A hybrid asymptotic-Kalman observer for bioprocesses

The exponential observers (extended Kalman or Luenberger observers, high gain observers) allow the use of a tuning parameter for managing the rate of convergence of the state estimate towards the true state. But their results are strongly dependent on the model quality (especially the kinetic model in bioprocesses). On the other hand, asymptotic observers (like the observer of Bastin and Dochain) have a rate of convergence which is a function of the experimental conditions (namely the dilution rate). However, this lack of tuning parameter is compensated by the absence of need for any kinetic model. In this paper, a hybrid technique is proposed which allows to jointly estimate the state and identify on-line the confidence on the kinetic model. The two limit cases (100 and 0 confidence) allow to recover rigorously the extended Kalman filter and the asymptotic observer of Bastin and Dochain. A simulation example (a fed-batch bacterial culture) is proposed and exhibits very satisfactory results.     

A hybrid representation approach for modelling complex dynamic bioprocesses

This paper considers the use of hybrid models to represent the dynamic behaviour of biotechnological processes. Each hybrid model consists of a set of non linear differential equations and a neural model. The set of differential equations attempts to describe as much as possible the phenomenology of the process whereas neural networks model predict some key parameters that are an essential part of the phenomenological model. The neural model is obtained indirectly, that is, using the prediction errors of one or more state variables to adjust its weights instead of successive presentations of input-output data of the neural network. This approach allows to use actual measurements to derive a suitable neural model that not only represents the variation of some key parameters but it is also able to partly include dynamic behaviour unaccounted for by the phenomenological model. The approach is described in detail using three test cases: (1) the fermentation of glucose to gluconic acid by the micro-organism Pseudomonas ovalis, (2) the growth of filamentous fungi in a solid state fermenter, and (3) the propagation of filamentous fungi growing on a 2-D solid substrate. Results for the three applications clearly demon- strate that using a hybrid model is a viable alternative for modelling complex biotechnological bioprocesses.     

A new sensitivity-based adaptive control vector parameterization approach for dynamic optimization of bioprocesses

Bioprocess and Biosystems Engineering - Dynamic optimization is a very effective way to increase the profitability or productivity of bioprocesses. As an important method of dynamic optimization,...     

A new generation of predictive models: The added value of hybrid models for manufacturing processes of therapeutic proteins

Due to the lack of complete understanding of metabolic networks and reaction pathways, establishing a universal mechanistic model for mammalian cell culture processes remains a challenge. Contrarily, data-driven approaches for modeling these processes lack extrapolation capabilities. Hybrid modeling is a technique that exploits the synergy between the two modeling methods. Although mammalian cell cultures are among the most relevant processes in biotechnology and indeed looks ideal for hybrid modeling, their application has only been proposed but never developed in the literature. This study provides a quantitative assessment of the improvement brought by hybrid models with respect to the state-of-the-art statistical predictive models in the context of therapeutic protein production. This is illustrated using a dataset obtained from a 3.5 L fed-batch experiment. With the goal to robustly define the process design space, hybrid models reveal a superior capability to predict the time evolution of different process variables using only the initial and process conditions in comparison to the statistical models. Hybrid models not only feature more accurate prediction results but also demonstrate better robustness and extrapolation capabilities. For the future application, this study highlights the added value of hybrid modeling for model-based process optimization and design of experiments.     

A new method for inferring hidden markov models from noisy time sequences

We present a new method for inferring hidden Markov models from noisy time sequences without the necessity of assuming a model architecture, thus allowing for the detection of degenerate states. This is based on the statistical prediction techniques developed by Crutchfield et al. and generates so called causal state models, equivalent in structure to hidden Markov models. The new method is applicable to any continuous data which clusters around discrete values and exhibits multiple transitions between these values such as tethered particle motion data or Fluorescence Resonance Energy Transfer (FRET) spectra. The algorithms developed have been shown to perform well on simulated data, demonstrating the ability to recover the model used to generate the data under high noise, sparse data conditions and the ability to infer the existence of degenerate states. They have also been applied to new experimental FRET data of Holliday Junction dynamics, extracting the expected two state model and providing values for the transition rates in good agreement with previous results and with results obtained using existing maximum likelihood based methods. The method differs markedly from previous Markov-model reconstructions in being able to uncover truly hidden states.     

A new method for dealing with residual spatial autocorrelation in species distribution models

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.     

A new method for dealing with measurement error in explanatory variables of regression models

We introduce a new method, moment reconstruction, of correcting for measurement error in covariates in regression models. The central idea is similar to regression calibration in that the values of the covariates that are measured with error are replaced by "adjusted" values. In regression calibration the adjusted value is the expectation of the true value conditional on the measured value. In moment reconstruction the adjusted value is the variance-preserving empirical Bayes estimate of the true value conditional on the outcome variable. The adjusted values thereby have the same first two moments and the same covariance with the outcome variable as the unobserved "true" covariate values. We show that moment reconstruction is equivalent to regression calibration in the case of linear regression, but leads to different results for logistic regression. For case-control studies with logistic regression and covariates that are normally distributed within cases and controls, we show that the resulting estimates of the regression coefficients are consistent. In simulations we demonstrate that for logistic regression, moment reconstruction carries less bias than regression calibration, and for case-control studies is superior in mean-square error to the standard regression calibration approach. Finally, we give an example of the use of moment reconstruction in linear discriminant analysis and a nonstandard problem where we wish to adjust a classification tree for measurement error in the explanatory variables.     

A new model-based RSA method validated using CAD models and models from reversed engineering

Roentgen stereophotogrammetric analysis (RSA) was developed to measure micromotion of an orthopaedic implant with respect to its surrounding bone. A disadvantage of conventional RSA is that it requires the implant to be marked with tantalum beads. This disadvantage can potentially be resolved with model-based RSA, whereby a 3D model of the implant is used for matching with the actual images and the assessment of position and rotation of the implant. In this study, a model-based RSA algorithm is presented and validated in phantom experiments. To investigate the influence of the accuracy of the implant models that were used for model-based RSA, we studied both computer aided design (CAD) models as well as models obtained by means of reversed engineering (RE) of the actual implant. The results demonstrate that the RE models provide more accurate results than the CAD models. If these RE models are derived from the very same implant, it is possible to achieve a maximum standard deviation of the error in the migration calculation of 0.06 mm for translations in x- and y-direction and 0.14 mm for the out of plane z-direction, respectively. For rotations about the y-axis, the standard deviation was about 0.1 degrees and for rotations about the x- and z-axis 0.05 degrees. Studies with clinical RSA-radiographs must prove that these results can also be reached in a clinical setting, making model-based RSA a possible alternative for marker-based RSA.     

A new instrumentation system for training rowers

A dry-land rowing system was developed to provide the coach and/or athlete with quantitative information about the athlete's kinetics and kinematics while the athlete trains. This system consists of a Concept II rowing ergometer instrumented with a force transducer and potentiometer, four electrogoniometers attached to the athlete's ankle, knee, hip, and elbow, and a data acquisition computer. The force transducer is used to quantify the athlete's pulling force. The potentiometer signal is used to locate the position of the handle. The electrogoniometers provide signals proportional to joint angles. A link segment model of the human body is used to locate joint centers based on limb lengths and joint angles. The computer is used to collect and process all the transducer signals, perform the link segment calculations and provide feedback to the coach or athlete in the form of a stick figure animation overlaid with kinematic and kinetic information. This system allows the coach and athlete to quickly study a rower's mechanics, to evaluate the effects that technique changes have on the power produced by the athlete, and to identify technique differences between athletes.     

A simple and rapid one-time method to evaluate the non-acidic gas content from bioprocesses

This paper presents a rapid less than 2 min and low-cost method involving the use of alkali solution to capture the acidic gasses from a biogas, thereby providing an estimate of the percentage of non-acidic gasses. Such a method was mentioned in the literature but never fully described or optimized. After sampling an aliquot of gas from bioprocess, gas was injected in a sealed flask with a 3 M NaOH solution, and after equilibrium was obtained, the non-acidic gas volume was measured. The method was first calibrated with certified gasses with an accuracy observed between 98 and 105 %. Regarding the validation step, certified standard gas mixtures and nine biogas-laboratory batch reactors were used, the overall accuracy reported was 103 + 3 %. This rapid and low-cost method may either be used in laboratory conditions as a quick and low cost alternative to standard analysis equipment or in addition as a routine field control method used on full-scale plants.     

Biotechnological potential of inulin for bioprocesses

Inulin consists of linear chains of β-2,1-linked d-fructofuranose molecules terminated by a glucose residue through a sucrose-type linkage at the reducing end. In this review article, inulin and its applications in bioprocesses are overviewed. The tubers of many plants, such as Jerusalem artichoke, chicory, dahlia, and yacon contain a large amount of inulin. Inulin can be actively hydrolyzed by microbial inulinases to produce fructose, glucose and inulooligosaccharides (IOS). The fructose and glucose formed can be further transformed into ethanol, single-cell protein, single cell oil and other useful products by different microorganisms. IOS formed have many functions. Therefore, inulin can be widely used in food, feed, pharmaceutical, chemical and biofuels industries.     

A new bayesian method for fitting evolutionary models to comparative data with intraspecific variation

Phylogenetic comparative methods that incorporate intraspecific variability are relatively new and, so far, not especially widely used in empirical studies. In the present short article we will describe a new Bayesian method for fitting evolutionary models to comparative data that incorporates intraspecific variability. This method differs from an existing likelihood-based approach in that it requires no a priori inference about species means and variances; rather it takes phenotypic values from individuals and a phylogenetic tree as input, and then samples species means and variances, along with the parameters of the evolutionary model, from their joint posterior probability distribution. One of the most novel and intriguing attributes of this approach is that jointly sampling the species means with the evolutionary model parameters means that the model and tree can influence our estimates of species mean trait values, not just the reverse. In the present implementation, we first apply this method to the most widely used evolutionary model for continuously valued phenotypic trait data (Brownian motion). However, the general approach has broad applicability, which we illustrate by also fitting the λ model, another simple model for quantitative trait evolution on a phylogeny. We test our approach via simulation and by analyzing two empirical datasets obtained from the literature. Finally, we have implemented the methods described herein in a new function for the R statistical computing environment, and this function will be distributed as part of the 'phytools' R library.     

A new semi-determinate tomato hybrid for early production

By using the knowledge of the genetic constitution and the characteristics of the available tomato lines for selection, there were created several dozens of hybrid combinations characterised by semi-determinate habit, with large fruits and early maturing. These hybrids were tested in preliminary and comparative trials with the hybrids Balkan F₁, Balca F₁, Žar F₁, and some others as standard varieties. These tomato hybrids were grown earlier but they are grown even today in the early production, for the market in green houses and in the open field. The hybrid Marco F₁ was the best of all. Its yield was 25 t/ha in the plastic-house (the same as in the standard), up to 32 t/ha in the open field (5 % more than in the standard). This hybrid was a few days later than Balkan, with the fruits weighting 200 grams (fruits were approximately 60 % heavier than in the standard). It possess the genetic V, F and Tm resistance.     

Classification of static behavior of a class of unstructured models of continuous bioprocesses

The stability characteristics of a class of unstructured models of continuous bioreactors are analyzed using elementary concepts of singularity theory and continuation techniques. The class consists of models for which the non-biomass product formation rate is linearly proportional to the utilization rate of limiting substrate. The kinetics expressions of cell growth and product synthesis are allowed to assume general forms of substrate and product. Global analytical conditions are derived that allow the construction of a practical picture in the multidimensional parameter space delineating the different static behavior these models can predict, including unique steady states, coexistence of non-trivial steady states with wash-out conditions, and multistability resulting from hysteresis. These general results are applied to specific examples of bioprocesses and allow the study of the effect of kinetic and operating parameters on the stability characteristics of these models.     

A hybrid ligand method for androgen receptor measurement

Existing techniques for androgen receptor (AR) assay are complicated by cross-reactivity of ligand binding affinities that can lead to incorrect estimation of receptor concentration. Two most frequently used ligands are [3H]dihydrotestosterone [( 3H]DHT) and [3H]methyltrienolone [( 3H]R1881), which in addition to binding to AR also bind to sex hormone binding globulin (SHBG; Kd = 1.5 nM) and progesterone receptors (PgR; Human Kd = 1 nM, rat Kd = 6 nM) respectively. Triamcinolone acetonide (TMA) is commonly used to block binding of [3H]R1881 to PgR, however at high concentrations TMA itself will bind AR (Kd = 7 microM). We have developed a hybrid ligand method for the measurement of AR in the presence of SHBG and PgR. This method used [3H]R1881 as the high specific activity labelled tracer and DHT as the unlabelled competitor of specific AR binding. Using this assay, 20% of human colorectal carcinomas were found to contain AR.     

A hybrid symbolic-numerical method for determining model structure

In this article, we present a method for determining whether a model is at least locally identifiable and in the case of non-identifiable models whether any of the parameters are individually at least locally identifiable. This method combines symbolic and numeric methods to create an algorithm that is extremely accurate compared to other numeric methods and computationally inexpensive. A series of generic computational steps are developed to create a method that is ideal for practitioners to use. The algorithm is compared to symbolic methods for two capture-recapture models and a compartment model.     

A sparse matrix method for renal models

A sparse matrix method for the numerical solution of nonlinear differential equations arising in modeling of the renal concentrating mechanism is given. The method involves a renumbering of the variables and equations such that the resulting Jacobian matrix has a block tridiagonal structure and the blocks above and below the main diagonal have a known set of complementary nonzero columns. The computer storage for the method is O(n). Results of some numerical experiments showing the stability of the method are given.     

ATP Photosynthetic vesicles for light-driven bioprocesses

We prepared ATP photosynthetic vesicles from inside-out membranes of Escherichia coli cells that express delta-rhodopsin (a novel light-driven H⁺ transporter) and TF₀F₁-ATP synthase (a thermo-stable ATP synthase). These vesicles showed light-dependent ATP synthesis. Furthermore, coupling the ATP photosynthetic vesicles with an ATP-hydrolyzing hexokinase enabled light-dependent glucose consumption. The ATP photosynthetic vesicles indicate their potential to applied to light-driven ATP-regenerating bioprocess for various ATP-hydrolyzing bioproductions.     

一种模型模拟结果的统计检验方法

模拟结果的准确性是衡量生态学模型是否成功的关键,但采用统计学方法判别模型模拟结果与观察值相符程度的报道较少。根据两个直线回归方程能否合并为一个方程的统计学检验方法,提出了通过检验观察值与模拟值直线回归方程和1∶1直线方程截距与斜率是否相同,进而在统计显著水平上判断生态学模型模拟值与观察值一致性的统计学检验方法。数据检验表明,此方法可以较好解决判断生态学模型模拟结果准确性的问题。