Otimização da metodologia de embriogênese somática visando a propagação clonal de genótipos elite de cacau (Theobroma cacao L.)

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Managing affect in learners' questions in undergraduate science

This is the author's accepted manuscript. The final published article is available from the link below. Copyright @ 2012 Society for Research into Higher Education.This article aims to position students' classroom questioning within the literature surrounding affect and its impact on learning. The article consists of two main sections. First, the act of questioning is discussed in order to highlight how affect shapes the process of questioning, and a four-part genesis to question-asking that we call CARE is described: the construction, asking, reception and evaluation of a learner's question. This work is contextualised through studies in science education and through our work with university students in undergraduate chemistry, although conducted in the firm belief that it has more general application. The second section focuses on teaching strategies to encourage and manage learners' questions, based here upon the conviction that university students in this case learn through questioning, and that an inquiry-based environment promotes better learning than a simple ‘transmission’ setting. Seven teaching strategies developed from the authors' work are described, where university teachers ‘scaffold’ learning through supporting learners' questions, and working with these to structure and organise the content and the shape of their teaching. The article concludes with a summary of the main issues, highlighting the impact of the affective dimension of learning through questioning, and a discussion of the implications for future research

Reverse Engineering Gene Networks with ANN: Variability in Network Inference Algorithms

Motivation :Reconstructing the topology of a gene regulatory network is one of the key tasks in systems biology. Despite of the wide variety of proposed methods, very little work has been dedicated to the assessment of their stability properties. Here we present a methodical comparison of the performance of a novel method (RegnANN) for gene network inference based on multilayer perceptrons with three reference algorithms (ARACNE, CLR, KELLER), focussing our analysis on the prediction variability induced by both the network intrinsic structure and the available data. Results: The extensive evaluation on both synthetic data and a selection of gene modules of "Escherichia coli" indicates that all the algorithms suffer of instability and variability issues with regards to the reconstruction of the topology of the network. This instability makes objectively very hard the task of establishing which method performs best. Nevertheless, RegnANN shows MCC scores that compare very favorably with all the other inference methods tested. Availability: The software for the RegnANN inference algorithm is distributed under GPL3 and it is available at the corresponding author home page (http://mpba.fbk.eu/grimaldi/regnann-supmat

DREAM4: Combining Genetic and Dynamic Information to Identify Biological Networks and Dynamical Models

Current technologies have lead to the availability of multiple genomic data types in sufficient quantity and quality to serve as a basis for automatic global network inference. Accordingly, there are currently a large variety of network inference methods that learn regulatory networks to varying degrees of detail. These methods have different strengths and weaknesses and thus can be complementary. However, combining different methods in a mutually reinforcing manner remains a challenge.We investigate how three scalable methods can be combined into a useful network inference pipeline. The first is a novel t-test-based method that relies on a comprehensive steady-state knock-out dataset to rank regulatory interactions. The remaining two are previously published mutual information and ordinary differential equation based methods (tlCLR and Inferelator 1.0, respectively) that use both time-series and steady-state data to rank regulatory interactions; the latter has the added advantage of also inferring dynamic models of gene regulation which can be used to predict the system's response to new perturbations.Our t-test based method proved powerful at ranking regulatory interactions, tying for first out of methods in the DREAM4 100-gene in-silico network inference challenge. We demonstrate complementarity between this method and the two methods that take advantage of time-series data by combining the three into a pipeline whose ability to rank regulatory interactions is markedly improved compared to either method alone. Moreover, the pipeline is able to accurately predict the response of the system to new conditions (in this case new double knock-out genetic perturbations). Our evaluation of the performance of multiple methods for network inference suggests avenues for future methods development and provides simple considerations for genomic experimental design. Our code is publicly available at http://err.bio.nyu.edu/inferelator/

Inferring gene regression networks with model trees

<p>Abstract</p> <p>Background</p> <p>Novel strategies are required in order to handle the huge amount of data produced by microarray technologies. To infer gene regulatory networks, the first step is to find direct regulatory relationships between genes building the so-called gene co-expression networks. They are typically generated using correlation statistics as pairwise similarity measures. Correlation-based methods are very useful in order to determine whether two genes have a strong global similarity but do not detect local similarities.</p> <p>Results</p> <p>We propose model trees as a method to identify gene interaction networks. While correlation-based methods analyze each pair of genes, in our approach we generate a single regression tree for each gene from the remaining genes. Finally, a graph from all the relationships among output and input genes is built taking into account whether the pair of genes is statistically significant. For this reason we apply a statistical procedure to control the false discovery rate. The performance of our approach, named R<smcaps>EG</smcaps>N<smcaps>ET</smcaps>, is experimentally tested on two well-known data sets: <it>Saccharomyces Cerevisiae </it>and E.coli data set. First, the biological coherence of the results are tested. Second the E.coli transcriptional network (in the Regulon database) is used as control to compare the results to that of a correlation-based method. This experiment shows that R<smcaps>EG</smcaps>N<smcaps>ET</smcaps> performs more accurately at detecting true gene associations than the Pearson and Spearman zeroth and first-order correlation-based methods.</p> <p>Conclusions</p> <p>R<smcaps>EG</smcaps>N<smcaps>ET</smcaps> generates gene association networks from gene expression data, and differs from correlation-based methods in that the relationship between one gene and others is calculated simultaneously. Model trees are very useful techniques to estimate the numerical values for the target genes by linear regression functions. They are very often more precise than linear regression models because they can add just different linear regressions to separate areas of the search space favoring to infer localized similarities over a more global similarity. Furthermore, experimental results show the good performance of R<smcaps>EG</smcaps>N<smcaps>ET</smcaps>.</p

The Escherichia coli transcriptome mostly consists of independently regulated modules

Underlying cellular responses is a transcriptional regulatory network (TRN) that modulates gene expression. A useful description of the TRN would decompose the transcriptome into targeted effects of individual transcriptional regulators. Here, we apply unsupervised machine learning to a diverse compendium of over 250 high-quality Escherichia coli RNA-seq datasets to identify 92 statistically independent signals that modulate the expression of specific gene sets. We show that 61 of these transcriptomic signals represent the effects of currently characterized transcriptional regulators. Condition-specific activation of signals is validated by exposure of E. coli to new environmental conditions. The resulting decomposition of the transcriptome provides: a mechanistic, systems-level, network-based explanation of responses to environmental and genetic perturbations; a guide to gene and regulator function discovery; and a basis for characterizing transcriptomic differences in multiple strains. Taken together, our results show that signal summation describes the composition of a model prokaryotic transcriptome

Reconstructing Gene Regulatory Networks That Control Hematopoietic Commitment.

Hematopoietic stem cells (HSCs) reside at the apex of the hematopoietic hierarchy, possessing the ability to self-renew and differentiate toward all mature blood lineages. Along with more specialized progenitor cells, HSCs have an essential role in maintaining a healthy blood system. Incorrect regulation of cell fate decisions in stem/progenitor cells can lead to an imbalance of mature blood cell populations-a situation seen in diseases such as leukemia. Transcription factors, acting as part of complex regulatory networks, are known to play an important role in regulating hematopoietic cell fate decisions. Yet, discovering the interactions present in these networks remains a big challenge. Here, we discuss a computational method that uses single-cell gene expression data to reconstruct Boolean gene regulatory network models and show how this technique can be applied to enhance our understanding of transcriptional regulation in hematopoiesis.Work in the author’s laboratory is supported by grants from the Wellcome, Bloodwise, Cancer Research UK, NIH-NIDDK and core support grants by the Wellcome to the Cambridge Institute for Medical Research and Wellcome & MRC Cambridge Stem Cell Institute. F.K.H. is a recipient of a Medical Research Council PhD Studentship

Gene Regulatory Networks from Multifactorial Perturbations Using Graphical Lasso: Application to the DREAM4 Challenge

A major challenge in the field of systems biology consists of predicting gene regulatory networks based on different training data. Within the DREAM4 initiative, we took part in the multifactorial sub-challenge that aimed to predict gene regulatory networks of size 100 from training data consisting of steady-state levels obtained after applying multifactorial perturbations to the original in silico network