共查询到20条相似文献,搜索用时 31 毫秒
1.
Pauliina Nurmi Bestamin Özkaya Anna H. Kaksonen Olli H. Tuovinen Jaakko A. Puhakka 《Bioprocess and biosystems engineering》2010,33(4):449-456
In this study, the applicability of three modelling approaches was determined in an effort to describe complex relationships
between process parameters and to predict the performance of an integrated process, which consisted of a fluidized bed bioreactor
for Fe3+ regeneration and a gravity settler for precipitative iron removal. Self-organizing maps were used to visually evaluate the
associations between variables prior to the comparison of two different modelling methods, the multiple regression modelling
and artificial neural network (ANN) modelling, for predicting Fe(III) precipitation. With the ANN model, an excellent match
between the predicted and measured data was obtained (R
2 = 0.97). The best-fitting regression model also gave a good fit (R
2 = 0.87). This study demonstrates that ANNs and regression models are robust tools for predicting iron precipitation in the
integrated process and can thus be used in the management of such systems. 相似文献
2.
Susan Fugett Abu‐Absi LiYing Yang Patrick Thompson Canping Jiang Sunitha Kandula Bernhard Schilling Abhinav A. Shukla 《Biotechnology and bioengineering》2010,106(6):894-905
The concept of design space has been taking root as a foundation of in‐process control strategies for biopharmaceutical manufacturing processes. During mapping of the process design space, the multidimensional combination of operational variables is studied to quantify the impact on process performance in terms of productivity and product quality. An efficient methodology to map the design space for a monoclonal antibody cell culture process is described. A failure modes and effects analysis (FMEA) was used as the basis for the process characterization exercise. This was followed by an integrated study of the inoculum stage of the process which includes progressive shake flask and seed bioreactor steps. The operating conditions for the seed bioreactor were studied in an integrated fashion with the production bioreactor using a two stage design of experiments (DOE) methodology to enable optimization of operating conditions. A two level Resolution IV design was followed by a central composite design (CCD). These experiments enabled identification of the edge of failure and classification of the operational parameters as non‐key, key or critical. In addition, the models generated from the data provide further insight into balancing productivity of the cell culture process with product quality considerations. Finally, process and product‐related impurity clearance was evaluated by studies linking the upstream process with downstream purification. Production bioreactor parameters that directly influence antibody charge variants and glycosylation in CHO systems were identified. Biotechnol. Bioeng. 2010;106: 894–905. © 2010 Wiley Periodicals, Inc. 相似文献
3.
Veronika Braunisch Joy Coppes Raphaël Arlettaz Rudi Suchant Hans Schmid Kurt Bollmann 《Ecography》2013,36(9):971-983
Correlative species distribution models are frequently used to predict species’ range shifts under climate change. However, climate variables often show high collinearity and most statistical approaches require the selection of one among strongly correlated variables. When causal relationships between species presence and climate parameters are unknown, variable selection is often arbitrary, or based on predictive performance under current conditions. While this should only marginally affect current range predictions, future distributions may vary considerably when climate parameters do not change in concert. We investigated this source of uncertainty using four highly correlated climate variables together with a constant set of landscape variables in order to predict current (2010) and future (2050) distributions of four mountain bird species in central Europe. Simulating different parameterization decisions, we generated a) four models including each of the climate variables singly, b) a model taking advantage of all variables simultaneously and c) an un‐weighted average of the predictions of a). We compared model accuracy under current conditions, predicted distributions under four scenarios of climate change, and – for one species – evaluated back‐projections using historical occurrence data. Although current and future variable‐correlations remained constant, and the models’ accuracy under contemporary conditions did not differ, future range predictions varied considerably in all climate change scenarios. Averaged models and models containing all climate variables simultaneously produced intermediate predictions; the latter, however, performed best in back‐projections. This pattern, consistent across different modelling methods, indicates a benefit from including multiple climate predictors in ambiguous situations. Variable selection proved to be an important source of uncertainty for future range predictions, difficult to control using contemporary information. Small, but diverging changes of climate variables, masked by constant overall correlation patterns, can cause substantial differences between future range predictions which need to be accounted for, particularly when outcomes are intended for conservation decisions. 相似文献
4.
Improved species‐occurrence predictions in data‐poor regions: using large‐scale data and bias correction with down‐weighted Poisson regression and Maxent
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Species distribution modelling (SDM) has become an essential method in ecology and conservation. In the absence of survey data, the majority of SDMs are calibrated with opportunistic presence‐only data, incurring substantial sampling bias. We address the challenge of correcting for sampling bias in the data‐sparse situations. We modelled the relative intensity of bat records in their entire range using three modelling algorithms under the point‐process modelling framework (GLMs with subset selection, GLMs fitted with an elastic‐net penalty, and Maxent). To correct for sampling bias, we applied model‐based bias correction by incorporating spatial information on site accessibility or sampling efforts. We evaluated the effect of bias correction on the models’ predictive performance (AUC and TSS), calculated on spatial‐block cross‐validation and a holdout data set. When evaluated with independent, but also sampling‐biased test data, correction for sampling bias led to improved predictions. The predictive performance of the three modelling algorithms was very similar. Elastic‐net models have intermediate performance, with slight advantage for GLMs on cross‐validation and Maxent on hold‐out evaluation. Model‐based bias correction is very useful in data‐sparse situations, where detailed data are not available to apply other bias correction methods. However, bias correction success depends on how well the selected bias variables describe the sources of bias. In this study, accessibility covariates described bias in our data better than the effort covariate, and their use led to larger changes in predictive performance. Objectively evaluating bias correction requires bias‐free presence–absence test data, and without them the real improvement for describing a species’ environmental niche cannot be assessed. 相似文献
5.
Species distribution modelling (SDM) is a widely used tool and has many applications in ecology and conservation biology. Spatial autocorrelation (SAC), a pattern in which observations are related to one another by their geographic distance, is common in georeferenced ecological data. SAC in the residuals of SDMs violates the ‘independent errors’ assumption required to justify the use of statistical models in modelling species’ distributions. The autologistic modelling approach accounts for SAC by including an additional term (the autocovariate) representing the similarity between the value of the response variable at a location and neighbouring locations. However, autologistic models have been found to introduce bias in the estimation of parameters describing the influence of explanatory variables on habitat occupancy. To address this problem we developed an extension to the autologistic approach by calculating the autocovariate on SAC in residuals (the RAC approach). Performance of the new approach was tested on simulated data with a known spatial structure and on strongly autocorrelated mangrove species’ distribution data collected in northern Australia. The RAC approach was implemented as generalized linear models (GLMs) and boosted regression tree (BRT) models. We found that the BRT models with only environmental explanatory variables can account for some SAC, but applying the standard autologistic or RAC approaches further reduced SAC in model residuals and substantially improved model predictive performance. The RAC approach showed stronger inferential performance than the standard autologistic approach, as parameter estimates were more accurate and statistically significant variables were accurately identified. The new RAC approach presented here has the potential to account for spatial autocorrelation while maintaining strong predictive and inferential performance, and can be implemented across a range of modelling approaches. 相似文献
6.
Impact of ecological redundancy on the performance of machine learning classifiers in vegetation mapping
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Paul D. Macintyre Adriaan Van Niekerk Mark P. Dobrowolski James L. Tsakalos Ladislav Mucina 《Ecology and evolution》2018,8(13):6728-6737
Vegetation maps are models of the real vegetation patterns and are considered important tools in conservation and management planning. Maps created through traditional methods can be expensive and time‐consuming, thus, new more efficient approaches are needed. The prediction of vegetation patterns using machine learning shows promise, but many factors may impact on its performance. One important factor is the nature of the vegetation–environment relationship assessed and ecological redundancy. We used two datasets with known ecological redundancy levels (strength of the vegetation–environment relationship) to evaluate the performance of four machine learning (ML) classifiers (classification trees, random forests, support vector machines, and nearest neighbor). These models used climatic and soil variables as environmental predictors with pretreatment of the datasets (principal component analysis and feature selection) and involved three spatial scales. We show that the ML classifiers produced more reliable results in regions where the vegetation–environment relationship is stronger as opposed to regions characterized by redundant vegetation patterns. The pretreatment of datasets and reduction in prediction scale had a substantial influence on the predictive performance of the classifiers. The use of ML classifiers to create potential vegetation maps shows promise as a more efficient way of vegetation modeling. The difference in performance between areas with poorly versus well‐structured vegetation–environment relationships shows that some level of understanding of the ecology of the target region is required prior to their application. Even in areas with poorly structured vegetation–environment relationships, it is possible to improve classifier performance by either pretreating the dataset or reducing the spatial scale of the predictions. 相似文献
7.
《Animal : an international journal of animal bioscience》2018,12(4):701-712
What is a good (useful) mathematical model in animal science? For models constructed for prediction purposes, the question of model adequacy (usefulness) has been traditionally tackled by statistical analysis applied to observed experimental data relative to model-predicted variables. However, little attention has been paid to analytic tools that exploit the mathematical properties of the model equations. For example, in the context of model calibration, before attempting a numerical estimation of the model parameters, we might want to know if we have any chance of success in estimating a unique best value of the model parameters from available measurements. This question of uniqueness is referred to as structural identifiability; a mathematical property that is defined on the sole basis of the model structure within a hypothetical ideal experiment determined by a setting of model inputs (stimuli) and observable variables (measurements). Structural identifiability analysis applied to dynamic models described by ordinary differential equations (ODEs) is a common practice in control engineering and system identification. This analysis demands mathematical technicalities that are beyond the academic background of animal science, which might explain the lack of pervasiveness of identifiability analysis in animal science modelling. To fill this gap, in this paper we address the analysis of structural identifiability from a practitioner perspective by capitalizing on the use of dedicated software tools. Our objectives are (i) to provide a comprehensive explanation of the structural identifiability notion for the community of animal science modelling, (ii) to assess the relevance of identifiability analysis in animal science modelling and (iii) to motivate the community to use identifiability analysis in the modelling practice (when the identifiability question is relevant). We focus our study on ODE models. By using illustrative examples that include published mathematical models describing lactation in cattle, we show how structural identifiability analysis can contribute to advancing mathematical modelling in animal science towards the production of useful models and, moreover, highly informative experiments via optimal experiment design. Rather than attempting to impose a systematic identifiability analysis to the modelling community during model developments, we wish to open a window towards the discovery of a powerful tool for model construction and experiment design. 相似文献
8.
Ana Antonic Emily S. Sena Jennifer S. Lees Taryn E. Wills Peta Skeers Peter E. Batchelor Malcolm R. Macleod David W. Howells 《PLoS biology》2013,11(12)
Spinal cord injury (SCI) is a devastating condition that causes substantial morbidity and mortality and for which no treatments are available. Stem cells offer some promise in the restoration of neurological function. We used systematic review, meta-analysis, and meta-regression to study the impact of stem cell biology and experimental design on motor and sensory outcomes following stem cell treatments in animal models of SCI. One hundred and fifty-six publications using 45 different stem cell preparations met our prespecified inclusion criteria. Only one publication used autologous stem cells. Overall, allogeneic stem cell treatment appears to improve both motor (effect size, 27.2%; 95% Confidence Interval [CI], 25.0%–29.4%; 312 comparisons in 5,628 animals) and sensory (effect size, 26.3%; 95% CI, 7.9%–44.7%; 23 comparisons in 473 animals) outcome. For sensory outcome, most heterogeneity between experiments was accounted for by facets of stem cell biology. Differentiation before implantation and intravenous route of delivery favoured better outcome. Stem cell implantation did not appear to improve sensory outcome in female animals and appeared to be enhanced by isoflurane anaesthesia. Biological plausibility was supported by the presence of a dose–response relationship. For motor outcome, facets of stem cell biology had little detectable effect. Instead most heterogeneity could be explained by the experimental modelling and the outcome measure used. The location of injury, method of injury induction, and presence of immunosuppression all had an impact. Reporting of measures to reduce bias was higher than has been seen in other neuroscience domains but were still suboptimal. Motor outcomes studies that did not report the blinded assessment of outcome gave inflated estimates of efficacy. Extensive recent preclinical literature suggests that stem-cell–based therapies may offer promise, however the impact of compromised internal validity and publication bias mean that efficacy is likely to be somewhat lower than reported here. 相似文献
9.
David G. García Münzer Margaritis Kostoglou Michael C. Georgiadis Efstratios N. Pistikopoulos Athanasios Mantalaris 《PLoS computational biology》2015,11(2)
Mammalian cell cultures are intrinsically heterogeneous at different scales (molecular to bioreactor). The cell cycle is at the centre of capturing heterogeneity since it plays a critical role in the growth, death, and productivity of mammalian cell cultures. Current cell cycle models use biological variables (mass/volume/age) that are non-mechanistic, and difficult to experimentally determine, to describe cell cycle transition and capture culture heterogeneity. To address this problem, cyclins—key molecules that regulate cell cycle transition—have been utilized. Herein, a novel integrated experimental-modelling platform is presented whereby experimental quantification of key cell cycle metrics (cell cycle timings, cell cycle fractions, and cyclin expression determined by flow cytometry) is used to develop a cyclin and DNA distributed model for the industrially relevant cell line, GS-NS0. Cyclins/DNA synthesis rates were linked to stimulatory/inhibitory factors in the culture medium, which ultimately affect cell growth. Cell antibody productivity was characterized using cell cycle-specific production rates. The solution method delivered fast computational time that renders the model’s use suitable for model-based applications. Model structure was studied by global sensitivity analysis (GSA), which identified parameters with a significant effect on the model output, followed by re-estimation of its significant parameters from a control set of batch experiments. A good model fit to the experimental data, both at the cell cycle and viable cell density levels, was observed. The cell population heterogeneity of disturbed (after cell arrest) and undisturbed cell growth was captured proving the versatility of the modelling approach. Cell cycle models able to capture population heterogeneity facilitate in depth understanding of these complex systems and enable systematic formulation of culture strategies to improve growth and productivity. It is envisaged that this modelling approach will pave the model-based development of industrial cell lines and clinical studies. 相似文献
10.
This study tested the performance of 15 predictive models in predicting the distribution of sponge assemblages on the Australian continental shelf using a common set of marine environmental variables. The models included traditional regression and more recently developed machine learning models. The results demonstrate that the spatial distribution of sponge assemblages can be successfully predicted, although the effectiveness of predictions varied among models. Overall, machine learning models achieved the best prediction performance. The direct variable of bottom-water temperature and the resource variables that describe bottom-water nutrient status were found to be useful surrogates for the distribution of sponge assemblages at the broad regional scale. A new method of deriving pseudo-absence data (weighted pseudo-absence) was compared with random pseudo-absence data — the new data were able to improve modelling performance for all the models both in terms of statistics (~ 10%) and in the predicted spatial distributions. Results from this study will further refine modelling methods used to predict the spatial distribution of marine biota at broad spatial scales, an outcome especially relevant to managers of marine resources. 相似文献
11.
The biopharmaceutical industry is moving toward a more quality by design (QbD) approach that seeks to increase product and process understanding and process control. Miniature bioreactor systems offer a high-throughput method enabling the assessment of numerous process variables in a controlled environment. However, the number of off/at-line samples that can be taken is restricted due to the small working volume of each vessel. This limitation may be resolved through the use of Raman spectroscopy due to its ability to obtain multianalyte data from small sample volumes fast. It can, however, be challenging to implement this technique for this application due to the complexity of the sample matrix and that analytes are often present in low concentration. Here, we present a design of experiments (DOE) approach to generate samples for calibrating robust multivariate predictive models measuring glucose, lactate, ammonium, viable cell concentration (VCC) and product concentration, for unclarified cell culture that improves the daily monitoring of each miniature bioreactor vessel. Furthermore, we demonstrate how the output of the glucose and VCC models can be used to control the glucose and main nutrient feed rate within miniature bioreactor cultures to within qualified critical limits set for larger scale vessels. The DOE approach used to generate the calibration sample set is shown to result in models more robust to process changes than by simply using samples taken from the “typical” process. © 2018 American Institute of Chemical Engineers Biotechnol. Prog., 35: e2740, 2019. 相似文献
12.
Verena Nold Lisa Junghans Lorenzo Bisgen Raphael Drerup Beate Presser Ingo Gorr Thomas Schwab Bettina Knapp Stefan Wieschalka 《Engineering in Life Science》2022,22(12):784
The analysis of data collected using design of experiments (DoE) is the current gold standard to determine the influence of input parameters and their interactions on process performance and product quality. In early development, knowledge on the bioprocess of a new product is limited. Many input parameters need to be investigated for a thorough investigation. For eukaryotic cell cultures, intensified DoE (iDoE) has been proposed as efficient tool, requiring fewer bioreactor runs by introducing setpoint changes during the bioprocess. We report the first successful application of iDoE to mammalian cell culture, performing sequential setpoint changes in the growth phase for the selected input parameters temperature and dissolved oxygen. The process performance data were analyzed using ordinary least squares regression. Our results indicate iDoE to be applicable to mammalian bioprocesses and to be a cost‐efficient option to inform modeling early on during process development. Even though only half the number of bioreactor runs were used in comparison to a classical DoE approach, the resulting models revealed comparable input‐output relations. Being able to examine several setpoint levels within one bioreactor run, we confirm iDoE to be a promising tool to speed up biopharmaceutical process development. 相似文献
13.
Magnus T Liu Y Parker GC Rao MS 《Philosophical transactions of the Royal Society of London. Series B, Biological sciences》2008,363(1489):9-22
Stem cells, although difficult to define, hold great promise as tools for understanding development and as therapeutic agents. However, as with any new field, uncritical enthusiasm can outstrip reality. In this review, we have listed nine common myths that we believe affect our approach to evaluating stem cells for therapy. We suggest that careful consideration needs to be given to each of these issues when evaluating a particular cell for its use in therapy. Data need to be collected and reported for failed as well as successful experiments and a rigorous scientific approach taken to evaluate the undeniable promise of stem cell biology. 相似文献
14.
15.
Iain S. Koolhof Simon M. Firestone Silvana Bettiol Michael Charleston Katherine B. Gibney Peter J. Neville Andrew Jardine Scott Carver 《PLoS neglected tropical diseases》2021,15(3)
BackgroundStatistical models are regularly used in the forecasting and surveillance of infectious diseases to guide public health. Variable selection assists in determining factors associated with disease transmission, however, often overlooked in this process is the evaluation and suitability of the statistical model used in forecasting disease transmission and outbreaks. Here we aim to evaluate several modelling methods to optimise predictive modelling of Ross River virus (RRV) disease notifications and outbreaks in epidemiological important regions of Victoria and Western Australia.Methodology/Principal findingsWe developed several statistical methods using meteorological and RRV surveillance data from July 2000 until June 2018 in Victoria and from July 1991 until June 2018 in Western Australia. Models were developed for 11 Local Government Areas (LGAs) in Victoria and seven LGAs in Western Australia. We found generalised additive models and generalised boosted regression models, and generalised additive models and negative binomial models to be the best fit models when predicting RRV outbreaks and notifications, respectively. No association was found with a model’s ability to predict RRV notifications in LGAs with greater RRV activity, or for outbreak predictions to have a higher accuracy in LGAs with greater RRV notifications. Moreover, we assessed the use of factor analysis to generate independent variables used in predictive modelling. In the majority of LGAs, this method did not result in better model predictive performance.Conclusions/SignificanceWe demonstrate that models which are developed and used for predicting disease notifications may not be suitable for predicting disease outbreaks, or vice versa. Furthermore, poor predictive performance in modelling disease transmissions may be the result of inappropriate model selection methods. Our findings provide approaches and methods to facilitate the selection of the best fit statistical model for predicting mosquito-borne disease notifications and outbreaks used for disease surveillance. 相似文献
16.
Recently developed perfusion micro-bioreactors offer the promise of more physiologic in vitro systems for tissue engineering. Successful application of such bioreactors will require a method to characterize the bioreactor environment required to elicit desired cell function. We present a mathematical model to describe nutrient/growth factor transport and cell growth inside a microchannel bioreactor. Using the model, we first show that the nature of spatial gradients in nutrient concentration can be controlled by both design and operating conditions and are a strong function of cell uptake rates. Next, we extend our model to investigate the spatial distributions of cell-secreted soluble autocrine/paracrine growth factors in the bioreactor. We show that the convective transport associated with the continuous cell culture and possible media recirculation can significantly alter the concentration distribution of the soluble signaling molecules as compared to static culture experiments and hence needs special attention when adapting static culture protocols for the bioreactor. Further, using an unsteady state model, we find that spatial gradients in nutrient/growth factor concentrations can bring about spatial variations in the cell density distribution inside the bioreactor, which can result in lowered working volume of the bioreactor. Finally, we show that the nutrient and spatial limitations can dramatically affect the composition of a co-cultured cell population. Our results are significant for the development, design, and optimization of novel micro-channel systems for tissue engineering. 相似文献
17.
van Riel NA 《Briefings in bioinformatics》2006,7(4):364-374
Systems biology applies quantitative, mechanistic modelling to study genetic networks, signal transduction pathways and metabolic networks. Mathematical models of biochemical networks can look very different. An important reason is that the purpose and application of a model are essential for the selection of the best mathematical framework. Fundamental aspects of selecting an appropriate modelling framework and a strategy for model building are discussed. Concepts and methods from system and control theory provide a sound basis for the further development of improved and dedicated computational tools for systems biology. Identification of the network components and rate constants that are most critical to the output behaviour of the system is one of the major problems raised in systems biology. Current approaches and methods of parameter sensitivity analysis and parameter estimation are reviewed. It is shown how these methods can be applied in the design of model-based experiments which iteratively yield models that are decreasingly wrong and increasingly gain predictive power. 相似文献
18.
T.J. Spencer L.A. Hidalgo‐Bastida S.H. Cartmell I. Halliday C.M. Care 《Biotechnology and bioengineering》2013,110(4):1221-1230
Computer simulations can potentially be used to design, predict, and inform properties for tissue engineering perfusion bioreactors. In this work, we investigate the flow properties that result from a particular poly‐L ‐lactide porous scaffold and a particular choice of perfusion bioreactor vessel design used in bone tissue engineering. We also propose a model to investigate the dynamic seeding properties such as the homogeneity (or lack of) of the cellular distribution within the scaffold of the perfusion bioreactor: a pre‐requisite for the subsequent successful uniform growth of a viable bone tissue engineered construct. Flows inside geometrically complex scaffolds have been investigated previously and results shown at these pore scales. Here, it is our aim to show accurately that through the use of modern high performance computers that the bioreactor device scale that encloses a scaffold can affect the flows and stresses within the pores throughout the scaffold which has implications for bioreactor design, control, and use. Central to this work is that the boundary conditions are derived from micro computed tomography scans of both a device chamber and scaffold in order to avoid generalizations and uncertainties. Dynamic seeding methods have also been shown to provide certain advantages over static seeding methods. We propose here a novel coupled model for dynamic seeding accounting for flow, species mass transport and cell advection‐diffusion‐attachment tuned for bone tissue engineering. The model highlights the timescale differences between different species suggesting that traditional homogeneous porous flow models of transport must be applied with caution to perfusion bioreactors. Our in silico data illustrate the extent to which these experiments have the potential to contribute to future design and development of large‐scale bioreactors. Biotechnol. Bioeng. 2013; 110: 1221–1230. © 2012 Wiley Periodicals, Inc. 相似文献
19.
Widespread cultivation of phototrophic microalgae for sustainable production of a variety of renewable products, for wastewater treatment, and for atmospheric carbon mitigation requires not only improved microorganisms but also significant improvements to process design and scaleup. The development of simulation tools capable of providing quantitative predictions for photobioreactor performance could contribute to improved reactor designs and it could also support process scaleup, as it has in the traditional petro-chemical industries. However, the complicated dependence of cell function on conditions in the microenvironment, such as light availability, temperature, nutrient concentration, and shear strain rate render simulation of photobioreactors much more difficult than chemical reactors. Although photobioreactor models with sufficient predictive ability suitable for reactor design and scaleup do not currently exist, progress towards this goal has occurred in recent years. The current status of algal photobioreactor simulations is reviewed here, with an emphasis on the integration of and interplay between sub-models describing hydrodynamics, radiation transport, and microalgal growth kinetics. Some limitations of widely used models and computational methods are identified, as well as current challenges and opportunities for the advancement of algal photobioreactor simulation. 相似文献
20.
《Biotechnology advances》2017,35(8):981-1003
Kinetic models are critical to predict the dynamic behaviour of metabolic networks. Mechanistic kinetic models for large networks remain uncommon due to the difficulty of fitting their parameters. Recent modelling frameworks promise new ways to overcome this obstacle while retaining predictive capabilities. In this review, we present an overview of the relevant mathematical frameworks for kinetic formulation, construction and analysis. Starting with kinetic formalisms, we next review statistical methods for parameter inference, as well as recent computational frameworks applied to the construction and analysis of kinetic models. Finally, we discuss opportunities and limitations hindering the development of larger kinetic reconstructions. 相似文献