28 research outputs found
Systems Biology: The Next Frontier for Bioinformatics
Biochemical systems biology augments more traditional disciplines, such as genomics, biochemistry and molecular biology, by championing (i) mathematical and computational modeling; (ii) the application of traditional engineering practices in the analysis of biochemical systems; and in the past decade increasingly (iii) the use of near-comprehensive data sets derived from ‘omics platform technologies, in particular “downstream” technologies relative to genome sequencing, including transcriptomics, proteomics and metabolomics. The future progress in understanding biological principles will increasingly depend on the development of temporal and spatial analytical techniques that will provide high-resolution data for systems analyses. To date, particularly successful were strategies involving (a) quantitative measurements of cellular components at the mRNA, protein and metabolite levels, as well as in vivo metabolic reaction rates, (b) development of mathematical models that integrate biochemical knowledge with the information generated by high-throughput experiments, and (c) applications to microbial organisms. The inevitable role bioinformatics plays in modern systems biology puts mathematical and computational sciences as an equal partner to analytical and experimental biology. Furthermore, mathematical and computational models are expected to become increasingly prevalent representations of our knowledge about specific biochemical systems
A dynamic programming approach for the alignment of signal peaks in multiple gas chromatography-mass spectrometry experiments
<p>Abstract</p> <p>Background</p> <p>Gas chromatography-mass spectrometry (GC-MS) is a robust platform for the profiling of certain classes of small molecules in biological samples. When multiple samples are profiled, including replicates of the same sample and/or different sample states, one needs to account for retention time drifts between experiments. This can be achieved either by the alignment of chromatographic profiles prior to peak detection, or by matching signal peaks after they have been extracted from chromatogram data matrices. Automated retention time correction is particularly important in non-targeted profiling studies.</p> <p>Results</p> <p>A new approach for matching signal peaks based on dynamic programming is presented. The proposed approach relies on both peak retention times and mass spectra. The alignment of more than two peak lists involves three steps: (1) all possible pairs of peak lists are aligned, and similarity of each pair of peak lists is estimated; (2) the guide tree is built based on the similarity between the peak lists; (3) peak lists are progressively aligned starting with the two most similar peak lists, following the guide tree until all peak lists are exhausted. When two or more experiments are performed on different sample states and each consisting of multiple replicates, peak lists within each set of replicate experiments are aligned first (within-state alignment), and subsequently the resulting alignments are aligned themselves (between-state alignment). When more than two sets of replicate experiments are present, the between-state alignment also employs the guide tree. We demonstrate the usefulness of this approach on GC-MS metabolic profiling experiments acquired on wild-type and mutant <it>Leishmania mexicana </it>parasites.</p> <p>Conclusion</p> <p>We propose a progressive method to match signal peaks across multiple GC-MS experiments based on dynamic programming. A sensitive peak similarity function is proposed to balance peak retention time and peak mass spectra similarities. This approach can produce the optimal alignment between an arbitrary number of peak lists, and models explicitly within-state and between-state peak alignment. The accuracy of the proposed method was close to the accuracy of manually-curated peak matching, which required tens of man-hours for the analyzed data sets. The proposed approach may offer significant advantages for processing of high-throughput metabolomics data, especially when large numbers of experimental replicates and multiple sample states are analyzed.</p
Protein secretion and outer membrane assembly in Alphaproteobacteria
The assembly of β-barrel proteins into membranes is a fundamental process that is essential in Gram-negative bacteria, mitochondria and plastids. Our understanding of the mechanism of β-barrel assembly is progressing from studies carried out in Escherichia coli and Neisseria meningitidis. Comparative sequence analysis suggests that while many components mediating β-barrel protein assembly are conserved in all groups of bacteria with outer membranes, some components are notably absent. The Alphaproteobacteria in particular seem prone to gene loss and show the presence or absence of specific components mediating the assembly of β-barrels: some components of the pathway appear to be missing from whole groups of bacteria (e.g. Skp, YfgL and NlpB), other proteins are conserved but are missing characteristic domains (e.g. SurA). This comparative analysis is also revealing important structural signatures that are vague unless multiple members from a protein family are considered as a group (e.g. tetratricopeptide repeat (TPR) motifs in YfiO, β-propeller signatures in YfgL). Given that the process of the β-barrel assembly is conserved, analysis of outer membrane biogenesis in Alphaproteobacteria, the bacterial group that gave rise to mitochondria, also promises insight into the assembly of β-barrel proteins in eukaryotes
The Essentials of Protein Import in the Degenerate Mitochondrion of Entamoeba histolytica
Several essential biochemical processes are situated in mitochondria. The metabolic transformation of mitochondria in distinct lineages of eukaryotes created proteomes ranging from thousands of proteins to what appear to be a much simpler scenario. In the case of Entamoeba histolytica, tiny mitochondria known as mitosomes have undergone extreme reduction. Only recently a single complete metabolic pathway of sulfate activation has been identified in these organelles. The E. histolytica mitosomes do not produce ATP needed for the sulfate activation pathway and for three molecular chaperones, Cpn60, Cpn10 and mtHsp70. The already characterized ADP/ATP carrier would thus be essential to provide cytosolic ATP for these processes, but how the equilibrium of inorganic phosphate could be maintained was unknown. Finally, how the mitosomal proteins are translocated to the mitosomes had remained unclear. We used a hidden Markov model (HMM) based search of the E. histolytica genome sequence to discover candidate (i) mitosomal phosphate carrier complementing the activity of the ADP/ATP carrier and (ii) membrane-located components of the protein import machinery that includes the outer membrane translocation channel Tom40 and membrane assembly protein Sam50. Using in vitro and in vivo systems we show that E. histolytica contains a minimalist set up of the core import components in order to accommodate a handful of mitosomal proteins. The anaerobic and parasitic lifestyle of E. histolytica has produced one of the simplest known mitochondrial compartments of all eukaryotes. Comparisons with mitochondria of another amoeba, Dictystelium discoideum, emphasize just how dramatic the reduction of the protein import apparatus was after the loss of archetypal mitochondrial functions in the mitosomes of E. histolytica
Extraction of pure components from overlapped signals in gas chromatography-mass spectrometry (GC-MS)
Gas chromatography-mass spectrometry (GC-MS) is a widely used analytical technique for the identification and quantification of trace chemicals in complex mixtures. When complex samples are analyzed by GC-MS it is common to observe co-elution of two or more components, resulting in an overlap of signal peaks observed in the total ion chromatogram. In such situations manual signal analysis is often the most reliable means for the extraction of pure component signals; however, a systematic manual analysis over a number of samples is both tedious and prone to error. In the past 30 years a number of computational approaches were proposed to assist in the process of the extraction of pure signals from co-eluting GC-MS components. This includes empirical methods, comparison with library spectra, eigenvalue analysis, regression and others. However, to date no approach has been recognized as best, nor accepted as standard. This situation hampers general GC-MS capabilities, and in particular has implications for the development of robust, high-throughput GC-MS analytical protocols required in metabolic profiling and biomarker discovery. Here we first discuss the nature of GC-MS data, and then review some of the approaches proposed for the extraction of pure signals from co-eluting components. We summarize and classify different approaches to this problem, and examine why so many approaches proposed in the past have failed to live up to their full promise. Finally, we give some thoughts on the future developments in this field, and suggest that the progress in general computing capabilities attained in the past two decades has opened new horizons for tackling this important problem
Metabolites: A Novel Platform for Converging Research on Metabolism and Metabolomics
Technological advances in analytical instrumentation and advances in data modeling are working in synergy to open up new perspectives and research agendas in metabolic research. Thanks to the legacy of the Human Genome Project and its continued impact in the post-genomic era, metabolism is now thought of in a whole-genome context, even when the focus is on a single metabolite and individual metabolic reactions. For a few model organisms we now have extensive, and in some cases complete, information about components that perform integrated metabolic functions. This promises a true paradigm shift in our understanding of the processes of metabolism, but also poses new challenges. As complex and coordinated global behaviors are observed in what were thought to be "simple" organisms, many challenges remain in the experimental domain, as well as in the integration of data generated by increasingly high-throughput analytical techniques. Indeed, in the new era of metabolic research, mathematical and computational modeling is expected to play an increasingly important role. For many complex biochemical phenomena, use of mathematical models may be the best way to build a consistent picture and generate testable hypotheses based on complex yet inevitably incomplete data sets.[...