Genome-scale metabolic network models for industrial microorganisms metabolic engineering: Current advances and future prospects

The construction of high-performance microbial cell factories (MCFs) is the centerpiece of biomanufacturing. However, the complex metabolic regulatory network of microorganisms poses great challenges for the efficient design and construction of MCFs. The genome-scale metabolic network models (GSMs) can systematically simulate the metabolic regulation process of microorganisms in silico, providing effective guidance for the rapid design and construction of MCFs. In this review, we summarized the development status of 16 important industrial microbial GSMs, and further outline the technologies or methods that continuously promote high-quality GSMs construction from five aspects: I) Databases and modeling tools facilitate GSMs reconstruction; II) evolving gap-filling technologies; III) constraint-based model reconstruction; IV) advances in algorithms; and V) developed visualization tools. In addition, we also summarized the applications of GSMs in guiding metabolic engineering from four aspects: I) exploring and explaining metabolic features; II) predicting the effects of genetic perturbations on metabolism; III) predicting the optimal phenotype; IV) guiding cell factories construction in practical experiment. Finally, we discussed the development of GSMs, aiming to provide a reference for efficiently reconstructing GSMs and guiding metabolic engineering.

N2O and CH4 fluxes from intensively managed grassland: The importance of biological and environmental drivers vs. management

Agriculture is the main contributor to anthropogenic nitrous oxide (N2O) and methane (CH4) emissions. Therefore, mitigation options are urgently needed. In contrast to carbon dioxide, eddy covariance measurements of N2O and CH4 fluxes are still scarce, and thus little is known how environmental and biotic drivers as well as management affect the net N2O and CH4 exchange in grasslands. Thus, we investigated the most important drivers of net ecosystem N2O and CH4 fluxes in a temperate grassland, and continued a N2O mitigation experiment (increased clover proportion vs. fertilization with slurry). Random forest gap-filling models were able to capture intermittent emission peaks, performing better for half-hourly N2O than for CH4 fluxes. The unfertilized clover parcel (parcel B) continued to show lower N2O emissions (4.4 and 2.7 kg N2O-N ha-1 yr-1) compared to the fertilized parcel (parcel A; 6.9 and 5.9 kg N2O-N ha-1 yr-1) for 2019 and 2020, respectively. Tier 1 nitrogen (N) emission factors of 2.6 % and 1.9 % were observed at the fertilized parcel during the study period. Lower soil N concentrations indicated a lower N leaching risk at the clover than at the fertilized parcel. Annual CH4 emissions (including periods with sheep grazing) were similar from both parcels, and ranged from 25 to 38.5 kg CH4-C ha-1. The most important drivers of both N2O and CH4 fluxes were lagged precipitation and water filled pore space, but also management (for N2O from parcel B; CH4 from parcel A). Biotic variables such as vegetation height and leaf area index were important predictors for the N2O exchange, while grazing temporarily increased CH4 emissions. Overall, reducing N fertilization and increasing the legume proportion were effective N2O reduction measures. In particular, adjusting N fertilization to plant N demands can help to avoid high N2O emissions from grasslands.

MTG-Link: leveraging barcode information from linked-reads to assemble specific loci

BACKGROUND

Local assembly with short and long reads has proven to be very useful in many applications: reconstruction of the sequence of a locus of interest, gap-filling in draft assemblies, as well as alternative allele reconstruction of large Structural Variants. Whereas linked-read technologies have a great potential to assemble specific loci as they provide long-range information while maintaining the power and accuracy of short-read sequencing, there is a lack of local assembly tools for linked-read data.

RESULTS

We present MTG-Link, a novel local assembly tool dedicated to linked-reads. The originality of the method lies in its read subsampling step which takes advantage of the barcode information contained in linked-reads mapped in flanking regions. We validated our approach on several datasets from different linked-read technologies. We show that MTG-Link is able to assemble successfully large sequences, up to dozens of Kb. We also demonstrate that the read subsampling step of MTG-Link considerably improves the local assembly of specific loci compared to other existing short-read local assembly tools. Furthermore, MTG-Link was able to fully characterize large insertion variants and deletion breakpoints in a human genome and to reconstruct dark regions in clinically-relevant human genes. It also improved the contiguity of a 1.3 Mb locus of biological interest in several individual genomes of the mimetic butterfly Heliconius numata.

CONCLUSIONS

MTG-Link is an efficient local assembly tool designed for different linked-read sequencing technologies. MTG-Link source code is available at https://github.com/anne-gcd/MTG-Link and as a Bioconda package.

Analysing the value of environmental citizen-generated data: Complementarity and cost per observation

The proliferation of Citizen Science initiatives has increased the expectations of practitioners who need data for design, analysis, management and research in environmental applications. Many Citizen Science experiences have reported tangible societal benefits related to improved governance of natural resources due to the involvement of citizens and communities. However, from the perspective of data generation, most of the literature on Citizen Science tends to regard it as a potentially cost-effective source of data, with major concerns about the quality of data. The Ground Truth 2.0 project brought the opportunity to examine the scope of this potential by analysing the value of citizen-generated data. We propose a methodology to account for the value of citizen observations as a function of their complementarity to existing environmental observations and the evolution of their costs in time. The application of the proposed methodology in the chosen case studies that were all established using a co-design approach shows that the cost of obtaining Citizen Science data is not as low as frequently stated in literature. This is because the costs associated with co-design events for creating a Citizen Science community, as well as the functional and technical design of the tools, are much higher than the costs of rolling out the actual observation campaigns. In none of the considered cases did an increment in the number of preparatory events translate into an immediate increase in the collected observations. Nevertheless, Citizen Science appears to have the greatest value in places where in-situ environmental monitoring is not implemented.

An interpretable self-adaptive deep neural network for estimating daily spatially-continuous PM2.5 concentrations across China

Accurate estimation of daily spatially-continuous PM_2.5 (fine particulate matter) concentration is a prerequisite to address environmental public health issues, and satellite-based aerosol optical depth (AOD) products have been widely used to estimate PM_2.5 concentrations using statistical-based or machine learning-based models. However, statistical-based models oversimplify the AOD-PM_2.5 relationships, whereas complex machine learning technologies ignore the spatiotemporal heterogeneity of the predictors and demonstrate shortage in interpretation. Besides, large AOD data gaps resulting in PM_2.5 estimation biases have been seldom imputed in previous studies, especially at national scales. To fill the above research gaps, this study attempts to present a feasible methodology to estimate daily spatially-continuous PM_2.5 concentrations in China. The AOD data gaps across China were first imputed via a random forest (RF) model. Then, an interpretable self-adaptive deep neural network (SADNN) model, incorporating AOD, meteorological and other auxiliary predictors, was developed to estimate daily spatially-continuous PM_2.5 concentrations from 2017 to 2018. Five-fold sample (site)-based cross-validation results showed a high accuracy of the SADNN model, with coefficient of determination and root mean square error values equal to 0.86 (0.84) and 13.07 (14.30) μg/m³, respectively, outperforming the standard DNN and the RF model. Furthermore, the SADNN model identified the spatiotemporal patterns of predictor importance, and demonstrated that the boundary layer height, elevation and AOD were the most important predictors both spatially and temporally. And the predictor importance in the Qinghai-Tibet Plateau was different from that in the rest of China. These results enhance our understanding of AOD-PM_2.5 relationships and elucidate the estimated PM_2.5 datasets with complete coverage are applicable for related air pollution studies and epidemiological cohort studies. Moreover, considering the effective nonlinear model capability and interpretability, the SADNN model is beneficial for not only PM_2.5 estimation but also other earth data and scenarios.

Estimating PM2.5 concentrations in Northeastern China with full spatiotemporal coverage, 2005-2016

Predicting long-term spatiotemporal characteristics of fine particulate matter (PM_2.5) is important in China to understand historical levels of PM_2.5, to support health effects research of both long-term and short-term exposures to PM_2.5, and to evaluate the efficacy of air pollution control policies. Satellite-retrieved aerosol optical depth (AOD) provides a unique opportunity to characterize the long-term trends of ground-level PM_2.5 at high spatial resolution. However, the missing rate of AOD in Northeastern China (NEC) is very high, especially in winter, and challenges the accuracy of long-term predictions of PM_2.5 if left unresolved. Using random forest algorithms, this study developed a gap-filling approach combing satellite AOD, meteorological data, land use parameters, population and visibility in the NEC during 2005-2016. The model, including all predictors, combined with a model without AOD was able to fill the gap of PM_2.5 predictions caused by missing AOD at 1-km resolution. The R² (RMSE) of the full-coverage predictions was 0.81 (18.5 μg/m³) at the daily level. Gap-filled PM_2.5 predictions on days with missing AOD reduced the relative prediction error from 28% to 2.5% in winter. The leave-one-year-out-cross-validation R² (RMSE) of the full-coverage predictions was 0.65 (16.3 μg/m³) at the monthly level, indicating relatively high accuracy of predicted historical PM_2.5 concentrations. Our results suggested that AOD helped increase the reliability of historical PM_2.5 prediction when ground PM_2.5 measurements were unavailable, even though predictions from the AOD model only accounted for approximate 37% of the whole dataset. Predicted PM_2.5 level in NEC have increased since 2005, reached its peak during 2013-2015, then saw a major decline in 2016. Our high-resolution predictions also showed a south to north gradient and many pollution hot spots in the city clusters surrounding provincial capitals, as well as within large cities. Overall, by combining predictions from the AOD model with higher accuracy and predictions from the non-AOD model to achieve full coverage, our modeling approach could produce long-term, full-coverage historical PM_2.5 levels in high-latitude areas in China, despite the widespread and persistent AOD missingness.

Systematically gap-filling the genome-scale metabolic model of CHO cells

OBJECTIVE

Chinese hamster ovary (CHO) cells are the leading cell factories for producing recombinant proteins in the biopharmaceutical industry. In this regard, constraint-based metabolic models are useful platforms to perform computational analysis of cell metabolism. These models need to be regularly updated in order to include the latest biochemical data of the cells, and to increase their predictive power. Here, we provide an update to iCHO1766, the metabolic model of CHO cells.

RESULTS

We expanded the existing model of Chinese hamster metabolism with the help of four gap-filling approaches, leading to the addition of 773 new reactions and 335 new genes. We incorporated these into an updated genome-scale metabolic network model of CHO cells, named iCHO2101. In this updated model, the number of reactions and pathways capable of carrying flux is substantially increased.

CONCLUSIONS

The present CHO model is an important step towards more complete metabolic models of CHO cells.

DATimeS: A machine learning time series GUI toolbox for gap-filling and vegetation phenology trends detection

Optical remotely sensed data are typically discontinuous, with missing values due to cloud cover. Consequently, gap-filling solutions are needed for accurate crop phenology characterization. The here presented Decomposition and Analysis of Time Series software (DATimeS) expands established time series interpolation methods with a diversity of advanced machine learning fitting algorithms (e.g., Gaussian Process Regression: GPR) particularly effective for the reconstruction of multiple-seasons vegetation temporal patterns. DATimeS is freely available as a powerful image time series software that generates cloud-free composite maps and captures seasonal vegetation dynamics from regular or irregular satellite time series. This work describes the main features of DATimeS, and provides a demonstration case using Sentinel-2 Leaf Area Index time series data over a Spanish site. GPR resulted as an optimum fitting algorithm with most accurate gap-filling performance and associated uncertainties. DATimeS further quantified LAI fluctuations among multiple crop seasons and provided phenological indicators for specific crop types.

Uncertainty in gap filling and estimating the annual sum of carbon dioxide exchange for the desert Tugai forest, Ebinur Lake Basin, Northwest China

In most eddy covariance (EC) studies, carbon flux measurements have a high defect rate for a variety of reasons. Obtaining the annual sum of carbon dioxide exchange requires imputation of data gaps with high precision and accuracy. This study used five methods to fill the gaps in carbon flux data and estimate the total annual carbon dioxide exchange of the Tugai forest in the arid desert ecosystem of Ebinur Lake Basin, Northwest China. The Monte Carlo method was used to estimate the random error and bias caused by gap filling. The results revealed that (1) there was a seasonal difference in the friction velocity threshold of nighttime flux, with values in the growing season and non-growing season of 0.12 and 0.10 m/s, respectively; (2) the five gap-filling methods explained 77–84% of the data variability in the fluxes, and the random errors estimated by these methods were characterized by non-normality and leptokurtic heavy tail features, following the Laplacian (or double-exponential) distribution; (3) estimates of the annual sum of carbon dioxide exchange using the five methods at the study site in 2015 ranged from −178.25 to −155.21 g C m⁻² year⁻¹, indicating that the Tugai forest in the Ebinur Lake Basin is a net carbon sink. The standard deviation of the total annual carbon dioxide exchange sums estimated by the five different methods ranged from 3.15 to 19.08 g C m⁻² year⁻¹, with bias errors ranging from −13.69 to 14.05 g C m⁻² year⁻¹. This study provides a theoretical basis for the carbon dioxide exchange and carbon source/sink assessment of the Tugai forest in an arid desert ecosystem. In order to explore the functioning of the Tugai forest at this site, a greater understanding of the underlying ecological mechanisms is necessary.

Impacts of snow and cloud covers on satellite-derived PM2.5 levels

Satellite aerosol optical depth (AOD) has been widely employed to evaluate ground fine particle (PM2.5) levels, whereas snow/cloud covers often lead to a large proportion of non-random missing AOD values. As a result, the fully covered and unbiased PM2.5 estimates will be hard to generate. Among the current approaches to deal with the data gap issue, few have considered the cloud-AOD relationship and none of them have considered the snow-AOD relationship. This study examined the impacts of snow and cloud covers on AOD and PM2.5 and made full- coverage PM2.5 predictions by considering these impacts. To estimate missing AOD values, daily gap-filling models with snow/cloud fractions and meteorological covariates were developed using the random forest algorithm. By using these models in New York State, a daily AOD data set with a 1-km resolution was generated with a complete coverage. The "out-of-bag" R2 of the gap-filling models averaged 0.93 with an interquartile range from 0.90 to 0.95. Subsequently, a random forest-based PM2.5 prediction model with the gap-filled AOD and covariates was built to predict fully covered PM2.5 estimates. A ten-fold cross-validation for the prediction model showed a good performance with an R2 of 0.82. In the gap-filling models, the snow fraction was of higher significance to the snow season compared with the rest of the year. The prediction models fitted with/without the snow fraction also suggested the discernible changes in PM2.5 patterns, further confirming the significance of this parameter. Compared with the methods without considering snow and cloud covers, our PM2.5 prediction surfaces showed more spatial details and reflected small-scale terrain-driven PM2.5 patterns. The proposed methods can be generalized to the areas with extensive snow/cloud covers and large proportions of missing satellite AOD data for predicting PM2.5 levels with high resolutions and complete coverage.

How accurate is automated gap filling of metabolic models?

Background

Reaction gap filling is a computational technique for proposing the addition of reactions to genome-scale metabolic models to permit those models to run correctly. Gap filling completes what are otherwise incomplete models that lack fully connected metabolic networks. The models are incomplete because they are derived from annotated genomes in which not all enzymes have been identified. Here we compare the results of applying an automated likelihood-based gap filler within the Pathway Tools software with the results of manually gap filling the same metabolic model. Both gap-filling exercises were applied to the same genome-derived qualitative metabolic reconstruction for Bifidobacterium longum subsp. longum JCM 1217, and to the same modeling conditions — anaerobic growth under four nutrients producing 53 biomass metabolites.

Results

The solution computed by the gap-filling program GenDev contained 12 reactions, but closer examination showed that solution was not minimal; two of the twelve reactions can be removed to yield a set of ten reactions that enable model growth. The manually curated solution contained 13 reactions, eight of which were shared with the 12-reaction computed solution. Thus, GenDev achieved recall of 61.5% and precision of 66.6%. These results suggest that although computational gap fillers are populating metabolic models with significant numbers of correct reactions, automatically gap-filled metabolic models also contain significant numbers of incorrect reactions.

Conclusions

Our conclusion is that manual curation of gap-filler results is needed to obtain high-accuracy models. Many of the differences between the manual and automatic solutions resulted from using expert biological knowledge to direct the choice of reactions within the curated solution, such as reactions specific to the anaerobic lifestyle of B. longum.

Electronic supplementary material

The online version of this article (10.1186/s12918-018-0593-7) contains supplementary material, which is available to authorized users.

MAIAC-based long-term spatiotemporal trends of PM2.5 in Beijing, China

Satellite-driven statistical models have been proven to be able to provide spatially resolved PM_2.5 estimates worldwide. The North China Plain has been suffering from severe PM_2.5 pollution in recent years. An accurate assessment of the spatiotemporal characteristics of PM_2.5 levels in this region is crucial to design effective air pollution control policy. Our objective is to estimate daily PM_2.5 concentrations at 1km spatial resolution from 2004 to 2014 in Beijing and its surrounding areas using the Multi-angle implementation of atmospheric correction (MAIAC) aerosol optical depth (AOD). A high-performance three-stage model was developed with AOD, meteorological, demographic and land use variables as predictors, which includes a custom-designed PM_2.5 gap-filling method. The 11-year average annual coverage increased from 177days to 279days and annual PM_2.5 prediction error decreased from 14.1μg/m³ to 8.3μg/m³ after gap-filling techniques were applied. Results show that the 11-year overall mean of predicted PM_2.5 was 67.1μg/m³ in our study domain. The cross-validation R² value of our model is 0.82 in 2013 and 0.79 in 2014. In addition, the models predicted historical PM_2.5 concentrations with relatively high accuracy at the seasonal and annual levels (R² ranged from 0.78 to 0.86). Our long-term PM_2.5 prediction filled the gaps left by ground monitors, which would be beneficial to PM_2.5 related epidemiological studies in Beijing.

Evaluation of reaction gap-filling accuracy by randomization

Background

Completion of genome-scale flux-balance models using computational reaction gap-filling is a widely used approach, but its accuracy is not well known.

Results

We report on computational experiments of reaction gap filling in which we generated degraded versions of the EcoCyc-20.0-GEM model by randomly removing flux-carrying reactions from a growing model. We gap-filled the degraded models and compared the resulting gap-filled models with the original model. Gap-filling was performed by the Pathway Tools MetaFlux software using its General Development Mode (GenDev) and its Fast Development Mode (FastDev). We explored 12 GenDev variants including two linear solvers (SCIP and CPLEX) for solving the Mixed Integer Linear Programming (MILP) problems for gap filling; three different sets of linear constraints were applied; and two MILP methods were implemented. We compared these 13 variants according to accuracy, speed, and amount of information returned to the user.

Conclusions

We observed large variation among the performance of the 13 gap-filling variants. Although no variant was best in all dimensions, we found one variant that was fast, accurate, and returned more information to the user. Some gap-filling variants were inaccurate, producing solutions that were non-minimum or invalid (did not enable model growth). The best GenDev variant showed a best average precision of 87% and a best average recall of 61%. FastDev showed an average precision of 71% and an average recall of 59%. Thus, using the most accurate variant, approximately 13% of the gap-filled reactions were incorrect (were not the reactions removed from the model), and 39% of gap-filled reactions were not found, suggesting that curation is still an important aspect of metabolic-model development.

Quality control and gap-filling of PM10 daily mean concentrations with the best linear unbiased estimator

According to the European Directive 2008/50/CE, the air quality assessment consists in the measurement of the concentration fields, and the evaluation of the mean, number of exceedances, etc. of some chemical species dangerous to human health. The measurements provided by an air quality ground-based monitoring network are the main information source but the availability of these data is often limited by several technical and operational problems. In this paper, the best linear unbiased estimator (BLUE) is proposed to validate the pollutant concentration values and to fill the gaps in the measurement of time series collected by a monitoring network. The BLUE algorithm is tested using the daily mean concentrations of particulate matter having aerodynamic diameter less than 10 μ (PM₁₀ concentrations) measured by the air quality monitoring sensors operating in the Lazio Region in Italy. The comparison between the estimated and measured data evidences an error comparable with the measurement uncertainty. Due to its simplicity and reliability, the BLUE will be used in the routine quality test procedures of the Lazio air quality monitoring network measurements.

Genome-Scale Modeling of Thermophilic Microorganisms

Thermophilic microorganisms are of increasing interest for many industries as their enzymes and metabolisms are highly efficient at elevated temperatures. However, their metabolic processes are often largely different from their mesophilic counterparts. These differences can lead to metabolic engineering strategies that are doomed to fail. Genome-scale metabolic modeling is an effective and highly utilized way to investigate cellular phenotypes and to test metabolic engineering strategies. In this review we chronicle a number of thermophilic organisms that have recently been studied with genome-scale models. The microorganisms spread across archaea and bacteria domains, and their study gives insights that can be applied in a broader context than just the species they describe. We end with a perspective on the future development and applications of genome-scale models of thermophilic organisms.

An effective approach for gap-filling continental scale remotely sensed time-series

The archives of imagery and modeled data products derived from remote sensing programs with high temporal resolution provide powerful resources for characterizing inter- and intra-annual environmental dynamics. The impressive depth of available time-series from such missions (e.g., MODIS and AVHRR) affords new opportunities for improving data usability by leveraging spatial and temporal information inherent to longitudinal geospatial datasets. In this research we develop an approach for filling gaps in imagery time-series that result primarily from cloud cover, which is particularly problematic in forested equatorial regions. Our approach consists of two, complementary gap-filling algorithms and a variety of run-time options that allow users to balance competing demands of model accuracy and processing time. We applied the gap-filling methodology to MODIS Enhanced Vegetation Index (EVI) and daytime and nighttime Land Surface Temperature (LST) datasets for the African continent for 2000-2012, with a 1 km spatial resolution, and an 8-day temporal resolution. We validated the method by introducing and filling artificial gaps, and then comparing the original data with model predictions. Our approach achieved R² values above 0.87 even for pixels within 500 km wide introduced gaps. Furthermore, the structure of our approach allows estimation of the error associated with each gap-filled pixel based on the distance to the non-gap pixels used to model its fill value, thus providing a mechanism for including uncertainty associated with the gap-filling process in downstream applications of the resulting datasets.