Hybrid modeling as a QbD/PAT tool in process development: an industrial <Emphasis Type="Italic">E. coli</Emphasis> case study

Process understanding is emphasized in the process analytical technology initiative and the quality by design paradigm to be essential for manufacturing of biopharmaceutical products with consistent high quality. A typical approach to developing a process understanding is applying a combination of design of experiments with statistical data analysis. Hybrid semi-parametric modeling is investigated as an alternative method to pure statistical data analysis. The hybrid model framework provides flexibility to select model complexity based on available data and knowledge. Here, a parametric dynamic bioreactor model is integrated with a nonparametric artificial neural network that describes biomass and product formation rates as function of varied fed-batch fermentation conditions for high cell density heterologous protein production with E. coli. Our model can accurately describe biomass growth and product formation across variations in induction temperature, pH and feed rates. The model indicates that while product expression rate is a function of early induction phase conditions, it is negatively impacted as productivity increases. This could correspond with physiological changes due to cytoplasmic product accumulation. Due to the dynamic nature of the model, rational process timing decisions can be made and the impact of temporal variations in process parameters on product formation and process performance can be assessed, which is central for process understanding.     

Application of high‐throughput mini‐bioreactor system for systematic scale‐down modeling,process characterization,and control strategy development

High‐throughput systems and processes have typically been targeted for process development and optimization in the bioprocessing industry. For process characterization, bench scale bioreactors have been the system of choice. Due to the need for performing different process conditions for multiple process parameters, the process characterization studies typically span several months and are considered time and resource intensive. In this study, we have shown the application of a high‐throughput mini‐bioreactor system viz. the Advanced Microscale Bioreactor (ambr15^TM), to perform process characterization in less than a month and develop an input control strategy. As a pre‐requisite to process characterization, a scale‐down model was first developed in the ambr system (15 mL) using statistical multivariate analysis techniques that showed comparability with both manufacturing scale (15,000 L) and bench scale (5 L). Volumetric sparge rates were matched between ambr and manufacturing scale, and the ambr process matched the pCO₂ profiles as well as several other process and product quality parameters. The scale‐down model was used to perform the process characterization DoE study and product quality results were generated. Upon comparison with DoE data from the bench scale bioreactors, similar effects of process parameters on process yield and product quality were identified between the two systems. We used the ambr data for setting action limits for the critical controlled parameters (CCPs), which were comparable to those from bench scale bioreactor data. In other words, the current work shows that the ambr15^TM system is capable of replacing the bench scale bioreactor system for routine process development and process characterization. © 2015 American Institute of Chemical Engineers Biotechnol. Prog., 31:1623–1632, 2015     

Integration of microbial kinetics and fluid dynamics toward model‐driven scale‐up of industrial bioprocesses

Scale‐up of bioprocesses is hampered by open questions, mostly related to poor mixing and mass transfer limitations. Concentration gradients of substrate, carbon dioxide, and oxygen in time and space, especially in large‐scale high‐cell density fed‐batch processes, are likely induced as the mixing time of the fermentor is usually longer than the relevant cellular reaction time. Cells in the fermentor are therefore repeatedly exposed to dynamic environments or perturbations. As a consequence, the heterogeneity in industrial practices often decreases either yield, titer, or productivity, or combinations thereof and increases by‐product formation as compared to well‐mixed small‐scale bioreactors, which is summarized as scale‐up effects. Identification of response mechanisms of the microorganism to various external perturbations is of great importance for pinpointing metabolic bottlenecks and targets for metabolic engineering. In this review, pulse response experimentation is proposed as an ideal way of obtaining kinetic information in combination with scale‐down approaches for in‐depth understanding of dynamic response mechanisms. As an emerging tool, computational fluid dynamics is able to draw a holistic picture of the fluid flow and concentration fields in the fermentor and finds its use in the optimization of fermentor design and process strategy. In the future, directed strain improvement and fermentor redesign are expected to largely depend on models, in which both microbial kinetics and fluid dynamics are thoroughly integrated.     

Efficient high‐throughput biological process characterization: Definitive screening design with the Ambr250 bioreactor system

The burgeoning pipeline for new biologic drugs has increased the need for high‐throughput process characterization to efficiently use process development resources. Breakthroughs in highly automated and parallelized upstream process development have led to technologies such as the 250‐mL automated mini bioreactor (ambr250?) system. Furthermore, developments in modern design of experiments (DoE) have promoted the use of definitive screening design (DSD) as an efficient method to combine factor screening and characterization. Here we utilize the 24‐bioreactor ambr250? system with 10‐factor DSD to demonstrate a systematic experimental workflow to efficiently characterize an Escherichia coli (E. coli) fermentation process for recombinant protein production. The generated process model is further validated by laboratory‐scale experiments and shows how the strategy is useful for quality by design (QbD) approaches to control strategies for late‐stage characterization. © 2015 American Institute of Chemical Engineers Biotechnol. Prog., 31:1388–1395, 2015     

Modeling and empirical validation of long‐term carbon sequestration in forests (France, 1850–2015)

The development of appropriate tools to quantify long‐term carbon (C) budgets following forest transitions, that is, shifts from deforestation to afforestation, and to identify their drivers are key issues for forging sustainable land‐based climate‐change mitigation strategies. Here, we develop a new modeling approach, CRAFT (CaRbon Accumulation in ForesTs) based on widely available input data to study the C dynamics in French forests at the regional scale from 1850 to 2015. The model is composed of two interconnected modules which integrate biomass stocks and flows (Module 1) with litter and soil organic C (Module 2) and build upon previously established coupled climate‐vegetation models. Our model allows to develop a comprehensive understanding of forest C dynamics by systematically depicting the integrated impact of environmental changes and land use. Model outputs were compared to empirical data of C stocks in forest biomass and soils, available for recent decades from inventories, and to a long‐term simulation using a bookkeeping model. The CRAFT model reliably simulates the C dynamics during France's forest transition and reproduces C‐fluxes and stocks reported in the forest and soil inventories, in contrast to a widely used bookkeeping model which strictly only depicts C‐fluxes due to wood extraction. Model results show that like in several other industrialized countries, a sharp increase in forest biomass and SOC stocks resulted from forest area expansion and, especially after 1960, from tree growth resulting in vegetation thickening (on average 7.8 Mt C/year over the whole period). The difference between the bookkeeping model, 0.3 Mt C/year in 1850 and 21 Mt C/year in 2015, can be attributed to environmental and land management changes. The CRAFT model opens new grounds for better quantifying long‐term forest C dynamics and investigating the relative effects of land use, land management, and environmental change.     

Bioenergy crop models: descriptions,data requirements,and future challenges

Field studies that address the production of lignocellulosic biomass as a source of renewable energy provide critical data for the development of bioenergy crop models. A literature survey revealed that 14 models have been used for simulating bioenergy crops including herbaceous and woody bioenergy crops, and for crassulacean acid metabolism (CAM) crops. These models simulate field‐scale production of biomass for switchgrass (ALMANAC, EPIC, and Agro‐BGC), miscanthus (MISCANFOR, MISCANMOD, and WIMOVAC), sugarcane (APSIM, AUSCANE, and CANEGRO), and poplar and willow (SECRETS and 3PG). Two models are adaptations of dynamic global vegetation models and simulate biomass yields of miscanthus and sugarcane at regional scales (Agro‐IBIS and LPJmL). Although it lacks the complexity of other bioenergy crop models, the environmental productivity index (EPI) is the only model used to estimate biomass production of CAM (Agave and Opuntia) plants. Except for the EPI model, all models include representations of leaf area dynamics, phenology, radiation interception and utilization, biomass production, and partitioning of biomass to roots and shoots. A few models simulate soil water, nutrient, and carbon cycle dynamics, making them especially useful for assessing the environmental consequences (e.g., erosion and nutrient losses) associated with the large‐scale deployment of bioenergy crops. The rapid increase in use of models for energy crop simulation is encouraging; however, detailed information on the influence of climate, soils, and crop management practices on biomass production is scarce. Thus considerable work remains regarding the parameterization and validation of process‐based models for bioenergy crops; generation and distribution of high‐quality field data for model development and validation; and implementation of an integrated framework for efficient, high‐resolution simulations of biomass production for use in planning sustainable bioenergy systems.     

Global Warming Potential of Carbon Dioxide Emissions from Biomass Stored in the Anthroposphere and Used for Bioenergy at End of Life

There is growing interest in understanding how storage or delayed emission of carbon in products based on bioresources might mitigate climate change, and how such activities could be credited. In this research we extend the recently introduced approach that integrates biogenic carbon dioxide (CO₂) fluxes with the global carbon cycle (using biogenic global warming potential [GWP_bio]) to consider the storage period of harvested biomass in the anthroposphere, with subsequent oxidation. We then examine how this affects the climate impact from a bioenergy resource. This approach is compared to several recent methods designed to address the same problem. Using both a 100‐ and a 500‐year fixed time horizon we calculate the GWP_bio factor for every combination of rotational and anthropogenic storage periods between 0 and 100 years. The resulting GWP_bio factors range from ?0.99 (1‐year rotation and 100‐year storage) to +0.44 (100‐year rotation and 0‐year storage). The approach proposed in this study includes the interface between biomass growth and emissions and the global carbon cycle, whereas other methods do not model this. These results and the characterization factors produced can determine the climate change benefits or impacts associated with the storage of biomass in the anthroposphere, and the subsequent release of biogenic CO₂ with the radiative forcing integrated in a fixed time window.     

Essential dynamics of the cold denaturation: pressure and temperature effects in yeast frataxin

The cold denaturation of globular proteins is a process that can be caused by increasing pressure or decreasing the temperature. Currently, the action mechanism of this process has not been clearly understood, raising an interesting debate on the matter. We have studied the process of cold denaturation using molecular dynamics simulations of the frataxin system Yfh1, which has a dynamic experimental characterization of unfolding at low and high temperatures. The frataxin model here studied allows a comparative analysis using experimental data. Furthermore, we monitored the cold denaturation process of frataxin and also investigated the effect under the high‐pressure regime. For a better understanding of the dynamics and structural properties of the cold denaturation, we also analyzed the MD trajectories using essentials dynamic. The results indicate that changes in the structure of water by the effect of pressure and low temperatures destabilize the hydrophobic interaction modifying the solvation and the system volume leading to protein denaturation. Proteins 2016; 85:125–136. © 2016 Wiley Periodicals, Inc.     

Modelling climate‐change‐induced nonlinear thresholds in cephalopod population dynamics

A significant global challenge lies in our current inability to anticipate, and therefore prepare for, critical ecological thresholds (i.e. tipping points in ecosystems). This deficit stems largely from an inadequate understanding of the many complex interactions between species and the environment at the ecosystem level, and the paucity of mechanistic models relating environment to population dynamics at the species level. In marine ecosystems, abundant, short‐lived and fast‐growing species such as anchovies or squids, consistently function as ‘keystone’ groups whose population dynamics affect entire ecosystems. Increasing exploitation coupled with climate change impacts has the potential to affect these ecological groups and consequently, the entire marine ecosystem. There are currently very few models that predict the impact of climate change on these keystone groups. Here we use a combination of individual‐based bioenergetics and stage‐structured population models to characterize the fundamental capacity of cephalopods to respond to climate change. We demonstrate the potential for, and mechanisms behind, two unfavourable climate‐change‐induced thresholds in future population dynamics. Although one threshold was the direct consequence of a decrease in incubation time caused by ocean warming, the other threshold was linked to survivorship, implying the possibility of management through a modification of fishing mortality. Additional substantive changes in phenology were also predicted, with a possible loss in population resilience. Our results demonstrate the feasibility of predicting complex nonlinear dynamics with a reasonably simplistic mechanistic model, and highlight the necessity of developing such approaches for other species if attempts to moderate the impact of climate change on natural resources are to be effective.     

Models of underlying autotrophic biomass dynamics fit to daily river ecosystem productivity estimates improve understanding of ecosystem disturbance and resilience

Directly observing autotrophic biomass at ecologically relevant frequencies is difficult in many ecosystems, hampering our ability to predict productivity through time. Since disturbances can impart distinct reductions in river productivity through time by modifying underlying standing stocks of biomass, mechanistic models fit to productivity time series can infer underlying biomass dynamics. We incorporated biomass dynamics into a river ecosystem productivity model for six rivers to identify disturbance flow thresholds and understand the resilience of primary producers. The magnitude of flood necessary to disturb biomass and thereby reduce ecosystem productivity was consistently lower than the more commonly used disturbance flow threshold of the flood magnitude necessary to mobilize river bed sediment. The estimated daily maximum percent increase in biomass (a proxy for resilience) ranged from 5% to 42% across rivers. Our latent biomass model improves understanding of disturbance thresholds and recovery patterns of autotrophic biomass within river ecosystems.     

Traces matter: Targeted optimization of monoclonal antibody N-glycosylation based on/by implementing automated high-throughput trace element screening

The use of high-throughput systems in cell culture process optimization offers various opportunities in biopharmaceutical process development. Here we describe the potential for acceleration and enhancement of product quality optimization and de novo bioprocess design regarding monoclonal antibody N-glycosylation by using an iterative statistical Design of Experiments (DoE) strategy based on our automated microtiter plate-based system for suspension cell culture. In our example, the combination of an initial screening of trace metal building blocks with a comprehensive DoE-based screening of 13 different trace elemental ions at three concentration levels in one run revealed most effective levers for N-glycan processing and biomass formation. Obtained results served to evaluate optimal concentration ranges and the right supplementation timing of relevant trace elements at shake flask and 2 L bioreactor scale. This setup identified manganese, copper, zinc, and iron as major factors. Manganese and copper acted as inverse key players in N-glycosylation, showing a positive effect of manganese and a negative effect of copper on glycan maturation in a zinc-dependent manner. Zinc and iron similarly improved cell growth and biomass formation. These findings allowed determining optimal concentration ranges for all four trace elements to establish control on desired product quality attributes regarding premature afucosylated and mature galactosylated glycan species. Our results demonstrates the power of combining robotics with DoE screening to enhance product quality optimization and to improve process understanding, thus, enabling targeted product quality control.     

A reinforcement learning-based hybrid modeling framework for bioprocess kinetics identification

Constructing predictive models to simulate complex bioprocess dynamics, particularly time-varying (i.e., parameters varying over time) and history-dependent (i.e., current kinetics dependent on historical culture conditions) behavior, has been a longstanding research challenge. Current advances in hybrid modeling offer a solution to this by integrating kinetic models with data-driven techniques. This article proposes a novel two-step framework: first (i) speculate and combine several possible kinetic model structures sourced from process and phenomenological knowledge, then (ii) identify the most likely kinetic model structure and its parameter values using model-free Reinforcement Learning (RL). Specifically, Step 1 collates feasible history-dependent model structures, then Step 2 uses RL to simultaneously identify the correct model structure and the time-varying parameter trajectories. To demonstrate the performance of this framework, a range of in-silico case studies were carried out. The results show that the proposed framework can efficiently construct high-fidelity models to quantify both time-varying and history-dependent kinetic behaviors while minimizing the risks of over-parametrization and over-fitting. Finally, the primary advantages of the proposed framework and its limitation were thoroughly discussed in comparison to other existing hybrid modeling and model structure identification techniques, highlighting the potential of this framework for general bioprocess modeling.     

The fate of Amazonian ecosystems over the coming century arising from changes in climate,atmospheric CO2, and land use

There is considerable interest in understanding the fate of the Amazon over the coming century in the face of climate change, rising atmospheric CO₂ levels, ongoing land transformation, and changing fire regimes within the region. In this analysis, we explore the fate of Amazonian ecosystems under the combined impact of these four environmental forcings using three terrestrial biosphere models (ED2, IBIS, and JULES) forced by three bias‐corrected IPCC AR4 climate projections (PCM1, CCSM3, and HadCM3) under two land‐use change scenarios. We assess the relative roles of climate change, CO₂ fertilization, land‐use change, and fire in driving the projected changes in Amazonian biomass and forest extent. Our results indicate that the impacts of climate change are primarily determined by the direction and severity of projected changes in regional precipitation: under the driest climate projection, climate change alone is predicted to reduce Amazonian forest cover by an average of 14%. However, the models predict that CO₂ fertilization will enhance vegetation productivity and alleviate climate‐induced increases in plant water stress, and, as a result, sustain high biomass forests, even under the driest climate scenario. Land‐use change and climate‐driven changes in fire frequency are predicted to cause additional aboveground biomass loss and reductions in forest extent. The relative impact of land use and fire dynamics compared to climate and CO₂ impacts varies considerably, depending on both the climate and land‐use scenario, and on the terrestrial biosphere model used, highlighting the importance of improved quantitative understanding of all four factors – climate change, CO₂ fertilization effects, fire, and land use – to the fate of the Amazon over the coming century.     

Vegetation succession among and within structural layers following wildfire in managed forests

Question: How does vegetation develop during the initial period following severe wildfire in managed forests? Location: Southwestern Oregon, USA. Methods: In severely burned plantations, dynamics of (1) shrub, herbaceous, and cryptogam richness; (2) cover; (3) topographic, overstory, and site influences were characterized on two contrasting aspects 2 to 4 years following fire. Analysis of variance was used to examine change in structural layer richness and cover over time. Non‐metric multidimensional scaling, multi‐response permutation procedure, and indicator species analysis were used to evaluate changes in community composition over time. Results: Vegetation established rapidly following wildfire in burned plantations, following an initial floristics model of succession among structural layers. Succession within structural layers followed a combination of initial and relay floristic models. Succession occurred simultaneously within and among structural layers following wildfire, but at different rates and with different drivers. Stochastic (fire severity and site history) and deterministic (species life history traits, topography, and pre‐disturbance plant community) factors determined starting points of succession. Multiple successional trajectories were evident in early succession. Conclusions: Mixed conifer forests are resilient to interacting effects of natural and human‐caused disturbances. Predicting the development of vegetation communities following disturbances requires an understanding of the various successional components, such as succession among and within structural layers, and the fire regime. Succession among and within structural layers can follow different successional models and trajectories, occurs at different rates, and is affected by multiple interacting factors.     

Susceptibility and tolerance in hybrid and pure‐line rice varieties to herbivore attack: biomass partitioning and resource‐based compensation in response to damage

Hybrid rice has been noted for its susceptibility to insects and diseases compared to pure‐line (conventional) rice varieties. We investigated herbivory by Nilaparvata lugens, Sogatella furcifera and Scirpophaga incertulas on replicated three‐line hybrid sets (parental and hybrid lines) in field and greenhouse experiments. In a field experiment, caterpillar densities and stemborer damage was similar among hybrid and parental lines. In field and greenhouse experiments, the cytoplasmic male sterile (CMS)‐lines and maintainer lines had higher densities of planthoppers (including N. lugens and S. furcifera) than restorer or hybrid lines likely because of their wild abortive CMS‐lineage. High nitrogen levels increased plant mortality due to N. lugens, but often reduced mortality from S. furcifera and S. incertulas: this was similar between hybrid and pure‐line varieties. The hybrids were generally more tolerant of herbivory (lower biomass reductions per unit weight of insect) than the inbred parental lines. The addition of nitrogen to both the hybrid and pure‐line varieties had contrasting effects on tolerance depending on the nature of the attacking insect: fertiliser increased tolerance to S. furcifera (lower losses of yield and shoot biomass per mg insect) and S. incertulas (lower yield, shoot and root biomass loss) but fertiliser reduced tolerance to N. lugens (higher loss of root biomass and no effects on yield and shoot biomass loss). Our results indicate that hybrid rice is not physiologically more susceptible to herbivores than are pure‐line varieties even under high nitrogen conditions, but does have higher tolerance to insect damage.     

Forecasting species ranges by statistical estimation of ecological niches and spatial population dynamics

Aim The study and prediction of species–environment relationships is currently mainly based on species distribution models. These purely correlative models neglect spatial population dynamics and assume that species distributions are in equilibrium with their environment. This causes biased estimates of species niches and handicaps forecasts of range dynamics under environmental change. Here we aim to develop an approach that statistically estimates process‐based models of range dynamics from data on species distributions and permits a more comprehensive quantification of forecast uncertainties. Innovation We present an approach for the statistical estimation of process‐based dynamic range models (DRMs) that integrate Hutchinson's niche concept with spatial population dynamics. In a hierarchical Bayesian framework the environmental response of demographic rates, local population dynamics and dispersal are estimated conditional upon each other while accounting for various sources of uncertainty. The method thus: (1) jointly infers species niches and spatiotemporal population dynamics from occurrence and abundance data, and (2) provides fully probabilistic forecasts of future range dynamics under environmental change. In a simulation study, we investigate the performance of DRMs for a variety of scenarios that differ in both ecological dynamics and the data used for model estimation. Main conclusions Our results demonstrate the importance of considering dynamic aspects in the collection and analysis of biodiversity data. In combination with informative data, the presented framework has the potential to markedly improve the quantification of ecological niches, the process‐based understanding of range dynamics and the forecasting of species responses to environmental change. It thereby strengthens links between biogeography, population biology and theoretical and applied ecology.     

Quantifying variation in forest disturbance,and its effects on aboveground biomass dynamics,across the eastern United States

The role of tree mortality in the global carbon balance is complicated by strong spatial and temporal heterogeneity that arises from the stochastic nature of carbon loss through disturbance. Characterizing spatio‐temporal variation in mortality (including disturbance) and its effects on forest and carbon dynamics is thus essential to understanding the current global forest carbon sink, and to predicting how it will change in future. We analyzed forest inventory data from the eastern United States to estimate plot‐level variation in mortality (relative to a long‐term background rate for individual trees) for nine distinct forest regions. Disturbances that produced at least a fourfold increase in tree mortality over an approximately 5 year interval were observed in 1–5% of plots in each forest region. The frequency of disturbance was lowest in the northeast, and increased southwards along the Atlantic and Gulf coasts as fire and hurricane disturbances became progressively more common. Across the central and northern parts of the region, natural disturbances appeared to reflect a diffuse combination of wind, insects, disease, and ice storms. By linking estimated covariation in tree growth and mortality over time with a data‐constrained forest dynamics model, we simulated the implications of stochastic variation in mortality for long‐term aboveground biomass changes across the eastern United States. A geographic gradient in disturbance frequency induced notable differences in biomass dynamics between the least‐ and most‐disturbed regions, with variation in mortality causing the latter to undergo considerably stronger fluctuations in aboveground stand biomass over time. Moreover, regional simulations showed that a given long‐term increase in mean mortality rates would support greater aboveground biomass when expressed through disturbance effects compared with background mortality, particularly for early‐successional species. The effects of increased tree mortality on carbon stocks and forest composition may thus depend partly on whether future mortality increases are chronic or episodic in nature.