Bacillus subtilis Cell Cycle as Studied by Fluorescence Microscopy: Constancy of Cell Length at Initiation of DNA Replication and Evidence for Active Nucleoid Partitioning

Fluorescence microscopic methods have been used to characterize the cell cycle of Bacillus subtilis at four different growth rates. The data obtained have been used to derive models for cell cycle progression. Like that of Escherichia coli, the period required by B. subtilis for chromosome replication at 37°C was found to be fairly constant (although a little longer, at about 55 min), as was the cell mass at initiation of DNA replication. The cell cycle of B. subtilis differed from that of E. coli in that changes in growth rate affected the average cell length but not the width and also in the relative variability of period between termination of DNA replication and septation. Overall movement of the nucleoid was found to occur smoothly, as in E. coli, but other aspects of nucleoid behavior were consistent with an underlying active partitioning machinery. The models for cell cycle progression in B. subtilis should facilitate the interpretation of data obtained from the recently introduced cytological methods for imaging the assembly and movement of proteins involved in cell cycle dynamics.     

Growth of a floating aquatic weed,Salvinia under standard conditions

1. Growth of the floating aquatic weed, Salvinia, in sterile culture was exponential for at least 2 weeks under standardized conditions.
2. Increase in light intensity or in CO₂ resulted in increases in growth rate, but did not extend the exponential period of growth.
3. This aquatic plant, like many others, discriminates against calcium relative to strontium.
4. In culture Salvinia exhibited luxury consumption of N and P.
5. Because of high C/N ratios, Salvinia may not be a favorable source of animal food, but might be useful in nutrient removal schemes.
6. In sterile culture, S. molesta produced fewer leaves than S. minima, but maintained a significant increase in leaf area and dry weight. This may be correlated with the ability of the first species to rapidly spread over tropical waterways.

     

Richards's equation and nonlinear mixed models applied to avian growth: why use them?

Postnatal growth is an important life‐history trait that varies widely across avian species, and several equations with a sigmoidal shape have been used to model it. Classical three‐parameter models have an inflection point fixed at a percentage of the upper asymptote which could be an unrealistic assumption generating biased fits. The Richards model emerged as an interesting alternative because it includes an extra parameter that determines the location of the inflection point which can move freely along the growth curve. Recently, nonlinear mixed models (NLMM) have been used in modeling avian growth because these models can deal with a lack of independence among data as typically occurs with multiple measurements on the same individual or on groups of related individuals. Here, we evaluated the usefulness of von Bertalanffy, Gompertz, logistic, U₄ and Richards's equations modeling chick growth in the imperial shag Phalacrocorax atriceps. We modelled growth in commonly used morphological traits, including body mass, bill length, head length and tarsus length, and compared the performance of models by using NLMM. Estimated adult size, age at maximum growth and maximum growth rates markedly differed across models. Overall, the most consistent performance in estimated adult size was obtained by the Richards model that showed deviations from mean adult size within 5%. Based on AICc values, the Richards equation was the best model for all traits analyzed. For tarsus length, both Richards and U₄ models provided indistinguishable fits because the relative inflection value estimated from the Richards model was very close to that assumed by the U₄ model. Our results highlight the bias incurred by three‐parameter models when the assumed inflection placement deviates from that derived from data. Thus, the application of the Richards equation using the NLMM framework represents a flexible and powerful tool for the analysis of avian growth.     

Application of some growth models to the black scallop,Chlamys varia (L.) from lanveoc,bay of brest

Linear day degree models were constructed, separately for each age group of the black scallop, Chlamys varia (L.) from Lanveoc, Bay of Brest and were found to be capable of predicting growth in terms of increase in shell height. Instantaneous growth rates, computed for evry month of the year, decreased with increase in age and showed minimal values in winter months. Q₁₀ values, calculated from instantaneous growth rates, demonstrated that growth did not depend upon temperature alone. Empirical models developed using temperature, food, and gonad index as independent variables, showed that both temperature and food together formed decisive factors for growth rates. The growth of the dry body tissue of the black scallop was also calculated for each age group and showed an obvious seasonal fluctuation.     

Predictive macroscopic modeling of cell growth,metabolism and monoclonal antibody production: Case study of a CHO fed-batch production

We describe a systematic approach to establish predictive models of CHO cell growth, cell metabolism and monoclonal antibody (mAb) formation during biopharmaceutical production. The prediction is based on a combination of an empirical metabolic model connecting extracellular metabolic fluxes with cellular growth and product formation with mixed Monod-inhibition type kinetics that we generalized to every possible external metabolite. We describe the maximum specific growth rate as a function of the integral viable cell density (IVCD). Moreover, we also take into account the accumulation of metabolites in intracellular pools that can influence cell growth. This is possible even without identification and quantification of these metabolites as illustrated with fed-batch cultures of Chinese Hamster Ovary (CHO) cells producing a mAb. The impact of cysteine and tryptophan on cell growth and cell productivity was assessed, and the resulting macroscopic model was successfully used to predict the impact of new, untested feeding strategies on cell growth and mAb production. This model combining piecewise linear relationships between metabolic rates, growth rate and production rate together with Monod-inhibition type models for cell growth did well in predicting cell culture performance in fed-batch cultures even outside the range of experimental data used for establishing the model. It could therefore also successfully be applied for in silico prediction of optimal operating conditions.     

Forecasting model of <Emphasis Type="Italic">Corylus</Emphasis>, <Emphasis Type="Italic">Alnus</Emphasis>, and <Emphasis Type="Italic">Betula</Emphasis> pollen concentration levels using spatiotemporal correlation properties of pollen count

The aim of the study was to create and evaluate models for predicting high levels of daily pollen concentration of Corylus, Alnus, and Betula using a spatiotemporal correlation of pollen count. For each taxon, a high pollen count level was established according to the first allergy symptoms during exposure. The dataset was divided into a training set and a test set, using a stratified random split. For each taxon and city, the model was built using a random forest method. Corylus models performed poorly. However, the study revealed the possibility of predicting with substantial accuracy the occurrence of days with high pollen concentrations of Alnus and Betula using past pollen count data from monitoring sites. These results can be used for building (1) simpler models, which require data only from aerobiological monitoring sites, and (2) combined meteorological and aerobiological models for predicting high levels of pollen concentration.     

Model-Based Tumor Growth Dynamics and Therapy Response in a Mouse Model of De Novo Carcinogenesis

Tumorigenesis is a complex, multistep process that depends on numerous alterations within the cell and contribution from the surrounding stroma. The ability to model macroscopic tumor evolution with high fidelity may contribute to better predictive tools for designing tumor therapy in the clinic. However, attempts to model tumor growth have mainly been developed and validated using data from xenograft mouse models, which fail to capture important aspects of tumorigenesis including tumor-initiating events and interactions with the immune system. In the present study, we investigate tumor growth and therapy dynamics in a mouse model of de novo carcinogenesis that closely recapitulates tumor initiation, progression and maintenance in vivo. We show that the rate of tumor growth and the effects of therapy are highly variable and mouse specific using a Gompertz model to describe tumor growth and a two-compartment pharmacokinetic/ pharmacodynamic model to describe the effects of therapy in mice treated with 5-FU. We show that inter-mouse growth variability is considerably larger than intra-mouse variability and that there is a correlation between tumor growth and drug kill rates. Our results show that in vivo tumor growth and regression in a double transgenic mouse model are highly variable both within and between subjects and that mathematical models can be used to capture the overall characteristics of this variability. In order for these models to become useful tools in the design of optimal therapy strategies and ultimately in clinical practice, a subject-specific modelling strategy is necessary, rather than approaches that are based on the average behavior of a given subject population which could provide erroneous results.     

Estimation of growth regulation in natural populations by extended family of growth curve models with fractional order derivative: Case studies from the global population dynamics database

Estimating the trend in population time series data using growth curve models is a central idea in population ecology. Several models, mainly governed by differential or difference equations, have been applied to real data sets to identify general growth pattern and make predictions. In this article, we analyze ecological time series data by fitting mathematical models governed by fractional differential equations (FDE). The order of the FDE (α) is used to quantify the evidence of memory in the population processes. The application of FDE is exemplified by analyzing time series data on two bird species Phalacrocorax carbo (Great cormorant) and Parus bicolor (Tufted titmouse) and two mammal species Castor canadensis (Beaver) and Ursus americanus (American black bear) extracted from the global population dynamics database. Five different population growth models were fitted to these data; density-independent exponential, negative density-dependent logistic and θ-logistic model, positive density-dependent exponential Allee and strong Allee model. Both ordinary and fractional derivative representations of these models were fitted to the time series data. Markov chain Monte Carlo (MCMC) method was used to estimate the model parameters and Akaike information criterion was used to select the best model. By estimating the return rate for each of the time series, we have shown that populations governed by FDE with a small value of α (high level of memory) return to the stable equilibrium faster. This demonstrates a synergistic interplay between memory and stability in natural populations.     

Comparison of occupancy modeling and radiotelemetry to estimate ungulate population dynamics

Radiotelemetry and unmarked occupancy modeling have been used to estimate animal population growth, but have not been compared for ungulates. We compared white-tailed deer (Odocoileus virginianus) population growth estimates from radiomarked individuals and occupancy modeling of unmarked individuals and evaluated advantages and disadvantages of each method. Estimates of population growth were obtained using remote camera (N = 54/year) detection/non-detection occupancy surveys of unmarked deer and from survival and recruitment data of radiomarked adult females (N = 87) and neonate fawns (N = 127) in a predominantly forested region of the Upper Peninsula of Michigan, USA, 2009–2011. We hypothesized that occupancy models and radiotelemetry data would have similar population growth trends because both methods sampled the same temporally closed population. Percent changes in camera trap data generally reflected finite population growth (λ) of radiomarked deer which increased (λ = 1.10 ± 0.01) from 2009 to 2010, but decreased (λ = 0.87 ± 0.02) from 2010 to 2011. Also, unmarked adult female abundance and fawn:adult female ratios generally reflected trends in radiomarked deer survival and recruitment. Royle–Nichols occupancy model abundance estimates had wide confidence intervals, which may preclude using this method from accurately estimating deer population growth. Radiotelemetry provided more precise population growth estimates, while allowing collection of vital rates and location data. However, the Royle–Nichols occupancy model may be preferred to radiotelemetry because it reflected yearly variation in population growth with reduced labor and no invasive marking. Researchers should consider the objectives and logistics of their study when choosing a specific method.     

Applicability of an Arrhenius Model for the Combined Effect of Temperature and CO2 Packaging on the Spoilage Microflora of Fish

The temperature behavior of the natural microflora on the Mediterranean fish red mullet (Mullus barbatus) was examined as a case study. The growth of the spoilage bacteria Pseudomonas spp., Shewanella putrefaciens, Brochothrix thermosphacta, and lactic acid bacteria was modeled as a function of temperature and the concentration of carbon dioxide in modified atmosphere packaging. Combined models were developed and comparatively assessed based on polynomial, Belehradek, and Arrhenius equations. The activation energy parameter of the Arrhenius model, E_A, was independent of the packaging atmosphere and ranged from 75 to 85 kJ/mol for the different bacteria, whereas the preexponential constant decreased exponentially with the packaging CO₂ concentration. We evaluated the applicability of the models developed by using experimental bacterial growth rates obtained from 42 independent experiments performed with three Mediterranean fish species and growth rates predicted from the models under the same temperature and packaging conditions. The accuracy factor and bias factor were used as statistical tools for evaluation, and the developed Arrhenius model and the Belehradek model were judged satisfactory overall.     

Density-dependent demographic variation determines extinction rate of experimental populations

Understanding population extinctions is a chief goal of ecological theory. While stochastic theories of population growth are commonly used to forecast extinction, models used for prediction have not been adequately tested with experimental data. In a previously published experiment, variation in available food was experimentally manipulated in 281 laboratory populations of Daphnia magna to test hypothesized effects of environmental variation on population persistence. Here, half of those data were used to select and fit a stochastic model of population growth to predict extinctions of populations in the other half. When density-dependent demographic stochasticity was detected and incorporated in simple stochastic models, rates of population extinction were accurately predicted or only slightly biased. However, when density-dependent demographic stochasticity was not accounted for, as is usual when forecasting extinction of threatened and endangered species, predicted extinction rates were severely biased. Thus, an experimental demonstration shows that reliable estimates of extinction risk may be obtained for populations in variable environments if high-quality data are available for model selection and if density-dependent demographic stochasticity is accounted for. These results suggest that further consideration of density-dependent demographic stochasticity is required if predicted extinction rates are to be relied upon for conservation planning.     

Heavy metal removal from a multi-metal solution and wastewater by Salvinia natans

Salvinia showed capacity to accumulate and hence remove more than one heavy metal from multi-metal solutions, though efficiency for heavy metal uptake varied for each metal present in different combinations. The pattern of heavy metal accumulation was confirmed by energy-dispersive X-ray fluorescence (EDXRF) analysis. There was a gradual decrease in heavy metal content in the wastewater samples when fresh biomass was replenished at definite time intervals of treatment. Zn, Cu, Ni and Cr removal to the extent of 84.8%, 73.8%, 56.8%, and 41.4%, respectively, was noted after four samplings of fresh biomass replenishment. Salvinia therefore can be recommended as a species for cleaning water contaminated with heavy metals.     

Range Expansion and Population Dynamics of an Invasive Species: The Eurasian Collared-Dove (Streptopelia decaocto)

Invasive species offer ecologists the opportunity to study the factors governing species distributions and population growth. The Eurasian Collared-Dove (Streptopelia decaocto) serves as a model organism for invasive spread because of the wealth of abundance records and the recent development of the invasion. We tested whether a set of environmental variables were related to the carrying capacities and growth rates of individual populations by modeling the growth trajectories of individual populations of the Collared-Dove using Breeding Bird Survey (BBS) and Christmas Bird Count (CBC) data. Depending on the fit of our growth models, carrying capacity and growth rate parameters were extracted and modeled using historical, geographical, land cover and climatic predictors. Model averaging and individual variable importance weights were used to assess the strength of these predictors. The specific variables with the greatest support in our models differed between data sets, which may be the result of temporal and spatial differences between the BBS and CBC. However, our results indicate that both carrying capacity and population growth rates are related to developed land cover and temperature, while growth rates may also be influenced by dispersal patterns along the invasion front. Model averaged multivariate models explained 35–48% and 41–46% of the variation in carrying capacities and population growth rates, respectively. Our results suggest that widespread species invasions can be evaluated within a predictable population ecology framework. Land cover and climate both have important effects on population growth rates and carrying capacities of Collared-Dove populations. Efforts to model aspects of population growth of this invasive species were more successful than attempts to model static abundance patterns, pointing to a potentially fruitful avenue for the development of improved invasive distribution models.     

Chemical composition of the water fern,salvinia molesta,and its potential as feed source for ruminants

The chemical composition of the now widespread tropical aquatic fern Salvinia molestawas analysed with regard to its suitability as a source of forage for ruminants. Three different stages of plant growth collected in Kerala, India, were air dried, brought to Germany, and used for Weende analysis, detergent fibre analysis, and the determination of gross energy, amino acids, tannins, and minerals. The concentration of 12.4% crude protein in sand-corrected dry matter (DM) and of some other nutrients in Salviniais comparable to that in conventional forage. However, the high content of crude ash (17.3% in DM) and of lignin (13.7%) and the presence of tannins (0.93%) may reduce acceptance as well as digestibility and therefore restrict the use of Salviniaas a potential feed source for ruminants.     

Macromolecular rate theory explains the temperature dependence of membrane conductance kinetics

The factor Q₁₀ is used in neuroscience to adjust reaction rates of voltage-activated membrane conductances to different temperatures and is widely assumed to be constant. By performing an analysis of published data of the reaction rates of sodium, potassium, and calcium membrane conductances, we demonstrate that 1) Q₁₀ is temperature dependent, 2) this relationship is similar across conductances, and 3) there is a strong effect at low temperatures (<15°C). We show that macromolecular rate theory (MMRT) explains this temperature dependency. MMRT predicts the existence of optimal temperatures at which reaction rates decrease as temperature increases, a phenomenon that we also found in the published data sets. We tested the consequences of using MMRT-adjusted reaction rates in the Hodgkin-Huxley model of the squid’s giant axon. The MMRT-adjusted model reproduces the temperature dependence of the rising and falling times of the action potential. Furthermore, the model also reproduces these properties for different squid species that live in different climates. In a second example, we compare spiking patterns of biophysical models based on human pyramidal neurons from the Allen Cell Types database at room and physiological temperatures. The original models, calibrated at 34°C, failed to generate realistic spikes at room temperature in more than half of the tested models, while the MMRT produces realistic spiking in all conditions. In another example, we show that using the MMRT correction in hippocampal pyramidal cell models results in 100% differences in voltage responses. Finally, we show that the shape of the Q₁₀ function results in systematic errors in predicting reaction rates. We propose that the optimal temperature could be a thermodynamical barrier to avoid over excitation in neurons. While this study is centered on membrane conductances, our results have important consequences for all biochemical reactions involved in cell signaling.     

Copepod production estimated by combining in situ data and specific temperature-dependent somatic growth models

Although growth in adult copepods is frequently assumed to be similar to juvenile growth, some evidence have pointed out that under in situ conditions, it can be lower, with, as a consequence, underestimation of secondary production. In addition, under field conditions, juvenile growth in copepods is close to maximum rates estimated at food-saturated conditions. Based on previous assumptions, this study aimed to test the applicability of a new approach for copepod production estimate, derived from temperature-dependent growth models and in situ data, such as seawater temperature and copepod biomass. For this purpose, site-specific copepod juvenile growth models, defined for Acartia tonsa and A. clausi populations from a Southern European estuary (Canal de Mira, Ria de Aveiro, Portugal), were used and copepod biomass was taken from zooplankton samples collected during 2 years at six sampling stations. By comparing the obtained results with published data, the feasibility of the approach for copepod secondary production estimates and its applicability in worldwide marine ecosystems was confirmed. Future studies should combine the estimates of adult and juvenile production in order to evaluate its relative contribution and to obtain a more precise estimate of secondary production.     

Addition of Escherichia coli K-12 Growth Observation and Gene Essentiality Data to the EcoCyc Database

The sets of compounds that can support growth of an organism are defined by the presence of transporters and metabolic pathways that convert nutrient sources into cellular components and energy for growth. A collection of known nutrient sources can therefore serve both as an impetus for investigating new metabolic pathways and transporters and as a reference for computational modeling of known metabolic pathways. To establish such a collection for Escherichia coli K-12, we have integrated data on the growth or nongrowth of E. coli K-12 obtained from published observations using a variety of individual media and from high-throughput phenotype microarrays into the EcoCyc database. The assembled collection revealed a substantial number of discrepancies between the high-throughput data sets, which we investigated where possible using low-throughput growth assays on soft agar and in liquid culture. We also integrated six data sets describing 16,119 observations of the growth of single-gene knockout mutants of E. coli K-12 into EcoCyc, which are relevant to antimicrobial drug design, provide clues regarding the roles of genes of unknown function, and are useful for validating metabolic models. To make this information easily accessible to EcoCyc users, we developed software for capturing, querying, and visualizing cellular growth assays and gene essentiality data.     

Maximal Sum of Metabolic Exchange Fluxes Outperforms Biomass Yield as a Predictor of Growth Rate of Microorganisms

Growth rate has long been considered one of the most valuable phenotypes that can be measured in cells. Aside from being highly accessible and informative in laboratory cultures, maximal growth rate is often a prime determinant of cellular fitness, and predicting phenotypes that underlie fitness is key to both understanding and manipulating life. Despite this, current methods for predicting microbial fitness typically focus on yields [e.g., predictions of biomass yield using GEnome-scale metabolic Models (GEMs)] or notably require many empirical kinetic constants or substrate uptake rates, which render these methods ineffective in cases where fitness derives most directly from growth rate. Here we present a new method for predicting cellular growth rate, termed SUMEX, which does not require any empirical variables apart from a metabolic network (i.e., a GEM) and the growth medium. SUMEX is calculated by maximizing the SUM of molar EXchange fluxes (hence SUMEX) in a genome-scale metabolic model. SUMEX successfully predicts relative microbial growth rates across species, environments, and genetic conditions, outperforming traditional cellular objectives (most notably, the convention assuming biomass maximization). The success of SUMEX suggests that the ability of a cell to catabolize substrates and produce a strong proton gradient enables fast cell growth. Easily applicable heuristics for predicting growth rate, such as what we demonstrate with SUMEX, may contribute to numerous medical and biotechnological goals, ranging from the engineering of faster-growing industrial strains, modeling of mixed ecological communities, and the inhibition of cancer growth.