Discriminative Topological Features Reveal Biological Network Mechanisms

Recent genomic and bioinformatic advances have motivated the development of numerous random network models purporting to describe graphs of biological, technological, and sociological origin. The success of a model has been evaluated by how well it reproduces a few key features of the real-world data, such as degree distributions, mean geodesic lengths, and clustering coefficients. Often pairs of models can reproduce these features with indistinguishable fidelity despite being generated by vastly different mechanisms. In such cases, these few target features are insufficient to distinguish which of the different models best describes real world networks of interest; moreover, it is not clear a priori that any of the presently-existing algorithms for network generation offers a predictive description of the networks inspiring them. To derive discriminative classifiers, we construct a mapping from the set of all graphs to a high-dimensional (in principle infinite-dimensional) ``word space.'' This map defines an input space for classification schemes which allow us for the first time to state unambiguously which models are most descriptive of the networks they purport to describe. Our training sets include networks generated from 17 models either drawn from the literature or introduced in this work, source code for which is freely available. We anticipate that this new approach to network analysis will be of broad impact to a number of communities.Comment: supplemental website: http://www.columbia.edu/itc/applied/wiggins/netclass

Inference of biomolecular interactions from sequence data

This thesis describes our work on the inference of biomolecular interactions from sequence data. In particular, the first part of the thesis focuses on proteins and describes computational methods that we have developed for the inference of both intra- and inter-protein interactions from genomic data. The second part of the thesis centers around protein-RNA interactions and describes a method for the inference of binding motifs of RNA-binding proteins from high-throughput sequencing data. The thesis is organized as follows. In the first part, we start by introducing a novel mathematical model for the characterization of protein sequences (chapter 1). We then show how, using genomic data, this model can be successfully applied to two different problems, namely to the inference of interacting amino acid residues in the tertiary structure of protein domains (chapter 2) and to the prediction of protein-protein interactions in large paralogous protein families (chapters 3 and 4). We conclude the first part by a discussion of potential extensions and generalizations of the methods presented (chapter 5). In the second part of this thesis, we first give a general introduction about RNA- binding proteins (chapter 6). We then describe a novel experimental method for the genome-wide identification of target RNAs of RNA-binding proteins and show how this method can be used to infer the binding motifs of RNA-binding proteins (chapter 7). Finally, we discuss a potential mechanism by which KH domain-containing RNA- binding proteins could achieve the specificity of interaction with their target RNAs and conclude the second part of the thesis by proposing a novel type of motif finding algorithm tailored for the inference of their recognition elements (chapter 8)

Reconstructing regulatory networks from high-throughput post-genomic data using MCMC methods

Modern biological research aims to understand when genes are expressed and how certain genes in uence the expression of other genes. For organizing and visualizing gene expression activity gene regulatory networks are used. The architecture of these networks holds great importance, as they enable us to identify inconsistencies between hypotheses and observations, and to predict the behavior of biological processes in yet untested conditions. Data from gene expression measurements are used to construct gene regulatory networks. Along with the advance of high-throughput technologies for measuring gene expression statistical methods to predict regulatory networks have also been evolving. This thesis presents a computational framework based on a Bayesian modeling technique using state space models (SSM) for the inference of gene regulatory networks from time-series measurements. A linear SSM consists of observation and hidden state equations. The hidden variables can unfold effects that cannot be directly measured in an experiment, such as missing gene expression. We have used a Bayesian MCMC approach based on Gibbs sampling for the inference of parameters. However the task of determining the dimension of the hidden state space variables remains crucial for the accuracy of network inference. For this we have used the Bayesian evidence (or marginal likelihood) as a yardstick. In addition, the Bayesian approach also provides the possibility of incorporating prior information, based on literature knowledge. We compare marginal likelihoods calculated from the Gibbs sampler output to the lower bound calculated by a variational approximation. Before using the algorithm for the analysis of real biological experimental datasets we perform validation tests using numerical experiments based on simulated time series datasets generated by in-silico networks. The robustness of our algorithm can be measured by its ability to recapture the input data and generating networks using the inferred parameters. Our developed algorithm, GBSSM, was used to infer a gene network using E. coli data sets from the different stress conditions of temperature shift and acid stress. The resulting model for the gene expression response under temperature shift captures the effects of global transcription factors, such as fnr that control the regulation of hundreds of other genes. Interestingly, we also observe the stress-inducible membrane protein OsmC regulating transcriptional activity involved in the adaptation mechanism under both temperature shift and acid stress conditions. In the case of acid stress, integration of metabolomic and transcriptome data suggests that the observed rapid decrease in the concentration of glycine betaine is the result of the activation of osmoregulators which may play a key role in acid stress adaptation

Human genome interaction: models for designing DNA sequences

Since the turn of the century, the scope and scale of Synthetic Biology projects have grown dramatically. Instead of limiting themselves to simple genetic circuits, researchers aim for genome-scale organism redesigns, revolutionary gene therapies, and high throughput, industrial scale natural product syntheses. However, the engineering principles adopted by the founders of the field have been applied to Biology in a way that does not fit many modern experiments. This has limited the usefulness of common sequence design paradigms. As experiments have become more complex, the sequence design process has taken up more and more intellectual bandwidth, partially because software tools for DNA design have remained largely unchanged. This thesis will explore software engineering, social science, and machine learning projects aiming to improve the ways in which researchers design novel DNA sequences for Synthetic Biology experiments. Popular DNA design tools will be reviewed, alongside an analysis of the key conceptual metaphors that underlie their workflows. Flaws in the ubiquitous parts-based design model will be demonstrated, and several alternatives will be explored. A tool called Part Crafter (partcrafter.com) will be presented, which aggregates sequence and annotation data from a variety of data sources to allow for rational search over genomic features, as well as the automated production of biological parts for Synthetic Biology experiments. However, Part Crafter’s mode of part creation is more flexible than traditional implementations of parts-based design in the field. Parts are abstracted away from specific manufacturing standards, and as much contextual information as possible is presented alongside parts of interest. Additionally, various types of machine learning models will be presented which predict histone modification occupancy in novel sequences. Current Synthetic Biology design paradigms largely ignore the epigenetic context of designed sequences. A gradient of increasingly complex models will be analysed in order to characterise the complexity of the combinatorial patterns of sequences of these epigenetic proteins. This work was exploratory, serving as a proof of concept for using a variety of increasingly complex models to represent genomic elements, and demonstrating that the parts-based design model is not the only option available to us. The aims of the field of Synthetic Biology become more ambitious every year. In order for the goals of the field to be accomplished, we must be able to better understand the sequences we are designing. The projects presented in this thesis were all completed with the aim of assisting Synthetic Biologists in designing sequences deliberately. By taking into account as much contextual information as possible, including epigenetic factors, researchers will be able to design sequences more quickly and reliably, increasing their chances of achieving the moon shot goals of the field

Application of coevolution-based methods and deep learning for structure prediction of protein complexes

The three-dimensional structures of proteins play a critical role in determining their biological functions and interactions. Experimental determination of protein and protein complex structures can be expensive and difficult. Computational prediction of protein and protein complex structures has therefore been an open challenge for decades. Recent advances in computational structure prediction techniques have resulted in increasingly accurate protein structure predictions. These techniques include methods that leverage information about coevolving residues to predict residue interactions and that apply deep learning techniques to enable better prediction of residue contacts and protein structures. Prior to the work outlined in this thesis, coevolution-based methods and deep learning had been shown to improve the prediction of single protein domains or single protein chains. Most proteins in living organisms do not function on their own but interact with other proteins either through transient interactions or by forming stable protein complexes. Knowledge of protein complex structures can be useful for biological and disease research, drug discovery and protein engineering. Unfortunately, a large number of protein complexes do not have experimental structures or close homolog structures that can be used as templates. In this thesis, methods previously developed and applied to the de novo prediction of single protein domains or protein monomer chains were modified and leveraged for the prediction of protein heterodimer and homodimer complexes. A number of coevolution-based tools and deep learning methods are explored for the purpose of predicting inter-chain and intra-chain residue contacts in protein dimers. These contacts are combined with existing protein docking methods to explore the prediction of homodimers and heterodimers. Overall, the work in this thesis demonstrates the promise of leveraging coevolution and deep-learning for the prediction of protein complexes, shows improvements in protein complex prediction tasks achieved using coevolution based methods and deep learning methods, and demonstrates remaining challenges in protein complex prediction

A Factor Graph Nested Effects Model To Identify Networks from Genetic Perturbations

Complex phenotypes such as the transformation of a normal population of cells into cancerous tissue result from a series of molecular triggers gone awry. We describe a method that searches for a genetic network consistent with expression changes observed under the knock-down of a set of genes that share a common role in the cell, such as a disease phenotype. The method extends the Nested Effects Model of Markowetz et al. (2005) by using a probabilistic factor graph to search for a network representing interactions among these silenced genes. The method also expands the network by attaching new genes at specific downstream points, providing candidates for subsequent perturbations to further characterize the pathway. We investigated an extension provided by the factor graph approach in which the model distinguishes between inhibitory and stimulatory interactions. We found that the extension yielded significant improvements in recovering the structure of simulated and Saccharomyces cerevisae networks. We applied the approach to discover a signaling network among genes involved in a human colon cancer cell invasiveness pathway. The method predicts several genes with new roles in the invasiveness process. We knocked down two genes identified by our approach and found that both knock-downs produce loss of invasive potential in a colon cancer cell line. Nested effects models may be a powerful tool for inferring regulatory connections and genes that operate in normal and disease-related processes

A Quantitative and High-Throughput Approach to Gene Regulation in Escherichia coli

Measurements in biology have reached a level of precision that demands quantitative modeling. This is particularly true in the field of gene regulation, where concepts from physics such as thermodynamics have allowed for accurate models to be made. Many issues remain. DNA sequencing is routine enough to sequence new genomes in days and cheap enough to use deep sequencing to perform precision measurements, but our ability to interpret the wealth of genomic data is lagging behind, especially in the realm of gene regulation. The primary reason is that we lack any information what so ever as to the basic regulatory details of approximately 65 percent of operons even in E. coli, the best understood organism in biology. As a result we cannot use our hard won modeling efforts to understand any of these operons. This work takes steps to address these issues. First we use 30 LacI mutants as a test case to prove that we can make quantitatively accurate models of gene expression and sequence-dependent binding energies of transcription factors and RNA polymerase. Next we note that much of the quantitative insight available on transcriptional regulation relies on work on only a few model regulatory systems such as LacI as was considered above. We develop an approach, through a combination of massively parallel reporter assays, mass spectrometry, and information-theoretic modeling that can be used to dissect bacterial promoters in a systematic and scalable way. We demonstrate that we can uncover a qualitative list of transcription factor binding sites as well as their associated quantitative details from both well-studied and previously uncharacterized promoters in E. coli. Finally we extend the above method to over 100 E. coli promoters using over 12 growth conditions. We show the method recapitulates known regulatory information. Then, we examine regulatory architectures for more than 80 promoters which previously had no known regulation. In many cases, we identify which transcription factors mediate their regulation. The method introduced clears a path for fully characterizing the regulatory genome of E. coli and advances towards the goal of using this method on a wide variety of other organisms including other prokaryotes and eukaryotes such as Drosophila melanogaster.</p

Bayesian statistical approach for protein residue-residue contact prediction

Despite continuous efforts in automating experimental structure determination and systematic target selection in structural genomics projects, the gap between the number of known amino acid sequences and solved 3D structures for proteins is constantly widening. While DNA sequencing technologies are advancing at an extraordinary pace, thereby constantly increasing throughput while at the same time reducing costs, protein structure determination is still labour intensive, time-consuming and expensive. This trend illustrates the essential importance of complementary computational approaches in order to bridge the so-called sequence-structure gap. About half of the protein families lack structural annotation and therefore are not amenable to techniques that infer protein structure from homologs. These protein families can be addressed by de novo structure prediction approaches that in practice are often limited by the immense computational costs required to search the conformational space for the lowest-energy conformation. Improved predictions of contacts between amino acid residues have been demonstrated to sufficiently constrain the overall protein fold and thereby extend the applicability of de novo methods to larger proteins. Residue-residue contact prediction is based on the idea that selection pressure on protein structure and function can lead to compensatory mutations between spatially close residues. This leaves an echo of correlation signatures that can be traced down from the evolutionary record. Despite the success of contact prediction methods, there are several challenges. The most evident limitation lies in the requirement of deep alignments, which excludes the majority of protein families without associated structural information that are the focus for contact guided de novo structure prediction. The heuristics applied by current contact prediction methods pose another challenge, since they omit available coevolutionary information. This work presents two different approaches for addressing the limitations of contact prediction methods. Instead of inferring evolutionary couplings by maximizing the pseudo-likelihood, I maximize the full likelihood of the statistical model for protein sequence families. This approach performed with comparable precision up to minor improvements over the pseudo-likelihood methods for protein families with few homologous sequences. A Bayesian statistical approach has been developed that provides posterior probability estimates for residue-residue contacts and eradicates the use of heuristics. The full information of coevolutionary signatures is exploited by explicitly modelling the distribution of statistical couplings that reflects the nature of residue-residue interactions. Surprisingly, the posterior probabilities do not directly translate into more precise predictions than obtained by pseudo-likelihood methods combined with prior knowledge. However, the Bayesian framework offers a statistically clean and theoretically solid treatment for the contact prediction problem. This flexible and transparent framework provides a convenient starting point for further developments, such as integrating more complex prior knowledge. The model can also easily be extended towards the Derivation of probability estimates for residue-residue distances to enhance the precision of predicted structures

High-Performance approaches for Phylogenetic Placement, and its application to species and diversity quantification

In den letzten Jahren haben Fortschritte in der Hochdurchsatz-Genesequenzierung, in Verbindung mit dem anhaltenden exponentiellen Wachstum und der Verfügbarkeit von Rechenressourcen, zu fundamental neuen analytischen Ansätzen in der Biologie geführt. Es ist nun möglich den genetischen Inhalt ganzer Organismengemeinschaften anhand einzelner Umweltproben umfassend zu sequenzieren. Solche Methoden sind besonders für die Mikrobiologie relevant. Die Mikrobiologie war zuvor weitgehend auf die Untersuchung jener Mikroben beschränkt, welche im Labor (d.h., in vitro) kultiviert werden konnten, was jedoch lediglich einen kleinen Teil der in der Natur vorkommenden Diversität abdeckt. Im Gegensatz dazu ermöglicht die Hochdurchsatzsequenzierung nun die direkte Erfassung der genetischen Sequenzen eines Mikrobioms, wie es in seiner natürlichen Umgebung vorkommt (d.h., in situ). Ein typisches Ziel von Mikrobiomstudien besteht in der taxonomischen Klassifizierung der in einer Probe enthaltenen Sequenzen (Querysequenzen). Üblicherweise werden phylogenetische Methoden eingesetzt, um detaillierte taxonomische Beziehungen zwischen Querysequenzen und vertrauenswürdigen Referenzsequenzen, die von bereits klassifizierten Organismen stammen, zu bestimmen. Aufgrund des hohen Volumens ($10 ^ 6$ bis $10 ^ 9$) von Querysequenzen, die aus einer Mikrobiom-Probe mittels Hochdurchsatzsequenzierung generiert werden können, ist eine akkurate phylogenetische Baumrekonstruktion rechnerisch nicht mehr möglich. Darüber hinaus erzeugen derzeit üblicherweise verwendete Sequenzierungstechnologien vergleichsweise kurze Sequenzen, die ein begrenztes phylogenetisches Signal aufweisen, was zu einer Instabilität bei der Inferenz der Phylogenien aus diesen Sequenzen führt. Ein weiteres typisches Ziel von Mikrobiomstudien besteht in der Quantifizierung der Diversität innerhalb einer Probe, bzw. zwischen mehreren Proben. Auch hierfür werden üblicherweise phylogenetische Methoden verwendet. Oftmals setzen diese Methoden die Inferenz eines phylogenetischen Baumes voraus, welcher entweder alle Sequenzen, oder eine geclusterte Teilmenge dieser Sequenzen, umfasst. Wie bei der taxonomischen Identifizierung können Analysen, die auf dieser Art von Bauminferenz basieren, zu ungenauen Ergebnissen führen und/oder rechnerisch nicht durchführbar sein. Im Gegensatz zu einer umfassenden phylogenetischen Inferenz ist die phylogenetische Platzierung eine Methode, die den phylogenetischen Kontext einer Querysequenz innerhalb eines etablierten Referenzbaumes bestimmt. Dieses Verfahren betrachtet den Referenzbaum typischerweise als unveränderlich, d.h. der Referenzbaum wird vor, während oder nach der Platzierung einer Sequenz nicht geändert. Dies erlaubt die phylogenetische Platzierung einer Sequenz in linearer Zeit in Bezug auf die Größe des Referenzbaums durchzuführen. In Kombination mit taxonomischen Informationen über die Referenzsequenzen ermöglicht die phylogenetische Platzierung somit die taxonomische Identifizierung einer Sequenz. Darüber hinaus erlaubt eine phylogenetische Platzierung die Anwendung einer Vielzahl zusätzlicher Analyseverfahren, die beispielsweise die Zuordnung der Zusammensetzungen humaner Mikrobiome zu klinisch-diagnostischen Eigenschaften ermöglicht. In dieser Dissertation präsentiere ich meine Arbeit bezüglich des Entwurfs, der Implementierung, und Verbesserung von EPA-ng, einer Hochleistungsimplementierung der phylogenetischen Platzierung anhand des Maximum-Likelihood Modells. EPA-ng wurde entwickelt um auf Milliarden von Querysequenzen zu skalieren und auf Tausenden von Kernen in Systemen mit gemeinsamem und verteiltem Speicher ausgeführt zu werden. EPA-ng beschleunigt auch die Verarbeitungsgeschwindigkeit auf einzelnen Kernen um das bis zu $30$-fache, im Vergleich zu dessen direkten Konkurrenzprogrammen. Vor kurzem haben wir eine zusätzliche Methode für EPA-ng eingeführt, welche die Platzierung in wesentlich größeren Referenzbäumen ermöglicht. Hierfür verwenden wir einen aktiven Speicherverwaltungsansatz, bei dem reduzierter Speicherverbrauch gegen größere Ausführungszeiten eingetauscht wird. Zusätzlich präsentiere ich einen massiv-parallelen Ansatz um die Diversität einer Probe zu quantifizieren, welcher auf den Ergebnissen phylogenetischer Platzierungen basiert. Diese Software, genannt \toolname{SCRAPP}, kombiniert aktuelle Methoden für die Maximum-Likelihood basierte phylogenetische Inferenz mit Methoden zur Abgrenzung molekularer Spezien. Daraus resultiert eine Verteilung der Artenanzahl auf den Kanten eines Referenzbaums für eine gegebene Probe. Darüber hinaus beschreibe ich einen neuartigen Ansatz zum Clustering von Platzierungsergebnissen, anhand dessen der Benutzer den Rechenaufwand reduzieren kann

Unsupervised inference methods for protein sequence data

L'abstract è presente nell'allegato / the abstract is in the attachmen