Network-level analysis of metabolic regulation in the human red blood cell using random sampling and singular value decomposition

BACKGROUND: Extreme pathways (ExPas) have been shown to be valuable for studying the functions and capabilities of metabolic networks through characterization of the null space of the stoichiometric matrix (S). Singular value decomposition (SVD) of the ExPa matrix P has previously been used to characterize the metabolic regulatory problem in the human red blood cell (hRBC) from a network perspective. The calculation of ExPas is NP-hard, and for genome-scale networks the computation of ExPas has proven to be infeasible. Therefore an alternative approach is needed to reveal regulatory properties of steady state solution spaces of genome-scale stoichiometric matrices. RESULTS: We show that the SVD of a matrix (W) formed of random samples from the steady-state solution space of the hRBC metabolic network gives similar insights into the regulatory properties of the network as was obtained with SVD of P. This new approach has two main advantages. First, it works with a direct representation of the shape of the metabolic solution space without the confounding factor of a non-uniform distribution of the extreme pathways and second, the SVD procedure can be applied to a very large number of samples, such as will be produced from genome-scale networks. CONCLUSION: These results show that we are now in a position to study the network aspects of the regulatory problem in genome-scale metabolic networks through the use of random sampling. Contact: [email protected]

Decomposing complex reaction networks using random sampling, principal component analysis and basis rotation

<p>Abstract</p> <p>Background</p> <p>Metabolism and its regulation constitute a large fraction of the molecular activity within cells. The control of cellular metabolic state is mediated by numerous molecular mechanisms, which in effect position the metabolic network flux state at specific locations within a mathematically-definable steady-state flux space. Post-translational regulation constitutes a large class of these mechanisms, and decades of research indicate that achieving a network flux state through post-translational metabolic regulation is both a complex and complicated regulatory problem. No analysis method for the objective, top-down assessment of such regulation problems in large biochemical networks has been presented and demonstrated.</p> <p>Results</p> <p>We show that the use of Monte Carlo sampling of the steady-state flux space of a cell-scale metabolic system in conjunction with Principal Component Analysis and eigenvector rotation results in a low-dimensional and biochemically interpretable decomposition of the steady flux states of the system. This decomposition comes in the form of a low number of small reaction sets whose flux variability accounts for nearly all of the flux variability in the entire system. This result indicates an underlying simplicity and implies that the regulation of a relatively low number of reaction sets can essentially determine the flux state of the entire network in the given growth environment.</p> <p>Conclusion</p> <p>We demonstrate how our top-down analysis of networks can be used to determine key regulatory requirements independent of specific parameters and mechanisms. Our approach complements the reductionist approach to elucidation of regulatory mechanisms and facilitates the development of our understanding of global regulatory strategies in biological networks.</p

Functional clustering of yeast proteins from the protein-protein interaction network

BACKGROUND: The abundant data available for protein interaction networks have not yet been fully understood. New types of analyses are needed to reveal organizational principles of these networks to investigate the details of functional and regulatory clusters of proteins. RESULTS: In the present work, individual clusters identified by an eigenmode analysis of the connectivity matrix of the protein-protein interaction network in yeast are investigated for possible functional relationships among the members of the cluster. With our functional clustering we have successfully predicted several new protein-protein interactions that indeed have been reported recently. CONCLUSION: Eigenmode analysis of the entire connectivity matrix yields both a global and a detailed view of the network. We have shown that the eigenmode clustering not only is guided by the number of proteins with which each protein interacts, but also leads to functional clustering that can be applied to predict new protein interactions

Generative Models of Biological Variations in Bulk and Single-cell RNA-seq

The explosive growth of next-generation sequencing data enhances our ability to understand biological process at an unprecedented resolution. Meanwhile organizing and utilizing this tremendous amount of data becomes a big challenge. High-throughput technology provides us a snapshot of all underlying biological activities, but this kind of extremely high-dimensional data is hard to interpret. Due to the curse of dimensionality, the measurement is sparse and far from enough to shape the actual manifold in the high-dimensional space. On the other hand, the measurements may contain structured noise such as technical or nuisance biological variation which can interfere downstream interpretation. Generative modeling is a powerful tool to make sense of the data and generate compact representations summarizing the embedded biological information. This thesis introduces three generative models that help amplifying biological signals buried in the noisy bulk and single-cell RNA-seq data. In Chapter 2, we propose a semi-supervised deconvolution framework called PLIER which can identify regulations in cell-type proportions and specific pathways that control gene expression. PLIER has inspired the development of MultiPLIER and has been used to infer context-specific genotype effects in the brain. In Chapter 3, we construct a supervised transformation named DataRemix to normalize bulk gene expression profiles in order to maximize the biological findings with respect to a variety of downstream tasks. By reweighing the contribution of hidden factors, we are able to reveal the hidden biological signals without any external dataset-specific knowledge. We apply DataRemix to the ROSMAP dataset and report the first replicable trans-eQTL effect in human brain. In Chapter 4, we focus on scRNA-seq and introduce NIFA which is an unsupervised decomposition framework that combines the desired properties of PCA, ICA and NMF. It simultaneously models uni- and multi-modal factors isolating discrete cell-type identity and continuous pathway-level variations into separate components. The work presented in Chapter 2 has been published as a journal article. The work in Chapter 3 and Chapter 4 are under submission and they are available as preprints on bioRxiv

Cell Type-specific Analysis of Human Interactome and Transcriptome

Cells are the fundamental building block of complex tissues in higher-order organisms. These cells take different forms and shapes to perform a broad range of functions. What makes a cell uniquely eligible to perform a task, however, is not well-understood; neither is the defining characteristic that groups similar cells together to constitute a cell type. Even for known cell types, underlying pathways that mediate cell type-specific functionality are not readily available. These functions, in turn, contribute to cell type-specific susceptibility in various disorders

Methods in Computational Biology

Modern biology is rapidly becoming a study of large sets of data. Understanding these data sets is a major challenge for most life sciences, including the medical, environmental, and bioprocess fields. Computational biology approaches are essential for leveraging this ongoing revolution in omics data. A primary goal of this Special Issue, entitled “Methods in Computational Biology”, is the communication of computational biology methods, which can extract biological design principles from complex data sets, described in enough detail to permit the reproduction of the results. This issue integrates interdisciplinary researchers such as biologists, computer scientists, engineers, and mathematicians to advance biological systems analysis. The Special Issue contains the following sections:•Reviews of Computational Methods•Computational Analysis of Biological Dynamics: From Molecular to Cellular to Tissue/Consortia Levels•The Interface of Biotic and Abiotic Processes•Processing of Large Data Sets for Enhanced Analysis•Parameter Optimization and Measuremen