Optimization and cost estimation of novel wheat biorefining for continuous production of fermentation feedstock

A wheat-based continuous process for the production of a nutrient-complete feedstock for bioethanol production by yeast fermentation has been cost-optimized. This process could substitute for the current wheat dry milling process employed in industry for bioethanol production. Each major wheat component (bran, gluten, starch) is extracted and processed for different end-uses. The separate stages, liquefaction and saccharification, used currently in industry for starch hydrolysis have been integrated into a simplified continuous process by exploiting the complex enzymatic consortium produced by on-site fungal bioconversions. A process producing 120 m3 h-1 nutrient-complete feedstock for bioethanol production containing 250 g L-1 glucose and 0.85 g L-1 free amino nitrogen would result in a production cost of $0.126/kg glucose.     

A non-parametric mixture model for genome-enabled prediction of genetic value for a quantitative trait

A Bayesian nonparametric form of regression based on Dirichlet process priors is adapted to the analysis of quantitative traits possibly affected by cryptic forms of gene action, and to the context of SNP-assisted genomic selection, where the main objective is to predict a genomic signal on phenotype. The procedure clusters unknown genotypes into groups with distinct genetic values, but in a setting in which the number of clusters is unknown a priori, so that standard methods for finite mixture analysis do not work. The central assumption is that genetic effects follow an unknown distribution with some “baseline” family, which is a normal process in the cases considered here. A Bayesian analysis based on the Gibbs sampler produces estimates of the number of clusters, posterior means of genetic effects, a measure of credibility in the baseline distribution, as well as estimates of parameters of the latter. The procedure is illustrated with a simulation representing two populations. In the first one, there are 3 unknown QTL, with additive, dominance and epistatic effects; in the second, there are 10 QTL with additive, dominance and additive × additive epistatic effects. In the two populations, baseline parameters are inferred correctly. The Dirichlet process model infers the number of unique genetic values correctly in the first population, but it produces an understatement in the second one; here, the true number of clusters is over 900, and the model gives a posterior mean estimate of about 140, probably because more replication of genotypes is needed for correct inference. The impact on inferences of the prior distribution of a key parameter (M), and of the extent of replication, was examined via an analysis of mean body weight in 192 paternal half-sib families of broiler chickens, where each sire was genotyped for nearly 7,000 SNPs. In this small sample, it was found that inference about the number of clusters was affected by the prior distribution of M. For a set of combinations of parameters of a given prior distribution, the effects of the prior dissipated when the number of replicate samples per genotype was increased. Thus, the Dirichlet process model seems to be useful for gauging the number of QTLs affecting the trait: if the number of clusters inferred is small, probably just a few QTLs code for the trait. If the number of clusters inferred is large, this may imply that standard parametric models based on the baseline distribution may suffice. However, priors may be influential, especially if sample size is not large and if only a few genotypic configurations have replicate phenotypes in the sample.     

Process simulation for recombinant protein production: cost estimation and sensitivity analysis for heparinase I expressed in Escherichia coli

Heparinase I from flavobacterium heparinum has several potential clinical applications; the resulting high demands on protein purity and quantity can be met by recombinant expression in Escherichia coli. Based on laboratory scale experiments with insoluble heparinase I expression followed by renaturation, a process for production of 3 kg/year of heparinase I was designed. We present a comparative analysis of the production costs of soluble and insoluble heparinase I expression, as well as a generalized approach to sensitivity analysis, based on perturbation around a base case design scenario. This may assist focusing further development on process steps for which improvements both are feasible and result in significant cost saving. (c) 1997 John Wiley & Sons, Inc. Biotechnol Bioeng 53: 575-582, 1997.     

A compartmentalised model for the estimation of the cost of coccidiosis to the world's chicken production industry

A compartmentalised model is presented for the estimation of the monetary losses suffered by the world's poultry industry resulting from coccidiosis of chickens and costs of its control. The model is designed so that the major elements of loss may be separately quantified for any chicken-producing entity, e.g., a farm, a poultry company, a country, etc. Examples are presented and the sources, reliability and geographical relevance of the data used for each parameter are provided. Loss elements for specific geographical areas should be recalculated at appropriate intervals to take into account local and international fluctuations in costs of chicks feed, labour, financial inflation and world currency exchange rates. Equations are given for relationships among numbers of chickens, liveweights, weights of carcasses, feed consumptions, feed conversion ratio (FCR), prices of feeds, prices of anticoccidial therapeutic and prophylactic drugs, values of chickens, chicken rearing costs; and effects of coccidiosis on mortality, weight gain and FCR. Using these equations, it is theoretically possible for an international team of representatives, each using reliable local data, to calculate simultaneously each relevant loss element for their respective countries. Addition of these elements could give, for the first time, an accurate global estimate of the losses due to chicken coccidiosis. The total cost of coccidiosis in chickens in the United Kingdom in 1995 is estimated to have been at least GB pound silver 38 588 795, of which 98.1% involved broilers (80.6% due to effects on mortality, weight gain and feed conversion, and 17.5% due to the cost of chemoprophylaxis and therapy). The costs of poor performance due to coccidiosis and its chemical control totalled 4.54% of the gross revenue from UK sales of live broilers. This model includes a new method for comparing the profitabilities of different treatments in commercial trials. providing actual costs rather than the arbitrary numerical scores of other methods. Although originally designed for the study of coccidiosis, the model is equally applicable to any disease. It should be of value to agricultural economists, the animal feed and poultry industries, animal health companies, and to research scientists (particularly for preparing grant applications).     

Species selection in a reactor-settler system

The competition between flocculating and nonflocculating microorganisms was investigated in a continuous reactor-settler system (e.g. activated sludge). Co existence states were found to be possible, over a certain domain of operating conditions, even with simple monotonic kinetics and simple competition. Multiple solutions exist when coexistence states are unstable. Coexistence solutions are stable when the flocculating bacteria grow faster at feed conditions as in the activated sludge problem. The analysis applies to one or several mixed or plug flow reactors. Other effects, such as enrichment of the recycle stream by the flocculating microorganism or substrate adsorption and storage, may change the structure of solution.     

A mannose selection system for production of fertile transgenic maize plants from protoplasts

 Maize (Zea mays L.) callus cultures cannot use mannose as a sole carbohydrate source, but can utilize fructose for that purpose. Phosphomannose isomerase (PMI) can convert mannose to fructose. Transgenic maize plants were obtained by selecting polyethylene glycol (PEG)-mediated transformed protoplasts on mannose (20 g/l) containing medium. Transgenic calluses and plants carrying the PMI structural gene, manA, were able to convert mannose to fructose. The PEG-mediated protoplast transformation frequency was 0.06%. Stable transformation was confirmed by PCR, PMI activity, germination tests, and by histochemical staining with 5-bromo-4-chloro-3-indolyl-β-D-glucuronide (X-Gluc). Stable integration of the transgenes into the maize genome was demonstrated in T₁ and T₂ plants. Results indicate that the mannose selection system can be used for maize PEG-mediated protoplast transformation. Received: 12 July 1999 / Revision received: 11 October 1999 / Accepted: 11 October 1999     

Performance-based selection of likelihood models for phylogeny estimation

Phylogenetic estimation has largely come to rely on explicitly model-based methods. This approach requires that a model be chosen and that that choice be justified. To date, justification has largely been accomplished through use of likelihood-ratio tests (LRTs) to assess the relative fit of a nested series of reversible models. While this approach certainly represents an important advance over arbitrary model selection, the best fit of a series of models may not always provide the most reliable phylogenetic estimates for finite real data sets, where all available models are surely incorrect. Here, we develop a novel approach to model selection, which is based on the Bayesian information criterion, but incorporates relative branch-length error as a performance measure in a decision theory (DT) framework. This DT method includes a penalty for overfitting, is applicable prior to running extensive analyses, and simultaneously compares all models being considered and thus does not rely on a series of pairwise comparisons of models to traverse model space. We evaluate this method by examining four real data sets and by using those data sets to define simulation conditions. In the real data sets, the DT method selects the same or simpler models than conventional LRTs. In order to lend generality to the simulations, codon-based models (with parameters estimated from the real data sets) were used to generate simulated data sets, which are therefore more complex than any of the models we evaluate. On average, the DT method selects models that are simpler than those chosen by conventional LRTs. Nevertheless, these simpler models provide estimates of branch lengths that are more accurate both in terms of relative error and absolute error than those derived using the more complex (yet still wrong) models chosen by conventional LRTs. This method is available in a program called DT-ModSel.     

Bias in error estimation when using cross-validation for model selection

Background  

Cross-validation (CV) is an effective method for estimating the prediction error of a classifier. Some recent articles have proposed methods for optimizing classifiers by choosing classifier parameter values that minimize the CV error estimate. We have evaluated the validity of using the CV error estimate of the optimized classifier as an estimate of the true error expected on independent data.     

Yeast selection for fuel ethanol production in Brazil

Brazil is one of the largest ethanol biofuel producers and exporters in the world and its production has increased steadily during the last three decades. The increasing efficiency of Brazilian ethanol plants has been evident due to the many technological contributions. As far as yeast is concerned, few publications are available regarding the industrial fermentation processes in Brazil. The present paper reports on a yeast selection program performed during the last 12 years aimed at selecting Saccharomyces cerevisiae strains suitable for fermentation of sugar cane substrates (cane juice and molasses) with cell recycle, as it is conducted in Brazilian bioethanol plants. As a result, some evidence is presented showing the positive impact of selected yeast strains in increasing ethanol yield and reducing production costs, due to their higher fermentation performance (high ethanol yield, reduced glycerol and foam formation, maintenance of high viability during recycling and very high implantation capability into industrial fermenters). Results also suggest that the great yeast biodiversity found in distillery environments could be an important source of strains. This is because during yeast cell recycling, selective pressure (an adaptive evolution) is imposed on cells, leading to strains with higher tolerance to the stressful conditions of the industrial fermentation.     

AGMIAL: implementing an annotation strategy for prokaryote genomes as a distributed system

We have implemented a genome annotation system for prokaryotes called AGMIAL. Our approach embodies a number of key principles. First, expert manual annotators are seen as a critical component of the overall system; user interfaces were cyclically refined to satisfy their needs. Second, the overall process should be orchestrated in terms of a global annotation strategy; this facilitates coordination between a team of annotators and automatic data analysis. Third, the annotation strategy should allow progressive and incremental annotation from a time when only a few draft contigs are available, to when a final finished assembly is produced. The overall architecture employed is modular and extensible, being based on the W3 standard Web services framework. Specialized modules interact with two independent core modules that are used to annotate, respectively, genomic and protein sequences. AGMIAL is currently being used by several INRA laboratories to analyze genomes of bacteria relevant to the food-processing industry, and is distributed under an open source license.     

Growing duckweed in swine wastewater for nutrient recovery and biomass production

Spirodela oligorrhiza, a promising duckweed identified in previous studies, was examined under different cropping conditions for nutrient recovery from swine wastewater and biomass production. To prevent algae bloom during the start-up of a duckweed system, inoculating 60% of the water surface with duckweed fronds was required. In the growing season, the duckweed system was capable of removing 83.7% and 89.4% of total nitrogen (TN) and total phosphorus (TP) respectively from 6% swine lagoon water in eight weeks at a harvest frequency of twice a week. The total biomass harvested was 5.30 times that of the starting amount. In winter, nutrients could still be substantially removed in spite of the limited duckweed growth, which was probably attributed to the improved protein accumulation of duckweed plants and the nutrient uptake by the attached biofilm (algae and bacteria) on duckweed and walls of the system.     

An analytical estimation of the energy cost for legged locomotion

Legged locomotion requires the determination of a number of parameters such as stride period, stride length, order of leg movements, leg trajectory, etc. How are these parameters determined? It has been reported that the locomotor patterns of many legged animals exhibit common characteristics, which suggests that there exists a basic strategy for legged locomotion. In this study we derive an equation to estimate the cost of transport for legged locomotion and examine a criterion of the minimization of the transport cost as a candidate of the strategy. The obtained optimal locomotor pattern that minimizes the cost suitably represents many characteristics of the pattern observed in legged animals. This suggests that the locomotor pattern of legged animals is well optimized with regard to the energetic cost. The result also suggests that the existence of specific gait patterns and the phase transition between them could be the result due to optimization; they are induced by the change in the distribution of ground reaction forces for each leg during locomotion.     

Frequency-dependent selection in a wild plant-pathogen system

This study investigated the occurrence of the rust fungus Puccinia chondrillina in clonal populations of its host plant, Chondrilla juncea. In 16 populations spread across eastern Turkey, 48 different multilocus isozyme phenotypes were identified in the host. Of these clones, 88% were restricted to single localities, while the remaining 12% were found in 2–11 populations. For 13 of the 16 plant populations the commonest host clone was always infected. Indeed, at ten sites this clone was the only one found to carry disease. In the remaining three populations the rusted plants were all of the second commonest isozyme type. The possibility of such a tight association of rust incidence with host clone frequency simultaneously across a wide geographic area is very low (P0.023), supporting the contention that the pathogen P. chondrillina may be imposing negative frequency-dependent selection on these C. juncea populations.     

Improving marginal likelihood estimation for Bayesian phylogenetic model selection

The marginal likelihood is commonly used for comparing different evolutionary models in Bayesian phylogenetics and is the central quantity used in computing Bayes Factors for comparing model fit. A popular method for estimating marginal likelihoods, the harmonic mean (HM) method, can be easily computed from the output of a Markov chain Monte Carlo analysis but often greatly overestimates the marginal likelihood. The thermodynamic integration (TI) method is much more accurate than the HM method but requires more computation. In this paper, we introduce a new method, steppingstone sampling (SS), which uses importance sampling to estimate each ratio in a series (the "stepping stones") bridging the posterior and prior distributions. We compare the performance of the SS approach to the TI and HM methods in simulation and using real data. We conclude that the greatly increased accuracy of the SS and TI methods argues for their use instead of the HM method, despite the extra computation needed.     

Optimal experiment selection for parameter estimation in biological differential equation models

ABSTRACT: BACKGROUND: Parameter estimation in biological models is a common yet challenging problem. In this work we explore the problem for gene regulatory networks modeled by differential equations with unknown parameters, such as decay rates, reaction rates, Michaelis-Menten constants, and Hill coefficients. We explore the question to what extent parameters can be efficiently estimated by appropriate experimental selection. RESULTS: A minimization formulation is used to find the parameter values that best fit the experiment data. When the data is insufficient, the minimization problem often has many local minima that fit the data reasonably well. We show that selecting a new experiment based on the local Fisher Information of one local minimum generates additional data that allows one to successfully discriminate among the many local minima. The parameters can be estimated to high accuracy by iteratively performing minimization and experiment selection. We show that the experiment choices are roughly independent of which local minima is used to calculate the local Fisher Information. CONCLUSIONS: We show that by an appropriate choice of experiments, one can, in principle, efficiently and accurately estimate all the parameters of gene regulatory network. In addition, we demonstrate that appropriate experiment selection can also allow one to restrict model predictions without constraining the parameters using many fewer experiments. We suggest that predicting model behaviors and inferring parameters represent two different approaches to model calibration with different requirements on data and experimental cost.     

Optimal sampling time selection for parameter estimation in dynamic pathway modeling

Systems Biology is an emerging research area, which considers mathematical representations of inter- and intra-cellular dynamics. Among the many research problems that have been addressed, dynamic modeling of signal transduction pathways has received increasing attention. The usual approach to represent intra-cellular dynamics are nonlinear, usually ordinary, differential equations. The purpose of the models is to test and generate hypothesis of specific pathways and it is therefore required to estimate model parameters from experimental data. The experiments to generate data are complex and expensive, as a consequence of which the time series available are usually rather short, with few if any replicates. Almost certainly, not all variables one would like to include in a model can be measured. Parameter estimation is therefore an important research problem in Systems Biology and the focus of this paper. In particular, we are interested in optimizing the sampling time selection in order to minimize the variance of the parameter estimation error. With few sampling time points feasible, their selection is of practical importance in experimental design. Finally, the theoretical results are supported with an application.