Ari Rantanen

FiD: a software for ab initio structural identification of product ions from tandem mass spectrometric data.

We present FiD (Fragment iDentificator), a software tool for the structural identification of product ions produced with tandem mass spectrometric measurement of low molecular weight organic compounds. Tandem mass spectrometry (MS/MS) has proven to be an indispensable tool in modern, cell-wide metabolomics and fluxomics studies. In such studies, the structural information of the MS(n) product ions is usually needed in the downstream analysis of the measurement data. The manual identification of the structures of MS(n) product ions is, however, a nontrivial task requiring expertise, and calls for computer assistance. Commercial software tools, such as Mass Frontier and ACD/MS Fragmenter, rely on fragmentation rule databases for the identification of MS(n) product ions. FiD, on the other hand, conducts a combinatorial search over all possible fragmentation paths and outputs a ranked list of alternative structures. This gives the user an advantage in situations where the MS/MS data of compounds with less well-known fragmentation mechanisms are processed. FiD software implements two fragmentation models, the single-step model that ignores intermediate fragmentation states and the multi-step model, which allows for complex fragmentation pathways. The software works for MS/MS data produced both in positive- and negative-ion modes. The software has an easy-to-use graphical interface with built-in visualization capabilities for structures of product ions and fragmentation pathways. In our experiments involving amino acids and sugar-phosphates, often found, e.g., in the central carbon metabolism of yeasts, FiD software correctly predicted the structures of product ions on average in 85% of the cases. The FiD software is free for academic use and is available for download from www.cs.helsinki.fi/group/sysfys/software/fragid.

An analytic and systematic framework for estimating metabolic flux ratios from 13C tracer experiments

BackgroundMetabolic fluxes provide invaluable insight on the integrated response of a cell to environmental stimuli or genetic modifications. Current computational methods for estimating the metabolic fluxes from 13C isotopomer measurement data rely either on manual derivation of analytic equations constraining the fluxes or on the numerical solution of a highly nonlinear system of isotopomer balance equations. In the first approach, analytic equations have to be tediously derived for each organism, substrate or labelling pattern, while in the second approach, the global nature of an optimum solution is difficult to prove and comprehensive measurements of external fluxes to augment the 13C isotopomer data are typically needed.ResultsWe present a novel analytic framework for estimating metabolic flux ratios in the cell from 13C isotopomer measurement data. In the presented framework, equation systems constraining the fluxes are derived automatically from the model of the metabolism of an organism. The framework is designed to be applicable with all metabolic network topologies, 13C isotopomer measurement techniques, substrates and substrate labelling patterns.By analyzing nuclear magnetic resonance (NMR) and mass spectrometry (MS) measurement data obtained from the experiments on glucose with the model micro-organisms Bacillus subtilis and Saccharomyces cerevisiae we show that our framework is able to automatically produce the flux ratios discovered so far by the domain experts with tedious manual analysis. Furthermore, we show by in silico calculability analysis that our framework can rapidly produce flux ratio equations – as well as predict when the flux ratios are unobtainable by linear means – also for substrates not related to glucose.ConclusionThe core of 13C metabolic flux analysis framework introduced in this article constitutes of flow and independence analysis of metabolic fragments and techniques for manipulating isotopomer measurements with vector space techniques. These methods facilitate efficient, analytic computation of the ratios between the fluxes of pathways that converge to a common junction metabolite. The framework can been seen as a generalization and formalization of existing tradition for computing metabolic flux ratios where equations constraining flux ratios are manually derived, usually without explicitly showing the formal proofs of the validity of the equations.

Planning optimal measurements of isotopomer distributions for estimation of metabolic fluxes†Preliminary version of this article appeared in the proceedings of German Conference on Bioinformatics 2005. Lecture Notes in Informatics Vol. P-71 (2005), pp. 177--191.

MOTIVATION Flux estimation using isotopomer information of metabolites is currently the most reliable method to obtain quantitative estimates of the activity of metabolic pathways. However, the development of isotopomer measurement techniques for intermediate metabolites is a demanding task. Careful planning of isotopomer measurements is thus needed to maximize the available flux information while minimizing the experimental effort. RESULTS In this paper we study the question of finding the smallest subset of metabolites to measure that ensure the same level of isotopomer information as the measurement of every metabolite in the metabolic network. We study the computational complexity of this optimization problem in the case of the so-called positional enrichment data, give methods for obtaining exact and fast approximate solutions, and evaluate empirically the efficacy of the proposed methods by analyzing a metabolic network that models the central carbon metabolism of Saccharomyces cerevisiae.

Novel computational tools in bakery process data analysis: a comparative study

We studied the potential of various machine learning and statistical methods in the prediction of product quality in industrial bakery processes. The methods included classification and regression tree, decision list, neural network, support vector machine and Bayesian learning algorithms as well as statistical multivariate methods. Our data originated from two industrial bakery processes: a sourdough rye bread and a Danish pastry process. In our studies, the Naive Bayesian algorithm turned out to be the best classifier building algorithm while the partial least squares (PLS) method was the best regression method. The prediction accuracy of these models improved significantly by pruning the original set of variables. In this study, two response variables could be predicted on a level that justifies further study: rye bread pH could be predicted with high accuracy with Naive Bayesian Classifier, and Danish pastry height could be predicted with a moderately high correlation with PLS.

ReMatch: a web-based tool to construct, store and share stoichiometric metabolic models with carbon maps for metabolic flux analysis

ReMatch is a web-based, user-friendly tool that constructs stoichiometric network models for metabolic flux analysis, integrating user-developed models into a database collected from several comprehensive metabolic data resources, including KEGG, MetaCyc and CheBI. Particularly, ReMatch augments the metabolic reactions of the model with carbon mappings to facilitate (13)C metabolic flux analysis. The construction of a network model consisting of biochemical reactions is the first step in most metabolic modelling tasks. This model construction can be a tedious task as the required information is usually scattered to many separate databases whose interoperability is suboptimal, due to the heterogeneous naming conventions of metabolites in different databases. Another, particularly severe data integration problem is faced in (13)C metabolic flux analysis, where the mappings of carbon atoms from substrates into products in the model are required. ReMatch has been developed to solve the above data integration problems. First, ReMatch matches the imported user-developed model against the internal ReMatch database while considering a comprehensive metabolite name thesaurus. This, together with wild card support, allows the user to specify the model quickly without having to look the names up manually. Second, ReMatch is able to augment reactions of the model with carbon mappings, obtained either from the internal database or given by the user with an easy-touse tool. The constructed models can be exported into 13C-FLUX and SBML file formats. Further, a stoichiometric matrix and visualizations of the network model can be generated. The constructed models of metabolic networks can be optionally made available to the other users of ReMatch. Thus, ReMatch provides a common repository for metabolic network models with carbon mappings for the needs of metabolic flux analysis community. ReMatch is freely available for academic use at http://www.cs.helsinki.fi/group/sysfys/software/rematch/.

Isotopomer distribution computation from tandem mass spectrometric data with overlapping fragment spectra

We present a method for determination of the isotopomer distributions of metabolites from the data generated by a tandem mass spectrometer. The method is an improvement over existing method as it is able to deal with overlapping fragments in the spectra. Our experiments indicate that the new method surpasses its predecessors in separating isotopomers from each other. When using the daughter ion scanning (collision induced dissociation) mode, the method was shown to be able to constrain the isotopomer distribution of different amino acids better than two existing methods. In particular, the isotopomer distributions of three amino acids, glycine, alanine and serine, can be fully uncovered with the method. However, due to the imperfect fragmentation of molecules in the tandem mass spectrometer, isotopomer distributions of larger amino acids still cannot be fully uncovered. In tests with isotope-labelled alanine, most accurate results were obtained using multiple reaction monitoring and 15 eV collision energy. The meausured isotopomer frequecies were in the range 99-106% of the theoretical value and the deviation between repetitions was in the range 1-10%.

A Method for Estimating Metabolic Fluxes from Incomplete Isotopomer Information

Metabolic flux estimation--the problem of finding out the rates of reactions in metabolic pathways--is an important problem area in the study of metabolism. The most accurate technique for this task today is the use of isotopic tracer experiments, where a mixture of differently isotope-labeled substrates is fed to a cell culture and the propagation of the labels is observed from the products and intermediate metabolites, where possible. We present a generic methodology for solving the fluxes of a metabolic network in a steady state. The method differs from most previous approaches by not making prior assumptions about the topology of the metabolic network. Also, only very mild assumptions are made about the available measurement data, for example, both positional enrichment and mass isotopomer data can be used.

A Computational Method for Reconstructing Gapless Metabolic Networks

We propose a computational method for reconstructing metabolic networks. The method utilizes optimization techniques and graph traversal algorithms to discover a set of biochemical reactions that is most likely catalyzed by the enzymatic genes of the target organism. Unlike most existing computational methods for metabolic reconstruction, our method generates networks that are structurally consistent, or in other terms, gapless. As many analyses of metabolic networks, like flux balance analysis, require gapless networks as inputs, our network offers a more realistic basis for metabolic modelling than the existing automated reconstruction methods. It is easy to incorporate existing information, like knowledge about experimentally discovered metabolic reactions or metabolites into the process. Thus, our method can be used to assist in the manual curation of metabolic network models as it is able to suggest good candidate reactions for filling gaps in the existing network models. However, it is not necessary to assume any prior knowledge on composition of complete biochemical pathways in the network. We argue that this makes the method well-suited to analysis of organisms that might differ considerably from previously known organisms. We demonstrate the viability of our method by analysing the metabolic network of the well-known organism Escherichia coli.

Finding feasible pathways in metabolic networks

Recent small-world studies of the global structure of metabolic networks have been based on the shortest-path distance. In this paper, we propose new distance measures that are based on the structure of feasible metabolic pathways between metabolites. We argue that these distances capture the complexity of the underlying biochemical processes more accurately than the shortest-path distance. To test our approach in practice, we calculated our distances and shortest-path distances in two microbial organisms, S. cerevisiae and E. coli. The results show that metabolite interconversion is significantly more complex than was suggested in previous small-world studies. We also studied the effect of reaction removals (gene knock-outs) on the connectivity of the S. cerevisiae network and found out that the network is not particularly robust against such mutations.

Equivalence of metabolite fragments and flow analysis of isotopomer distributions for flux estimation

The most accurate estimates of the activity of metabolic pathways are obtained by conducting isotopomer tracer experiments. The success of this method, however, is intimately dependent on the quality and amount of data on isotopomer distributions of intermediate metabolites. In this paper we present a novel method for discovering sets of metabolite fragments that always have identical isotopomer distributions, regardless of the velocities of the reactions in the metabolic network. We outline several applications of this equivalence concept, including improved propagation of measurements, experiment planning and consistency checking of metabolic network. Our computational experiments in measurement propagation indicate that the improvement via the use of this technique may be substantial.