基于MODIS的中国草地NPP综合估算模型

草地生态系统是陆地生态系统分布最广的生态系统类型之一,其碳储量的估算在全球变化中的作用越来越受到重视。为了快速、便捷地实现中国草地净初级生产力(NPP)的估算,在获取野外调查资料与同期遥感影像数据的基础上,利用归一化植被指数(NDVI)以及气候数据,构建了草地NPP综合估算模型。模型包括叶面积指数(LAI)和光合累积量(PA)两个子模型,其中LAI子模型利用了遥感数据NDVI,PA子模型利用了温度、降水和辐射等气候数据。通过建模以外独立的实测数据的验证,模拟值与实测值之间有很好的相关性,R2为0.8519,相关性达到极显著水平。RMSE和RRMSE均较小,表明模型的模拟结果比较可靠。同时模拟值与实测值之间的平均相对误差仅为1.97%,模拟结果的准确度较高,因此利用上述模型估算中国草地NPP是可行的。以上结果为中国草地NPP估算提供了新的方法。     

A distributed delay routine-based simulation model of Beauveria bassiana conidial stability in response to environmental stressors

Using published data and equations on therelationship between spore longevity of theentomopathogenic hyphomycetes, Metarhiziumanisopliae var. acridum and Beauveria bassiana (Balsamo) Vuillemin(Deuteromycota: Hyphomycetes) and temperatureand moisture content, a model of sporeviability was constructed based on adistributed-delay routine. The model ismodified via average spore survival time or byincluding an additional attrition (mortality)rate. The model was parameterized usingpublished values from studies on M. a.var. acridum spores, and output comparedfavorably with germination data and with apreviously-developed model. After initializingthe model using parameter estimates of B.bassiana spores from the laboratory andpublished data on changes in (1) spore viabilitywith respect to temperature and moisturecontent, and (2) spore moisture content withrespect to temperature and relative humidity,the model was run using daily min/maxtemperature and relative humidity data andcompared with data from four field experimentsof Mycotech B. bassiana isolate GHAsprayed on canteloupe plants. For two of theexperiments, observed viability trends werecompared to model outputs using weather datafrom both a weather station and fromwithin-canopy temperature and humidity probes. Output using weather station data fitobservations much better than output usingwithin-canopy probe data. For the tworemaining sets of field data, both earlier inthe season, only weather station data wereavailable and the resulting output fitobservations poorly. An attrition rate of 98%was needed to fit output to field data early inthe growing season, and a rate of 74% wasneeded for data collected four weeks later. These attrition rates can be consideredestimates for the proportion of spores dyingfor reasons other than temperature and relativehumidity, and they were attributed largely toUVB radiation due to the more open canopyearlier in the season.     

基于经验模型和机理模型的杉木人工林生物量估测对比研究

为了揭示间伐干扰下杉木人工林生物量的变化规律,研究利用江西省吉水县石阳林场的36块杉木人工林样地的实测数据和研究区气候数据,通过基于经验的引入地位指数(SI)的生物量生长方程组和基于机理的3-PG模型,模拟并预估林分生物量,分析在间伐和非间伐的情况下,不同立地的林分其生物量0-50a的变化。结果表明:(1)构建了生物量生长方程组,并在参数a、b、c中引入地位指数SI,发现改进后的模型对于基础模型拟合精度更高,且对数似然比检验表明,改进效果显著(P<0.05)。(2)通过对3-PG模型预测精度验证发现,预估值和实测值之间有很高的一致性,各因子的决定系数(R²)在0.65-0.96之间,其中,胸径和树高的R²均高于0.92;各因子的平均相对误差(MRE)不超过26%。(3)通过比较经验模型和机理模型的生物量预测发现,经验模型的预测误差MRE为16.50%,机理模型为23.52%,经验模型预估精度更高。进一步对未来预测对比分析表明,机理模型预估值高于经验模型。(4)两个模型模拟的杉木人工林生物量规律一致,即随着林龄的增加,杉木人工林林分总生物量均表现出先快速增加,后逐渐平稳的趋势;并且间伐不会改变这种趋势,但间伐林分在间伐后的生物量生长速率高于无间伐林分。此外,由于SI对经验模型影响显著,改进模型拟合效果更好,更具有生态学意义。参数化后的3-PG模型模预估精度较高,能够为江西杉木人工林生长规律研究提供依据。虽然经验模型和机理模型在对研究区杉木人工林生物量的预估上均具有较好的表现,但各具特点和局限性。经验模型参数较易获得,且经验模型预测生物量、林分胸高断面积和林分平均树高的R²、MRE均优于机理模型;但模型对于建模数据内的评价效果较好,对于建模数据外的应用具有局限性,即经验模型更适合模拟生长期间的某一阶段的林分生物量。机理模型虽然需要的参数较多,但是考虑了生态学原理,弥补了经验模型的不足,可较好解释和模拟环境因子对树木生长的影响,对校正数据之外生长阶段的林分生物量预测更有优势。     

Modeling competition between Laurencia obtusa (Ceramiales,Rhodophyta) and Hypnea spinella (Gigartinales,Rhodophyta) at Cabo Frio Island,Rio de Janeiro,Brazil

The Lotka-Volterra competition model was used to represent the interaction between Laurencia obtusa and Hypnea spinella. A new model that considers effects of competition on algal carrying capacity is suggested. To test the models, data from field experiments conducted in an intertidal region at Cabo Frio Island, Rio de Janeiro, Brazil, were used. Both models showed that Hypnea was a stronger competitor than Laurencia. The model of interaction through the carrying capacity showed a stable coexistence between the algal populations and better represented the experimental data.     

基于高光谱数据的互花米草叶片功能性状反演

互花米草成功入侵的关键是其生长繁殖能力以及对环境的适应能力,叶片含水率、相对叶绿素含量、碳氮比、总氮、总磷以及比叶面积等叶片功能性状反应的是互花米草对资源的利用能力以及环境的适应能力。以江苏盐城滨海湿地为研究对象,进行互花米草叶片功能性状与高光谱数据的关系研究。通过对原始光谱数据以及一阶微分转换光谱数据进行主成分分析提取新的主成分变量作为自变量分别建立不同性状的逐步回归、BP神经网络、支持向量机、随机森林4种预测模型,通过比较构建模型的R²以及RMSE选择最优模型,进而基于相关性分析得到的敏感波段构建最优模型,验证其准确性和适用性。研究结果发现：（1）一阶微分数据的建模效果优于原始光谱数据;（2）通过对不同功能性状的预测建模,发现4种模型的预测效果排序为：随机森林>支持向量机>BP神经网络>逐步回归,其中随机森林模型的准确性高、稳定性强,明显优于其他3种模型,而逐步回归模型的效果最差,不适用于互花米草叶片功能性状的高光谱建模;（3）通过对相关性分析得到的敏感波段建立随机森林模型,建模R²均大于0.90,验证R²介于0.73-0.95之间,进一步证实了随机森林模型的准确性和稳定性。研究结果表明,高光谱数据可以作为快速监测互花米草生长状况的有力手段,而随机森林模型可以作为高精度模型实现对互花米草不同叶片功能性状的估测。     

Modeling of exo-inulinase biosynthesis by Kluyveromyces marxianus in fed-batch mode: correlating production kinetics and metabolic heat fluxes

A metabolic heat-based model was used for estimating the growth of Kluyveromyces marxianus, and the modified Luedeking-Piret kinetic model was used for describing the inulinase production kinetics. For the first time, a relationship was developed to relate inulinase production kinetics directly to metabolic heat generated, which corroborated well with the experimental data (with R ² values of above 0.9). It also demonstrated the predominantly growth-associated nature of the inulinase production with Luedeking-Piret parameters α and β, having values of 0.75 and 0.033, respectively, in the exponential feeding experiment. MATLAB was used for simulating the inulinase production kinetics which demonstrated the model’s utility in performing real-time prediction of inulinase concentration with metabolic heat data as input. To validate the model predictions, a biocalorimetric (Bio RC1e) experiment for inulinase production by K. marxianus was performed. The inulinase concentration (IU/mL) values acquired from the model in were validated with the experimental values and the metabolic heat data. This modeling approach enabled the optimization, monitoring, and control of inulinase production process using the real-time biocalorimetric (Bio RC1e) data. Gas chromatography and mass spectrometry analysis were carried out to study the overflow metabolism taking place in K. marxianus inulinase production.

     

Construction of a robust prognostic model for adult adrenocortical carcinoma: Results from bioinformatics and real-world data

This study aims to construct a robust prognostic model for adult adrenocortical carcinoma (ACC) by large-scale multiomics analysis and real-world data. The RPPA data, gene expression profiles and clinical information of adult ACC patients were obtained from The Cancer Proteome Atlas (TCPA), Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA). Integrated prognosis-related proteins (IPRPs) model was constructed. Immunohistochemistry was used to validate the prognostic value of the IPRPs model in Fudan University Shanghai Cancer Center (FUSCC) cohort. 76 ACC cases from TCGA and 22 ACC cases from GSE10927 in NCBI’s GEO database with full data for clinical information and gene expression were utilized to validate the effectiveness of the IPRPs model. Higher FASN (P = .039), FIBRONECTIN (P < .001), TFRC (P < .001), TSC1 (P < .001) expression indicated significantly worse overall survival for adult ACC patients. Risk assessment suggested significantly a strong predictive capacity of IPRPs model for poor overall survival (P < .05). IPRPs model showed a little stronger ability for predicting prognosis than Ki-67 protein in FUSCC cohort (P = .003, HR = 3.947; P = .005, HR = 3.787). In external validation of IPRPs model using gene expression data, IPRPs model showed strong ability for predicting prognosis in TCGA cohort (P = .005, HR = 3.061) and it exhibited best ability for predicting prognosis in GSE10927 cohort (P = .0898, HR = 2.318). This research constructed IPRPs model for predicting adult ACC patients’ prognosis using proteomic data, gene expression data and real-world data and this prognostic model showed stronger predictive value than other biomarkers (Ki-67, Beta-catenin, etc) in multi-cohorts.     

A Simulation Model for Assessing Soybean Rust Epidemics

A soybean rust (causal agent Phakopsora pachyrhizt) simulation model was developed for assessing disease epidemics as a part of pest risk analysis. Equations describing environmental effects on disease components were developed by re-analyzing previous data with a view toward a systems approach. The infection rate was predicted well using dew period and temperature after inoculation as independent variables (R²=0.88, P < 0.0001). The exponential models which used physiological day as an independent variable explained 98% of the variations of latent period and senescence of disease lesions. The simulation model was validated with data from 72 sequential planting experiments during 1980 and 1981 in Taiwan. Time of onset for these epidemics varied from 25—60 days and 50—80 days after planting soybean cultivars TK 5 and G 8587, respectively. The epidemic periods were 75—95 for TK 5 and 100—120 days for G 8587. Variation of epidemics was accurately predicted by the simulator. Predicted disease curves fit well the observed disease curves for the recognized cropping seasons, spring- and autumn-seeded crops. For G 8587, which is very sensitive to photoperiod, the data from spring and autumn gave a better fit compared with data from pre-summer planting. The model underestimated disease epidemics during the winter, probably because the plant growth model failed to reflect the photoperiod rection of soybean. The simulation model was validated with data from other experiments conducted in three cropping seasons in 1979 and 1980. Determination coefficients of the regression between observed and predicted disease severity were significant.     

Prediction of Pharmacokinetic Profile of Valsartan in Humans Based on in vitro Uptake‐Transport Data

The aim of this study was to evaluate a physiologically based pharmacokinetic (PBPK) model for predicting PK profiles in humans based on a model refined in rats and humans in vitro uptake‐transport data using valsartan as a probe substrate. Valsartan is eliminated unchanged, mostly through biliary excretion, both in humans and rats. It was, therefore, chosen as model compound to predict in vivo elimination based on in vitro hepatic uptake‐transport data using a fully mechanistic PBPK model. Plated rat and human hepatocytes, and cell lines overexpressing human OATP1B1 and OATP1B3 were used for in vitro uptake experiments. A mechanistic two‐compartment model was used to derive the active and passive transport parameters, namely uptake Michaelis–Menten parameters (V_max and K_m,u) together with passive diffusion (P_dif). These transport parameters were then used as input in a whole body physiologically based pharmacokinetic (PBPK) model. The uptake rate of valsartan was higher for rat hepatocytes (K_m,u=28.4±3.7 μM , V_max=1320±180 pmol/mg/min, and P_dif =1.21±0.42 μl/mg/min) compared to human hepatocytes (K_m,u=44.4±14.6 μM , V_max=304±85 pmol/mg/min, and P_dif=0.724±0.271 μl/mg/min). OATP1B1 and ‐1B3 parameters were correlated to human hepatocyte data, using experimentally established relative activity factors (RAF). Resulting PBPK simulations were compared for plasma‐ (humans and rats) and bile‐ (rats) concentration–time profiles following iv bolus administration of valsartan. Plasma clearances (CL_P) for rats and humans were predicted within twofold relative to predictions based on respective in vitro data. The simulations were extended to simulate the impact of either OATP1B1 or ‐1B3 inhibition on plasma profile. The limited data set indicates that the mechanistic model allowed for accurate evaluation of in vitro transport data; and the resulting hepatic uptake transport kinetic parameters enabled the prediction of in vivo PK profiles and plasma clearances, using PBPK modelling. Moreover, the interspecies difference in elimination rate observed in vivo was correctly reflected in the transport parameters determined in vitro.     

盐城滨海湿地优势植物碳氮磷生态化学计量高光谱反演

滨海湿地是海陆交界的生态过渡带,是自然界生物多样性最丰富的生态系统之一。湿地植物作为湿地生态系统重要的组成部分,研究其碳、氮、磷生态化学计量特征是了解植物生长状况与适应策略的有效途径。以江苏盐城滨海湿地为研究区,采集互花米草(Spartina alterifora)、芦苇(Phragmites australis)、白茅(Imperata cylindrica)、柽柳(Tamarix chinensis)、盐地碱蓬(Suaeda salsa)共5种优势湿地植物样本及冠层高光谱数据,对植物的碳、氮、磷生态化学计量特征进行高光谱反演研究。结果表示白茅、柽柳与芦苇的最佳反演模型为随机森林(RF)模型,对互花米草反演效果最好的是偏最小二乘(PLSR)模型,而对盐地碱蓬反演精度最高的是BP神经网络(BPNN)模型。研究表明利用高光谱数据可以实现湿地植物碳、氮、磷生态化学计量特征的准确反演,不同模型对于不同湿地植物的反演存在差异,RF模型的反演稳定性最强,是反演湿地植物生态化学计量特征的较优模型。     

A fine‐grained spatial prediction model for the red‐listed vascular plant Scorzonera humilis

We explored the applied use of distribution modelling as a tool for making spatial predictions of occurrences of the red‐listed vascular plant species Scorzonera humilis in a study area in southeast Norway. Scorzonera is typical of extensively managed semi‐natural grasslands. A Maxent model was trained on all known records of the species, accurately georeferenced and gridded to fine resolution (grid cells of 25×25 m). Model performance was assessed on the training data by data‐splitting (by which some records were set off for evaluation) and on independent evaluation data collected in the field. Of the eight predictor variables used in the modelling, distance to roads and to arable land were most important followed by land‐cover class and altitude. Judged from the area under curve (AUC), the model was good to excellent and a significant, positive relationship was found between relative probabilities of occurrence predicted by the model and true probability of presence provided by the independently collected evaluation data. The model was used together with the evaluation data to estimate presence of Scorzonera humilis in 0.7% of the grid cells in the study area. The grid cells in which the model predicted highest probability for Scorzonera to be present had a true probability of presence of ca 12%, i.e. 17×higher than in an average cell. The present study demonstrates that, even when only simple predictor variables are available, spatial prediction modelling contributes important knowledge about rare species such as prevalence estimates, spatial prediction maps and insights into the species’ autecology. Spatial prediction modelling also makes cost‐efficient monitoring of rare species possible. However, it is pointed out that these benefits require evaluation of the model on independently sampled evaluation data.     

Approximate Bayesian estimation of extinction rate in the Finnish Daphnia magna metapopulation

Approximate Bayesian computation (ABC) is useful for parameterizing complex models in population genetics. In this study, ABC was applied to simultaneously estimate parameter values for a model of metapopulation coalescence and test two alternatives to a strict metapopulation model in the well‐studied network of Daphnia magna populations in Finland. The models shared four free parameters: the subpopulation genetic diversity (θ_S), the rate of gene flow among patches (4Nm), the founding population size (N₀) and the metapopulation extinction rate (e) but differed in the distribution of extinction rates across habitat patches in the system. The three models had either a constant extinction rate in all populations (strict metapopulation), one population that was protected from local extinction (i.e. a persistent source), or habitat‐specific extinction rates drawn from a distribution with specified mean and variance. Our model selection analysis favoured the model including a persistent source population over the two alternative models. Of the closest 750 000 data sets in Euclidean space, 78% were simulated under the persistent source model (estimated posterior probability = 0.769). This fraction increased to more than 85% when only the closest 150 000 data sets were considered (estimated posterior probability = 0.774). Approximate Bayesian computation was then used to estimate parameter values that might produce the observed set of summary statistics. Our analysis provided posterior distributions for e that included the point estimate obtained from previous data from the Finnish D. magna metapopulation. Our results support the use of ABC and population genetic data for testing the strict metapopulation model and parameterizing complex models of demography.     

Analysis of methylation-driven genes for predicting the prognosis of patients with head and neck squamous cell carcinoma

To help provide evidence for prognosis prediction and personalized targeted therapy for patients with head and neck squamous cell carcinoma (HNSCC), we investigated prognosis-specific methylation-driven genes in HNSCC. Survival time data, RNA sequencing data, and methylation data for HNSCC patients were downloaded from The Cancer Genome Atlas. The MethylMix R package based on the β mixture model was utilized to screen genes with different methylation statuses in tumor tissues and adjacent normal tissues, and a total of 182 HNSCC-related methylation-driven genes were then identified. A survival prediction scoring model based on multivariate Cox analysis was developed to screen the genes related to the prognosis of HNSCC, and a linear risk model of the methylation status of six genes (INA, LINC01354, TSPYL4, MAGEB2, EPHX3, and ZNF134) was constructed. The prognostic values of the six genes were further independently explored by survival analysis combined with methylation and gene expression analyses. The 5-year survival rate in the high-risk group of patients in the test set was 30.4% (95% CI: 22.7%-40.8%) and that in the low-risk group of patients was 65.5% (95% CI: 56.1%-76.5%). The area under the receiver operating characteristic curve for the model was 0.723, which further verified the specificity and sensitivity of the model. In addition, subsequent combined survival analysis revealed that all six genes could be used as independent prognostic markers and thus might be potential drug targets. The innovative method provides new insight into the molecular mechanism and prognosis of HNSCC.     

An energetics model of an aquatic predator and its application to life-history optima

Summary A bioenergetics simulation model of the growth and life history of the aquatic predator Nephelopsis obscura Verrill was developed and validated using both experimentation and sensitivity analysis. Sensitivity analysis demonstrated that the model's internal feedbacks resulted in stability similar to homeostatic biological mechanisms. The experimental validation showed the model very accurately predicts growth at 10°C and 15°C but is slightly biased at 20°C. Simulation output was also consistent with the observed data on Nephelopsis from the site from which the simulation input data were obtained and indicated that Nephelopsis growth is more sensitive to prey variation among years than to temperature variation. Although built using data from a population at one extreme of the spectrum observed in life history and growth, the model was able to emulate the growth of Nephelopsis throughout its range. Thus, the variability in size and life history observed in the field can be explained as the result of a plastic phenotype responding to different habitat conditions.     

Predicting distributions of seven bitterling fishes in northern Kyushu,Japan

The distributions of seven bitterling species and subspecies—Tanakia lanceolata, T. limbata, Acheilognathus tabira nakamurae, A. rhombeus, Rhodeus ocellatus kurumeus, R. ocellatus ocellatus, and R. atremius atremius—in northern Kyushu were predicted using generalized linear models (GLMs) in order to provide information helpful for conserving native bitterlings and preventing the expansion of alien bitterling species. Predictions were made according to the following procedure: (1) a set of GLMs for each species was formulated using environmental data from 710 sites that were derived using digital maps and GIS software, from which the best fit model for each species was selected using the Akaike information criterion for predicting the fish occurrence, (2) model performance was evaluated based on the receiver-operating characteristics (ROC) analysis using occurrence and environmental data from 362 sites, and (3) potential distributions of the bitterling were analyzed using the best fit models and environmental data for 1,272 sites, of which 200 data points without occurrence data were prepared. The best fit models revealed that 4–6 environmental factors were important in predicting seven bitterling distributions, which was supported by the area under the ROC curve (AUC) values of these fishes ranging from 0.753 to 0.927. The AUC values in model evaluation were significantly greater than 0.5 for six fishes, suggesting the moderate accuracies of these best fit models for predicting the fish distributions. These predictive models can be used for evaluating potential native bitterling richness and the potential distribution expansion of an alien subspecies.     

Comparative Epidemiology of Three Virus Diseases on Zucchini Squash

Zucchini yellow mosaic virus (ZYMV), Papaya ringspot virus – type W (PRSV‐W) and Zucchini lethal chlorosis virus (ZLCV) cause important diseases on zucchini squash crops in Brazil. ZYMV and PRSV‐W belong to the genus Potyvirus and are transmitted by aphids, whereas ZLCV belongs to Tospovirus and is transmitted by the thrips Frankliniella zucchini. These three viruses may occur simultaneously in the field, and the epidemiology of the corresponding diseases may be determined by interactions among viruses, hosts and vectors. In this work, the progress of the diseases caused by these viruses was studied over a temporal and geographic range for three planting seasons (PS). For the lethal chlorosis (ZLCV), a monomolecular model was found to be the best fit for the data, though only during the third PS. For data collected during the first two PS, the Gompertz model was found to fit the data best. The spatial distribution of disease indicated disease aggregation at the end of the crop cycle. For the yellow mosaic (ZYMV), the model that best fit in the 1st PS was the logistic and in the 2nd and 3rd PS was monomolecular. The spatial pattern of the disease was random when the disease incidence was low but aggregated when the disease incidence was high. The common mosaic (PRSV‐W) showed the lowest incidence in all three PS. An exponential model was the best fit for data collected during all PS, and the spatial pattern of the disease was random. Interactions among the three viruses apparently did not result in changes in the epidemiology of the diseases. Removal of sources of inoculum and planting at an unfavourable time for reproduction of virus vectors are the two main measures recommended for the control of these diseases. The use of insecticide is indicated only for the control of the F. zucchini.     

A coupled model of photosynthesis-transpiration based on the stomatal behavior for maize (Zea mays L.) grown in the field

The study presents a theoretical basis of a stomatal behavior-based coupled model for estimating photosynthesis, A, and transpiration, E. Outputs of the model were tested against data observed in a maize (Zea mays L.) field. The model was developed by introducing the internal conductance, g _ic, to CO₂ assimilation, and the general equation of stomatal conductance, g _sw, to H₂O diffusion, into models of CO₂ and H₂O diffusion through the stomata of plant leaves. The coupled model is easier for practical use since the model only includes environmental variables, such as ambient CO₂ concentration, leaf temperature, humidity and photosynthetic photon flux received at the leaves within the canopy. Moreover, concept of g _ic, and factors controlling A and E were discussed, and applicability of the model was examined with the data collected in the maize field.     

A model that predicts the attachment behavior of Ulva linza zoospores on surface topography

A predictive model for the attachment of spores of the green alga Ulva on patterned topographical surfaces was developed using a constant refinement approach. This ‘attachment model’ incorporated two historical data sets and a modified version of the previously-described Engineered Roughness Index. Two sets of newly-designed surfaces were used to evaluate the effect of two components of the model on spore settlement. Spores attached in fewer numbers when the area fraction of feature tops increased or when the number of distinct features in the design increased, as predicted by the model. The model correctly predicted the spore attachment density on three previously-untested surfaces relative to a smooth surface. The two historical data sets and two new data sets showed high correlation (R ² = 0.88) with the model. This model may be useful for designing new antifouling topographies.     

Conformation and packing analysis of polysaccharides and derivatives. I: Mannan

A previously described procedure for simultaneous optimization of bond lengths and angles was used to test different models for mannan I. Potential hydrogen bonds and the glycosidic angle were included in the optimization. A conformational model with bifurcated intramolecular hydrogen bonds of the type observed in the methyl cellobioside methanol complex showed the best agreement with available exprerimental data. The coordinates of this model were provided by computer calculations. The available X-ray data, however, were not sufficient for selecting this model; rather, ir data were necessary to furnish the needed information. The different conformational models tested all showed an almost constant virtual bond length O(1)–O(4) of the β-pyranose residue. This was in contrast to the previously obtained results for the α-pyranose residues.     

Genome‐scale metabolic reconstruction and constraint‐based modelling of the Antarctic bacterium Pseudoalteromonas haloplanktis TAC125

The Antarctic strain Pseudoalteromonas haloplanktis TAC125 is one of the model organisms of cold‐adapted bacteria and is currently exploited as a new alternative expression host for numerous biotechnological applications. Here, we investigated several metabolic features of this strain through in silico modelling and functional integration of –omics data. A genome‐scale metabolic model of P. haloplanktis TAC125 was reconstructed, encompassing information on 721 genes, 1133 metabolites and 1322 reactions. The predictive potential of this model was validated against a set of experimentally determined growth rates and a large dataset of growth phenotypic data. Furthermore, evidence synthesis from proteomics, phenomics, physiology and metabolic modelling data revealed possible drawbacks of cold‐dependent changes in gene expression on the overall metabolic network of P. haloplanktis TAC125. These included, for example, variations in its central metabolism, amino acid degradation and fatty acid biosynthesis. The genome‐scale metabolic model described here is the first one reconstructed so far for an Antarctic microbial strain. It allowed a system‐level investigation of variations in cellular metabolic fluxes following a temperature downshift. It represents a valuable platform for further investigations on P. haloplanktis TAC125 cellular functional states and for the design of more focused strategies for its possible biotechnological exploitation.