Stochastic Sensitivity Analysis and Kernel Inference via Distributional Data

AbstractCellular processes are noisy due to the stochastic nature of biochemical reactions. As such, it is impossible to predict the exact quantity of a molecule or other attributes at the single-cell level. However, the distribution of a molecule over a population is often deterministic and is governed by the underlying regulatory networks relevant to the cellular functionality of interest. Recent studies have started to exploit this property to infer network states. To facilitate the analysis of distributional data in a general experimental setting, we introduce a computational framework to efficiently characterize the sensitivity of distributional output to changes in external stimuli. Further, we establish a probability-divergence-based kernel regression model to accurately infer signal level based on distribution measurements. Our methodology is applicable to any biological system subject to stochastic dynamics and can be used to elucidate how population-based information processing may contribute to organism-level functionality. It also lays the foundation for engineering synthetic biological systems that exploit population decoding to more robustly perform various biocomputation tasks, such as disease diagnostics and environmental-pollutant sensing

Deciphering the functional organization of molecular networks via graphlets-based methods and network embedding techniques

[eng] Advances in capturing technologies have yielded a massive production of large-scale molecular data that describe different aspects of cellular functioning. These data are often modeled as networks, in which nodes are molecular entities, and the edges connecting them represent their relationships. These networks are a valuable source of biological information, but they need to be untangled by new algorithms to reveal the information hidden in their wiring patterns. State-of-the-art approaches for deciphering these complex networks are based on graphlets and network embeddings. This thesis focuses on the development of novel algorithms to overcome the limitations of the current graphlet and network embedding methodologies in the field of biology. Graphlets are a powerful tool for characterizing the local wiring patterns of molecular networks. However, current graphlet-based methods are mostly applicable to unweighted networks, whereas real-world molecular networks may have weighted edges that represent the probability of an interaction occurring in the cell. This probabilistic information is commonly discarded when applying thresholds to generate unweighted networks, which may lead to information loss. To address this challenge, we introduce probabilistic graphlets, a novel approach that can capture the local wiring patterns of weighted networks and uncover hidden probabilistic relationships between molecular entities. We use probabilistic graphlets to generalize the graphlet methods and apply these to the probabilistic representation of real-world molecular interactions. We show that probabilistic graphlets robustly un- cover relevant biological information from the molecular networks. Furthermore, we demonstrate that probabilistic graphlets exhibit a higher sensitivity to identifying condition-specific functions compared to their unweighted counterparts. Network embedding algorithms learn a low-dimensional vectorial representation for each gene in the network while preserving the structural information of the molecular network. Current, available embedding approaches strictly focus on clustering the genes’ embedding vectors and interpreting such clusters to reveal the hidden information of the biological networks. Thus, we investigate new perspectives and methods that go beyond gene-centric approaches. First, we shift the exploration of the embedding space’s functional organization from the genes to their functions. We introduce the Functional Mapping Matrix and apply it to investigate the changes in the organization of cancer and control network embedding spaces from a functional perspective. We demonstrate that our methodology identifies novel cancer-related functions and genes that the currently available methods for gene-centric analyses cannot identify. Finally, we go even further and switch the perspective from the organization of the embedded entities (genes and functions) in the embedding space to the space itself. We annotate axes of the network embedding spaces of six species with both, functional annotations and genes. We demonstrate that the embedding space axes represent coherent cellular functions and offer a functional fingerprint of the cell’s functional organization. Moreover, we show that the analysis of the axes reveals new functional evolutionary connections between species

Personalized Medicine: Studies of Pharmacogenomics in Yeast and Cancer

Advances in microarray and sequencing technology enable the era of personalized medicine. With increasing availability of genomic assays, clinicians have started to utilize genetics and gene expression of patients to guide clinical care. Signatures of gene expression and genetic variation in genes have been associated with disease risks and response to clinical treatment. It is therefore not difficult to envision a future where each patient will have clinical care that is optimized based on his or her genetic background and genomic profiles. However, many challenges exist towards the full realization of the potential personalized medicine. The human genome is complex and we have yet to gain a better understanding of how to associate genomic data with phenotype. First, the human genome is very complex: more than 50 million sequence variants and more than 20,000 genes have been reported. Many efforts have been devoted to genome-wide association studies (GWAS) in the last decade, associating common genetic variants with common complex traits and diseases. While many associations have been identified by genome-wide association studies, most of our phenotypic variation remains unexplained, both at the level of the variants involved and the underlying mechanism. Finally, interaction between genetics and environment presents additional layer of complexity governing phenotypic variation. Currently, there is much research developing computational methods to help associate genomic features with phenotypic variation. Modeling techniques such as machine learning have been very useful in uncovering the intricate relationships between genomics and phenotype. Despite some early successes, the performance of most models is disappointing. Many models lack robustness and predictions do not replicate. In addition, many successful models work as a black box, giving good predictions of phenotypic variation but unable to reveal the underlying mechanism. In this thesis I propose two methods addressing this challenge. First, I describe an algorithm that focuses on identifying causal genomic features of phenotype. My approach assumes genomic features predictive of phenotype are more likely to be causal. The algorithm builds models that not only accurately predict the traits, but also uncover molecular mechanisms that are responsible for these traits. . The algorithm gains its power by combining regularized linear regression, causality testing and Bayesian statistics. I demonstrate the application of the algorithm on a yeast dataset, where genotype and gene expression are used to predict drug sensitivity and elucidate the underlying mechanisms. The accuracy and robustness of the algorithm are both evaluated statistically and experimentally validated. The second part of the thesis takes on a much more complicated system: cancer. The availability of genomic and drug sensitivity data of cancer cell lines has recently been made available. The challenge here is not only the increasing complexity of the system (e.g. size of genome), but also the fundamental differences between cancers and tissues. Different cancers or tissues provide different contexts influencing regulatory networks and signaling pathways. In order to account for this, I propose a method to associate contextual genomic features with drug sensitivity. The algorithm is based on information theory, Bayesian statistics, and transfer learning. The algorithm demonstrates the importance of context specificity in predictive modeling of cancer pharmacogenomics. The two complementary algorithms highlight the challenges faced in personalized medicine and the potential solutions. This thesis detailed the results and analysis that demonstrate the importance of causality and context specificity in predictive modeling of drug response, which will be crucial for us towards bringing personalized medicine in practice

Integration and mining of malaria molecular, functional and pharmacological data: how far are we from a chemogenomic knowledge space?

The organization and mining of malaria genomic and post-genomic data is highly motivated by the necessity to predict and characterize new biological targets and new drugs. Biological targets are sought in a biological space designed from the genomic data from Plasmodium falciparum, but using also the millions of genomic data from other species. Drug candidates are sought in a chemical space containing the millions of small molecules stored in public and private chemolibraries. Data management should therefore be as reliable and versatile as possible. In this context, we examined five aspects of the organization and mining of malaria genomic and post-genomic data: 1) the comparison of protein sequences including compositionally atypical malaria sequences, 2) the high throughput reconstruction of molecular phylogenies, 3) the representation of biological processes particularly metabolic pathways, 4) the versatile methods to integrate genomic data, biological representations and functional profiling obtained from X-omic experiments after drug treatments and 5) the determination and prediction of protein structures and their molecular docking with drug candidate structures. Progresses toward a grid-enabled chemogenomic knowledge space are discussed.Comment: 43 pages, 4 figures, to appear in Malaria Journa

Transcription regulation: models for combinatorial regulation and functional specificity

Gene regulation id controlled by transcription factor proteins that bind to specific DNA sequences, known as transcription factor binding sites (TFBSs). Combinations of transcription factors working, co-operatively in cis-regulatory modules (CRMs), play a role in regulating gene expression. Current computational methods for TFBS prediction cannot distinguish between functional and non-functional sites, and predict very large numbers of false positives. The thesis focuses on the development of a novel computational model, based on artificial neural networks (ANNs), for the identification of functional TFBSs, and the CRMs within which they operate in the human genome. Datasets of 12,239 experimentally verified true positive (TP) TFBSs and 130,199 false positive (FP) TFBSs were extracted using a combination of position weight matrices from the JASPAR database and experimentally verified sites from the Encyclopedia of DNA elements (ENCODE). A number of machine learning alsgorithms were tested using a range of genetic information including gene expression, necleosome positioning, DNA methylation states and DNA entropy. The best model, that gave a mean area under the curve under a receiver operator characteristic curve of 0.800, was based on a feedforward ANN using backpropagation. This model was then used to predict functional TFBSs in a number of gene sets from the human genome. The predictions, combined with experimentally proven TFBSs from ENCODE, were used to investigate combinatorial [atterns of TFBSs operating in CRMs. CRM patterns have been analysed in disease-associated genes located in linkage disequilibrium blocks containing SNPs obtained from Genome Wide Association Studies (GWAS). The potential for the model to make functional TFBS predictions to aid in the annotation of orphan genes of unknown function is discussed. In addition this thesis presents computational work on a number of smaller published studies

Fuzzy-Granular Based Data Mining for Effective Decision Support in Biomedical Applications

Due to complexity of biomedical problems, adaptive and intelligent knowledge discovery and data mining systems are highly needed to help humans to understand the inherent mechanism of diseases. For biomedical classification problems, typically it is impossible to build a perfect classifier with 100% prediction accuracy. Hence a more realistic target is to build an effective Decision Support System (DSS). In this dissertation, a novel adaptive Fuzzy Association Rules (FARs) mining algorithm, named FARM-DS, is proposed to build such a DSS for binary classification problems in the biomedical domain. Empirical studies show that FARM-DS is competitive to state-of-the-art classifiers in terms of prediction accuracy. More importantly, FARs can provide strong decision support on disease diagnoses due to their easy interpretability. This dissertation also proposes a fuzzy-granular method to select informative and discriminative genes from huge microarray gene expression data. With fuzzy granulation, information loss in the process of gene selection is decreased. As a result, more informative genes for cancer classification are selected and more accurate classifiers can be modeled. Empirical studies show that the proposed method is more accurate than traditional algorithms for cancer classification. And hence we expect that genes being selected can be more helpful for further biological studies