Mathematical models of cell factories: moving towards the core of industrial biotechnology

Optimization and Scale-Up of Fermentation Processes Driven by Models

In the era of sustainable development, the use of cell factories to produce various compounds by fermentation has attracted extensive attention; however, industrial fermentation requires not only efficient production strains, but also suitable extracellular conditions and medium components, as well as scaling-up. In this regard, the use of biological models has received much attention, and this review will provide guidance for the rapid selection of biological models. This paper first introduces two mechanistic modeling methods, kinetic modeling and constraint-based modeling (CBM), and generalizes their applications in practice. Next, we review data-driven modeling based on machine learning (ML), and highlight the application scope of different learning algorithms. The combined use of ML and CBM for constructing hybrid models is further discussed. At the end, we also discuss the recent strategies for predicting bioreactor scale-up and culture behavior through a combination of biological models and computational fluid dynamics (CFD) models.

Insights on the Advancements of In Silico Metabolic Studies of Succinic Acid Producing Microorganisms: A Review with Emphasis on Actinobacillus succinogenes

Succinic acid (SA) is one of the top candidate value-added chemicals that can be produced from biomass via microbial fermentation. A considerable number of cell factories have been proposed in the past two decades as native as well as non-native SA producers. Actinobacillus succinogenes is among the best and earliest known natural SA producers. However, its industrial application has not yet been realized due to various underlying challenges. Previous studies revealed that the optimization of environmental conditions alone could not entirely resolve these critical problems. On the other hand, microbial in silico metabolic modeling approaches have lately been the center of attention and have been applied for the efficient production of valuable commodities including SA. Then again, literature survey results indicated the absence of up-to-date reviews assessing this issue, specifically concerning SA production. Hence, this review was designed to discuss accomplishments and future perspectives of in silico studies on the metabolic capabilities of SA producers. Herein, research progress on SA and A. succinogenes, pathways involved in SA production, metabolic models of SA-producing microorganisms, and status, limitations and prospects on in silico studies of A. succinogenes were elaborated. All in all, this review is believed to provide insights to understand the current scenario and to develop efficient mathematical models for designing robust SA-producing microbial strains.

Biovalorization of raw agro-industrial waste through a bioprocess development platform for boosting alkaline phosphatase production by Lysinibacillus sp. strain APSO

This study highlighted the exploitation of mathematical models for optimizing the growth conditions that give the highest phosphatase productivity from a newfound Lysinibacillus sp. strain APSO isolated from a slime sample. Mathematical models facilitate data interpretation and provide a strategy to solve fermentation problems. Alkaline phosphatase (ALP) throughput was enhanced by 16.5-fold compared to basal medium based on a sequential optimization strategy that depended on two-level Plackett–Burman design and central composite design. The additional improvement for volumetric productivity and specific production yield was followed in a 7 L bench-top bioreactor to evaluate microbial growth kinetics under controlled and uncontrolled pH conditions. The pH-controlled batch cultivation condition neither supported cell growth nor enhanced ALP productivity. In contrast, the uncontrolled pH batch cultivation condition provided the highest ALP output (7119.4 U L⁻¹) and specific growth rate (µ = 0.188 h⁻¹) at 15 h from incubation time, which was augmented > 20.75-fold compared to the basal medium. To the authors’ knowledge, this study is the second report that deals with how to reduce the production cost of the ALP production process via utilization of agro-industrial waste, such as molasses and food waste (eggshell), as a nutrimental source for the improvement of the newfound Lysinibacillus sp. strain APSO ALP throughput.

An Improved Scatter Search Algorithm for Parameter Estimation in Large-Scale Kinetic Models of Biochemical Systems

Background:

Mathematical models play a central role in facilitating researchers to better understand and comprehensively analyze various processes in biochemical systems. Their usage is beneficial in metabolic engineering as they help predict and improve desired products. However, one of the primary challenges in model building is parameter estimation. It is the process to find nearoptimal values of kinetic parameters which may culminate in the best fit of model prediction to experimental data.

Methods:

This paper proposes an improved scatter search algorithm to address the challenging parameter estimation problem. The improved algorithm is based on hybridization of quasi opposition-based learning in enhanced scatter search (QOBLESS) method. The algorithm is tested using a large-scale metabolic model of Chinese Hamster Ovary (CHO) cells.

Results:

The experimental result shows that the proposed algorithm performs better than other algorithms in terms of convergence speed and the minimum value of the objective function (loglikelihood). The estimated parameters from the experiment produce a better model by means of obtaining a reasonable good fit of model prediction to the experimental data.

Conclusion:

The kinetic parameters’ value obtained from our work was able to result in a reasonable best fit of model prediction to the experimental data, which contributes to a better understanding and produced more accurate model. Based on the results, the QOBLESS method can be used as an efficient parameter estimation method in large-scale kinetic model building.

Constraint-based modeling in microbial food biotechnology

Genome-scale metabolic network reconstruction offers a means to leverage the value of the exponentially growing genomics data and integrate it with other biological knowledge in a structured format. Constraint-based modeling (CBM) enables both the qualitative and quantitative analyses of the reconstructed networks. The rapid advancements in these areas can benefit both the industrial production of microbial food cultures and their application in food processing. CBM provides several avenues for improving our mechanistic understanding of physiology and genotype–phenotype relationships. This is essential for the rational improvement of industrial strains, which can further be facilitated through various model-guided strain design approaches. CBM of microbial communities offers a valuable tool for the rational design of defined food cultures, where it can catalyze hypothesis generation and provide unintuitive rationales for the development of enhanced community phenotypes and, consequently, novel or improved food products. In the industrial-scale production of microorganisms for food cultures, CBM may enable a knowledge-driven bioprocess optimization by rationally identifying strategies for growth and stability improvement. Through these applications, we believe that CBM can become a powerful tool for guiding the areas of strain development, culture development and process optimization in the production of food cultures. Nevertheless, in order to make the correct choice of the modeling framework for a particular application and to interpret model predictions in a biologically meaningful manner, one should be aware of the current limitations of CBM.

Controlled gas exchange in whole lung bioreactors

In cellular, tissue-level or whole organ bioreactors, the level of dissolved oxygen is one of the most important factors requiring control. Hypoxic environments may lead to cellular apoptosis, while hyperoxic environments may lead to cellular damage or dedifferentiation, both resulting in loss of overall tissue function. This manuscript describes the creation, characterization and validation of a bioreactor system that can control oxygen delivery based on real-time metabolic demand of cultured whole lung tissue. A mathematical model describing and predicting gas exchange within the tunable bioreactor system is developed. In addition, the inherent gas exchange properties of the bioreactor and the inherent oxygen consumption rates of native rat lungs are determined, thereby providing a quantitative relationship between system parameters and levels of dissolved oxygen. Finally, the mathematical model is validated during whole lung culture under a range of system parameters. The system presented here provides a quantitative relationship between the concentration of dissolved oxygen, tissue oxygen consumption rates, and controllable system parameters that introduce gasses into the bioreactor. This relationship not only enables the maintenance of constant levels of dissolved oxygen throughout a culture period during which cells are replicating, but also provides noninvasive and real-time estimation of the metabolic and proliferative states of native or engineered lung tissue simply through dissolved oxygen measurements. Copyright © 2017 John Wiley & Sons, Ltd.

Fine-tuning the P. pastoris iMT1026 genome-scale metabolic model for improved prediction of growth on methanol or glycerol as sole carbon sources

The methylotrophic yeast Pichia pastoris (Komagataella spp.) is widely used as cell factory for recombinant protein production. In the past recent years, important breakthroughs in the systems-level quantitative analysis of its physiology have been achieved. This wealth of information has allowed the development of genome-scale metabolic models, which make new approaches possible for host cell and bioprocess engineering. Nevertheless, the predictive accuracy of the previous consensus model required to be upgraded and validated with new experimental data sets for P. pastoris growing on glycerol or methanol as sole carbon sources, two of the most relevant substrates for this cell factory. In this study, we have characterized P. pastoris growing in chemostat cultures using glycerol or methanol as sole carbon sources over a wide range of growth rates, thereby providing physiological data on the effect of growth rate and culture conditions on biomass macromolecular and elemental composition. In addition, these data sets were used to improve the performance of the P. pastoris consensus genomic-scale metabolic model iMT1026. Thereupon, new experimentally determined bounds, including the representation of biomass composition for these growth conditions, have been incorporated. As a result, here, we present version 3 (v3.0) of the consensus P. pastoris genome-scale metabolic model as an update of the iMT1026 model. The v3.0 model was validated for growth on glycerol and methanol as sole carbon sources, demonstrating improved prediction capabilities over an extended substrate range including two biotechnologically relevant carbon sources.

Using Genome-scale Models to Predict Biological Capabilities

Constraint-based reconstruction and analysis (COBRA) methods at the genome scale have been under development since the first whole-genome sequences appeared in the mid-1990s. A few years ago, this approach began to demonstrate the ability to predict a range of cellular functions, including cellular growth capabilities on various substrates and the effect of gene knockouts at the genome scale. Thus, much interest has developed in understanding and applying these methods to areas such as metabolic engineering, antibiotic design, and organismal and enzyme evolution. This Primer will get you started.

Flux balance analysis in the production of clavulanic acid by Streptomyces clavuligerus

In this work, in silico flux balance analysis is used for predicting the metabolic behavior of Streptomyces clavuligerus during clavulanic acid production. To choose the best objective function for use in the analysis, three different optimization problems are evaluated inside the flux balance analysis formulation: (i) maximization of the specific growth rate, (ii) maximization of the ATP yield, and (iii) maximization of clavulanic acid production. Maximization of ATP yield showed the best predictions for the cellular behavior. Therefore, flux balance analysis using ATP as objective function was used for analyzing different scenarios of nutrient limitations toward establishing the effect of limiting the carbon, nitrogen, phosphorous, and oxygen sources on the growth and clavulanic acid production rates. Obtained results showed that ammonia and phosphate limitations are the ones most strongly affecting clavulanic acid biosynthesis. Furthermore, it was possible to identify the ornithine flux from the urea cycle and the α-ketoglutarate flux from the TCA cycle as the most determinant internal fluxes for promoting clavulanic acid production.

Erratum to: A coupled thermodynamic and metabolic control analysis methodology and its evaluation on glycerol biosynthesis in Saccharomyces cerevisiae

A coupled in silico thermodynamic and probabilistic metabolic control analysis methodology was verified by applying it to the glycerol biosynthetic pathway in Saccharomyces cerevisiae. The methodology allows predictions even when detailed knowledge of the enzyme kinetics is lacking. In a metabolic steady state, we found that glycerol-3-phosphate dehydrogenase operates far from thermodynamic equilibrium ([Formula: see text]  -15.9 to -47.5 kJ mol(-1), where [Formula: see text] is the transformed Gibbs energy of the reaction). Glycerol-3-phosphatase operates in modes near the thermodynamic equilibrium, far from the thermodynamic equilibrium or in between ([Formula: see text] ≈ 0 to -23.7 kJ mol(-1)). From the calculated distribution of the scaled flux control coefficients (median = 0.81), we inferred that the pathway flux is primarily controlled by glycerol-3-phosphate dehydrogenase. This prediction is consistent with previous findings, verifying the efficacy of the proposed methodology.

(Post-)genomics approaches in fungal research

To date, hundreds of fungal genomes have been sequenced and many more are in progress. This wealth of genomic information has provided new directions to study fungal biodiversity. However, to further dissect and understand the complicated biological mechanisms involved in fungal life styles, functional studies beyond genomes are required. Thanks to the developments of current -omics techniques, it is possible to produce large amounts of fungal functional data in a high-throughput fashion (e.g. transcriptome, proteome, etc.). The increasing ease of creating -omics data has also created a major challenge for downstream data handling and analysis. Numerous databases, tools and software have been created to meet this challenge. Facing such a richness of techniques and information, hereby we provide a brief roadmap on current wet-lab and bioinformatics approaches to study functional genomics in fungi.

Kinetic models in industrial biotechnology - Improving cell factory performance

An increasing number of industrial bioprocesses capitalize on living cells by using them as cell factories that convert sugars into chemicals. These processes range from the production of bulk chemicals in yeasts and bacteria to the synthesis of therapeutic proteins in mammalian cell lines. One of the tools in the continuous search for improved performance of such production systems is the development and application of mathematical models. To be of value for industrial biotechnology, mathematical models should be able to assist in the rational design of cell factory properties or in the production processes in which they are utilized. Kinetic models are particularly suitable towards this end because they are capable of representing the complex biochemistry of cells in a more complete way compared to most other types of models. They can, at least in principle, be used to in detail understand, predict, and evaluate the effects of adding, removing, or modifying molecular components of a cell factory and for supporting the design of the bioreactor or fermentation process. However, several challenges still remain before kinetic modeling will reach the degree of maturity required for routine application in industry. Here we review the current status of kinetic cell factory modeling. Emphasis is on modeling methodology concepts, including model network structure, kinetic rate expressions, parameter estimation, optimization methods, identifiability analysis, model reduction, and model validation, but several applications of kinetic models for the improvement of cell factories are also discussed.

Yeast 5 - an expanded reconstruction of the Saccharomyces cerevisiae metabolic network

Background

Efforts to improve the computational reconstruction of the Saccharomyces cerevisiae biochemical reaction network and to refine the stoichiometrically constrained metabolic models that can be derived from such a reconstruction have continued since the first stoichiometrically constrained yeast genome scale metabolic model was published in 2003. Continuing this ongoing process, we have constructed an update to the Yeast Consensus Reconstruction, Yeast 5. The Yeast Consensus Reconstruction is a product of efforts to forge a community-based reconstruction emphasizing standards compliance and biochemical accuracy via evidence-based selection of reactions. It draws upon models published by a variety of independent research groups as well as information obtained from biochemical databases and primary literature.

Results

Yeast 5 refines the biochemical reactions included in the reconstruction, particularly reactions involved in sphingolipid metabolism; updates gene-reaction annotations; and emphasizes the distinction between reconstruction and stoichiometrically constrained model. Although it was not a primary goal, this update also improves the accuracy of model prediction of viability and auxotrophy phenotypes and increases the number of epistatic interactions. This update maintains an emphasis on standards compliance, unambiguous metabolite naming, and computer-readable annotations available through a structured document format. Additionally, we have developed MATLAB scripts to evaluate the model’s predictive accuracy and to demonstrate basic model applications such as simulating aerobic and anaerobic growth. These scripts, which provide an independent tool for evaluating the performance of various stoichiometrically constrained yeast metabolic models using flux balance analysis, are included as Additional files 1, 2 and 3.

Conclusions

Yeast 5 expands and refines the computational reconstruction of yeast metabolism and improves the predictive accuracy of a stoichiometrically constrained yeast metabolic model. It differs from previous reconstructions and models by emphasizing the distinction between the yeast metabolic reconstruction and the stoichiometrically constrained model, and makes both available as Additional file 4 and Additional file 5 and at http://yeast.sf.net/ as separate systems biology markup language (SBML) files. Through this separation, we intend to make the modeling process more accessible, explicit, transparent, and reproducible.