Recent transcriptome data from yeast, worm, plants, and humans has shown that alternative polyadenylation (APA), a mechanism that enables expression of multiple 3′UTR isoforms for the same gene, is widespread in eukaryotic organisms. It is still poorly understood why metazoans require multiple 3′UTRs for the same gene, but accumulating evidence suggests that APA is largely regulated at a tissue-specific level. APA may direct combinatorial variation between cis-elements and microRNAs, perhaps to regulate gene expression in a tissue-specific manner. Apart from a few single gene anecdotes, this idea has not been systematically explored.
This dissertation research employs a systems biology approach to study the somatic tissue dynamics of APA and its impact on microRNA targeting networks in the small nematode C. elegans. In the first aim, tools were developed and applied to isolate and sequence mRNA from worm intestine and muscle tissues, which revealed pervasive tissue-specific APA correlated with microRNA regulation. The second aim provides genetic evidence that two worm genes use APA to escape repression by microRNAs in the body muscle. Finally, in aim three, mRNA from five additional somatic worm tissues was sequenced and their 3′ends mapped, allowing for an integrative study of APA and microRNA targeting dynamics in worms. Together, this work provides evidence that APA is a pervasive mechanism operating in somatic tissues of C. elegans with the potential to significantly rearrange their microRNA regulatory networks and precisely dose their gene expression.
This project utilized computational tools to analyze large data sets and interpreted the results from historical and philosophical perspectives. Tools deployed were derived from scientometrics, corpus linguistics, text-based analysis, network analysis, and GIS analysis to analyze more than 9000 articles (metadata and text) on systems biology. The application of these tools to a HPS project represents a novel approach.
The dissertation shows that systems biology has transitioned from a more mathematical, computational, and engineering-oriented discipline focusing on modeling to a more biology-oriented discipline that uses modeling as a means to address real biological problems. Also, the results show that bioengineering and medical research has increased within systems biology. This is reflected in the increase of the centrality of biology-related concepts such as cancer, over time. The dissertation also compares the development of systems biology in China with some other parts of the world, and reveals regional differences, such as a unique trajectory of systems biology in China related to a focus on traditional Chinese medicine.
This dissertation adds to the historiography of modern biology where few studies have focused on systems biology compared with the history of molecular biology and evolutionary biology.
The first chapter revisits some of the key experiments that contributed to the development of the repression model of genetic regulation in the lac operon and concludes that the early research on gene expression and genetic regulation depict an iterative and integrative process, which was neither reductionist nor holist. In doing so, it challenges a common application of a conceptual framework in the history of biology and offers an alternative framework. The second chapter argues that the concept of emergence in the history and philosophy of biology is too ambiguous to account for the current research in post-genomic molecular biology and it is often erroneously used to argue against some reductionist theses. The third chapter investigates the use of network representations of gene expression in developmental evolution research and takes up some of the conceptual and methodological problems it has generated. The concluding comments present potential avenues for future research arising from each substantial chapter.
In sum, this dissertation argues that the epistemic practices of gene expression research are an iterative and integrative process, which produces theoretical representations of the complex interactions in gene expression as networks. Moreover, conceptualizing these interactions as networks constrains empirical research strategies by the limited number of ways in which gene expression can be controlled through general rules of network interactions. Making these strategies explicit helps to clarify how they can explain the dynamic and adaptive features of genomes.
Background:
Drosophila gene expression pattern images document the spatiotemporal dynamics of gene expression during embryogenesis. A comparative analysis of these images could provide a fundamentally important way for studying the regulatory networks governing development. To facilitate pattern comparison and searching, groups of images in the Berkeley Drosophila Genome Project (BDGP) high-throughput study were annotated with a variable number of anatomical terms manually using a controlled vocabulary. Considering that the number of available images is rapidly increasing, it is imperative to design computational methods to automate this task.
Results:
We present a computational method to annotate gene expression pattern images automatically. The proposed method uses the bag-of-words scheme to utilize the existing information on pattern annotation and annotates images using a model that exploits correlations among terms. The proposed method can annotate images individually or in groups (e.g., according to the developmental stage). In addition, the proposed method can integrate information from different two-dimensional views of embryos. Results on embryonic patterns from BDGP data demonstrate that our method significantly outperforms other methods.
Conclusion:
The proposed bag-of-words scheme is effective in representing a set of annotations assigned to a group of images, and the model employed to annotate images successfully captures the correlations among different controlled vocabulary terms. The integration of existing annotation information from multiple embryonic views improves annotation performance.
Drosophila melanogaster has been established as a model organism for investigating the developmental gene interactions. The spatio-temporal gene expression patterns of Drosophila melanogaster can be visualized by in situ hybridization and documented as digital images. Automated and efficient tools for analyzing these expression images will provide biological insights into the gene functions, interactions, and networks. To facilitate pattern recognition and comparison, many web-based resources have been created to conduct comparative analysis based on the body part keywords and the associated images. With the fast accumulation of images from high-throughput techniques, manual inspection of images will impose a serious impediment on the pace of biological discovery. It is thus imperative to design an automated system for efficient image annotation and comparison.
Results
We present a computational framework to perform anatomical keywords annotation for Drosophila gene expression images. The spatial sparse coding approach is used to represent local patches of images in comparison with the well-known bag-of-words (BoW) method. Three pooling functions including max pooling, average pooling and Sqrt (square root of mean squared statistics) pooling are employed to transform the sparse codes to image features. Based on the constructed features, we develop both an image-level scheme and a group-level scheme to tackle the key challenges in annotating Drosophila gene expression pattern images automatically. To deal with the imbalanced data distribution inherent in image annotation tasks, the undersampling method is applied together with majority vote. Results on Drosophila embryonic expression pattern images verify the efficacy of our approach.
Conclusion
In our experiment, the three pooling functions perform comparably well in feature dimension reduction. The undersampling with majority vote is shown to be effective in tackling the problem of imbalanced data. Moreover, combining sparse coding and image-level scheme leads to consistent performance improvement in keywords annotation.
Learning Sparse Representations for Fruit-Fly Gene Expression Pattern Image Annotation and Retrieval
Fruit fly embryogenesis is one of the best understood animal development systems, and the spatiotemporal gene expression dynamics in this process are captured by digital images. Analysis of these high-throughput images will provide novel insights into the functions, interactions, and networks of animal genes governing development. To facilitate comparative analysis, web-based interfaces have been developed to conduct image retrieval based on body part keywords and images. Currently, the keyword annotation of spatiotemporal gene expression patterns is conducted manually. However, this manual practice does not scale with the continuously expanding collection of images. In addition, existing image retrieval systems based on the expression patterns may be made more accurate using keywords.
Results
In this article, we adapt advanced data mining and computer vision techniques to address the key challenges in annotating and retrieving fruit fly gene expression pattern images. To boost the performance of image annotation and retrieval, we propose representations integrating spatial information and sparse features, overcoming the limitations of prior schemes.
Conclusions
We perform systematic experimental studies to evaluate the proposed schemes in comparison with current methods. Experimental results indicate that the integration of spatial information and sparse features lead to consistent performance improvement in image annotation, while for the task of retrieval, sparse features alone yields better results.