Recon 2.2: from reconstruction to model of human metabolism.

IntroductionThe human genome-scale metabolic reconstruction details all known metabolic reactions occurring in humans, and thereby holds substantial promise for studying complex diseases and phenotypes. Capturing the whole human metabolic reconstruction is an on-going task and since the last community effort generated a consensus reconstruction, several updates have been developed.ObjectivesWe report a new consensus version, Recon 2.2, which integrates various alternative versions with significant additional updates. In addition to re-establishing a consensus reconstruction, further key objectives included providing more comprehensive annotation of metabolites and genes, ensuring full mass and charge balance in all reactions, and developing a model that correctly predicts ATP production on a range of carbon sources.MethodsRecon 2.2 has been developed through a combination of manual curation and automated error checking. Specific and significant manual updates include a respecification of fatty acid metabolism, oxidative phosphorylation and a coupling of the electron transport chain to ATP synthase activity. All metabolites have definitive chemical formulae and charges specified, and these are used to ensure full mass and charge reaction balancing through an automated linear programming approach. Additionally, improved integration with transcriptomics and proteomics data has been facilitated with the updated curation of relationships between genes, proteins and reactions.ResultsRecon 2.2 now represents the most predictive model of human metabolism to date as demonstrated here. Extensive manual curation has increased the reconstruction size to 5324 metabolites, 7785 reactions and 1675 associated genes, which now are mapped to a single standard. The focus upon mass and charge balancing of all reactions, along with better representation of energy generation, has produced a flux model that correctly predicts ATP yield on different carbon sources.ConclusionThrough these updates we have achieved the most complete and best annotated consensus human metabolic reconstruction available, thereby increasing the ability of this resource to provide novel insights into normal and disease states in human. The model is freely available from the Biomodels database (http://identifiers.org/biomodels.db/MODEL1603150001)

Avoiding the Enumeration of Infeasible Elementary Flux Modes by Including Transcriptional Regulatory Rules in the Enumeration Process Saves Computational Costs.

Despite the significant progress made in recent years, the computation of the complete set of elementary flux modes of large or even genome-scale metabolic networks is still impossible. We introduce a novel approach to speed up the calculation of elementary flux modes by including transcriptional regulatory information into the analysis of metabolic networks. Taking into account gene regulation dramatically reduces the solution space and allows the presented algorithm to constantly eliminate biologically infeasible modes at an early stage of the computation procedure. Thereby, computational costs, such as runtime, memory usage, and disk space, are extremely reduced. Moreover, we show that the application of transcriptional rules identifies non-trivial system-wide effects on metabolism. Using the presented algorithm pushes the size of metabolic networks that can be studied by elementary flux modes to new and much higher limits without the loss of predictive quality. This makes unbiased, system-wide predictions in large scale metabolic networks possible without resorting to any optimization principle

What can mathematical modelling say about CHO metabolism and protein glycosylation?

Chinese hamster ovary cells have been in the spotlight for process optimization in recent years, due to being the major, long established cell factory for the production of recombinant proteins. A deep, quantitative understanding of CHO metabolism and mechanisms involved in protein glycosylation has proven to be attainable through the development of high throughput technologies. Here we review the most notable accomplishments in the field of modelling CHO metabolism and protein glycosylation. MSC 2010: 00-01, 99-00, Keywords: CHO cells, Metabolic modelling, Glycosylation, MFA, Kinetic model, 13C-labellin

Reducing Recon 2 for steady-state flux analysis of HEK cell culture

A representative stoichiometric model is essential to perform metabolic flux analysis (MFA) using experimentally measured consumption (or production) rates as constraints. For Human Embryonic Kidney (HEK) cell culture, there is the opportunity to use an extremely well-curated and annotated human genome-scale model Recon 2 for MFA. Performing MFA using Recon 2 without any modification would have implied that cells have access to all functionality encoded by the genome, which is not realistic. The majority of intracellular fluxes are poorly determined as only extracellular exchange rates are measured. This is compounded by the fact that there is no suitable metabolic objective function to suppress non-specific fluxes. We devised a heuristic to systematically reduce Recon 2 to emphasize flux through core metabolic reactions. This implies that cells would engage these dominant metabolic pathways to grow, and any significant changes in gross metabolic phenotypes would have invoked changes in these pathways. The reduced metabolic model becomes a functionalized version of Recon 2 used for identifying significant metabolic changes in cells by flux analysis

Mode matrix <i>R</i> after the first iteration step (left) converting reaction <i>R2</i> from numerical to binary form and after the last iteration step (right) for an ordinary EFM analysis.

<p>After the final step <i>R</i> contains only binary values. In the final matrix M6 is a futile 2-cycle mode and can be removed.</p

Initial mode matrix <i>R</i> for EFM calculation.

<p>Note that the order of the reactions has changed to maximize the number of leading rows that can directly be converted to binary form in the pre-iteration phase. The first ten rows were already transformed to the binary representation. Here “t” represents a binary “1” (true) and indicates that the reaction carries flux. “f” stands for a binary “0” (false) and indicates that the reaction flux is zero.</p

Comparison of runs with (green) and without (red) resorting the reaction order for a case with four rules (GR1, GR2, GR3, and GR4).

<p>The diagrams show the accumulated runtime (a), the number of adjacency candidates (b), the number of intermediate modes (c), and the number of modes eliminated by the rules (d) as a function of the iteration step.</p

Number of modes as a function of all possible combinations of the four iteration phase rules.

<p>The green bars show the average mode number for cases with one, two, or three gene rules.</p

Comparison of EFM calculation with and without taking into account gene regulatory information.

<p>The required disk space is given for a result file containing all modes in text format using double precision. The line ‘max. adjacent candidates’ shows the maximum number of potentially occurring adjacent pairs.</p

Kernel matrix <i>K</i> of the extended stoichiometric matrix shown in S2 Table.

<p>Kernel matrix <i>K</i> of the extended stoichiometric matrix shown in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0129840#pone.0129840.s004" target="_blank">S2 Table</a>.</p