scholarly journals The Expanding Computational Toolbox for Engineering Microbial Phenotypes at the Genome Scale

2020 ◽  
Vol 8 (12) ◽  
pp. 2050
Author(s):  
Daniel Craig Zielinski ◽  
Arjun Patel ◽  
Bernhard O. Palsson
Keyword(s):  
Data Science ◽  
Strain Engineering ◽  
Chemical Production ◽  
Protein Structure Analysis ◽  
Biological Design ◽  
Strain Design ◽  
Microbial Strains ◽  
Active Research ◽  
Constraint Based Modeling ◽  
Predictive Design

Microbial strains are being engineered for an increasingly diverse array of applications, from chemical production to human health. While traditional engineering disciplines are driven by predictive design tools, these tools have been difficult to build for biological design due to the complexity of biological systems and many unknowns of their quantitative behavior. However, due to many recent advances, the gap between design in biology and other engineering fields is closing. In this work, we discuss promising areas of development of computational tools for engineering microbial strains. We define five frontiers of active research: (1) Constraint-based modeling and metabolic network reconstruction, (2) Kinetics and thermodynamic modeling, (3) Protein structure analysis, (4) Genome sequence analysis, and (5) Regulatory network analysis. Experimental and machine learning drivers have enabled these methods to improve by leaps and bounds in both scope and accuracy. Modern strain design projects will require these tools to be comprehensively applied to the entire cell and efficiently integrated within a single workflow. We expect that these frontiers, enabled by the ongoing revolution of big data science, will drive forward more advanced and powerful strain engineering strategies.

scholarly journals Automation assisted anaerobic phenotyping for metabolic engineering

2021 ◽  
Vol 20 (1) ◽  
Author(s):  
Kaushik Raj ◽  
Naveen Venayak ◽  
Patrick Diep ◽  
Sai Akhil Golla ◽  
Alexander F. Yakunin ◽  
...  
Keyword(s):  
Metabolic Engineering ◽  
High Throughput ◽  
Large Scale ◽  
Plastic Waste ◽  
Small Scale ◽  
Chemical Production ◽  
Liquid Handling ◽  
Strain Design ◽  
Wide Range ◽  
Scale Down

Abstract Background Microorganisms can be metabolically engineered to produce a wide range of commercially important chemicals. Advancements in computational strategies for strain design and synthetic biological techniques to construct the designed strains have facilitated the generation of large libraries of potential candidates for chemical production. Consequently, there is a need for high-throughput laboratory scale techniques to characterize and screen these candidates to select strains for further investigation in large scale fermentation processes. Several small-scale fermentation techniques, in conjunction with laboratory automation have enhanced the throughput of enzyme and strain phenotyping experiments. However, such high throughput experimentation typically entails large operational costs and generate massive amounts of laboratory plastic waste. Results In this work, we develop an eco-friendly automation workflow that effectively calibrates and decontaminates fixed-tip liquid handling systems to reduce tip waste. We also investigate inexpensive methods to establish anaerobic conditions in microplates for high-throughput anaerobic phenotyping. To validate our phenotyping platform, we perform two case studies—an anaerobic enzyme screen, and a microbial phenotypic screen. We used our automation platform to investigate conditions under which several strains of E. coli exhibit the same phenotypes in 0.5 L bioreactors and in our scaled-down fermentation platform. We also propose the use of dimensionality reduction through t-distributed stochastic neighbours embedding (t-SNE) in conjunction with our phenotyping platform to effectively cluster similarly performing strains at the bioreactor scale. Conclusions Fixed-tip liquid handling systems can significantly reduce the amount of plastic waste generated in biological laboratories and our decontamination and calibration protocols could facilitate the widespread adoption of such systems. Further, the use of t-SNE in conjunction with our automation platform could serve as an effective scale-down model for bioreactor fermentations. Finally, by integrating an in-house data-analysis pipeline, we were able to accelerate the ‘test’ phase of the design-build-test-learn cycle of metabolic engineering.

scholarly journals Constraint-based modeling in microbial food biotechnology

2018 ◽  
Vol 46 (2) ◽  
pp. 249-260 ◽  
Author(s):  
Martin H. Rau ◽  
Ahmad A. Zeidan
Keyword(s):  
Rational Design ◽  
Correct Choice ◽  
Biological Knowledge ◽  
Scale Production ◽  
Modeling Framework ◽  
Strain Design ◽  
Industrial Strains ◽  
Food Cultures ◽  
Constraint Based Modeling ◽  
Culture Development

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.

scholarly journals Automation assisted anaerobic phenotyping for metabolic engineering

2021 ◽  
Author(s):  
Kaushik Raj ◽  
Naveen Venayak ◽  
Patrick Diep ◽  
Sai Akhil Golla ◽  
Alexander F. Yakunin ◽  
...  
Keyword(s):  
Metabolic Engineering ◽  
High Throughput ◽  
Large Scale ◽  
Small Scale ◽  
Chemical Production ◽  
E Coli ◽  
Strain Design ◽  
Phenotypic Screen ◽  
Wide Range ◽  
Scale Down

Microorganisms can be metabolically engineered to produce a wide range of commercially important chemicals. Advancements in computational strategies for strain design and synthetic biological techniques to construct the designed strains have facilitated the generation of large libraries of potential candidates for chemical production. Consequently, there is a need for a high-throughput, laboratory scale methods to characterize and screen these candidates to select strains for further investigation in large scale fermentation processes. Several small-scale fermentation techniques, in conjunction with laboratory automation have enhanced the throughput of enzyme and strain phenotyping experiments. However, such high throughput experimentation typically entails large operational costs and generate massive amounts of laboratory plastic waste. In this work, we develop an eco-friendly automation workflow that effectively calibrates and decontaminates fixed-tip liquid handling systems to reduce tip waste. We also investigate inexpensive methods to establish anaerobic conditions in microplates for high-throughput anaerobic phenotyping. To validate our phenotyping platform, we assess its performance in two case studies - an anaerobic enzyme screen, and a microbial phenotypic screen. We used our automation platform to investigate conditions under which several strains of E. coli exhibit the same phenotypes in 0.5 L bioreactors and in our scaled-down fermentation platform. Further, we propose the use of dimensionality reduction through t-distributed stochastic neighbours embedding, in conjunction with our phenotyping platform to serve as an effective scale-down model for bioreactor phenotypes. By integrating an in-house data-analysis pipeline, we were able to accelerate the 'test' phase of the design-build-test-learn cycle of metabolic engineering.

scholarly journals Clean manufacturing powered by biology: how Amyris has deployed technology and aims to do it better

2020 ◽  
Vol 47 (11) ◽  
pp. 965-975 ◽  
Author(s):  
Paul Hill ◽  
Kirsten Benjamin ◽  
Binita Bhattacharjee ◽  
Fernando Garcia ◽  
Joshua Leng ◽  
...  
Keyword(s):  
Data Science ◽  
Process Development ◽  
Scale Up ◽  
Strain Engineering ◽  
Lessons Learned ◽  
Product Portfolio ◽  
Fermentation Products ◽  
The World ◽  
Clean Manufacturing ◽  
Transfer Procedures

AbstractAmyris is a fermentation product company that leverages synthetic biology and has been bringing novel fermentation products to the market since 2009. Driven by breakthroughs in genome editing, strain construction and testing, analytics, automation, data science, and process development, Amyris has commercialized nine separate fermentation products over the last decade. This has been accomplished by partnering with the teams at 17 different manufacturing sites around the world. This paper begins with the technology that drives Amyris, describes some key lessons learned from early scale-up experiences, and summarizes the technology transfer procedures and systems that have been built to enable moving more products to market faster. Finally, the breadth of the Amyris product portfolio continues to expand; thus the steps being taken to overcome current challenges (e.g. automated strain engineering can now outpace the rest of the product commercialization timeline) are described.

scholarly journals Comparison of Multi-Objective Evolutionary Algorithms to Solve the Modular Cell Design Problem for Novel Biocatalysis

Processes ◽  
2019 ◽  
Vol 7 (6) ◽  
pp. 361 ◽  
Author(s):  
Sergio Garcia ◽  
Cong T. Trinh
Keyword(s):  
Evolutionary Algorithms ◽  
Design Problem ◽  
Optimization Problems ◽  
Strain Engineering ◽  
Cell Design ◽  
Multi Objective Optimization ◽  
Large Space ◽  
Multi Objective ◽  
Strain Design ◽  
Application Problem

A large space of chemicals with broad industrial and consumer applications could be synthesized by engineered microbial biocatalysts. However, the current strain optimization process is prohibitively laborious and costly to produce one target chemical and often requires new engineering efforts to produce new molecules. To tackle this challenge, modular cell design based on a chassis strain that can be combined with different product synthesis pathway modules has recently been proposed. This approach seeks to minimize unexpected failure and avoid task repetition, leading to a more robust and faster strain engineering process. In our previous study, we mathematically formulated the modular cell design problem based on the multi-objective optimization framework. In this study, we evaluated a library of state-of-the-art multi-objective evolutionary algorithms (MOEAs) to identify the most effective method to solve the modular cell design problem. Using the best MOEA, we found better solutions for modular cells compatible with many product synthesis modules. Furthermore, the best performing algorithm could provide better and more diverse design options that might help increase the likelihood of successful experimental implementation. We identified key parameter configurations to overcome the difficulty associated with multi-objective optimization problems with many competing design objectives. Interestingly, we found that MOEA performance with a real application problem, e.g., the modular strain design problem, does not always correlate with artificial benchmarks. Overall, MOEAs provide powerful tools to solve the modular cell design problem for novel biocatalysis.

scholarly journals OptDesign: Identifying Optimum Design Strategies in Strain Engineering for Biochemical Production

2021 ◽  
Author(s):  
Shouyong Jiang
Keyword(s):  
Metabolic Model ◽  
Strain Engineering ◽  
Design Strategies ◽  
E Coli ◽  
Success Stories ◽  
Strain Design ◽  
Optimality Principles ◽  
Biochemical Production ◽  
Genome Scale ◽  
Flux Difference

Computational tools have been widely adopted for strain optimisation in metabolic engineering, contributing to numerous success stories of producing industrially relevant biochemicals. However, most of these tools focus on single metabolic intervention strategies (either gene/reaction knockout or amplification alone) and rely on hypothetical optimality principles (e.g., maximisation of growth) and precise gene expression (e.g., fold changes) for phenotype prediction. This paper introduces OptDesign, a new two-step strain design strategy. In the first step, OptDesign selects regulation candidates that have a noticeable flux difference between the wild type and production strains. In the second step, it computes optimal design strategies with limited manipulations (combining regulation and knockout) leading to high biochemical production. The usefulness 1and capabilities of OptDesign are demonstrated for the production of three biochemicals in E. coli using the latest genome-scale metabolic model iML1515, showing highly consistent results with previous studies while suggesting new manipulations to boost strain performance. Source code is available at https://github.com/chang88ye/OptDesign.

scholarly journals Automation Assisted Anaerobic Phenotyping For Metabolic Engineering

2021 ◽  
Author(s):  
Kaushik Venkatesan ◽  
Naveen Venayak ◽  
Patrick Diep ◽  
Sai Akhil Golla ◽  
Alexander Yakunin ◽  
...  
Keyword(s):  
Metabolic Engineering ◽  
High Throughput ◽  
Large Scale ◽  
Plastic Waste ◽  
Small Scale ◽  
Chemical Production ◽  
Liquid Handling ◽  
Strain Design ◽  
Wide Range ◽  
Scale Down

Abstract Background: Microorganisms can be metabolically engineered to produce a wide range of commercially important chemicals. Advancements in computational strategies for strain design and synthetic biological techniques to construct the designed strains have facilitated the generation of large libraries of potential candidates for chemical production. Consequently, there is a need for a high-throughput, laboratory scale techniques to characterize and screen these candidates to select strains for further investigation in large scale fermentation processes. Several small-scale fermentation techniques, in conjunction with laboratory automation have enhanced the throughput of enzyme and strain phenotyping experiments. However, such high throughput experimentation typically entails large operational costs and generate massive amounts of laboratory plastic waste. Results: In this work, we develop an eco-friendly automation work ow that effectively calibrates and decontaminates fixed-tip liquid handling systems to reduce tip waste. We also investigate inexpensive methods to establish anaerobic conditions in microplates for high-throughput anaerobic phenotyping. To validate our phenotyping platform, we perform two case studies - an anaerobic enzyme screen, and a microbial phenotypic screen. We used our automation platform to investigate conditions under which several strains of E. coli exhibit the same phenotypes in 0.5 L bioreactors and in our scaled-down fermentation platform. We also propose the use of dimensionality reduction through t-distributed stochastic neighbours embedding (t-SNE) in conjunction with our phenotyping platform to effectively cluster similarly performing strains at the bioreactor scale. Conclusions: Fixed-tip liquid handling systems can significantly reduce the amount of plastic waste generated in biological laboratories and our decontamination and calibration protocols could facilitate the widespread adoption of such systems. Further, the use of t-SNE in conjunction with our automation platform could serve as an effective scale-down model for bioreactor fermentations. Finally, by integrating an in-house data-analysis pipeline, we were able to accelerate the 'test' phase of the design-build-test-learn cycle of metabolic engineering.

scholarly journals Comparison of Multi-objective Evolutionary Algorithms to Solve the Modular Cell Design Problem for Novel Biocatalysis

2019 ◽  
Author(s):  
Sergio Garcia ◽  
Cong Trinh
Keyword(s):  
Evolutionary Algorithms ◽  
Design Problem ◽  
Optimization Problems ◽  
Strain Engineering ◽  
Cell Design ◽  
Multi Objective Optimization ◽  
Large Space ◽  
Multi Objective ◽  
Strain Design ◽  
Application Problem

AbstractA large space of chemicals with broad industrial and consumer applications could be synthesized by engineered microbial biocatalysts. However, the current strain optimization process is prohibitively laborious and costly to produce one target chemical and often requires new engineering efforts to produce new molecules. To tackle this challenge, modular cell design based on a chassis strain that can be combined with different product synthesis pathway modules has been recently proposed. This approach seeks to minimize unexpected failure and avoid task repetition, leading to a more robust and faster strain engineering process. The modular cell design problem was mathematically formulated using a multi-objective optimization framework.[1] In this study, we evaluated a library of the state-of-the-art multi-objective evolutionary algorithms (MOEAs) to identify the most effective method to solve the modular cell design problem. Using the best MOEA, we found better solutions for modular cells compatible with many product synthesis modules. Furthermore, the best performing algorithm could provide better and more diverse design options that might help increase the likelihood of successful experimental implementation. We identified key parameter configurations to overcome the difficulty associated with multi-objective optimization problems with many competing design objectives. Interestingly, we found that MOEA performance with a real application problem, e.g., the modular strain design problem, does not always correlate with artificial benchmarks. Overall, MOEAs provide powerful tools to solve the modular cell design problem for novel biocatalysis.

scholarly journals Homotopy continuation for the spectra of persistent Laplacians

2021 ◽  
Vol 0 (0) ◽  
pp. 0
Author(s):  
Xiaoqi Wei ◽  
Guo-Wei Wei
Keyword(s):  
Characteristic Polynomial ◽  
Algebraic Topology ◽  
Data Science ◽  
Persistent Homology ◽  
Betti Numbers ◽  
Laplacian Matrix ◽  
Machine Learning Algorithms ◽  
Homotopy Continuation ◽  
Combinatorial Laplacian ◽  
Active Research

<p style='text-indent:20px;'>The <inline-formula><tex-math id="M1">\begin{document}$ p $\end{document}</tex-math></inline-formula>-persistent <inline-formula><tex-math id="M2">\begin{document}$ q $\end{document}</tex-math></inline-formula>-combinatorial Laplacian defined for a pair of simplicial complexes is a generalization of the <inline-formula><tex-math id="M3">\begin{document}$ q $\end{document}</tex-math></inline-formula>-combinatorial Laplacian. Given a filtration, the spectra of persistent combinatorial Laplacians not only recover the persistent Betti numbers of persistent homology but also provide extra multiscale geometrical information of the data. Paired with machine learning algorithms, the persistent Laplacian has many potential applications in data science. Seeking different ways to find the spectrum of an operator is an active research topic, becoming interesting when ideas are originated from multiple fields. In this work, we explore an alternative approach for the spectrum of persistent Laplacians. As the eigenvalues of a persistent Laplacian matrix are the roots of its characteristic polynomial, one may attempt to find the roots of the characteristic polynomial by homotopy continuation, and thus resolving the spectrum of the corresponding persistent Laplacian. We consider a set of simple polytopes and small molecules to prove the principle that algebraic topology, combinatorial graph, and algebraic geometry can be integrated to understand the shape of data.</p>

Postgraduate Specialization Fellowship Training for Audiologists: Survey Results From Educators, Supervisors, and Students

2020 ◽  
Vol 29 (2) ◽  
pp. 290-299
Author(s):  
Julie G. Arenberg ◽  
Ray H. Hull ◽  
Lisa Hunter
Keyword(s):  
Fellowship Training ◽  
Professional Organization ◽  
Student Responses ◽  
Academic Affiliation ◽  
Job Opportunities ◽  
Working Groups ◽  
Survey Results ◽  
Active Research ◽  
Funding Opportunities ◽  
Exceptional Patient

Purpose From the Audiology Education Summit held in 2017, several working groups were formed to explore ideas about improving the quality and consistency in graduate education in audiology and externship training. The results are described here from one of the working groups formed to examine postgraduate specialization fellowships. Method Over the course of a year, the committee designed and implemented two surveys: one directed toward faculty and one toward students. The rationale for the survey and the results are presented. Comparisons between faculty and student responses are made for similar questions. Results Overall, the results demonstrate that the majority of both students and faculty believe that postgraduation specialization fellowships are needed for either 1 year or a flexible length. There was a consensus of opinion that the fellowship should be paid, as these would be designed for licensed audiologists. Most believed that the fellowships should be “governed by a professional organization (e.g., American Speech-Language-Hearing Association, American Academy of Audiology, American Doctors of Audiology, etc.),” or less so, a “separate body for this specific purpose.” Potential topics for specialization identified were the following: tinnitus, vestibular, cochlear implants, pediatrics, and intraoperative monitoring. The highest priority attributes for a specialization site were “abundant access to patient populations,” “staff of clinical experts,” and “active research.” The weight put toward these attributes differed between faculty and students with faculty prioritizing “university/academic centers,” and “access to academic coursework in the fellowship area.” The faculty rated “caseload diversity,” “minimum hours,” “research,” and “academic affiliation” as requirements for a fellowship site, with less weight for “coursework” and “other.” Finally, the students valued “improved personal ability to provide exceptional patient care,” “the potential for increased job opportunities,” and the “potential for a higher salary” as benefits most important to them, with lower ratings for “recognition as a subject matter expert” or “potential pathway to Ph.D. program.” Conclusions As a result of the survey, further exploration of a postgraduate specialization fellowship is warranted, especially to determine funding opportunities to offset cost for the sites and to ensure that fellows are paid adequately.

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