A roadmap for interpreting 13C metabolite labeling patterns from cells

Measuring intracellular metabolism has increasingly led to important insights in biomedical research. [superscript 13]C tracer analysis, although less information-rich than quantitative [superscript 13]C flux analysis that requires computational data integration, has been established as a time-efficient method to unravel relative pathway activities, qualitative changes in pathway contributions, and nutrientcontributions. Here, we review selected key issues in interpreting [superscript 13]C metabolite labeling patterns, with the goal of drawing accurate conclusions from steady state and dynamic stable isotopic tracer experiment

Bioinformatics and Handwriting/Speech Reconition: Uncoventional Applications of Similarity Search Tools

This work introduces two unconventional applications for sequence alignment algorithms outside the domain of bioinformatics: handwriting recognition and speech recognition. In each application we treated data samples, such as the path of a and written pen stroke, as a protein sequence and use the FastA sequence alignment tool to classify unknown data samples, such as a written character. That is, we handle the handwriting and speech recognition problems like the protein annotation problem: given a sequence of unknown function, we annotate the sequence via sequence alignment. This approach achieves classification rates of 99.65% and 93.84% for the handwriting and speech recognition respectively. In addition, we provide a framework for applying sequence alignment to a variety of other non–traditional problems.Singapore-MIT Alliance (SMA

A Functional Protein Chip for Combinatorial Pathway Optimization and In Vitro Metabolic Engineering

Pathway optimization is, in general, a very demanding task due to the complex, nonlinear and largely unknown interactions of enzymes, regulators and metabolites. While in vitro reconstruction and pathway analysis is a viable alternative, a major limitation of this approach is the availability of the pathway enzymes for reliable pathway reconstruction. Here, we report the application of RNA display methods for the construction of fusion (chimeric) molecules, comprising mRNA and the protein they express, that can be used for the above purpose. The chimeric molecule is immobilized via hybridization of its mRNA end with homologous capture DNA spotted on a substrate surface. We show that the protein (enzyme) end of the fusion molecule retains its function under immobilized conditions and that the enzymatic activity is proportional to the amount of capture DNA spotted on the surface of a microarray or 96-well microplate. The relative amounts of all pathway enzymes can thus be changed at will by changing the amount of the corresponding capture DNA. Hence, entire pathways can be reconstructed and optimized in vitro from genomic information alone by generating chimeric molecules for all pathway enzymes in a single in vitro translation step and hybridizing on 96-well microplates where each well contains a different combination of capture DNA. We provide validation of this concept with the sequential reactions catalyzed by luciferase and nucleoside diphosphate kinase and further illustrate this method with the optimization of the five-step pathway for trehalose synthesis. Multi-enzyme pathways leading to the synthesis of specialty molecules can thus be optimized from genomic information about the pathway enzymes, provided the latter retain their activity under the in vitro immobilized conditions.Singapore-MIT Alliance (SMA

Inverse Metabolic Engineering of Synechocystis PCC 6803 for Improved Growth Rate and Poly-3-hydroxybutyrate Production

Synechocystis PCC 6803 is a photosynthetic bacterium that has the potential to make bioproducts from carbon dioxide and light. Biochemical production from photosynthetic organisms is attractive because it replaces the typical bioprocessing steps of crop growth, milling, and fermentation, with a one-step photosynthetic process. However, low yields and slow growth rates limit the economic potential of such endeavors. Rational metabolic engineering methods are hindered by limited cellular knowledge and inadequate models of Synechocystis. Instead, inverse metabolic engineering, a scheme based on combinatorial gene searches which does not require detailed cellular models, but can exploit sequence data and existing molecular biological techniques, was used to find genes that (1) improve the production of the biopolymer poly-3-hydroxybutyrate (PHB) and (2) increase the growth rate. A fluorescence activated cell sorting assay was developed to screen for high PHB producing clones. Separately, serial sub-culturing was used to select clones that improve growth rate. Novel gene knock-outs were identified that increase PHB production and others that increase the specific growth rate. These improvements make this system more attractive for industrial use and demonstrate the power of inverse metabolic engineering to identify novel phenotype-associated genes in poorly understood systems.Singapore-MIT Alliance (SMA

Activation Ratios For Reconstruction Of Signal Transduction Networks

We have developed a novel framework that can be applied for the analysis of signal transduction networks, both to facilitate reconstruction of the network structure and quantitatively characterize the interaction between network components. This approach, termed activation ratio analysis, involves the ratio between active and inactive forms of signaling intermediates at steady state. The activation ratio of an intermediate is shown to depend linearly upon the concentration of the activating enzyme. The slope of the line is defined as the activation factor, and is determined by the kinetic parameters of activation and inactivation. When activation ratios for simple signaling systems are considered, a set of rules develop that can be used to transform a set of experimental data to a proposed model network structure, with activation factors yielding a measure of activation potential between intermediates.Singapore-MIT Alliance (SMA

Machine Learning Approaches to Modeling the Physiochemical Properties of Small Peptides

Peptide and protein sequences are most commonly represented as a strings: a series of letters selected from the twenty character alphabet of abbreviations for the naturally occurring amino acids. Here, we experiment with representations of small peptide sequences that incorporate more physiochemical information. Specifically, we develop three different physiochemical representations for a set of roughly 700 HIV–I protease substrates. These different representations are used as input to an array of six different machine learning models which are used to predict whether or not a given peptide is likely to be an acceptable substrate for the protease. Our results show that, in general, higher–dimensional physiochemical representations tend to have better performance than representations incorporating fewer dimensions selected on the basis of high information content. We contend that such representations are more biologically relevant than simple string–based representations and are likely to more accurately capture peptide characteristics that are functionally important.Singapore-MIT Alliance (SMA

Third Generation Photovoltaics: Advanced Solar Energy Conversion

material contributors to primary energy supply, whereas cost-related constraints dominate the ultimate commercial potential of PV-derived solar energy conversion and storage systems. One approach to storing electrical energy in chemical bonds is through electrolysis, in which water is split into H 2 and O 2 in an electrolyzer. However, Pt-based electrolysis in acidic or neutral media is expensive and unlikely to be scalable to the levels that would be required for this process to be material in global primary energy production. Ni-based electrolysis in basic aqueous solutions is cheaper but requires scrubbing the input stream to remove the CO 2 (13); additionally, even the best fuel cells are only 50 to 60% energy-efficient and the best electrolysis units are 50 to 70% energyefficient (13), so the full-cycle energy storage/ discharge efficiency of such a system is currently only 25 to 30%. Clearly, better catalysts for the multielectron transformations involved in fuel formation are needed. Nature provides the existence proof for such catalysts, with the hydrogenase enzymes operating at the thermodynamic potential for production of H 2 from H 2 O, and with the oxygen-evolving complex of photosystem II producing O 2 from H 2 O in an energy-efficient fashion. However, no humanmade catalyst systems, either molecular or heterogeneous, have yet been identified that show performance even close to that of the natural enzymatic systems. Development of such catalysts would provide a key enabling technology for a full solar energy conversion and storage system. Whether the fuel-forming system is separate, as in a PV-electrolysis combination, or integrated, as in a fully artificial photosynthetic system that uses the incipient charge-separated electron-hole pairs to directly produce fuels with no wires and with only water and sunlight as the inputs, is an interesting point of discussion from both cost and engineering perspectives. However, the key components needed to enable the whole system remain the same in either case: cost-effective and efficient capture, conversion, and storage of sunlight. Each of these functions has its own challenges, and integration of them into a fully functioning, synergistic, globally scalable system will require further advances in both basic science and engineering. Such advances, together with advances in existing technologies, will be required if the full potential of solar energy is to be realized. PERSPECTIVES Challenges in Engineering Microbes for Biofuels Production Gregory Stephanopoulos Economic and geopolitical factors (high oil prices, environmental concerns, and supply instability) have been prompting policy-makers to put added emphasis on renewable energy sources. For the scientific community, recent advances, embodied in new insights into basic biology and technology that can be applied to metabolic engineering, are generating considerable excitement. There is justified optimism that the full potential of biofuel production from cellulosic biomass will be obtainable in the next 10 to 15 years

Multiresolution analysis in statistical mechanics. II. The wavelet transform as a basis for Monte Carlo simulations on lattices

In this paper, we extend our analysis of lattice systems using the wavelet transform to systems for which exact enumeration is impractical. For such systems, we illustrate a wavelet-accelerated Monte Carlo (WAMC) algorithm, which hierarchically coarse-grains a lattice model by computing the probability distribution for successively larger block spins. We demonstrate that although the method perturbs the system by changing its Hamiltonian and by allowing block spins to take on values not permitted for individual spins, the results obtained agree with the analytical results in the preceding paper, and ``converge'' to exact results obtained in the absence of coarse-graining. Additionally, we show that the decorrelation time for the WAMC is no worse than that of Metropolis Monte Carlo (MMC), and that scaling laws can be constructed from data performed in several short simulations to estimate the results that would be obtained from the original simulation. Although the algorithm is not asymptotically faster than traditional MMC, because of its hierarchical design, the new algorithm executes several orders of magnitude faster than a full simulation of the original problem. Consequently, the new method allows for rapid analysis of a phase diagram, allowing computational time to be focused on regions near phase transitions.Comment: 11 pages plus 7 figures in PNG format (downloadable separately

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. Contact: [email protected] Supplementary information: Supplementary data are available at Bioinformatics onlin