ALUminating the Path of Atherosclerosis Progression: Chaos Theory Suggests a Role for Alu Repeats in the Development of Atherosclerotic Vascular Disease

Atherosclerosis (ATH) and coronary artery disease (CAD) are chronic inflammatory diseases with an important genetic background; they derive from the cumulative effect of multiple common risk alleles, most of which are located in genomic noncoding regions. These complex diseases behave as nonlinear dynamical systems that show a high dependence on their initial conditions; thus, long-term predictions of disease progression are unreliable. One likely possibility is that the nonlinear nature of ATH could be dependent on nonlinear correlations in the structure of the human genome. In this review, we show how chaos theory analysis has highlighted genomic regions that have shared specific structural constraints, which could have a role in ATH progression. These regions were shown to be enriched with repetitive sequences of the Alu family, genomic parasites that have colonized the human genome, which show a particular secondary structure and are involved in the regulation of gene expression. Here, we show the impact of Alu elements on the mechanisms that regulate gene expression, especially highlighting the molecular mechanisms via which the Alu elements alter the inflammatory response. We devote special attention to their relationship with the long noncoding RNA (lncRNA); antisense noncoding RNA in the INK4 locus (ANRIL), a risk factor for ATH; their role as microRNA (miRNA) sponges; and their ability to interfere with the regulatory circuitry of the (nuclear factor kappa B) NF-κB response. We aim to characterize ATH as a nonlinear dynamic system, in which small initial alterations in the expression of a number of repetitive elements are somehow amplified to reach phenotypic significance

DNA entropy reveals a significant difference in complexity between housekeeping and tissue specific gene promoters

BACKGROUND The complexity of DNA can be quantified using estimates of entropy. Variation in DNA complexity is expected between the promoters of genes with different transcriptional mechanisms; namely housekeeping (HK) and tissue specific (TS). The former are transcribed constitutively to maintain general cellular functions, and the latter are transcribed in restricted tissue and cells types for specific molecular events. It is known that promoter features in the human genome are related to tissue specificity, but this has been difficult to quantify on a genomic scale. If entropy effectively quantifies DNA complexity, calculating the entropies of HK and TS gene promoters as profiles may reveal significant differences. RESULTS Entropy profiles were calculated for a total dataset of 12,003 human gene promoters and for 501 housekeeping (HK) and 587 tissue specific (TS) human gene promoters. The mean profiles show the TS promoters have a significantly lower entropy (p<2.2e-16) than HK gene promoters. The entropy distributions for the 3 datasets show that promoter entropies could be used to identify novel HK genes. CONCLUSION Functional features comprise DNA sequence patterns that are non-random and hence they have lower entropies. The lower entropy of TS gene promoters can be explained by a higher density of positive and negative regulatory elements, required for genes with complex spatial and temporary expression

Complexity, Emergent Systems and Complex Biological Systems:\ud Complex Systems Theory and Biodynamics. [Edited book by I.C. Baianu, with listed contributors (2011)]

An overview is presented of System dynamics, the study of the behaviour of complex systems, Dynamical system in mathematics Dynamic programming in computer science and control theory, Complex systems biology, Neurodynamics and Psychodynamics.\u

The Reasonable Effectiveness of Randomness in Scalable and Integrative Gene Regulatory Network Inference and Beyond

Gene regulation is orchestrated by a vast number of molecules, including transcription factors and co-factors, chromatin regulators, as well as epigenetic mechanisms, and it has been shown that transcriptional misregulation, e.g., caused by mutations in regulatory sequences, is responsible for a plethora of diseases, including cancer, developmental or neurological disorders. As a consequence, decoding the architecture of gene regulatory networks has become one of the most important tasks in modern (computational) biology. However, to advance our understanding of the mechanisms involved in the transcriptional apparatus, we need scalable approaches that can deal with the increasing number of large-scale, high-resolution, biological datasets. In particular, such approaches need to be capable of efficiently integrating and exploiting the biological and technological heterogeneity of such datasets in order to best infer the underlying, highly dynamic regulatory networks, often in the absence of sufficient ground truth data for model training or testing. With respect to scalability, randomized approaches have proven to be a promising alternative to deterministic methods in computational biology. As an example, one of the top performing algorithms in a community challenge on gene regulatory network inference from transcriptomic data is based on a random forest regression model. In this concise survey, we aim to highlight how randomized methods may serve as a highly valuable tool, in particular, with increasing amounts of large-scale, biological experiments and datasets being collected. Given the complexity and interdisciplinary nature of the gene regulatory network inference problem, we hope our survey maybe helpful to both computational and biological scientists. It is our aim to provide a starting point for a dialogue about the concepts, benefits, and caveats of the toolbox of randomized methods, since unravelling the intricate web of highly dynamic, regulatory events will be one fundamental step in understanding the mechanisms of life and eventually developing efficient therapies to treat and cure diseases

Image Representations of DNA allow Classification by Convolutional Neural Networks

In metagenomic analyses the rapid and accurate identification of DNA sequences is important. This is confounded by the existence of novel species not contained in databases. There exist many methods to identify sequences, but with the increasing amounts of sequencing data from high-throughput technologies, the use of new deep learning methods are made more viable. In an attempt to address this it was decided to use Convolutional Neural Networks (CNNs) to classify DNA sequences of archaea, which are important in anaerobic digestion. CNNs were trained on two different image representations of DNA sequences, Chaos Game Representation (CGR) and Reshape. Three phyla of archaea and randomly generated sequences were used. These were compared against simpler machine learning models trained on the 4-mer and 7-mer frequencies of the same sequences. It was found that the simpler models performed better than CNNs trained on either image representation, and that Reshape was the poorest representation. However, by shuffling sequences whilst preserving 4-mer count it was found that the Reshape model had learnt 4-mers as an important feature. It was also found that the Reshape model was able to perform equally well without depending on the use of 4-mers, indicating that certain training regimes may uncover novel features. The errors of these models were also random or in weak disagreement, suggesting ensemble methods would be viable and help to identify problematic sequences

Modern Computing Techniques for Solving Genomic Problems

With the advent of high-throughput genomics, biological big data brings challenges to scientists in handling, analyzing, processing and mining this massive data. In this new interdisciplinary field, diverse theories, methods, tools and knowledge are utilized to solve a wide variety of problems. As an exploration, this dissertation project is designed to combine concepts and principles in multiple areas, including signal processing, information-coding theory, artificial intelligence and cloud computing, in order to solve the following problems in computational biology: (1) comparative gene structure detection, (2) DNA sequence annotation, (3) investigation of CpG islands (CGIs) for epigenetic studies. Briefly, in problem #1, sequences are transformed into signal series or binary codes. Similar to the speech/voice recognition, similarity is calculated between two signal series and subsequently signals are stitched/matched into a temporal sequence. In the nature of binary operation, all calculations/steps can be performed in an efficient and accurate way. Improving performance in terms of accuracy and specificity is the key for a comparative method. In problem #2, DNA sequences are encoded and transformed into numeric representations for deep learning methods. Encoding schemes greatly influence the performance of deep learning algorithms. Finding the best encoding scheme for a particular application of deep learning is significant. Three applications (detection of protein-coding splicing sites, detection of lincRNA splicing sites and improvement of comparative gene structure identification) are used to show the computing power of deep neural networks. In problem #3, CpG sites are assigned certain energy and a Gaussian filter is applied to detection of CpG islands. By using the CpG box and Markov model, we investigate the properties of CGIs and redefine the CGIs using the emerging epigenetic data. In summary, these three problems and their solutions are not isolated; they are linked to modern techniques in such diverse areas as signal processing, information-coding theory, artificial intelligence and cloud computing. These novel methods are expected to improve the efficiency and accuracy of computational tools and bridge the gap between biology and scientific computing

Transcriptional regulation of the Arabidopsis thaliana flowering-time gene GIGANTEA

Plants adjust their developmental programmes to the surrounding environment, which allows them to colonise almost every habitat on earth. One key player in regulating different developmental processes in response to the environment in Arabidopsis thaliana is GIGANTEA (GI), a circadian-clock regulated protein that is most abundant in the evening. The precise timing of GI transcription is proposed to be crucial for it to fulfil its different functions such as the regulation of flowering time, raising the question of how GI itself is transcriptionally regulated. A combination of phylogenetic and genome-wide bioinformatic analysis as well as the study of transgenic promoter-reporter and complementation lines demonstrated that a highly conserved 700bp block within the GI promoter is important for many aspects of GI regulation and function. These include the response to light and temperature, control of hypocotyl growth and the regulation of flowering time. Moreover, conserved Evening Element (EE) motifs within this block were shown to be important for several specific features of GI transcription. Having shown the importance of EEs within the GI promoter, all EEs were mapped on a genome-wide level and co-occurrences with other circadian-clock related cis-regulatory elements were determined. This analysis revealed striking patterns between EEs and between other cis-elements that gave insights into the general transcriptional code in plants. Taken together, this thesis demonstrates that the pleiotropic functions of GI in light signalling, the circadian clock, freezing tolerance and the regulation of flowering time are reflected within its promoter. This work not only contributed to understanding the complex transcriptional regulation of GI and its function in the plant, but also provided novel insights into the regulation of co-expressed genes and the general transcriptional code in plants

Opportunities and obstacles for deep learning in biology and medicine

Deep learning describes a class of machine learning algorithms that are capable of combining raw inputs into layers of intermediate features. These algorithms have recently shown impressive results across a variety of domains. Biology and medicine are data-rich disciplines, but the data are complex and often ill-understood. Hence, deep learning techniques may be particularly well suited to solve problems of these fields. We examine applications of deep learning to a variety of biomedical problems-patient classification, fundamental biological processes and treatment of patients-and discuss whether deep learning will be able to transform these tasks or if the biomedical sphere poses unique challenges. Following from an extensive literature review, we find that deep learning has yet to revolutionize biomedicine or definitively resolve any of the most pressing challenges in the field, but promising advances have been made on the prior state of the art. Even though improvements over previous baselines have been modest in general, the recent progress indicates that deep learning methods will provide valuable means for speeding up or aiding human investigation. Though progress has been made linking a specific neural network\u27s prediction to input features, understanding how users should interpret these models to make testable hypotheses about the system under study remains an open challenge. Furthermore, the limited amount of labelled data for training presents problems in some domains, as do legal and privacy constraints on work with sensitive health records. Nonetheless, we foresee deep learning enabling changes at both bench and bedside with the potential to transform several areas of biology and medicine

On the Analysis of DNA Methylation

Recent genome-wide studies lend support to the idea that the patterns of DNA methylation are in some way related either causally or as a readout of cell-type specific protein binding. We lay the groundwork for a framework to test whether the pattern of DNA methylation levels in a cell combined with protein binding models is sufficient to completely describe the location of the component of proteins binding to its genome in an assayed context. There is only one method, whole-genome bisulfite sequencing, WGBS, available to study DNA methylation genome-wide at such high resolution, however its accuracy has not been determined on the scale of individual binding locations. We address this with a two-fold approach. First, we developed an alternative high-resolution, whole-genome assay using a combination of an enrichment-based and a restriction-enzyme-based assay of methylation, methylCRF. While both assays are considered inferior to WGBS, by using two distinct assays, this method has the advantage that each assay in part cancels out the biases of the other. Additionally, this method is up to 15 times lower in cost than WGBS. By formulating the estimation of methylation from the two methods as a structured prediction problem using a conditional random field, this work will also address the general problem of incorporating data of varying qualities -a common characteristic of biological data- for the purpose of prediction. We show that methylCRF is concordant with WGBS within the range of two WGBS methylomes. Due to the lower cost, we were able to analyze at high-resolution, methylation across more cell-types than previously possible and estimate that 28% of CpGs, in regions comprising 11% of the genome, show variable methylation and are enriched in regulatory regions. Secondly, we show that WGBS has inherent resulution limitations in a read count dependent manner and that the identification of unmethylated regions is highly affected by GC-bias in the underlying protocol suggesting simple estimate procedures may not be sufficient for high-resolution analysis. To address this, we propose a novel approach to DNA methylation analysis using change point detection instead of estimating methylation level directly. However, we show that current change-point detection methods are not robust to methylation signal, we therefore explore how to extend current non-parametric methods to simultaneously find change-points as well as characteristic methylation levels. We believe this framework may have the power to examine the connection between changes in methylation and transcription factor binding in the context of cell-type specific behaviors

Identification and analysis of patterns in DNA sequences, the genetic code and transcriptional gene regulation

The present cumulative work consists of six articles linked by the topic ”Identification and Analysis of Patterns in DNA sequences, the Genetic Code and Transcriptional Gene Regulation”. We have applied a binary coding, to efficiently findpatterns within nucleotide sequences. In the first and second part of my work one single bit to encode all four nucleotides is used. The three possibilities of a one - bit coding are: keto (G,U) - amino (A,C) bases, strong (G,C) - weak (A,U) bases, and purines (G,A) - pyrimidines (C,U). We found out that the best pattern could be observed using the purine - pyrimidine coding. Applying this coding we have succeeded in finding a new representation of the genetic code which has been published under the title ”A New Classification Scheme of the Genetic Code” in ”Journal of Molecular Biology” and ”A Purine-Pyrimidine Classification Scheme of the Genetic Code” in ”BIOForum Europe”. This new representation enables to reduce the common table of the genetic code from 64 to 32 fields maintaining the same information content. It turned out that all known and even new patterns of the genetic code can easily be recognized in this new scheme. Furthermore, our new representation allows us for speculations about the origin and evolution of the translation machinery and the genetic code. Thus, we found a possible explanation for the contemporary codon - amino acid assignment and wide support for an early doublet code. Those explanations have been published in ”Journal of Bioinformatics and Computational Biology” under the title ”The New Classification Scheme of the Genetic Code, its Early Evolution, and tRNA Usage”. Assuming to find these purine - pyrimidine patterns at the DNA level itself, we examined DNA binding sites for the occurrence of binary patterns. A comprehensive statistic about the largest class of restriction enzymes (type II) has shown a very distinctive purine - pyrimidine pattern. Moreover, we have observed a higher G+C content for the protein binding sequences. For both observations we have provided and discussed several explanations published under the title ”Common Patterns in Type II Restriction Enzyme Binding Sites” in ”Nucleic Acid Research”. The identified patterns may help to understand how a protein finds its binding site. In the last part of my work two submitted articles about the analysis of Boolean functions are presented. Boolean functions are used for the description and analysis of complex dynamic processes and make it easier to find binary patterns within biochemical interaction networks. It is well known that not all functions are necessary to describe biologically relevant gene interaction networks. In the article entitled ”Boolean Networks with Biologically Relevant Rules Show Ordered Behavior”, submitted to ”BioSystems”, we have shown, that the class of required Boolean functions can strongly be restricted. Furthermore, we calculated the exact number of hierarchically canalizing functions which are known to be biologically relevant. In our work ”The Decomposition Tree for Analysis of Boolean Functions” submitted to ”Journal of Complexity”, we introduced an efficient data structure for the classification and analysis of Boolean functions. This permits the recognition of biologically relevant Boolean functions in polynomial time