2,267 research outputs found
Methods for identifying regulatory grammars
Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2013.Cataloged from PDF version of thesis.Includes bibliographical references (p. [37]-40).Recent advancements in sequencing technology have made it possible to study the mechanisms of gene regulation, such as protein-DNA binding, at greater resolution and on a greater scale than was previously possible. We present an expectation-maximization learning algorithm that identifies enriched spatial relationships between motifs in sets of DNA sequences. For example, the method will identify spatially constrained motifs colocated in the same regulatory region. We apply our method to biological sequence data and recover previously known prokaryotic promoter spacing constraints demonstrating that joint learning of motifs and spacing constraints is superior to other methods for this task.by Tahin Fahmid Syed.S.M
CORECLUST: identification of the conserved CRM grammar together with prediction of gene regulation
Identification of transcriptional regulatory regions and tracing their internal organization are important for understanding the eukaryotic cell machinery. Cis-regulatory modules (CRMs) of higher eukaryotes are believed to possess a regulatory ‘grammar’, or preferred arrangement of binding sites, that is crucial for proper regulation and thus tends to be evolutionarily conserved. Here, we present a method CORECLUST (COnservative REgulatory CLUster STructure) that predicts CRMs based on a set of positional weight matrices. Given regulatory regions of orthologous and/or co-regulated genes, CORECLUST constructs a CRM model by revealing the conserved rules that describe the relative location of binding sites. The constructed model may be consequently used for the genome-wide prediction of similar CRMs, and thus detection of co-regulated genes, and for the investigation of the regulatory grammar of the system. Compared with related methods, CORECLUST shows better performance at identification of CRMs conferring muscle-specific gene expression in vertebrates and early-developmental CRMs in Drosophila
Precis of neuroconstructivism: how the brain constructs cognition
Neuroconstructivism: How the Brain Constructs Cognition proposes a unifying framework for the study of cognitive development that brings together (1) constructivism (which views development as the progressive elaboration of increasingly complex structures), (2) cognitive neuroscience (which aims to understand the neural mechanisms underlying behavior), and (3) computational modeling (which proposes formal and explicit specifications of information processing). The guiding principle of our approach is context dependence, within and (in contrast to Marr [1982]) between levels of organization. We propose that three mechanisms guide the emergence of representations: competition, cooperation, and chronotopy; which themselves allow for two central processes: proactivity and progressive specialization. We suggest that the main outcome of development is partial representations, distributed across distinct functional circuits. This framework is derived by examining development at the level of single neurons, brain systems, and whole organisms. We use the terms encellment, embrainment, and embodiment to describe the higher-level contextual influences that act at each of these levels of organization. To illustrate these mechanisms in operation we provide case studies in early visual perception, infant habituation, phonological development, and object representations in infancy. Three further case studies are concerned with interactions between levels of explanation: social development, atypical development and within that, developmental dyslexia. We conclude that cognitive development arises from a dynamic, contextual change in embodied neural structures leading to partial representations across multiple brain regions and timescales, in response to proactively specified physical and social environment
Current approaches to gene regulatory network modelling
Many different approaches have been developed to model and simulate gene regulatory networks. We proposed the following categories for gene regulatory network models: network parts lists, network topology models, network control logic models, and dynamic models. Here we will describe some examples for each of these categories. We will study the topology of gene regulatory networks in yeast in more detail, comparing a direct network derived from transcription factor binding data and an indirect network derived from genome-wide expression data in mutants. Regarding the network dynamics we briefly describe discrete and continuous approaches to network modelling, then describe a hybrid model called Finite State Linear Model and demonstrate that some simple network dynamics can be simulated in this model
Computational identification of transcriptional regulatory elements in DNA sequence
Identification and annotation of all the functional elements in the genome, including genes and the regulatory sequences, is a fundamental challenge in genomics and computational biology. Since regulatory elements are frequently short and variable, their identification and discovery using computational algorithms is difficult. However, significant advances have been made in the computational methods for modeling and detection of DNA regulatory elements. The availability of complete genome sequence from multiple organisms, as well as mRNA profiling and high-throughput experimental methods for mapping protein-binding sites in DNA, have contributed to the development of methods that utilize these auxiliary data to inform the detection of transcriptional regulatory elements. Progress is also being made in the identification of cis-regulatory modules and higher order structures of the regulatory sequences, which is essential to the understanding of transcription regulation in the metazoan genomes. This article reviews the computational approaches for modeling and identification of genomic regulatory elements, with an emphasis on the recent developments, and current challenges
Predicting tissue specific cis-regulatory modules in the human genome using pairs of co-occurring motifs
<p>Abstract</p> <p>Background</p> <p>Researchers seeking to unlock the genetic basis of human physiology and diseases have been studying gene transcription regulation. The temporal and spatial patterns of gene expression are controlled by mainly non-coding elements known as cis-regulatory modules (CRMs) and epigenetic factors. CRMs modulating related genes share the regulatory signature which consists of transcription factor (TF) binding sites (TFBSs). Identifying such CRMs is a challenging problem due to the prohibitive number of sequence sets that need to be analyzed.</p> <p>Results</p> <p>We formulated the challenge as a supervised classification problem even though experimentally validated CRMs were not required. Our efforts resulted in a software system named CrmMiner. The system mines for CRMs in the vicinity of related genes. CrmMiner requires two sets of sequences: a mixed set and a control set. Sequences in the vicinity of the related genes comprise the mixed set, whereas the control set includes random genomic sequences. CrmMiner assumes that a large percentage of the mixed set is made of background sequences that do not include CRMs. The system identifies pairs of closely located motifs representing vertebrate TFBSs that are enriched in the training mixed set consisting of 50% of the gene loci. In addition, CrmMiner selects a group of the enriched pairs to represent the tissue-specific regulatory signature. The mixed and the control sets are searched for candidate sequences that include any of the selected pairs. Next, an optimal Bayesian classifier is used to distinguish candidates found in the mixed set from their control counterparts. Our study proposes 62 tissue-specific regulatory signatures and putative CRMs for different human tissues and cell types. These signatures consist of assortments of ubiquitously expressed TFs and tissue-specific TFs. Under controlled settings, CrmMiner identified known CRMs in noisy sets up to 1:25 signal-to-noise ratio. CrmMiner was 21-75% more precise than a related CRM predictor. The sensitivity of the system to locate known human heart enhancers reached up to 83%. CrmMiner precision reached 82% while mining for CRMs specific to the human CD4<sup>+ </sup>T cells. On several data sets, the system achieved 99% specificity.</p> <p>Conclusion</p> <p>These results suggest that CrmMiner predictions are accurate and likely to be tissue-specific CRMs. We expect that the predicted tissue-specific CRMs and the regulatory signatures broaden our knowledge of gene transcription regulation.</p
An ensemble learning approach to reverse-engineering transcriptional regulatory networks from time-series gene expression data
Background
One of the most challenging tasks in the post-genomic era is to reconstruct the transcriptional regulatory networks. The goal is to reveal, for each gene that responds to a certain biological event, which transcription factors affect its expression, and how a set of transcription factors coordinate to accomplish temporal and spatial specific regulations. Results
Here we propose a supervised machine learning approach to address these questions. We focus our study on the gene transcriptional regulation of the cell cycle in the budding yeast, thanks to the large amount of data available and relatively well-understood biology, although the main ideas of our method can be applied to other data as well. Our method starts with building an ensemble of decision trees for each microarray data to capture the association between the expression levels of yeast genes and the binding of transcription factors to gene promoter regions, as determined by chromatin immunoprecipitation microarray (ChIP-chip) experiment. Cross-validation experiments show that the method is more accurate and reliable than the naive decision tree algorithm and several other ensemble learning methods. From the decision tree ensembles, we extract logical rules that explain how a set of transcription factors act in concert to regulate the expression of their targets. We further compute a profile for each rule to show its regulation strengths at different time points. We also propose a spline interpolation method to integrate the rule profiles learned from several time series expression data sets that measure the same biological process. We then combine these rule profiles to build a transcriptional regulatory network for the yeast cell cycle. Compared to the results in the literature, our method correctly identifies all major known yeast cell cycle transcription factors, and assigns them into appropriate cell cycle phases. Our method also identifies many interesting synergetic relationships among these transcription factors, most of which are well known, while many of the rest can also be supported by other evidences. Conclusion
The high accuracy of our method indicates that our method is valid and robust. As more gene expression and transcription factor binding data become available, we believe that our method is useful for reconstructing large-scale transcriptional regulatory networks in other species as well
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