Development and in silico analysis of a new nitrogen-limited feeding strategy for fed-batch cultures of Pichia pastoris based on a simple pH-control system

Irani, Zahra Azimzadeh; Maghsoudi, Amir; Shojaosadati, Seyed Abbas; Motamedian, Ehsan

doi:10.1016/j.bej.2015.02.016

Cited by 16 publications

(6 citation statements)

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“…[ 2,37 ] Since medium components, nutritional supplements, and culture additives directly affect cellular metabolism, [ 38 ] CBM can be a suitable approach to analyze the effect of medium components on cell growth and productivity. CBM‐based methods have been extensively used to analyze and optimize the culture medium and develop appropriate feeding strategies to overproduce a wide range of products such as recombinant proteins, [ 39–43 ] biofuels, [ 44–46 ] lipids, [ 47,48 ] chemicals, [ 49–51 ] and foods. [ 23 ] Swayambhu et al.…”

Section: Cbm: a Mechanistic‐driven Approachmentioning

confidence: 99%

Synergisms of machine learning and constraint‐based modeling of metabolism for analysis and optimization of fermentation parameters

Khaleghi

Savizi

Lewis

et al. 2021

Biotechnology Journal

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Recent noteworthy advances in developing high‐performing microbial and mammalian strains have enabled the sustainable production of bio‐economically valuable substances such as bio‐compounds, biofuels, and biopharmaceuticals. However, to obtain an industrially viable mass‐production scheme, much time and effort are required. The robust and rational design of fermentation processes requires analysis and optimization of different extracellular conditions and medium components, which have a massive effect on growth and productivity. In this regard, knowledge‐ and data‐driven modeling methods have received much attention. Constraint‐based modeling (CBM) is a knowledge‐driven mathematical approach that has been widely used in fermentation analysis and optimization due to its ability to predict the cellular phenotype from genotype through high‐throughput means. On the other hand, machine learning (ML) is a data‐driven statistical method that identifies the data patterns within sophisticated biological systems and processes, where there is inadequate knowledge to represent underlying mechanisms. Furthermore, ML models are becoming a viable complement to constraint‐based models in a reciprocal manner when one is used as a pre‐step of another. As a result, a more predictable model is produced. This review highlights the applications of CBM and ML independently and the combination of these two approaches for analyzing and optimizing fermentation parameters.

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Section: Cbm: a Mechanistic‐driven Approachmentioning

confidence: 99%

Synergisms of machine learning and constraint‐based modeling of metabolism for analysis and optimization of fermentation parameters

Khaleghi

Savizi

Lewis

et al. 2021

Biotechnology Journal

Self Cite

View full text Add to dashboard Cite

show abstract

“…Since medium components, nutritional supplements, and culture additives directly affect cellular metabolism [53], CBM can be a suitable approach to analyze the effect of medium components on cell growth and productivity. CBM-based methods have been extensively used to analyze and optimize the culture medium and develop appropriate feeding strategies to overproduce a wide range of products such as recombinant proteins [54][55][56][57][58], biofuels [59][60][61], lipids [62,63], chemicals [64][65][66], and foods [36]. For a more detailed example, Swayambhu et al used FBA and its derivatives to identify gene deletion/overexpression targets and culture medium components to enhance the production of siderophore compounds in recombinant Escherichia coli .…”

Section: Instances Of Cbm Applications In Fermentation Optimizationmentioning

confidence: 99%

Constraint-based modeling and machine learning applications for analysis and optimization of fermentation parameters

Isp

Lewis

et al. 2021

Preprint

Self Cite

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Recent noteworthy advances in the development of high-performing microbial and mammalian strains have enabled the sustainable production of bio-economically valuable substances such as bio-compounds, biofuels, and biopharmaceuticals. However, to obtain an industrially viable mass-production scheme, much time and effort are required. The robust and rational design of fermentation processes requires analysis and optimization of different extracellular conditions and medium components, which have a massive effect on growth and productivity. In this regard, knowledge- and data-driven modeling methods have received much attention. Constraint-based modeling (CBM) is a knowledge-driven mathematical approach that has been widely used in fermentation analysis and optimization due to its capabilities of predicting the cellular phenotype from genotype through high-throughput means. On the other hand, machine learning (ML) is a data-driven statistical method that identifies the data patterns within sophisticated biological systems and processes, where there is inadequate knowledge to represent underlying mechanisms. Furthermore, ML models are becoming a viable complement to constraint-based models in a reciprocal manner when one is used as a pre-step of another. As a result, more predictable models are produced. This review highlights the applications of CBM and ML independently and the combination of these two approaches for analyzing and optimizing fermentation parameters.

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“…There are three independently constructed GEMs of this organism (Caspeta, Shoaie, Agren, Nookaew, & Nielsen, ; Chung et al, ; Sohn et al, ). Although these models have potential for the in silico identification of genetic modification targets (Nocon et al, ) or the development of new cultivation strategies (Irani, Maghsoudi, Shojaosadati, & Motamedian, ), their lack of standard annotation limits our ability to compare and analyze these models and also to improve these models in the light of current information. Moreover, these models do not make use of valuable information about the biomass composition of the K. phaffii , which has been garnered under a range of experimental conditions (Carnicer et al, ; Jordà, de Jesus, Peltier, Ferrer, & Albiol, ).…”

Section: Introductionmentioning

confidence: 99%

Metabolic modeling to identify engineering targets for Komagataella phaffii: The effect of biomass composition on gene target identification

Cankorur-Cetinkaya

Dikicioglu

Oliver

2017

Biotech & Bioengineering

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Genome‐scale metabolic models are valuable tools for the design of novel strains of industrial microorganisms, such as Komagataella phaffii (syn. Pichia pastoris). However, as is the case for many industrial microbes, there is no executable metabolic model for K. phaffiii that confirms to current standards by providing the metabolite and reactions IDs, to facilitate model extension and reuse, and gene‐reaction associations to enable identification of targets for genetic manipulation. In order to remedy this deficiency, we decided to reconstruct the genome‐scale metabolic model of K. phaffii by reconciling the extant models and performing extensive manual curation in order to construct an executable model (Kp.1.0) that conforms to current standards. We then used this model to study the effect of biomass composition on the predictive success of the model. Twelve different biomass compositions obtained from published empirical data obtained under a range of growth conditions were employed in this investigation. We found that the success of Kp1.0 in predicting both gene essentiality and growth characteristics was relatively unaffected by biomass composition. However, we found that biomass composition had a profound effect on the distribution of the fluxes involved in lipid, DNA, and steroid biosynthetic processes, cellular alcohol metabolic process, and oxidation‐reduction process. Furthermore, we investigated the effect of biomass composition on the identification of suitable target genes for strain development. The analyses revealed that around 40% of the predictions of the effect of gene overexpression or deletion changed depending on the representation of biomass composition in the model. Considering the robustness of the in silico flux distributions to the changing biomass representations enables better interpretation of experimental results, reduces the risk of wrong target identification, and so both speeds and improves the process of directed strain development.

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Development and in silico analysis of a new nitrogen-limited feeding strategy for fed-batch cultures of Pichia pastoris based on a simple pH-control system

Cited by 16 publications

References 33 publications

Synergisms of machine learning and constraint‐based modeling of metabolism for analysis and optimization of fermentation parameters

Synergisms of machine learning and constraint‐based modeling of metabolism for analysis and optimization of fermentation parameters

Constraint-based modeling and machine learning applications for analysis and optimization of fermentation parameters

Metabolic modeling to identify engineering targets for Komagataella phaffii: The effect of biomass composition on gene target identification

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