From annotated genomes to metabolic flux models and kinetic parameter fitting

Significant advances in system-level modeling of cellular behavior can be achieved based on constraints derived from genomic information and on optimality hypotheses. For steady-state models of metabolic networks, mass conservation and reaction stoichiometry impose linear constraints on metabolic fluxes. Different objectives, such as maximization of growth rate or minimization of flux distance from a reference state, can be tested in different organisms and conditions. In particular, we have suggested that the metabolic properties of mutant bacterial strains are best described by an algorithm that performs a minimization of metabolic adjustment (MOMA) upon gene deletion. The increasing availability of many annotated genomes paves the way for a systematic application of these flux balance methods to a large variety of organisms. However, such a high throughput goal crucially depends on our capacity to build metabolic flux models in a fully automated fashion. Here we describe a pipeline for generating models from annotated genomes and discuss the current obstacles to full automation. In addition, we propose a framework for the integration of flux modeling results and high throughput proteomic data, which can potentially help in the inference of whole-cell kinetic parameters.     

Evaluation of regression models in metabolic physiology: predicting fluxes from isotopic data without knowledge of the pathway

This study explores the ability of regression models, with no knowledge of the underlying physiology, to estimate physiological parameters relevant for metabolism and endocrinology. Four regression models were compared: multiple linear regression (MLR), principal component regression (PCR), partial least-squares regression (PLS) and regression using artificial neural networks (ANN). The pathway of mammalian gluconeogenesis was analyzed using [U−¹³C]glucose as tracer. A set of data was simulated by randomly selecting physiologically appropriate metabolic fluxes for the 9 steps of this pathway as independent variables. The isotope labeling patterns of key intermediates in the pathway were then calculated for each set of fluxes, yielding 29 dependent variables. Two thousand sets were created, allowing independent training and test data. Regression models were asked to predict the nine fluxes, given only the 29 isotopomers. For large training sets (>50) the artificial neural network model was superior, capturing 95% of the variability in the gluconeogenic flux, whereas the three linear models captured only 75%. This reflects the ability of neural networks to capture the inherent non-linearities of the metabolic system. The effect of error in the variables and the addition of random variables to the data set was considered. Model sensitivities were used to find the isotopomers that most influenced the predicted flux values. These studies provide the first test of multivariate regression models for the analysis of isotopomer flux data. They provide insight for metabolomics and the future of isotopic tracers in metabolic research where the underlying physiology is complex or unknown.We acknowledge the support of NIH Grant DK58533 and the DuPont-MIT Alliance.     

MICRA: an automatic pipeline for fast characterization of microbial genomes from high-throughput sequencing data

The increase in available sequence data has advanced the field of microbiology; however, making sense of these data without bioinformatics skills is still problematic. We describe MICRA, an automatic pipeline, available as a web interface, for microbial identification and characterization through reads analysis. MICRA uses iterative mapping against reference genomes to identify genes and variations. Additional modules allow prediction of antibiotic susceptibility and resistance and comparing the results of several samples. MICRA is fast, producing few false-positive annotations and variant calls compared to current methods, making it a tool of great interest for fully exploiting sequencing data.     

Origin and evolution of the Notch signalling pathway: an overview from eukaryotic genomes

Background  

Of the 20 or so signal transduction pathways that orchestrate cell-cell interactions in metazoans, seven are involved during development. One of these is the Notch signalling pathway which regulates cellular identity, proliferation, differentiation and apoptosis via the developmental processes of lateral inhibition and boundary induction. In light of this essential role played in metazoan development, we surveyed a wide range of eukaryotic genomes to determine the origin and evolution of the components and auxiliary factors that compose and modulate this pathway.     

Renewable energy from Cyanobacteria: energy production optimization by metabolic pathway engineering

The need to develop and improve sustainable energy resources is of eminent importance due to the finite nature of our fossil fuels. This review paper deals with a third generation renewable energy resource which does not compete with our food resources, cyanobacteria. We discuss the current state of the art in developing different types of bioenergy (ethanol, biodiesel, hydrogen, etc.) from cyanobacteria. The major important biochemical pathways in cyanobacteria are highlighted, and the possibility to influence these pathways to improve the production of specific types of energy forms the major part of this review.     

Random models of Menzerath-Altmann law in genomes

Recently, a random breakage model has been proposed to explain the negative correlation between mean chromosome length and chromosome number that is found in many groups of species and is consistent with Menzerath-Altmann law, a statistical law that defines the dependency between the mean size of the whole and the number of parts in quantitative linguistics. Here, the central assumption of the model, namely that genome size is independent from chromosome number is reviewed. This assumption is shown to be unrealistic from the perspective of chromosome structure and the statistical analysis of real genomes. A general class of random models, including that random breakage model, is analyzed. For any model within this class, a power law with an exponent of -1 is predicted for the expectation of the mean chromosome size as a function of chromosome length, a functional dependency that is not supported by real genomes. The random breakage and variants keeping genome size and chromosome number independent raise no serious objection to the relevance of correlations consistent with Menzerath-Altmann law across taxonomic groups and the possibility of a connection between human language and genomes through that law.     

Hyaluronan catabolism: a new metabolic pathway

A new pathway of intermediary metabolism is described involving the catabolism of hyaluronan. The cell surface hyaluronan receptor, CD44, two hyaluronidases, Hyal-1 and Hyal-2, and two lysosomal enzymes, beta-glucuronidase and beta-N-acetylglucosaminidase, are involved. This metabolic cascade begins in lipid raft invaginations at the cell membrane surface. Degradation of the high-molecular-weight extracellular hyaluronan occurs in a series of discreet steps generating hyaluronan chains of decreasing sizes. The biological functions of the oligomers at each quantum step differ widely, from the space-filling, hydrating, anti-angiogenic, immunosuppressive 10(4)-kDa extracellular polymer, to 20-kDa intermediate polymers that are highly angiogenic, immuno-stimulatory, and inflammatory. This is followed by degradation to small oligomers that can induce heat shock proteins and that are anti-apoptotic. The single sugar products, glucuronic acid and a glucosamine derivative are released from lysosomes to the cytoplasm, where they become available for other metabolic cycles. There are 15 g of hyaluronan in the 70-kg individual, of which 5 g are cycled daily through this pathway. Some of the steps in this catabolic cascade can be commandeered by cancer cells in the process of growth, invasion, and metastatic spread.     

Identification of metabolic network models from incomplete high-throughput datasets

MOTIVATION: High-throughput measurement techniques for metabolism and gene expression provide a wealth of information for the identification of metabolic network models. Yet, missing observations scattered over the dataset restrict the number of effectively available datapoints and make classical regression techniques inaccurate or inapplicable. Thorough exploitation of the data by identification techniques that explicitly cope with missing observations is therefore of major importance. RESULTS: We develop a maximum-likelihood approach for the estimation of unknown parameters of metabolic network models that relies on the integration of statistical priors to compensate for the missing data. In the context of the linlog metabolic modeling framework, we implement the identification method by an Expectation-Maximization (EM) algorithm and by a simpler direct numerical optimization method. We evaluate performance of our methods by comparison to existing approaches, and show that our EM method provides the best results over a variety of simulated scenarios. We then apply the EM algorithm to a real problem, the identification of a model for the Escherichia coli central carbon metabolism, based on challenging experimental data from the literature. This leads to promising results and allows us to highlight critical identification issues.     

Decoupling dynamical systems for pathway identification from metabolic profiles

RATIONALE: Modern molecular biology is generating data of unprecedented quantity and quality. Particularly exciting for biochemical pathway modeling and proteomics are comprehensive, time-dense profiles of metabolites or proteins that are measurable, for instance, with mass spectrometry, nuclear magnetic resonance or protein kinase phosphorylation. These profiles contain a wealth of information about the structure and dynamics of the pathway or network from which the data were obtained. The retrieval of this information requires a combination of computational methods and mathematical models, which are typically represented as systems of ordinary differential equations. RESULTS: We show that, for the purpose of structure identification, the substitution of differentials with estimated slopes in non-linear network models reduces the coupled system of differential equations to several sets of decoupled algebraic equations, which can be processed efficiently in parallel or sequentially. The estimation of slopes for each time series of the metabolic or proteomic profile is accomplished with a 'universal function' that is computed directly from the data by cross-validated training of an artificial neural network (ANN). CONCLUSIONS: Without preprocessing, the inverse problem of determining structure from metabolic or proteomic profile data is challenging and computationally expensive. The combination of system decoupling and data fitting with universal functions simplifies this inverse problem very significantly. Examples show successful estimations and current limitations of the method. AVAILABILITY: A preliminary Web-based application for ANN smoothing is accessible at http://bioinformatics.musc.edu/webmetabol/. S-systems can be interactively analyzed with the user-friendly freeware PLAS (http://correio.cc.fc.ul.pt/~aenf/plas.html) or with the MATLAB module BSTLab (http://bioinformatics.musc.edu/bstlab/), which is currently being beta-tested.     

From cancer genomes to cancer models: bridging the gaps

Cancer genome projects are now being expanded in an attempt to provide complete landscapes of the mutations that exist in tumours. Although the importance of cataloguing genome variations is well recognized, there are obvious difficulties in bridging the gaps between high‐throughput resequencing information and the molecular mechanisms of cancer evolution. Here, we describe the current status of the high‐throughput genomic technologies, and the current limitations of the associated computational analysis and experimental validation of cancer genetic variants. We emphasize how the current cancer‐evolution models will be influenced by the high‐throughput approaches, in particular through efforts devoted to monitoring tumour progression, and how, in turn, the integration of data and models will be translated into mechanistic knowledge and clinical applications.     

Parameter estimation of kinetic models from metabolic profiles: two-phase dynamic decoupling method

MOTIVATION: Time-series measurements of metabolite concentration have become increasingly more common, providing data for building kinetic models of metabolic networks using ordinary differential equations (ODEs). In practice, however, such time-course data are usually incomplete and noisy, and the estimation of kinetic parameters from these data is challenging. Practical limitations due to data and computational aspects, such as solving stiff ODEs and finding global optimal solution to the estimation problem, give motivations to develop a new estimation procedure that can circumvent some of these constraints. RESULTS: In this work, an incremental and iterative parameter estimation method is proposed that combines and iterates between two estimation phases. One phase involves a decoupling method, in which a subset of model parameters that are associated with measured metabolites, are estimated using the minimization of slope errors. Another phase follows, in which the ODE model is solved one equation at a time and the remaining model parameters are obtained by minimizing concentration errors. The performance of this two-phase method was tested on a generic branched metabolic pathway and the glycolytic pathway of Lactococcus lactis. The results showed that the method is efficient in getting accurate parameter estimates, even when some information is missing.     

Frontiers in metabolic reconstruction and modeling of plant genomes

A major goal of post-genomic biology is to reconstruct and model in silico the metabolic networks of entire organisms. Work on bacteria is well advanced, and is now under way for plants and other eukaryotes. Genome-scale modelling in plants is much more challenging than in bacteria. The challenges come from features characteristic of higher organisms (subcellular compartmentation, tissue differentiation) and also from the particular severity in plants of a general problem: genome content whose functions remain undiscovered. This problem results in thousands of genes for which no function is known ('undiscovered genome content') and hundreds of enzymatic and transport functions for which no gene is yet identified. The severity of the undiscovered genome content problem in plants reflects their genome size and complexity. To bring the challenges of plant genome-scale modelling into focus, we first summarize the current status of plant genome-scale models. We then highlight the challenges - and ways to address them - in three areas: identifying genes for missing processes, modelling tissues as opposed to single cells, and finding metabolic functions encoded by undiscovered genome content. We also discuss the emerging view that a significant fraction of undiscovered genome content encodes functions that counter damage to metabolites inflicted by spontaneous chemical reactions or enzymatic mistakes.     

The metabolic pathway collection: an update.

The Metabolic Pathway Collection from EMP is an extraction of data from the larger Enzymes and Metabolic Pathways database (EMP). This extraction has been made publicly available in the hope that others will find it useful for a variety of purposes. The original release in October 1995 contained 1814 distinct pathways. The current collection contains 2180. Metabolic reconstructions for the first completely sequenced organisms-Haemophilus influenzae,Mycoplasma genitalium,Saccharomyces cerevisiaeandMethanococcus janaschii-are all included in the current release. All of the pathways in the collections are available as ASCII files in the form generated by the main curator, Evgeni Selkov. In addition, we are offering a more structured encoding of a subset of the collection; our initial release of this subcollection includes all of the pathways inMycoplasma genitalium, and we ultimately intend to offer the entire collection in this form as well.     

Analysis of mechanistic pathway models in drug discovery: p38 pathway

Mechanistic models of signal transduction have emerged as valuable tools for untangling complex signaling networks and gaining detailed insight into pathway dynamics. The natural extension of these tools is for the design of therapeutic strategies. We have generated a novel computational model of lipopolysaccharide-induced p38 signaling in the context of TNF-alpha production in inflammatory disease. Using experimental measurement of protein levels and phospho-protein time courses, populations of model parameters were estimated. With a collection of parameter sets, reflecting virtual diversity, we step through analysis of the p38 signaling pathway model to answer specific drug discovery questions regarding target prioritization, inhibitor simulation, model robustness and co-drugging. We demonstrate that target selection cannot be assessed independently from inhibitor mechanism of action and is also linked with robustness to cellular variability. Finally, we assert that in the face of parameter uncertainty one can still uncover consistent findings that can guide drug discovery efforts.     

Evolution of tryptophan biosynthetic pathway in microbial genomes: a comparative genetic study

Biosynthetic pathway evolution needs to consider the evolution of a group of genes that code for enzymes catalysing the multiple chemical reaction steps leading to the final end product. Tryptophan biosynthetic pathway has five chemical reaction steps that are highly conserved in diverse microbial genomes, though the genes of the pathway enzymes show considerable variations in arrangements, operon structure (gene fusion and splitting) and regulation. We use a combined bioinformatic and statistical analyses approach to address the question if the pathway genes from different microbial genomes, belonging to a wide range of groups, show similar evolutionary relationships within and between them. Our analyses involved detailed study of gene organization (fusion/splitting events), base composition, relative synonymous codon usage pattern of the genes, gene expressivity, amino acid usage, etc. to assess inter- and intra-genic variations, between and within the pathway genes, in diverse group of microorganisms. We describe these genetic and genomic variations in the tryptophan pathway genes in different microorganisms to show the similarities across organisms, and compare the same genes across different organisms to find the possible variability arising possibly due to horizontal gene transfers. Such studies form the basis for moving from single gene evolution to pathway evolutionary studies that are important steps towards understanding the systems biology of intracellular pathways.