Recent advances on conversion and co-production of acetone-butanol-ethanol into high value-added bioproducts

Butanol is an important bulk chemical and has been regarded as an advanced biofuel. Large-scale production of butanol has been applied for more than 100 years, but its production through acetone–butanol–ethanol (ABE) fermentation process by solventogenic Clostridium species is still not economically viable due to the low butanol titer and yield caused by the toxicity of butanol and a by-product, such as acetone. Renewed interest in biobutanol as a biofuel has spurred technological advances to strain modification and fermentation process design. Especially, with the development of interdisciplinary processes, the sole product or even the mixture of ABE produced through ABE fermentation process can be further used as platform chemicals for high value added product production through enzymatic or chemical catalysis. This review aims to comprehensively summarize the most recent advances on the conversion of acetone, butanol and ABE mixture into various products, such as isopropanol, butyl-butyrate and higher-molecular mass alkanes. Additionally, co-production of other value added products with ABE was also discussed.     

Recent advances and state-of-the-art strategies in strain and process engineering for biobutanol production by Clostridium acetobutylicum

Butanol as an advanced biofuel has gained great attention due to its environmental benefits and superior properties compared to ethanol. However, the cost of biobutanol production via conventional acetone-butanol-ethanol (ABE) fermentation by Clostridium acetobutylicum is not economically competitive, which has hampered its industrial application. The strain performance and downstream process greatly impact the economics of biobutanol production. Although various engineered strains with carefully orchestrated metabolic and sporulation-specific pathways have been developed, none of them is ideal for industrial biobutanol production. For further strain improvement, it is necessary to develop advanced genome editing tools and a deep understanding of cellular functioning of genes in metabolic and regulatory pathways. Processes with integrated product recovery can increase fermentation productivity by continuously removing inhibitory products while generating butanol (ABE) in a concentrated solution. In this review, we provide an overview of recent advances in C. acetobutylicum strain engineering and process development focusing on in situ product recovery. With deep understanding of systematic cellular bioinformatics, the exploration of state-of-the-art genome editing tools such as CRISPR-Cas for targeted gene knock-out and knock-in would play a vital role in Clostridium cell engineering for biobutanol production. Developing advanced hybrid separation processes for in situ butanol recovery, which will be discussed with a detailed comparison of advantages and disadvantages of various recovery techniques, is also imperative to the economical development of biobutanol.     

Thematic review series: patient-oriented research. Design and analysis of lipoprotein tracer kinetics studies in humans

Lipoprotein tracer kinetics studies have for many years provided new and important knowledge of the metabolism of lipoproteins. Our understanding of kinetics defects in lipoprotein metabolism has resulted from the use of tracer kinetics studies and mathematical modeling. This review discusses all aspects of the performance of kinetics studies, including the development of hypotheses, experimental design, statistical considerations, tracer administration and sampling schedule, and the development of compartmental models for the interpretation of tracer data. In addition to providing insight into new metabolic pathways, such models provide quantitative information on the effect of interventions on lipoprotein metabolism. Compartment models are useful tools to describe experimental data but can also be used to aid in experimental design and hypothesis generation. The SAAM II program provides an easy-to-use interface with which to develop and test compartmental models against experimental models. The development of a model requires that certain checks be performed to ensure that the model describes the experimental data and that the model parameters can be estimated with precision. In addition to methodologic aspects, several compartment models of apoprotein and lipid metabolism are reviewed.     

Anaerobic biotechnological approaches for production of liquid energy carriers from biomass

In recent years, increasing attention has been paid to the use of renewable biomass for energy production. Anaerobic biotechnological approaches for production of liquid energy carriers (ethanol and a mixture of acetone, butanol and ethanol) from biomass can be employed to decrease environmental pollution and reduce dependency on fossil fuels. There are two major biological processes that can convert biomass to liquid energy carriers via anaerobic biological breakdown of organic matter: ethanol fermentation and mixed acetone, butanol, ethanol (ABE) fermentation. The specific product formation is determined by substrates and microbial communities available as well as the operating conditions applied. In this review, we evaluate the recent biotechnological approaches employed in ethanol and ABE fermentation. Practical applicability of different technologies is discussed taking into account the microbiology and biochemistry of the processes.     

Systems Metabolic Engineering Meets Machine Learning: A New Era for Data‐Driven Metabolic Engineering

The recent increase in high‐throughput capacity of ‘omics datasets combined with advances and interest in machine learning (ML) have created great opportunities for systems metabolic engineering. In this regard, data‐driven modeling methods have become increasingly valuable to metabolic strain design. In this review, the nature of ‘omics is discussed and a broad introduction to the ML algorithms combining these datasets into predictive models of metabolism and metabolic rewiring is provided. Next, this review highlights recent work in the literature that utilizes such data‐driven methods to inform various metabolic engineering efforts for different classes of application including product maximization, understanding and profiling phenotypes, de novo metabolic pathway design, and creation of robust system‐scale models for biotechnology. Overall, this review aims to highlight the potential and promise of using ML algorithms with metabolic engineering and systems biology related datasets.     

多约束代谢网络模型的研究进展

基于约束的基因组尺度代谢网络模型(genome-scale metabolic models,GEMs)分析已被广泛应用于代谢表型的预测.而实际细胞中代谢速率除计量学约束外,还受到酶资源可用性和反应热力学可行性等其他因素影响,在GEMs中整合酶资源约束或者热力学约束构建多约束代谢网络模型可以进一步缩小优化解空间,提升细...     

New developments in solid-state fermentation: II. Rational approaches to the design, operation and scale-up of bioreactors

Over the last decade there has been a significant improvement in understanding how to design, operate and scale-up solid-state fermentation bioreactors. The key to these advances has been the application of mathematical modeling techniques to describe the biological and transport phenomena within the system. This review focuses on the advances in understanding that have come from this modeling work, and the insights it has given us into bioreactor design, operation and scale-up. It also highlights two promising bioreactor designs that have emerged over the last decade or so. For processes in which the substrate bed must remain static throughout the fermentation, the most promising design is the Zymotis design of ORSTOM at Montpellier, France, which involves closely spaced internal heat transfer plates within a packed-bed bioreactor. For those processes in which mixing can be tolerated, the stirred bioreactor developed at INRA, in Dijon, France, has been successfully demonstrated at scales of 1–25 t of substrate. Theoretical work suggests that mathematical models will be useful tools in the scale-up process, however, there are no reports that they have been used in the development of any current large-scale process. Rather, the models have been validated against data obtained from laboratory-scale bioreactors. There is an urgent need to test the accuracy and robustness of the models by applying them within real process development.     

发酵过程多尺度解析与调控的研究进展

工业发酵科学致力于实现高产量、高转化率和高生产强度的相对统一。通过从分子、细胞和反应器进行发酵过程多尺度解析与调控,实施全局与动态的优化与控制能够确保发酵过程高效、转化定向、过程稳定和系统有序。本文从发酵微生物代谢途径动力学模型、细胞代谢特性、发酵提取相耦合与反应器设计四个方面,总结和讨论发酵过程多尺度解析与调控的研究进展。整合分析发酵过程不同尺度特征并且针对性地开展多尺度整合调控是实现高效工业微生物发酵的重要策略。     

Lumping kinetics of ABE fermentation wastewater treatment by oleaginous yeast Trichosporon cutaneum

Lumping kinetics models were built for the biological treatment of acetone–butanol–ethanol (ABE) fermentation wastewater by oleaginous yeast Trichosporon cutaneum with different fermentation temperatures. Compared with high temperature (33°C, 306?K) and low temperature (23°C, 296?K), medium temperature (28°C, 301?K) was beneficial for the cell growth and chemical oxygen demand (COD) degradation during the early stage of fermentation but the final yeast biomass and COD removal were influenced little. By lumping method, the materials in the bioconversion network were divided into five lumps (COD, lipid, polysaccharide, other intracellular products, other extracellular products), and the nine rate constants (k₁–k₉) for the models can well explain the bioconversion laws. The Gibbs free energy (G) for this bioconversion was positive, showing that it cannot happen spontaneous, but the existence of yeast can after the chemical equilibrium and make the bioconversion to be possible. Overall, the possibility of using lumping kinetics for elucidating the laws of materials conversion in the biological treatment of ABE fermentation wastewater by T. cutaneum has been initially proved and this method has great potential for further application.     

Leveraging knowledge engineering and machine learning for microbial bio-manufacturing

Genome scale modeling (GSM) predicts the performance of microbial workhorses and helps identify beneficial gene targets. GSM integrated with intracellular flux dynamics, omics, and thermodynamics have shown remarkable progress in both elucidating complex cellular phenomena and computational strain design (CSD). Nonetheless, these models still show high uncertainty due to a poor understanding of innate pathway regulations, metabolic burdens, and other factors (such as stress tolerance and metabolite channeling). Besides, the engineered hosts may have genetic mutations or non-genetic variations in bioreactor conditions and thus CSD rarely foresees fermentation rate and titer. Metabolic models play important role in design-build-test-learn cycles for strain improvement, and machine learning (ML) may provide a viable complementary approach for driving strain design and deciphering cellular processes. In order to develop quality ML models, knowledge engineering leverages and standardizes the wealth of information in literature (e.g., genomic/phenomic data, synthetic biology strategies, and bioprocess variables). Data driven frameworks can offer new constraints for mechanistic models to describe cellular regulations, to design pathways, to search gene targets, and to estimate fermentation titer/rate/yield under specified growth conditions (e.g., mixing, nutrients, and O₂). This review highlights the scope of information collections, database constructions, and machine learning techniques (such as deep learning and transfer learning), which may facilitate “Learn and Design” for strain development.     

Modeling and simulation of biological systems with stochasticity

Mathematical modeling is a powerful approach for understanding the complexity of biological systems. Recently, several successful attempts have been made for simulating complex biological processes like metabolic pathways, gene regulatory networks and cell signaling pathways. The pathway models have not only generated experimentally verifiable hypothesis but have also provided valuable insights into the behavior of complex biological systems. Many recent studies have confirmed the phenotypic variability of organisms to an inherent stochasticity that operates at a basal level of gene expression. Due to this reason, development of novel mathematical representations and simulations algorithms are critical for successful modeling efforts in biological systems. The key is to find a biologically relevant representation for each representation. Although mathematically rigorous and physically consistent, stochastic algorithms are computationally expensive, they have been successfully used to model probabilistic events in the cell. This paper offers an overview of various mathematical and computational approaches for modeling stochastic phenomena in cellular systems.     

Metabolic ensemble modeling for strain engineers

Previous mathematical modeling efforts have made significant contributions to the development of systems biology for predicting biological behavior quantitatively. However, dynamic metabolic model construction remains challenging due to uncertainties in mechanistic structures and parameters. In addition, parameter estimation and model validation often require designated experiments conducted only for purpose of modeling. Such difficulties have hampered the progress of modeling in biology and biotechnology. To circumvent these problems, ensemble approaches have been used to account for uncertainties in model structure and parameters. Specifically, this review focuses on approaches that utilize readily available fermentation data for parameter screening and model validation. Time course data for metabolite measurements, if available, can further calibrate the model. The basis for this approach is explained in non-mathematical terms accessible to experimentalists. Information gained from such an approach has been shown to be useful in designing Escherichia coli strains for metabolic engineering and synthetic biology.     

Modeling microbial community structure and functional diversity across time and space

Microbial communities exhibit exquisitely complex structure. Many aspects of this complexity, from the number of species to the total number of interactions, are currently very difficult to examine directly. However, extraordinary efforts are being made to make these systems accessible to scientific investigation. While recent advances in high-throughput sequencing technologies have improved accessibility to the taxonomic and functional diversity of complex communities, monitoring the dynamics of these systems over time and space - using appropriate experimental design - is still expensive. Fortunately, modeling can be used as a lens to focus low-resolution observations of community dynamics to enable mathematical abstractions of functional and taxonomic dynamics across space and time. Here, we review the approaches for modeling bacterial diversity at both the very large and the very small scales at which microbial systems interact with their environments. We show that modeling can help to connect biogeochemical processes to specific microbial metabolic pathways.     

影响灵芝三萜类物质液态发酵合成的关键因素研究进展

灵芝是我国名贵的食药两用型菌类,具有广泛的药用价值,其三萜类物质为灵芝中最主要的药理活性物质之一。灵芝液态发酵因具有生长周期短、环境条件可控、目标产物质量稳定及易实现规模化制备等特点而成为获得灵芝三萜类物质最有前景的方式。灵芝三萜代谢途径、发酵工艺及参数、溶解氧控制等是影响灵芝三萜类物质液态发酵合成的关键因素。本文总结了灵芝三萜生物合成的代谢途径和相关的酶(基因)、液态发酵方式和发酵参数调节的溶解氧控制这3个层面对灵芝三萜类物质生物合成的影响,并对今后的研究方向进行了展望,为液态培养灵芝三萜类物质调控及高产提供参考,也为下一步研究提供借鉴。     

High-titer n-butanol production by clostridium acetobutylicum JB200 in fed-batch fermentation with intermittent gas stripping

Acetone–butanol–ethanol (ABE) fermentation with a hyper‐butanol producing Clostridium acetobutylicum JB200 was studied for its potential to produce a high titer of butanol that can be readily recovered with gas stripping. In batch fermentation without gas stripping, a final butanol concentration of 19.1 g/L was produced from 86.4 g/L glucose consumed in 78 h, and butanol productivity and yield were 0.24 g/L h and 0.21 g/g, respectively. In contrast, when gas stripping was applied intermittently in fed‐batch fermentation, 172 g/L ABE (113.3 g/L butanol, 49.2 g/L acetone, 9.7 g/L ethanol) were produced from 474.9 g/L glucose in six feeding cycles over 326 h. The overall productivity and yield were 0.53 g/L h and 0.36 g/g for ABE and 0.35 g/L h and 0.24 g/g for butanol, respectively. The higher productivity was attributed to the reduced butanol concentration in the fermentation broth by gas stripping that alleviated butanol inhibition, whereas the increased butanol yield could be attributed to the reduced acids accumulation as most acids produced in acidogenesis were reassimilated by cells for ABE production. The intermittent gas stripping produced a highly concentrated condensate containing 195.9 g/L ABE or 150.5 g/L butanol that far exceeded butanol solubility in water. After liquid–liquid demixing or phase separation, a final product containing ～610 g/L butanol, ～40 g/L acetone, ～10 g/L ethanol, and no acids was obtained. Compared to conventional ABE fermentation, the fed‐batch fermentation with intermittent gas stripping has the potential to reduce at least 90% of energy consumption and water usage in n‐butanol production from glucose. Biotechnol. Bioeng. 2012; 109: 2746–2756. © 2012 Wiley Periodicals, Inc.     

Efficient Acetone–Butanol–Ethanol Production from Corncob with a New Pretreatment Technology—Wet Disk Milling

Acetone–butanol–ethanol (ABE) production from corncob was achieved using an integrated process combining wet disk milling (WDM) pretreatment with enzymatic hydrolysis and fermentation by Clostridium acetobutylicum SE-1. Sugar yields of 71.3 % for glucose and 39.1 % for xylose from pretreated corncob were observed after enzymatic hydrolysis. The relationship between sugar yields and particle size of the pretreated corncob was investigated, suggesting a smaller particle size benefits enzymatic hydrolysis with the WDM pretreatment approach. Analysis of the correlation between parameters representing particle size and efficiency of enzymatic hydrolysis predicted that frequency 90 % is the best parameter representing particle size for the indication of the readiness of the material for enzymatic hydrolysis. ABE production from corncob was carried out with both separate hydrolysis and fermentation (SHF) and simultaneous saccharification and fermentation (SSF) processes using C. acetobutylicum SE-1. Interestingly, when considering the time for fermentation as the time for ABE production, a comparable rate of sugar consumption and ABE production in the SHF process (0.55 g/l·h sugar consumption and 0.20 g/l·h ABE production) could be observed when glucose (0.50 g/l·h sugar consumption and 0.17 g/l·h ABE production) or a mixture of glucose and xylose (0.68 g/l·h sugar consumption and 0.22 g/l·h ABE production) mimicking the corncob hydrolysate was used as the substrate for fermentation. This result suggested that the WDM is a suitable pretreatment method for ABE production from corncob owing to the mild conditions. A higher ABE production rate could be observed with the SSF process (0.15 g/l·h) comparing with SHF process (0.12 g/l·h) when combining the time for saccharification and fermentation and consider it as the time for ABE production. This is possibly a result of low sustained sugar level during fermentation. These investigations lead to the suggestion that this new WDM pretreatment method has the potentials to be exploited for efficient ABE production from corncob.     

Lactate modeling and its application to endurance training

1. 1. In this short review, previous studies regarding the modeling of lactate (La) response to exercise and its application to endurance training have been summarized.

2. 2. Additionally the result of a recent study by the present authors are shown.

3. 3. Several models for La response to step and ramp exercise are already proposed and deductions derived from them are used for practical purposes such as the prediction of race performance in middle-and long-distance runners as well as for construction of their training regimens.

4. 4. Only a limited number of models however have tried to quantify whole body La kinetics to exercise in humans concomitantly with describing physiological mechanisms underlying the observed phenomenon.

5. 5. In a recent study described further in this paper a 2 compartment model was used for the purpose of clarifying the current “La production vs degradation” controversy during La adaptation to training.

6. 6. It was determined from this investigation that the La metabolic clearance rate during recovery is enhanced by the endurance training.

7. 7. This is in accordance with another recent observation of an increased La metabolic clearance rate at high absolute work rates and all relative work rates during exercise.

A guide to metabolic flux analysis in metabolic engineering: Methods,tools and applications

The field of metabolic engineering is primarily concerned with improving the biological production of value-added chemicals, fuels and pharmaceuticals through the design, construction and optimization of metabolic pathways, redirection of intracellular fluxes, and refinement of cellular properties relevant for industrial bioprocess implementation. Metabolic network models and metabolic fluxes are central concepts in metabolic engineering, as was emphasized in the first paper published in this journal, “Metabolic fluxes and metabolic engineering” (Metabolic Engineering, 1: 1–11, 1999). In the past two decades, a wide range of computational, analytical and experimental approaches have been developed to interrogate the capabilities of biological systems through analysis of metabolic network models using techniques such as flux balance analysis (FBA), and quantify metabolic fluxes using constrained-based modeling approaches such as metabolic flux analysis (MFA) and more advanced experimental techniques based on the use of stable-isotope tracers, i.e. ¹³C-metabolic flux analysis (¹³C-MFA). In this review, we describe the basic principles of metabolic flux analysis, discuss current best practices in flux quantification, highlight potential pitfalls and alternative approaches in the application of these tools, and give a broad overview of pragmatic applications of flux analysis in metabolic engineering practice.