the bottom up. We formulate conceptual networks of biochemical reactions that implement elementary algebraic operations over the domain and range of positive real numbers. We discuss how the steady state behavior relates to algebraic functions, and study the stability of the networks' fixed points. The primitive networks are then combined in feed-forward networks, allowing us to compute a diverse range of algebraic functions, such as polynomials. With this systematic approach, we explore the range of mathematical functions that can be constructed with these networks.

In this paper we present a machine learning system that finds the scope of negation in biomedical texts. The system consists of two memory-based engines, one that decides if the tokens in a sentence are negation signals, and another that finds the full scope of these negation signals. Our approach to negation detection differs in two main aspects from existing research on negation. First, we focus on finding the scope of negation signals, instead of determining whether a term is negated or not. Second, we apply supervised machine learning techniques, whereas most existing systems apply rule-based algorithms. As far as we know, this way of approaching the negation scope finding task is novel.

Sequence analysis of 13 microRNA (miRNA) genes expressed in the human brain and located in genomic regions associated with schizophrenia and/or bipolar disorder, in a northern Swedish patient/control population, resulted in the discovery of two functional variants in the MIR137 gene. On the basis of their location and the allele frequency differences between patients and controls, we explored the hypothesis that the discovered variants impact the expression of the mature miRNA and consequently influence global mRNA expression affecting normal brain functioning. Using neuronal-like SH-SY5Y cells, we demonstrated significantly reduced mature miR-137 levels in the cells expressing the variant miRNA gene. Subsequent transcriptome analysis showed that the reduction in miR-137 expression led to the deregulation of gene sets involved in synaptogenesis and neuronal transmission, all implicated in psychiatric disorders. Our functional findings add to the growing data, which implicate that miR-137 has an important role in the etiology of psychiatric disorders and emphasizes its involvement in nervous system development and proper synaptic function.

Over the last years, genome-wide studies consistently showed an increased burden of rare copy number variants (CNVs) in schizophrenia patients, supporting the "common disease, rare variant" hypothesis in at least a subset of patients. We hypothesize that in families with a high burden of disease, and thus probably a high genetic load influencing disease susceptibility, rare CNVs might be involved in the etiology of schizophrenia. We performed a genome-wide CNV analysis in the index patients of eight families with multiple schizophrenia affected members, and consecutively performed a detailed family analysis for the most relevant CNVs. One index patient showed a DRD5 containing duplication. A second index patient presented with an NRXN1 containing deletion and two adjacent duplications containing MYT1L and SNTG2. Detailed analysis in the subsequent families showed segregation of the identified CNVs. With this study we show the importance of screening high burden families for rare CNVs, which will not only broaden our knowledge concerning the molecular genetic mechanisms involved in schizophrenia but also allow the use of the obtained genetic data to provide better clinical care to these families in general and to non-symptomatic causal CNV carriers in particular.

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