Hybrid dynamic modeling of Escherichia coli central metabolic network combining Michaelis–Menten and approximate kinetic equations

The construction of dynamic metabolic models at reaction network level requires the use of mechanistic enzymatic rate equations that comprise a large number of parameters. The lack of knowledge on these equations and the difficulty in the experimental identification of their associated parameters, represent nowadays the limiting factor in the construction of such models. In this study, we compare four alternative modeling approaches based on Michaelis–Menten kinetics for the bi-molecular reactions and different types of simplified rate equations for the remaining reactions (generalized mass action, convenience kinetics, lin-log and power-law). Using the mechanistic model for Escherichia coli central carbon metabolism as a benchmark, we investigate the alternative modeling approaches through comparative simulations analyses. The good dynamic behavior and the powerful predictive capabilities obtained using the hybrid model composed of Michaelis–Menten and the approximate lin-log kinetics indicate that this is a possible suitable approach to model complex large-scale networks where the exact rate laws are unknown.     

Evolution of taxis responses in virtual bacteria: non-adaptive dynamics

Bacteria are able to sense and respond to a variety of external stimuli, with responses that vary from stimuli to stimuli and from species to species. The best-understood is chemotaxis in the model organism Escherichia coli, where the dynamics and the structure of the underlying pathway are well characterised. It is not clear, however, how well this detailed knowledge applies to mechanisms mediating responses to other stimuli or to pathways in other species. Furthermore, there is increasing experimental evidence that bacteria integrate responses from different stimuli to generate a coherent taxis response. We currently lack a full understanding of the different pathway structures and dynamics and how this integration is achieved. In order to explore different pathway structures and dynamics that can underlie taxis responses in bacteria, we perform a computational simulation of the evolution of taxis. This approach starts with a population of virtual bacteria that move in a virtual environment based on the dynamics of the simple biochemical pathways they harbour. As mutations lead to changes in pathway structure and dynamics, bacteria better able to localise with favourable conditions gain a selective advantage. We find that a certain dynamics evolves consistently under different model assumptions and environments. These dynamics, which we call non-adaptive dynamics, directly couple tumbling probability of the cell to increasing stimuli. Dynamics that are adaptive under a wide range of conditions, as seen in the chemotaxis pathway of E. coli, do not evolve in these evolutionary simulations. However, we find that stimulus scarcity and fluctuations during evolution results in complex pathway dynamics that result both in adaptive and non-adaptive dynamics depending on basal stimuli levels. Further analyses of evolved pathway structures show that effective taxis dynamics can be mediated with as few as two components. The non-adaptive dynamics mediating taxis responses provide an explanation for experimental observations made in mutant strains of E. coli and in wild-type Rhodobacter sphaeroides that could not be explained with standard models. We speculate that such dynamics exist in other bacteria as well and play a role linking the metabolic state of the cell and the taxis response. The simplicity of mechanisms mediating such dynamics makes them a candidate precursor of more complex taxis responses involving adaptation. This study suggests a strong link between stimulus conditions during evolution and evolved pathway dynamics. When evolution was simulated under conditions of scarce and fluctuating stimulus conditions, the evolved pathway contained features of both adaptive and non-adaptive dynamics, suggesting that these two types of dynamics can have different advantages under distinct environmental circumstances.     

Inference of S-system models of gene regulatory networks using immune algorithm

The S-system model is one of the nonlinear differential equation models of gene regulatory networks, and it can describe various dynamics of the relationships among genes. If we successfully infer rigorous S-system model parameters that describe a target gene regulatory network, we can simulate gene expressions mathematically. However, the problem of finding an optimal S-system model parameter is too complex to be solved analytically. Thus, some heuristic search methods that offer approximate solutions are needed for reducing the computational time. In previous studies, several heuristic search methods such as Genetic Algorithms (GAs) have been applied to the parameter search of the S-system model. However, they have not achieved enough estimation accuracy. One of the conceivable reasons is that the mechanisms to escape local optima. We applied an Immune Algorithm (IA) to search for the S-system parameters. IA is also a heuristic search method, which is inspired by the biological mechanism of acquired immunity. Compared to GA, IA is able to search large solution space, thereby avoiding local optima, and have multiple candidates of the solutions. These features work well for searching the S-system model. Actually, our algorithm showed higher performance than GA for both simulation and real data analyses.     

An intelligent two-stage evolutionary algorithm for dynamic pathway identification from gene expression profiles

From gene expression profiles, it is desirable to rebuild cellular dynamic regulation networks to discover more delicate and substantial functions in molecular biology, biochemistry, bioengineering and pharmaceutics. S-system model is suitable to characterize biochemical network systems and capable to analyze the regulatory system dynamics. However, inference of an S-system model of N-gene genetic networks has 2N(N+1) parameters in a set of non-linear differential equations to be optimized. This paper proposes an intelligent two-stage evolutionary algorithm (iTEA) to efficiently infer the S-system models of genetic networks from time-series data of gene expression. To cope with curse of dimensionality, the proposed algorithm consists of two stages where each uses a divide-and-conquer strategy. The optimization problem is first decomposed into N subproblems having 2(N+1) parameters each. At the first stage, each subproblem is solved using a novel intelligent genetic algorithm (IGA) with intelligent crossover based on orthogonal experimental design (OED). At the second stage, the obtained N solutions to the N subproblems are combined and refined using an OED-based simulated annealing algorithm for handling noisy gene expression profiles. The effectiveness of iTEA is evaluated using simulated expression patterns with and without noise running on a single-processor PC. It is shown that 1) IGA is efficient enough to solve subproblems; 2) IGA is significantly superior to the existing method SPXGA; and 3) iTEA performs well in inferring S-system models for dynamic pathway identification.     

Analysis of operating principles with S-system models

Operating principles address general questions regarding the response dynamics of biological systems as we observe or hypothesize them, in comparison to a priori equally valid alternatives. In analogy to design principles, the question arises: Why are some operating strategies encountered more frequently than others and in what sense might they be superior? It is at this point impossible to study operation principles in complete generality, but the work here discusses the important situation where a biological system must shift operation from its normal steady state to a new steady state. This situation is quite common and includes many stress responses. We present two distinct methods for determining different solutions to this task of achieving a new target steady state. Both methods utilize the property of S-system models within Biochemical Systems Theory (BST) that steady states can be explicitly represented as systems of linear algebraic equations. The first method uses matrix inversion, a pseudo-inverse, or regression to characterize the entire admissible solution space. Operations on the basis of the solution space permit modest alterations of the transients toward the target steady state. The second method uses standard or mixed integer linear programming to determine admissible solutions that satisfy criteria of functional effectiveness, which are specified beforehand. As an illustration, we use both methods to characterize alternative response patterns of yeast subjected to heat stress, and compare them with observations from the literature.     

Strategies for representing metabolic pathways within biochemical systems theory: reversible pathways

The search for systematic methods to deal with the integrated behavior of complex biochemical systems has over the past two decades led to the proposal of several theories of biochemical systems. Among the most promising is biochemical systems theory (BST). Recent comparisons of this theory with several others that have recently been proposed have demonstrated that all are variants of BST and share a common underlying formalism. Hence, the different variants can be precisely related and ranked according to their completeness and operational utility. The original and most fruitful variant within BST is based on a particular representation, called an S-system (for synergistic and saturable systems), that exhibits many advantages not found among alternative representations. Even within the preferred S-system representation there are options, depending on the method of aggregating fluxes, that become especially apparent when one considers reversible pathways. In this paper we focus on the paradigm situation and clearly distinguish the two most common strategies for generating an S-system representation. The first is called the "reversible" strategy because it involves aggregating incoming fluxes separately from outgoing fluxes for each metabolite to define a net flux that can be positive, negative, or zero. The second is the "irreversible" strategy, which involves aggregating forward and reverse fluxes through each reaction to define a net flux that is always positive. This second strategy has been used almost exclusively in all variants of BST. The principal results of detailed analyses are the following: (1) All S-system representations predict the same changes in dependent concentrations for a given change in an independent concentration. (2) The reversible strategy is superior to the irreversible on the basis of several criteria, including accuracy in predicting steady-state flux, accuracy in predicting transient responses, and robustness of representation. (3) Only the reversible strategy yields a representation that is able to capture the characteristic feature of amphibolic pathways, namely, the reversal of nets flux under physiological conditions. Finally, the results document the wide range of variation over which the S-system representation can accurately predict the behavior of intact biochemical systems and confirm similar results of earlier studies [Voit and Savageau, Biochemistry 26: 6869-6880 (1987)].     

Consistency and Diversity of Spike Dynamics in the Neurons of Bed Nucleus of Stria Terminalis of the Rat: A Dynamic Clamp Study

Neurons display a high degree of variability and diversity in the expression and regulation of their voltage-dependent ionic channels. Under low level of synaptic background a number of physiologically distinct cell types can be identified in most brain areas that display different responses to standard forms of intracellular current stimulation. Nevertheless, it is not well understood how biophysically different neurons process synaptic inputs in natural conditions, i.e., when experiencing intense synaptic bombardment in vivo. While distinct cell types might process synaptic inputs into different patterns of action potentials representing specific “motifs” of network activity, standard methods of electrophysiology are not well suited to resolve such questions. In the current paper we performed dynamic clamp experiments with simulated synaptic inputs that were presented to three types of neurons in the juxtacapsular bed nucleus of stria terminalis (jcBNST) of the rat. Our analysis on the temporal structure of firing showed that the three types of jcBNST neurons did not produce qualitatively different spike responses under identical patterns of input. However, we observed consistent, cell type dependent variations in the fine structure of firing, at the level of single spikes. At the millisecond resolution structure of firing we found high degree of diversity across the entire spectrum of neurons irrespective of their type. Additionally, we identified a new cell type with intrinsic oscillatory properties that produced a rhythmic and regular firing under synaptic stimulation that distinguishes it from the previously described jcBNST cell types. Our findings suggest a sophisticated, cell type dependent regulation of spike dynamics of neurons when experiencing a complex synaptic background. The high degree of their dynamical diversity has implications to their cooperative dynamics and synchronization.     

Non-linear dynamics of carbon and hydrogen isotopic signatures based on a biological kinetic model of nitrite-dependent methane oxidation by “Candidatus Methylomirabilis oxyfera”

System dynamics of nitrite-dependent anaerobic methane oxidation (N-DAMO) in a “Candidatus Methylomirabilis oxyfera” culture are described using a mathematical model based on chemical kinetics, microbial growth dynamics and equations for ¹³C and ²H isotopic fractionation. Experimental data for the N-DAMO model were taken from Rasigraf et al. (2012), who studied N-DAMO in a batch culture of “Ca. M. oxyfera” started at two different conditions with varying methane, nitrite and biomass concentrations. In the model, instead of using concentrations of each isotopologue (¹²C and ¹³C, ¹H and ²H), total concentrations and respective isotope ratios were considered as variables. The empirical Monod equations, which included methane and nitrite as two rate-limiting substrates, a threshold methane concentration CH _4min below which there was no biomass growth, and the same kinetic coefficients for the separate batch experiments, fitted the experimental data much better than apparent first-order kinetics that required rather different kinetic coefficients for the two experiments. Non-linear dynamics of ¹³C and ²H isotopic signatures were obtained based on the N-DAMO model. It was shown that rate limitation by methane or nitrite concentrations significantly affected the dynamics of carbon and hydrogen isotopic signatures. Fractionation rate increased at higher initial biomass concentration. The non-linear N-DAMO model satisfactorily described experimental data presented in the two-dimensional plot of hydrogen versus carbon stable isotopic signatures.     

Responses of two populations of an Iranian nickel-hyperaccumulating serpentine plant,Alyssum inflatum Nyar., to substrate Ca/Mg quotient and nickel

We selected two geographically close serpentine and non-serpentine populations of a Ni-hyperaccumulating plant (Alyssum inflatum) to investigate the influence of two common factors of serpentine soils: high Ni concentrations and low Ca/Mg quotients. Soils and plants were sampled from serpentine and non-serpentine substrates, and concentrations of Ca, Mg and Ni were measured. A hydroponic culture was used to compare growth and elemental composition responses of serpentine and non-serpentine plants to different Ca/Mg quotients and Ni concentrations in the nutrient solution. The Ca/Mg quotient for non-serpentine soils was 15 times higher than for serpentine soils, but there was no difference in the Ca/Mg quotient of plants from the two populations. In hydroponic culture, plants from both populations were able to survive at high Ca/Mg quotients. This result suggests that serpentine plants of A. inflatum do not necessarily need a substrate with a low Ca/Mg quotient for survival. Decreases in the Ca/Mg quotient in hydroponics decreased growth. The magnitude of this decrease was significantly greater in non-serpentine plants, suggesting a greater resistance of serpentine plants to low Ca/Mg quotients. Total Ni concentration in serpentine soils was 13 times higher than in non-serpentine soils, but ammonium nitrate-extractable concentrations of Ni in both soil types were similar. Ni concentrations in non-serpentine plants from their natural habitat were significantly lower than in serpentine plants, but there was no significant difference in Ni accumulation by plants of the two populations in hydroponic culture. However, increased concentrations of Ni in the hydroponic medium caused similar decreases in growth of both populations, indicating that Ni tolerance of the two populations was similar.     

Biodegradation of aged diesel in diverse soil matrixes: impact of environmental conditions and bioavailability on microbial remediation capacity

While bioremediation of total petroleum hydrocarbons (TPH) is in general a robust technique, heterogeneity in terms of contaminant and environmental characteristics can impact the extent of biodegradation. The current study investigates the implications of different soil matrix types (anthropogenic fill layer, peat, clay, and sand) and bioavailability on bioremediation of an aged diesel contamination from a heterogeneous site. In addition to an uncontaminated sample for each soil type, samples representing two levels of contamination (high and low) were also used; initial TPH concentrations varied between 1.6 and 26.6 g TPH/kg and bioavailability between 36 and 100 %. While significant biodegradation occurred during 100 days of incubation under biostimulating conditions (64.4–100 % remediation efficiency), low bioavailability restricted full biodegradation, yielding a residual TPH concentration. Respiration levels, as well as the abundance of alkB, encoding mono-oxygenases pivotal for hydrocarbon metabolism, were positively correlated with TPH degradation, demonstrating their usefulness as a proxy for hydrocarbon biodegradation. However, absolute respiration and alkB presence were dependent on soil matrix type, indicating the sensitivity of results to initial environmental conditions. Through investigating biodegradation potential across a heterogeneous site, this research illuminates the interplay between soil matrix type, bioavailability, and bioremediation and the implications of these parameters for the effectiveness of an in situ treatment.     

Application of micro-segmented flow for two-dimensional characterization of the combinatorial effect of zinc and copper ions on metal-tolerant Streptomyces strains

The cultivation and growth behavior of metal-tolerant strains of Streptomyce acidiscabies E13 and Streptomyces sp. F4 were studied under droplet-based microfluidics conditions. It was shown that the technique of micro segmented flow is well suited for the investigation of dependence of bacterial growth on different concentrations of either single metal ions or combinations of them. This study confirms higher tolerance to Zn than to Cu by our test organism. The highly resolved dose–response curves reflect two transitions between the different growth behaviors, separating initial responses to Cu concentration ranges into those with (a) intense growth, (b) moderate growth, and (c) growth inhibition. For Streptomyces sp. F4, an initial stimulation was shown in the sublethal range of zinc sulfate. Two-dimensional screenings using computer-controlled fluid actuation and in situ micro flow-through fluorimetry reflected a strong growth stimulation of strain F4 by zinc sulfate in the presence of sublethal Cu concentrations. This stimulatory effect on binary mixtures may be useful in providing optimal growth conditions in bioremediation procedures.     

Kinetic, dynamic, and pathway studies of glycerol metabolism by Klebsiella pneumoniae in anaerobic continuous culture: III. Enzymes and fluxes of glycerol dissimilation and 1,3-propanediol formation

The initial steps of glycerol dissimilation and 1,3-propanediol (1, 3-PD) formation by Klebsiella pneumoniae anaerobically grown on glycerol were studied by quantifying the in vitro and in vivo activities of enzymes in continuous culture under conditions of steady state and oscillation and during transient phases. The enzymes studied included glycerol dehydrogenase (GDH), glycerol dehydratase (GDHt), and 1,3-propanediol oxidoreductase (PDOR). Three conclusions can be drawn from the steady-state results. First, glycerol concentration in the culture is a key parameter that inversely affects the in vitro activities (concentrations) of all three enzymes, but has a positive effect on their in vivo activities. Growth rate significantly affects the ratio of in vitro and in vivo enzyme activities under low glycerol concentrations, but not under glycerol excess. Second, whereas the flux through the oxidative pathway of glycerol dissimilation is governed mainly by the regulation of in vivo enzyme activity on a metabolic level, the flux through the reductive pathway is largely controlled by the synthesis of enzymes. Third, GDHt is a major rate-liming enzyme for the consumption of glycerol and the formation of 1,3-PD in K. pneumoniae at high glycerol concentrations. Results from oscillating cultures revealed that both in vitro and in vivo activities of the enzymes oscillated. The average values of the in vitro activities during an oscillation cycle agreed well with their corresponding values for nonoscillating cultures under similar environmental conditions. Experiments with step changes in the feed concentration of glycerol demonstrated that growth and product formation are very sensitive to changes of substrate concentration in the culture. This sensitivity is due to the dynamic responses of the genetic and metabolic networks. They should be considered when modeling the dynamics of the culture and attempting to improve the formation of 1,3-PD.     

Symmetries of S-systems.

An S-system is a set of first-order nonlinear differential equations that all have the same structure: The derivative of a variable is equal to the difference of two products of power-law functions. S-systems have been used as models for a variety of problems, primarily in biology. In addition, S-systems possess the interesting property that large classes of differential equations can be recast exactly as S-systems, a feature that has been proven useful in statistics and numerical analysis. Here, simple criteria are introduced that determine whether an S-system possesses certain types of symmetries and how the underlying transformation groups can be constructed. If a transformation group exists, families of solutions can be characterized, the number of S-system equations necessary for solution can be reduced, and some boundary value problems can be reduced to initial value problems.     

A new framework for the estimation of control parameters in metabolic pathways using lin-log kinetics.

The control properties of biochemical pathways can be described by control coefficients and elasticities, as defined in the framework of metabolic control analysis. The determination of these parameters using the traditional metabolic control analysis relationships is, however, limited by experimental difficulties (e.g. realizing and measuring small changes in biological systems) and lack of appropriate mathematical procedures (e.g. when the more practical large changes are made). In this paper, the recently developed lin-log approach is proposed to avoid the above-mentioned problems and is applied to estimate control parameters from measurements obtained in steady state experiments. The lin-log approach employs approximative linear-logarithmic kinetics parameterized by elasticities and provides analytical solutions for fluxes and metabolite concentrations when large changes are made. Published flux and metabolite concentration data are used, obtained from a reconstructed section of glycolysis converting 3-phosphoglycerate to pyruvate [Giersch, C. (1995) Eur. J. Biochem. 227, 194-201]. With the lin-log approach, all data from different experiments can be combined to give realistic elasticity and flux control coefficient estimates by linear regression. Despite the large changes, a good agreement of fluxes and metabolite concentrations is obtained between the measured and calculated values according to the lin-log model. Furthermore, it is shown that the lin-log approach allows a rigorous statistical evaluation to identify the optimal reference state and the optimal model structure assumption. In conclusion, the lin-log approach addresses practical problems encountered in the traditional metabolic control analysis-based methods by introducing suitable nonlinear kinetics, thus providing a novel framework with improved procedures for the estimation of elasticities and control parameters from large perturbation experiments.     

High-Throughput 3D Screening Reveals Differences in Drug Sensitivities between Culture Models of JIMT1 Breast Cancer Cells

The traditional method for studying cancer in vitro is to grow immortalized cancer cells in two-dimensional monolayers on plastic. However, many cellular features are impaired in these artificial conditions, and large changes in gene expression compared to tumors have been reported. Three-dimensional cell culture models have become increasingly popular and are suggested to be better models than two-dimensional monolayers due to improved cell-to-cell contact and structures that resemble in vivo architecture. The aim of this study was to develop a simple high-throughput three-dimensional drug screening method and to compare drug responses in JIMT1 breast cancer cells when grown in two dimensions, in poly(2-hydroxyethyl methacrylate) induced anchorage-independent three-dimensional models, and in Matrigel three-dimensional cell culture models. We screened 102 compounds with multiple concentrations and biological replicates for their effects on cell proliferation. The cells were either treated immediately upon plating, or they were allowed to grow in three-dimensional cultures for 4 days before the drug treatment. Large variations in drug responses were observed between the models indicating that comparisons of culture model-influenced drug sensitivities cannot be made based on the effects of a single drug. However, we show with the 63 most prominent drugs that, in general, JIMT1 cells grown on Matrigel were significantly more sensitive to drugs than cells grown in two-dimensional cultures, while the responses of cells grown in poly(2-hydroxyethyl methacrylate) resembled those of the two-dimensional cultures. Furthermore, comparing the gene expression profiles of the cell culture models to xenograft tumors indicated that cells cultured in Matrigel and as xenografts most closely resembled each other. In this study, we also suggest that three-dimensional cultures can provide a platform for systematic experimentation of larger compound collections in a high-throughput mode and be used as alternatives to traditional two-dimensional screens for better comparability to the in vivo state.     

A scaffold-free in vitro model for osteogenesis of human mesenchymal stem cells

For studying cellular processes three-dimensional (3D) in vitro models are of a high importance. For tissue engineering approaches osseous differentiation is performed on 3D scaffolds, but material depending influences promote cellular processes like adhesion, proliferation and differentiation. To investigate developmental processes of mesenchymal stem cells without cell-substrate interactions, self-contained in vitro models mimicking physiological condition are required. However, with respect to scientific investigations and pharmaceutical tests, it is essential that these tissue models are well characterised and are of a high reproducibility. In order to establish an appropriate in vitro model for bone formation, different protocols are compared and optimised regarding their aggregate formation efficiency, homogeneity of the aggregates, the viability and their ability to induce differentiation into the osteogenic lineage. The protocols for the generation of 3D cell models are based on rotation culture, hanging drop technique, and the cultivation in non adhesive culture vessels (single vessels as well as 96 well plates). To conclude, the cultivation of hMSCs in 96 well non adhesive plates facilitates an easy way to cultivate homogenous cellular aggregates with high performance efficiency in parallel. The size can be controlled by the initial cell density per well and within this spheroids, bone formation has been induced.     

Kinetic modeling of microbial growth,enzyme activity,and gene deletions: An integrated model of β-glucosidase function in Cellvibrio japonicus

Understanding the complex growth and metabolic dynamics in microorganisms requires advanced kinetic models containing both metabolic reactions and enzymatic regulation to predict phenotypic behaviors under different conditions and perturbations. Most current kinetic models lack gene expression dynamics and are separately calibrated to distinct media, which consequently makes them unable to account for genetic perturbations or multiple substrates. This challenge limits our ability to gain a comprehensive understanding of microbial processes towards advanced metabolic optimizations that are desired for many biotechnology applications. Here, we present an integrated computational and experimental approach for the development and optimization of mechanistic kinetic models for microbial growth and metabolic and enzymatic dynamics. Our approach integrates growth dynamics, gene expression, protein secretion, and gene-deletion phenotypes. We applied this methodology to build a dynamic model of the growth kinetics in batch culture of the bacterium Cellvibrio japonicus grown using either cellobiose or glucose media. The model parameters were inferred from an experimental data set using an evolutionary computation method. The resulting model was able to explain the growth dynamics of C. japonicus using either cellobiose or glucose media and was also able to accurately predict the metabolite concentrations in the wild-type strain as well as in β-glucosidase gene deletion mutant strains. We validated the model by correctly predicting the non-diauxic growth and metabolite consumptions of the wild-type strain in a mixed medium containing both cellobiose and glucose, made further predictions of mutant strains growth phenotypes when using cellobiose and glucose media, and demonstrated the utility of the model for designing industrially-useful strains. Importantly, the model is able to explain the role of the different β-glucosidases and their behavior under genetic perturbations. This integrated approach can be extended to other metabolic pathways to produce mechanistic models for the comprehensive understanding of enzymatic functions in multiple substrates.     

Germination and anaerobic metabolism of seeds of Tabebuia cassinoides (Lam.) DC subjected to flooding and anoxia

Tabebuia cassinoides (Lam.) DC (Bignoniaceae) is an arboreal species common in seasonally or permanently waterlogged areas of the “restinga” forest (a type of forest that occurs on the sandbanks of the coastal plains of southeastern Brazil). The objectives of the present study were to establish seed germination responses of this species to flooding and anoxia and investigate the end products of the anaerobic metabolism of seeds subjected to these conditions, with the goal of understanding the adaptive strategies that enable this species to dominate flood prone areas of “restinga”, as well as determine reserves stored in their seeds. Seeds of T. cassinoides did not germinate under anoxia or complete submergence, but remained viable under these conditions for 15 and 20 days, respectively. Due to their membranaceous wings, the seeds float very well and reached 100% germination in this condition, an important adaptation to overcome the initial stages of development in flooded habitats. In relation to anaerobic metabolism, ethanol is the most important end product, while lactate is produced in lower concentrations. Seeds of T. cassinoides have very little endosperm and the reserves, mainly glycoproteins, lipids and free sugars, accumulate in the cotyledons. Free sugars may provide the substrate for the initial metabolism of seed germination, but the level of these reserves was not enough to allow germination under oxygen deprivation. Possibly, carbohydrate reserves were enough only to maintain seed viability for a relative short period under this condition.     

Microbial dynamics and soil physicochemical properties explain large‐scale variations in soil organic carbon

First‐order organic matter decomposition models are used within most Earth System Models (ESMs) to project future global carbon cycling; these models have been criticized for not accurately representing mechanisms of soil organic carbon (SOC) stabilization and SOC response to climate change. New soil biogeochemical models have been developed, but their evaluation is limited to observations from laboratory incubations or few field experiments. Given the global scope of ESMs, a comprehensive evaluation of such models is essential using in situ observations of a wide range of SOC stocks over large spatial scales before their introduction to ESMs. In this study, we collected a set of in situ observations of SOC, litterfall and soil properties from 206 sites covering different forest and soil types in Europe and China. These data were used to calibrate the model MIMICS (The MIcrobial‐MIneral Carbon Stabilization model), which we compared to the widely used first‐order model CENTURY. We show that, compared to CENTURY, MIMICS more accurately estimates forest SOC concentrations and the sensitivities of SOC to variation in soil temperature, clay content and litter input. The ratios of microbial biomass to total SOC predicted by MIMICS agree well with independent observations from globally distributed forest sites. By testing different hypotheses regarding (using alternative process representations) the physicochemical constraints on SOC deprotection and microbial turnover in MIMICS, the errors of simulated SOC concentrations across sites were further decreased. We show that MIMICS can resolve the dominant mechanisms of SOC decomposition and stabilization and that it can be a reliable tool for predictions of terrestrial SOC dynamics under future climate change. It also allows us to evaluate at large scale the rapidly evolving understanding of SOC formation and stabilization based on laboratory and limited filed observation.