Genome-scale mapping models and algorithms for stationary and instationary MFA-based metabolic flux elucidation

Metabolic models used in 13C metabolic flux analysis (13C-MFA) generally include a limited number of reactions primarily from central metabolism, neglecting degradation pathways and atom transition contributions for reactions outside central metabolism. This study addresses the impact on prediction fidelity of scaling-up core bacterial and cyanobacterial mapping models to a genome-scale carbon mapping (GSCM) models, imEco726 (668 reaction and 566 metabolites) and imSyn711 (731 reactions, 679 metabolites) for E. coli and Synechocystis PCC 6803, respectively, representing a ten-fold increase in model size. The GSCM models are constructed using the CLCA algorithm following reduction of the corresponding metabolic models, iAF1260 and iSyn731, using experimentally measured biomass and product yield during growth on glucose and CO2, respectively. The mapping models are then deployed for flux elucidation using isotopic steady-state MFA for E. coli to recapitulate experimentally observed labeling distributions of 12 measured amino acids, and isotopic instationary MFA for Synechocystis, to recapitulate labeling dynamics of 15 central metabolites. In both models, 80% of all fluxes varies less than onetenth of the basis carbon substrate uptake rate primarily due to the flux coupling with biomass production. Overall, we find that both the topology and estimated values of the metabolic fluxes remain largely consistent between the core and GSMM models for E. coli. Stepping up to a genome-scale mapping model leads to wider flux inference ranges for 20 key reactions present in the core model. The glycolysis flux range doubles due to the possibility of active gluconeogenesis, the TCA flux range expanded by 80% due to the availability of a bypass through arginine consistent with labeling data, and the transhydrogenase reaction flux was essentially unresolved due to the presence of as many as five routes for the inter-conversion of NADPH to NADH afforded by the genome-scale model. By globally accounting for ATP demands in the GSMM model the unused ATP decreased drastically with the lower bound matching the maintenance ATP requirement. A non-zero flux for the arginine degradation pathway was identified to meet biomass precursor demands as detailed in the iAF1260 model. Significant flux range shifts were observed using a GSCM model compared to a core model in Synechocystis arising from the inclusion of 18 novel carbon paths in the GSCM model. In particular, no flux is channeled through the oxidative pentose phosphate pathway, resulting in a reduced carbon fixation flux. In addition, a higher flux is seen through the Transaldolase reaction, serving as a bypass route to Fructose bisphosphatase. Serine and glycine are found to be synthesized from 3-phosphoglycerate and the photorespiratory pathway, respectively. Pyruvate is synthesized exclusively via the malate bypass with trace contributions from pyruvate kinase. Furthermore, trace flux is predicted through the lower TCA cycle indicating either pathway incompleteness or dispensability during photoautotrophic growth. Despite these differences, 80% of all reactions in both genome-scale models are resolved to within 10% of the respective substrate uptake rate due to the presence of 411 and 407 growth-coupled reactions in E. coli and Synechocystis, respectively. Flux ranges obtained with GSCM models are compared with those obtained upon projecting core model ranges on to a genome-scale metabolic model to elucidate the loss of information and erroneous biological inferences about pathway usage arising from assumptions contained within core models, reaffirming the importance of using mapping models with global carbon path coverage in 13C metabolic flux analysis

Reconstruction and Validation of a Genome-Scale Metabolic Model for the Filamentous Fungus Neurospora crassa Using FARM

The filamentous fungus Neurospora crassa played a central role in the development of twentieth-century genetics, biochemistry and molecular biology, and continues to serve as a model organism for eukaryotic biology. Here, we have reconstructed a genome-scale model of its metabolism. This model consists of 836 metabolic genes, 257 pathways, 6 cellular compartments, and is supported by extensive manual curation of 491 literature citations. To aid our reconstruction, we developed three optimization-based algorithms, which together comprise Fast Automated Reconstruction of Metabolism (FARM). These algorithms are: LInear MEtabolite Dilution Flux Balance Analysis (limed-FBA), which predicts flux while linearly accounting for metabolite dilution; One-step functional Pruning (OnePrune), which removes blocked reactions with a single compact linear program; and Consistent Reproduction Of growth/no-growth Phenotype (CROP), which reconciles differences between in silico and experimental gene essentiality faster than previous approaches. Against an independent test set of more than 300 essential/non-essential genes that were not used to train the model, the model displays 93% sensitivity and specificity. We also used the model to simulate the biochemical genetics experiments originally performed on Neurospora by comprehensively predicting nutrient rescue of essential genes and synthetic lethal interactions, and we provide detailed pathway-based mechanistic explanations of our predictions. Our model provides a reliable computational framework for the integration and interpretation of ongoing experimental efforts in Neurospora, and we anticipate that our methods will substantially reduce the manual effort required to develop high-quality genome-scale metabolic models for other organisms

MetRxn: a knowledgebase of metabolites and reactions spanning metabolic models and databases

<p>Abstract</p> <p>Background</p> <p>Increasingly, metabolite and reaction information is organized in the form of genome-scale metabolic reconstructions that describe the reaction stoichiometry, directionality, and gene to protein to reaction associations. A key bottleneck in the pace of reconstruction of new, high-quality metabolic models is the inability to directly make use of metabolite/reaction information from biological databases or other models due to incompatibilities in content representation (i.e., metabolites with multiple names across databases and models), stoichiometric errors such as elemental or charge imbalances, and incomplete atomistic detail (e.g., use of generic R-group or non-explicit specification of stereo-specificity).</p> <p>Description</p> <p>MetRxn is a knowledgebase that includes standardized metabolite and reaction descriptions by integrating information from BRENDA, KEGG, MetaCyc, Reactome.org and 44 metabolic models into a single unified data set. All metabolite entries have matched synonyms, resolved protonation states, and are linked to unique structures. All reaction entries are elementally and charge balanced. This is accomplished through the use of a workflow of lexicographic, phonetic, and structural comparison algorithms. MetRxn allows for the download of standardized versions of existing genome-scale metabolic models and the use of metabolic information for the rapid reconstruction of new ones.</p> <p>Conclusions</p> <p>The standardization in description allows for the direct comparison of the metabolite and reaction content between metabolic models and databases and the exhaustive prospecting of pathways for biotechnological production. This ever-growing dataset currently consists of over 76,000 metabolites participating in more than 72,000 reactions (including unresolved entries). MetRxn is hosted on a web-based platform that uses relational database models (MySQL).</p

MAPs: a database of modular antibody parts for predicting tertiary structures and designing affinity matured antibodies

BACKGROUND: The de novo design of a novel protein with a particular function remains a formidable challenge with only isolated and hard-to-repeat successes to date. Due to their many structurally conserved features, antibodies are a family of proteins amenable to predictable rational design. Design algorithms must consider the structural diversity of possible naturally occurring antibodies. The human immune system samples this design space (2 10(12)) by randomly combining variable, diversity, and joining genes in a process known as V-(D)-J recombination. DESCRIPTION: By analyzing structural features found in affinity matured antibodies, a database of Modular Antibody Parts (MAPs) analogous to the variable, diversity, and joining genes has been constructed for the prediction of antibody tertiary structures. The database contains 929 parts constructed from an analysis of 1168 human, humanized, chimeric, and mouse antibody structures and encompasses all currently observed structural diversity of antibodies. CONCLUSIONS: The generation of 260 antibody structures shows that the MAPs database can be used to reliably predict antibody tertiary structures with an average all-atom RMSD of 1.9 Å. Using the broadly neutralizing anti-influenza antibody CH65 and anti-HIV antibody 4E10 as examples, promising starting antibodies for affinity maturation are identified and amino acid changes are traced as antibody affinity maturation occurs

Cyanobacterial Alkanes Modulate Photosynthetic Cyclic Electron Flow to Assist Growth under Cold Stress

All cyanobacterial membranes contain diesel-range C15-C19 hydrocarbons at concentrations similar to chlorophyll. Recently, two universal but mutually exclusive hydrocarbon production pathways in cyanobacteria were discovered. We engineered a mutant of Synechocystis sp. PCC 6803 that produces no alkanes, which grew poorly at low temperatures. We analyzed this defect by assessing the redox kinetics of PSI. The mutant exhibited enhanced cyclic electron flow (CEF), especially at low temperature. CEF raises the ATP:NADPH ratio from photosynthesis and balances reductant requirements of biosynthesis with maintaining the redox poise of the electron transport chain. We conducted in silico flux balance analysis and showed that growth rate reaches a distinct maximum for an intermediate value of CEF equivalent to recycling 1 electron in 4 from PSI to the plastoquinone pool. Based on this analysis, we conclude that the lack of membrane alkanes causes higher CEF, perhaps for maintenance of redox poise. In turn, increased CEF reduces growth by forcing the cell to use less energy-efficient pathways, lowering the quantum efficiency of photosynthesis. This study highlights the unique and universal role of medium-chain hydrocarbons in cyanobacterial thylakoid membranes: they regulate redox balance and reductant partitioning in these oxygenic photosynthetic cells under stress

Cyanobacterial Alkanes Modulate Photosynthetic Cyclic Electron Flow to Assist Growth under Cold Stress

All cyanobacterial membranes contain diesel-range C15-C19 hydrocarbons at concentrations similar to chlorophyll. Recently, two universal but mutually exclusive hydrocarbon production pathways in cyanobacteria were discovered. We engineered a mutant of Synechocystis sp. PCC 6803 that produces no alkanes, which grew poorly at low temperatures. We analyzed this defect by assessing the redox kinetics of PSI. The mutant exhibited enhanced cyclic electron flow (CEF), especially at low temperature. CEF raises the ATP:NADPH ratio from photosynthesis and balances reductant requirements of biosynthesis with maintaining the redox poise of the electron transport chain. We conducted in silico flux balance analysis and showed that growth rate reaches a distinct maximum for an intermediate value of CEF equivalent to recycling 1 electron in 4 from PSI to the plastoquinone pool. Based on this analysis, we conclude that the lack of membrane alkanes causes higher CEF, perhaps for maintenance of redox poise. In turn, increased CEF reduces growth by forcing the cell to use less energy-efficient pathways, lowering the quantum efficiency of photosynthesis. This study highlights the unique and universal role of medium-chain hydrocarbons in cyanobacterial thylakoid membranes: they regulate redox balance and reductant partitioning in these oxygenic photosynthetic cells under stress

Large-scale inference and graph theoretical analysis of gene-regulatory networks in B. stubtilis

We present the methods and results of a two-stage modeling process that generates candidate gene-regulatory networks of the bacterium B. subtilis from experimentally obtained, yet mathematically underdetermined microchip array data. By employing a computational, linear correlative procedure to generate these networks, and by analyzing the networks from a graph theoretical perspective, we are able to verify the biological viability of our inferred networks, and we demonstrate that our networks' graph theoretical properties are remarkably similar to those of other biological systems. In addition, by comparing our inferred networks to those of a previous, noisier implementation of the linear inference process [17], we are able to identify trends in graph theoretical behavior that occur both in our networks as well as in their perturbed counterparts. These commonalities in behavior at multiple levels of complexity allow us to ascertain the level of complexity to which our process is robust to noise.Comment: 22 pages, 4 figures, accepted for publication in Physica A (2006

IPRO+/-: a computational protein design tool allowing not only for amino acid changes but also insertions and deletions

The need for enzymes with new or improved catalytic properties and specificities underpins many challenges in both biotechnology and pharmaceutical industry. This is typically carried out by changing the native amino acid composition through single or multiple mutations or recombination. Many computational strategies have been developed for suggesting amino acid changes (i.e., mutations) likely to usher an altered substrate or cofactor specificity, improved thermostability or higher turnover. However, by perusing protein family alignments one can immediately notice the ubiquitous presence of gaps. These gaps imply that not all active enzyme variants have the same backbone length with insertions and deletions (indels) contributing significantly to the possibilities of altering enzyme activity by drastically affecting protein repacking. Currently, no algorithms exist which can systemically position multiple insertions or deletions during in silico protein redesign. In this contribution we introduce IPRO+/-, a first of its kind integrated environment for protein redesign with respect to a single or multiple binding imperatives by not only predicting amino acid changes, but also insertions and deletions (see Figure 1). IPRO+/- allows the user to run standalone programs for (a) predicting energy minimized structural models of an enzyme with a desired indels and/or mutations, (b) computing binding free energies between proteins and small molecules, and (c) performing energy minimization on any protein or protein complex. The contribution will provide an overview of the tasks involved in IPRO+/-, input language terminology, algorithmic details, software implementation specifics and application highlights. IPRO+/- will be made freely downloadable from http://www.maranasgroup.com/software.htm upon publication. Please click Additional Files below to see the full abstract