1
|
Kundu P, Beura S, Mondal S, Das AK, Ghosh A. Machine learning for the advancement of genome-scale metabolic modeling. Biotechnol Adv 2024; 74:108400. [PMID: 38944218 DOI: 10.1016/j.biotechadv.2024.108400] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2023] [Revised: 05/13/2024] [Accepted: 06/23/2024] [Indexed: 07/01/2024]
Abstract
Constraint-based modeling (CBM) has evolved as the core systems biology tool to map the interrelations between genotype, phenotype, and external environment. The recent advancement of high-throughput experimental approaches and multi-omics strategies has generated a plethora of new and precise information from wide-ranging biological domains. On the other hand, the continuously growing field of machine learning (ML) and its specialized branch of deep learning (DL) provide essential computational architectures for decoding complex and heterogeneous biological data. In recent years, both multi-omics and ML have assisted in the escalation of CBM. Condition-specific omics data, such as transcriptomics and proteomics, helped contextualize the model prediction while analyzing a particular phenotypic signature. At the same time, the advanced ML tools have eased the model reconstruction and analysis to increase the accuracy and prediction power. However, the development of these multi-disciplinary methodological frameworks mainly occurs independently, which limits the concatenation of biological knowledge from different domains. Hence, we have reviewed the potential of integrating multi-disciplinary tools and strategies from various fields, such as synthetic biology, CBM, omics, and ML, to explore the biochemical phenomenon beyond the conventional biological dogma. How the integrative knowledge of these intersected domains has improved bioengineering and biomedical applications has also been highlighted. We categorically explained the conventional genome-scale metabolic model (GEM) reconstruction tools and their improvement strategies through ML paradigms. Further, the crucial role of ML and DL in omics data restructuring for GEM development has also been briefly discussed. Finally, the case-study-based assessment of the state-of-the-art method for improving biomedical and metabolic engineering strategies has been elaborated. Therefore, this review demonstrates how integrating experimental and in silico strategies can help map the ever-expanding knowledge of biological systems driven by condition-specific cellular information. This multiview approach will elevate the application of ML-based CBM in the biomedical and bioengineering fields for the betterment of society and the environment.
Collapse
Affiliation(s)
- Pritam Kundu
- School School of Energy Science and Engineering, Indian Institute of Technology Kharagpur, West Bengal 721302, India
| | - Satyajit Beura
- Department of Bioscience and Biotechnology, Indian Institute of Technology, Kharagpur, West Bengal 721302, India
| | - Suman Mondal
- P.K. Sinha Centre for Bioenergy and Renewables, Indian Institute of Technology Kharagpur, West Bengal 721302, India
| | - Amit Kumar Das
- Department of Bioscience and Biotechnology, Indian Institute of Technology, Kharagpur, West Bengal 721302, India
| | - Amit Ghosh
- School School of Energy Science and Engineering, Indian Institute of Technology Kharagpur, West Bengal 721302, India; P.K. Sinha Centre for Bioenergy and Renewables, Indian Institute of Technology Kharagpur, West Bengal 721302, India.
| |
Collapse
|
2
|
Turanli B, Gulfidan G, Aydogan OO, Kula C, Selvaraj G, Arga KY. Genome-scale metabolic models in translational medicine: the current status and potential of machine learning in improving the effectiveness of the models. Mol Omics 2024; 20:234-247. [PMID: 38444371 DOI: 10.1039/d3mo00152k] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/07/2024]
Abstract
The genome-scale metabolic model (GEM) has emerged as one of the leading modeling approaches for systems-level metabolic studies and has been widely explored for a broad range of organisms and applications. Owing to the development of genome sequencing technologies and available biochemical data, it is possible to reconstruct GEMs for model and non-model microorganisms as well as for multicellular organisms such as humans and animal models. GEMs will evolve in parallel with the availability of biological data, new mathematical modeling techniques and the development of automated GEM reconstruction tools. The use of high-quality, context-specific GEMs, a subset of the original GEM in which inactive reactions are removed while maintaining metabolic functions in the extracted model, for model organisms along with machine learning (ML) techniques could increase their applications and effectiveness in translational research in the near future. Here, we briefly review the current state of GEMs, discuss the potential contributions of ML approaches for more efficient and frequent application of these models in translational research, and explore the extension of GEMs to integrative cellular models.
Collapse
Affiliation(s)
- Beste Turanli
- Marmara University, Faculty of Engineering, Department of Bioengineering, Istanbul, Turkey.
- Health Biotechnology Joint Research and Application Center of Excellence, Istanbul, Turkey
| | - Gizem Gulfidan
- Marmara University, Faculty of Engineering, Department of Bioengineering, Istanbul, Turkey.
| | - Ozge Onluturk Aydogan
- Marmara University, Faculty of Engineering, Department of Bioengineering, Istanbul, Turkey.
| | - Ceyda Kula
- Marmara University, Faculty of Engineering, Department of Bioengineering, Istanbul, Turkey.
- Health Biotechnology Joint Research and Application Center of Excellence, Istanbul, Turkey
| | - Gurudeeban Selvaraj
- Concordia University, Centre for Research in Molecular Modeling & Department of Chemistry and Biochemistry, Quebec, Canada
- Saveetha Institute of Medical and Technical Sciences (SIMATS), Saveetha Dental College and Hospital, Department of Biomaterials, Bioinformatics Unit, Chennai, India
| | - Kazim Yalcin Arga
- Marmara University, Faculty of Engineering, Department of Bioengineering, Istanbul, Turkey.
- Health Biotechnology Joint Research and Application Center of Excellence, Istanbul, Turkey
- Marmara University, Genetic and Metabolic Diseases Research and Investigation Center, Istanbul, Turkey
| |
Collapse
|
3
|
Theorell A, Jadebeck JF, Wiechert W, McFadden J, Nöh K. Rethinking 13C-metabolic flux analysis - The Bayesian way of flux inference. Metab Eng 2024; 83:137-149. [PMID: 38582144 DOI: 10.1016/j.ymben.2024.03.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2023] [Revised: 03/22/2024] [Accepted: 03/23/2024] [Indexed: 04/08/2024]
Abstract
Metabolic reaction rates (fluxes) play a crucial role in comprehending cellular phenotypes and are essential in areas such as metabolic engineering, biotechnology, and biomedical research. The state-of-the-art technique for estimating fluxes is metabolic flux analysis using isotopic labelling (13C-MFA), which uses a dataset-model combination to determine the fluxes. Bayesian statistical methods are gaining popularity in the field of life sciences, but the use of 13C-MFA is still dominated by conventional best-fit approaches. The slow take-up of Bayesian approaches is, at least partly, due to the unfamiliarity of Bayesian methods to metabolic engineering researchers. To address this unfamiliarity, we here outline similarities and differences between the two approaches and highlight particular advantages of the Bayesian way of flux analysis. With a real-life example, re-analysing a moderately informative labelling dataset of E. coli, we identify situations in which Bayesian methods are advantageous and more informative, pointing to potential pitfalls of current 13C-MFA evaluation approaches. We propose the use of Bayesian model averaging (BMA) for flux inference as a means of overcoming the problem of model uncertainty through its tendency to assign low probabilities to both, models that are unsupported by data, and models that are overly complex. In this capacity, BMA resembles a tempered Ockham's razor. With the tempered razor as a guide, BMA-based 13C-MFA alleviates the problem of model selection uncertainty and is thereby capable of becoming a game changer for metabolic engineering by uncovering new insights and inspiring novel approaches.
Collapse
Affiliation(s)
- Axel Theorell
- Institute of Bio- and Geosciences, IBG-1: Biotechnology, Forschungszentrum Jülich GmbH, 52425 Jülich, Germany
| | - Johann F Jadebeck
- Institute of Bio- and Geosciences, IBG-1: Biotechnology, Forschungszentrum Jülich GmbH, 52425 Jülich, Germany; Computational Systems Biotechnology (AVT.CSB), RWTH Aachen University, 52062 Aachen, Germany
| | - Wolfgang Wiechert
- Institute of Bio- and Geosciences, IBG-1: Biotechnology, Forschungszentrum Jülich GmbH, 52425 Jülich, Germany; Computational Systems Biotechnology (AVT.CSB), RWTH Aachen University, 52062 Aachen, Germany
| | - Johnjoe McFadden
- Department of Microbial and Cellular Sciences, University of Surrey, GU2 7XH Guildford, United Kingdom
| | - Katharina Nöh
- Institute of Bio- and Geosciences, IBG-1: Biotechnology, Forschungszentrum Jülich GmbH, 52425 Jülich, Germany.
| |
Collapse
|
4
|
Goshisht MK. Machine Learning and Deep Learning in Synthetic Biology: Key Architectures, Applications, and Challenges. ACS OMEGA 2024; 9:9921-9945. [PMID: 38463314 PMCID: PMC10918679 DOI: 10.1021/acsomega.3c05913] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/11/2023] [Revised: 01/19/2024] [Accepted: 01/30/2024] [Indexed: 03/12/2024]
Abstract
Machine learning (ML), particularly deep learning (DL), has made rapid and substantial progress in synthetic biology in recent years. Biotechnological applications of biosystems, including pathways, enzymes, and whole cells, are being probed frequently with time. The intricacy and interconnectedness of biosystems make it challenging to design them with the desired properties. ML and DL have a synergy with synthetic biology. Synthetic biology can be employed to produce large data sets for training models (for instance, by utilizing DNA synthesis), and ML/DL models can be employed to inform design (for example, by generating new parts or advising unrivaled experiments to perform). This potential has recently been brought to light by research at the intersection of engineering biology and ML/DL through achievements like the design of novel biological components, best experimental design, automated analysis of microscopy data, protein structure prediction, and biomolecular implementations of ANNs (Artificial Neural Networks). I have divided this review into three sections. In the first section, I describe predictive potential and basics of ML along with myriad applications in synthetic biology, especially in engineering cells, activity of proteins, and metabolic pathways. In the second section, I describe fundamental DL architectures and their applications in synthetic biology. Finally, I describe different challenges causing hurdles in the progress of ML/DL and synthetic biology along with their solutions.
Collapse
Affiliation(s)
- Manoj Kumar Goshisht
- Department of Chemistry, Natural and
Applied Sciences, University of Wisconsin—Green
Bay, Green
Bay, Wisconsin 54311-7001, United States
| |
Collapse
|
5
|
Qin J, Kurt E, LBassi T, Sa L, Xie D. Biotechnological production of omega-3 fatty acids: current status and future perspectives. Front Microbiol 2023; 14:1280296. [PMID: 38029217 PMCID: PMC10662050 DOI: 10.3389/fmicb.2023.1280296] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2023] [Accepted: 10/25/2023] [Indexed: 12/01/2023] Open
Abstract
Omega-3 fatty acids, including alpha-linolenic acids (ALA), eicosapentaenoic acid (EPA), and docosahexaenoic acid (DHA), have shown major health benefits, but the human body's inability to synthesize them has led to the necessity of dietary intake of the products. The omega-3 fatty acid market has grown significantly, with a global market from an estimated USD 2.10 billion in 2020 to a predicted nearly USD 3.61 billion in 2028. However, obtaining a sufficient supply of high-quality and stable omega-3 fatty acids can be challenging. Currently, fish oil serves as the primary source of omega-3 fatty acids in the market, but it has several drawbacks, including high cost, inconsistent product quality, and major uncertainties in its sustainability and ecological impact. Other significant sources of omega-3 fatty acids include plants and microalgae fermentation, but they face similar challenges in reducing manufacturing costs and improving product quality and sustainability. With the advances in synthetic biology, biotechnological production of omega-3 fatty acids via engineered microbial cell factories still offers the best solution to provide a more stable, sustainable, and affordable source of omega-3 fatty acids by overcoming the major issues associated with conventional sources. This review summarizes the current status, key challenges, and future perspectives for the biotechnological production of major omega-3 fatty acids.
Collapse
Affiliation(s)
| | | | | | | | - Dongming Xie
- Department of Chemical Engineering, University of Massachusetts Lowell, Lowell, MA, United States
| |
Collapse
|
6
|
Gonçalves DM, Henriques R, Costa RS. Predicting metabolic fluxes from omics data via machine learning: Moving from knowledge-driven towards data-driven approaches. Comput Struct Biotechnol J 2023; 21:4960-4973. [PMID: 37876626 PMCID: PMC10590844 DOI: 10.1016/j.csbj.2023.10.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2023] [Revised: 10/01/2023] [Accepted: 10/01/2023] [Indexed: 10/26/2023] Open
Abstract
The accurate prediction of phenotypes in microorganisms is a main challenge for systems biology. Genome-scale models (GEMs) are a widely used mathematical formalism for predicting metabolic fluxes using constraint-based modeling methods such as flux balance analysis (FBA). However, they require prior knowledge of the metabolic network of an organism and appropriate objective functions, often hampering the prediction of metabolic fluxes under different conditions. Moreover, the integration of omics data to improve the accuracy of phenotype predictions in different physiological states is still in its infancy. Here, we present a novel approach for predicting fluxes under various conditions. We explore the use of supervised machine learning (ML) models using transcriptomics and/or proteomics data and compare their performance against the standard parsimonious FBA (pFBA) approach using case studies of Escherichia coli organism as an example. Our results show that the proposed omics-based ML approach is promising to predict both internal and external metabolic fluxes with smaller prediction errors in comparison to the pFBA approach. The code, data, and detailed results are available at the project's repository[1].
Collapse
Affiliation(s)
- Daniel M. Gonçalves
- INESC-ID, Rua Alves Redol, 9, Lisbon, 1000-029, Portugal
- Instituto Superior Técnico, Av. Rovisco Pais, 1, Lisbon, 1049-001, Portugal
- LAQV-REQUIMTE, Department of Chemistry, NOVA School of Science and Technology, Universidade NOVA de Lisboa, Caparica, 2829-516, Portugal
| | - Rui Henriques
- INESC-ID, Rua Alves Redol, 9, Lisbon, 1000-029, Portugal
- Instituto Superior Técnico, Av. Rovisco Pais, 1, Lisbon, 1049-001, Portugal
| | - Rafael S. Costa
- LAQV-REQUIMTE, Department of Chemistry, NOVA School of Science and Technology, Universidade NOVA de Lisboa, Caparica, 2829-516, Portugal
| |
Collapse
|
7
|
Wu C, Guarnieri M, Xiong W. FreeFlux: A Python Package for Time-Efficient Isotopically Nonstationary Metabolic Flux Analysis. ACS Synth Biol 2023; 12:2707-2714. [PMID: 37561998 PMCID: PMC10510750 DOI: 10.1021/acssynbio.3c00265] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2023] [Indexed: 08/12/2023]
Abstract
13C metabolic flux analysis is a powerful tool for metabolism characterization in metabolic engineering and synthetic biology. However, the widespread adoption of this tool is hindered by limited software availability and computational efficiency. Currently, the most widely accepted 13C-flux tools, such as INCA and 13CFLUX2, are developed in a closed-source environment. While several open-source packages or software are available, they are either computationally inefficient or only suitable for flux estimation at isotopic steady state. To address the need for a time-efficient computational tool for the more complicated flux analysis at an isotopically nonstationary state, especially for understanding the single-carbon substrate metabolism, we present FreeFlux. FreeFlux is an open-source Python package that performs labeling pattern simulation and flux analysis at both isotopic steady state and transient state, enabling a more comprehensive analysis of cellular metabolism. FreeFlux provides a set of interfaces to manipulate the objects abstracted from a labeling experiment and computational process, making it easy to integrate into other programs or pipelines. The flux estimation by FreeFlux is fast and reliable, and its validity has been confirmed by comparison with results from other computational tools using both synthetic and experimental data. FreeFlux is freely available at https://github.com/Chaowu88/freeflux with a detailed online tutorial and documentation provided at https://freeflux.readthedocs.io/en/latest/index.html.
Collapse
Affiliation(s)
- Chao Wu
- Biosciences Center, National
Renewable Energy Laboratory, Golden, Colorado 80401, United States
| | - Michael Guarnieri
- Biosciences Center, National
Renewable Energy Laboratory, Golden, Colorado 80401, United States
| | - Wei Xiong
- Biosciences Center, National
Renewable Energy Laboratory, Golden, Colorado 80401, United States
| |
Collapse
|
8
|
Karlsen ST, Rau MH, Sánchez BJ, Jensen K, Zeidan AA. From genotype to phenotype: computational approaches for inferring microbial traits relevant to the food industry. FEMS Microbiol Rev 2023; 47:fuad030. [PMID: 37286882 PMCID: PMC10337747 DOI: 10.1093/femsre/fuad030] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Revised: 05/31/2023] [Accepted: 06/06/2023] [Indexed: 06/09/2023] Open
Abstract
When selecting microbial strains for the production of fermented foods, various microbial phenotypes need to be taken into account to achieve target product characteristics, such as biosafety, flavor, texture, and health-promoting effects. Through continuous advances in sequencing technologies, microbial whole-genome sequences of increasing quality can now be obtained both cheaper and faster, which increases the relevance of genome-based characterization of microbial phenotypes. Prediction of microbial phenotypes from genome sequences makes it possible to quickly screen large strain collections in silico to identify candidates with desirable traits. Several microbial phenotypes relevant to the production of fermented foods can be predicted using knowledge-based approaches, leveraging our existing understanding of the genetic and molecular mechanisms underlying those phenotypes. In the absence of this knowledge, data-driven approaches can be applied to estimate genotype-phenotype relationships based on large experimental datasets. Here, we review computational methods that implement knowledge- and data-driven approaches for phenotype prediction, as well as methods that combine elements from both approaches. Furthermore, we provide examples of how these methods have been applied in industrial biotechnology, with special focus on the fermented food industry.
Collapse
Affiliation(s)
- Signe T Karlsen
- Bioinformatics & Modeling, R&D Digital Innovation, Chr. Hansen A/S, Bøge Allé 10-12, 2970 Hørsholm, Denmark
| | - Martin H Rau
- Bioinformatics & Modeling, R&D Digital Innovation, Chr. Hansen A/S, Bøge Allé 10-12, 2970 Hørsholm, Denmark
| | - Benjamín J Sánchez
- Bioinformatics & Modeling, R&D Digital Innovation, Chr. Hansen A/S, Bøge Allé 10-12, 2970 Hørsholm, Denmark
| | - Kristian Jensen
- Bioinformatics & Modeling, R&D Digital Innovation, Chr. Hansen A/S, Bøge Allé 10-12, 2970 Hørsholm, Denmark
| | - Ahmad A Zeidan
- Bioinformatics & Modeling, R&D Digital Innovation, Chr. Hansen A/S, Bøge Allé 10-12, 2970 Hørsholm, Denmark
| |
Collapse
|
9
|
Helleckes LM, Hemmerich J, Wiechert W, von Lieres E, Grünberger A. Machine learning in bioprocess development: from promise to practice. Trends Biotechnol 2023; 41:817-835. [PMID: 36456404 DOI: 10.1016/j.tibtech.2022.10.010] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2022] [Revised: 10/20/2022] [Accepted: 10/27/2022] [Indexed: 11/30/2022]
Abstract
Fostered by novel analytical techniques, digitalization, and automation, modern bioprocess development provides large amounts of heterogeneous experimental data, containing valuable process information. In this context, data-driven methods like machine learning (ML) approaches have great potential to rationally explore large design spaces while exploiting experimental facilities most efficiently. Herein we demonstrate how ML methods have been applied so far in bioprocess development, especially in strain engineering and selection, bioprocess optimization, scale-up, monitoring, and control of bioprocesses. For each topic, we will highlight successful application cases, current challenges, and point out domains that can potentially benefit from technology transfer and further progress in the field of ML.
Collapse
Affiliation(s)
- Laura M Helleckes
- Institute for Bio- and Geosciences (IBG-1), Forschungszentrum Jülich GmbH, 52428 Jülich, Germany; RWTH Aachen University, Templergraben 55, 52062 Aachen, Germany
| | - Johannes Hemmerich
- Institute for Bio- and Geosciences (IBG-1), Forschungszentrum Jülich GmbH, 52428 Jülich, Germany
| | - Wolfgang Wiechert
- Institute for Bio- and Geosciences (IBG-1), Forschungszentrum Jülich GmbH, 52428 Jülich, Germany; RWTH Aachen University, Templergraben 55, 52062 Aachen, Germany
| | - Eric von Lieres
- Institute for Bio- and Geosciences (IBG-1), Forschungszentrum Jülich GmbH, 52428 Jülich, Germany; RWTH Aachen University, Templergraben 55, 52062 Aachen, Germany
| | - Alexander Grünberger
- Multiscale Bioengineering, Technical Faculty, Bielefeld University, Universitätsstr. 25, 33615 Bielefeld, Germany; Center for Biotechnology (CeBiTec), Bielefeld University, Universitätsstr. 25, 33615 Bielefeld, Germany; Institute of Process Engineering in Life Sciences, Section III: Microsystems in Bioprocess Engineering, Karlsruhe Institute of Technology, Fritz-Haber-Weg 2, 76131, Karlsruhe, Germany.
| |
Collapse
|
10
|
Huntington T, Baral NR, Yang M, Sundstrom E, Scown CD. Machine learning for surrogate process models of bioproduction pathways. BIORESOURCE TECHNOLOGY 2023; 370:128528. [PMID: 36574885 DOI: 10.1016/j.biortech.2022.128528] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/29/2022] [Revised: 12/20/2022] [Accepted: 12/21/2022] [Indexed: 06/17/2023]
Abstract
Technoeconomic analysis and life-cycle assessment are critical to guiding and prioritizing bench-scale experiments and to evaluating economic and environmental performance of biofuel or biochemical production processes at scale. Traditionally, commercial process simulation tools have been used to develop detailed models for these purposes. However, developing and running such models can be costly and computationally intensive, which limits the degree to which they can be shared and reproduced in the broader research community. This study evaluates the potential of an automated machine learning approach to develop surrogate models based on conventional process simulation models. The analysis focuses on several high-value biofuels and bioproducts for which pathways of production from biomass feedstocks have been well-established. The results demonstrate that surrogate models can be an accurate and effective tool for approximating the cost, mass and energy balance outputs of more complex process simulations at a fraction of the computational expense.
Collapse
Affiliation(s)
- Tyler Huntington
- Life-cycle, Economics, and Agronomy Division, Joint BioEnergy Institute, 5885 Hollis Street, Emeryville, CA 94608, USA; Biosciences Area, Lawrence Berkeley National Laboratory, 1 Cyclotron Road, Berkeley, CA 94720, USA
| | - Nawa Raj Baral
- Life-cycle, Economics, and Agronomy Division, Joint BioEnergy Institute, 5885 Hollis Street, Emeryville, CA 94608, USA; Biosciences Area, Lawrence Berkeley National Laboratory, 1 Cyclotron Road, Berkeley, CA 94720, USA
| | - Minliang Yang
- Life-cycle, Economics, and Agronomy Division, Joint BioEnergy Institute, 5885 Hollis Street, Emeryville, CA 94608, USA; Biosciences Area, Lawrence Berkeley National Laboratory, 1 Cyclotron Road, Berkeley, CA 94720, USA
| | - Eric Sundstrom
- Biosciences Area, Lawrence Berkeley National Laboratory, 1 Cyclotron Road, Berkeley, CA 94720, USA; Advanced Biofuels and Bioproducts Process Development Unit, 5885 Hollis Street, Emeryville, CA 94608, USA
| | - Corinne D Scown
- Life-cycle, Economics, and Agronomy Division, Joint BioEnergy Institute, 5885 Hollis Street, Emeryville, CA 94608, USA; Biosciences Area, Lawrence Berkeley National Laboratory, 1 Cyclotron Road, Berkeley, CA 94720, USA; Energy Technologies Area, Lawrence Berkeley National Laboratory, 1 Cyclotron Road, Berkeley, CA, 94720, USA; Energy & Biosciences Institute, University of California, Berkeley, 282 Koshland Hall, Berkeley, CA 94720, USA.
| |
Collapse
|
11
|
Sieow BFL, De Sotto R, Seet ZRD, Hwang IY, Chang MW. Synthetic Biology Meets Machine Learning. Methods Mol Biol 2023; 2553:21-39. [PMID: 36227537 DOI: 10.1007/978-1-0716-2617-7_2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
This chapter outlines the myriad applications of machine learning (ML) in synthetic biology, specifically in engineering cell and protein activity, and metabolic pathways. Though by no means comprehensive, the chapter highlights several prominent computational tools applied in the field and their potential use cases. The examples detailed reinforce how ML algorithms can enhance synthetic biology research by providing data-driven insights into the behavior of living systems, even without detailed knowledge of their underlying mechanisms. By doing so, ML promises to increase the efficiency of research projects by modeling hypotheses in silico that can then be tested through experiments. While challenges related to training dataset generation and computational costs remain, ongoing improvements in ML tools are paving the way for smarter and more streamlined synthetic biology workflows that can be readily employed to address grand challenges across manufacturing, medicine, engineering, agriculture, and beyond.
Collapse
Affiliation(s)
- Brendan Fu-Long Sieow
- NUS Synthetic Biology for Clinical and Technological Innovation (SynCTI), National University of Singapore, Singapore, Singapore
- Synthetic Biology Translational Research Programme, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- NUS Graduate School for Integrative Sciences and Engineering Programme, National University of Singapore, Singapore, Singapore
| | - Ryan De Sotto
- NUS Synthetic Biology for Clinical and Technological Innovation (SynCTI), National University of Singapore, Singapore, Singapore
- Synthetic Biology Translational Research Programme, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Zhi Ren Darren Seet
- NUS Synthetic Biology for Clinical and Technological Innovation (SynCTI), National University of Singapore, Singapore, Singapore
- Synthetic Biology Translational Research Programme, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - In Young Hwang
- NUS Synthetic Biology for Clinical and Technological Innovation (SynCTI), National University of Singapore, Singapore, Singapore
- Synthetic Biology Translational Research Programme, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Matthew Wook Chang
- NUS Synthetic Biology for Clinical and Technological Innovation (SynCTI), National University of Singapore, Singapore, Singapore.
- Synthetic Biology Translational Research Programme, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.
- Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.
| |
Collapse
|
12
|
Patra P, B R D, Kundu P, Das M, Ghosh A. Recent advances in machine learning applications in metabolic engineering. Biotechnol Adv 2023; 62:108069. [PMID: 36442697 DOI: 10.1016/j.biotechadv.2022.108069] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2022] [Revised: 10/18/2022] [Accepted: 11/22/2022] [Indexed: 11/27/2022]
Abstract
Metabolic engineering encompasses several widely-used strategies, which currently hold a high seat in the field of biotechnology when its potential is manifesting through a plethora of research and commercial products with a strong societal impact. The genomic revolution that occurred almost three decades ago has initiated the generation of large omics-datasets which has helped in gaining a better understanding of cellular behavior. The itinerary of metabolic engineering that has occurred based on these large datasets has allowed researchers to gain detailed insights and a reasonable understanding of the intricacies of biosystems. However, the existing trail-and-error approaches for metabolic engineering are laborious and time-intensive when it comes to the production of target compounds with high yields through genetic manipulations in host organisms. Machine learning (ML) coupled with the available metabolic engineering test instances and omics data brings a comprehensive and multidisciplinary approach that enables scientists to evaluate various parameters for effective strain design. This vast amount of biological data should be standardized through knowledge engineering to train different ML models for providing accurate predictions in gene circuits designing, modification of proteins, optimization of bioprocess parameters for scaling up, and screening of hyper-producing robust cell factories. This review briefs on the premise of ML, followed by mentioning various ML methods and algorithms alongside the numerous omics datasets available to train ML models for predicting metabolic outcomes with high-accuracy. The combinative interplay between the ML algorithms and biological datasets through knowledge engineering have guided the recent advancements in applications such as CRISPR/Cas systems, gene circuits, protein engineering, metabolic pathway reconstruction, and bioprocess engineering. Finally, this review addresses the probable challenges of applying ML in metabolic engineering which will guide the researchers toward novel techniques to overcome the limitations.
Collapse
Affiliation(s)
- Pradipta Patra
- School School of Energy Science and Engineering, Indian Institute of Technology Kharagpur, West Bengal 721302, India
| | - Disha B R
- B.M.S College of Engineering, Basavanagudi, Bengaluru, Karnataka 560019, India
| | - Pritam Kundu
- School School of Energy Science and Engineering, Indian Institute of Technology Kharagpur, West Bengal 721302, India
| | - Manali Das
- School of Bioscience, Indian Institute of Technology Kharagpur, West Bengal 721302, India
| | - Amit Ghosh
- School School of Energy Science and Engineering, Indian Institute of Technology Kharagpur, West Bengal 721302, India; P.K. Sinha Centre for Bioenergy and Renewables, Indian Institute of Technology Kharagpur, West Bengal 721302, India.
| |
Collapse
|
13
|
Duong-Trung N, Born S, Kim JW, Schermeyer MT, Paulick K, Borisyak M, Cruz-Bournazou MN, Werner T, Scholz R, Schmidt-Thieme L, Neubauer P, Martinez E. When Bioprocess Engineering Meets Machine Learning: A Survey from the Perspective of Automated Bioprocess Development. Biochem Eng J 2022. [DOI: 10.1016/j.bej.2022.108764] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
|
14
|
Arduino Soft Sensor for Monitoring Schizochytrium sp. Fermentation, a Proof of Concept for the Industrial Application of Genome-Scale Metabolic Models in the Context of Pharma 4.0. Processes (Basel) 2022. [DOI: 10.3390/pr10112226] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
Schizochytrium sp. is a microorganism cultured for producing docosahexaenoic acid (DHA). Genome-scale metabolic modeling (GEM) is a promising technique for describing gen-protein-reactions in cells, but with still limited industrial application due to its complexity and high computation requirements. In this work, we simplified GEM results regarding the relationship between the specific oxygen uptake rate (−rO2), the specific growth rate (µ), and the rate of lipid synthesis (rL) using an evolutionary algorithm for developing a model that can be used by a soft sensor for fermentation monitoring. The soft sensor estimated the concentration of active biomass (X), glutamate (N), lipids (L), and DHA in a Schizochytrium sp. fermentation using the dissolved oxygen tension (DO) and the oxygen mass transfer coefficient (kLa) as online input variables. The soft sensor model described the biomass concentration response of four reported experiments characterized by different kLa values. The average range normalized root-mean-square error for X, N, L, and DHA were equal to 1.1, 1.3, 1.1, and 3.2%, respectively, suggesting an acceptable generalization capacity. The feasibility of implementing the soft sensor over a low-cost electronic board was successfully tested using an Arduino UNO, showing a novel path for applying GEM-based soft sensors in the context of Pharma 4.0.
Collapse
|
15
|
Du YH, Wang MY, Yang LH, Tong LL, Guo DS, Ji XJ. Optimization and Scale-Up of Fermentation Processes Driven by Models. Bioengineering (Basel) 2022; 9:bioengineering9090473. [PMID: 36135019 PMCID: PMC9495923 DOI: 10.3390/bioengineering9090473] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2022] [Revised: 09/05/2022] [Accepted: 09/09/2022] [Indexed: 11/16/2022] Open
Abstract
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.
Collapse
Affiliation(s)
- Yuan-Hang Du
- School of Food Science and Pharmaceutical Engineering, Nanjing Normal University, Nanjing 210023, China
| | - Min-Yu Wang
- State Key Laboratory of Materials-Oriented Chemical Engineering, College of Biotechnology and Pharmaceutical Engineering, Nanjing Tech University, Nanjing 211816, China
| | - Lin-Hui Yang
- School of Food Science and Pharmaceutical Engineering, Nanjing Normal University, Nanjing 210023, China
| | - Ling-Ling Tong
- School of Food Science and Pharmaceutical Engineering, Nanjing Normal University, Nanjing 210023, China
| | - Dong-Sheng Guo
- School of Food Science and Pharmaceutical Engineering, Nanjing Normal University, Nanjing 210023, China
- Correspondence: (D.-S.G.); (X.-J.J.)
| | - Xiao-Jun Ji
- State Key Laboratory of Materials-Oriented Chemical Engineering, College of Biotechnology and Pharmaceutical Engineering, Nanjing Tech University, Nanjing 211816, China
- Correspondence: (D.-S.G.); (X.-J.J.)
| |
Collapse
|
16
|
Yan S, Bhawal R, Yin Z, Thannhauser TW, Zhang S. Recent advances in proteomics and metabolomics in plants. MOLECULAR HORTICULTURE 2022; 2:17. [PMID: 37789425 PMCID: PMC10514990 DOI: 10.1186/s43897-022-00038-9] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/24/2022] [Accepted: 06/20/2022] [Indexed: 10/05/2023]
Abstract
Over the past decade, systems biology and plant-omics have increasingly become the main stream in plant biology research. New developments in mass spectrometry and bioinformatics tools, and methodological schema to integrate multi-omics data have leveraged recent advances in proteomics and metabolomics. These progresses are driving a rapid evolution in the field of plant research, greatly facilitating our understanding of the mechanistic aspects of plant metabolisms and the interactions of plants with their external environment. Here, we review the recent progresses in MS-based proteomics and metabolomics tools and workflows with a special focus on their applications to plant biology research using several case studies related to mechanistic understanding of stress response, gene/protein function characterization, metabolic and signaling pathways exploration, and natural product discovery. We also present a projection concerning future perspectives in MS-based proteomics and metabolomics development including their applications to and challenges for system biology. This review is intended to provide readers with an overview of how advanced MS technology, and integrated application of proteomics and metabolomics can be used to advance plant system biology research.
Collapse
Affiliation(s)
- Shijuan Yan
- Guangdong Key Laboratory for Crop Germplasm Resources Preservation and Utilization, Agro-biological Gene Research Center, Guangdong Academy of Agricultural Sciences, Guangzhou, China
| | - Ruchika Bhawal
- Proteomics and Metabolomics Facility, Institute of Biotechnology, Cornell University, 139 Biotechnology Building, 526 Campus Road, Ithaca, NY, 14853, USA
| | - Zhibin Yin
- Guangdong Key Laboratory for Crop Germplasm Resources Preservation and Utilization, Agro-biological Gene Research Center, Guangdong Academy of Agricultural Sciences, Guangzhou, China
| | | | - Sheng Zhang
- Proteomics and Metabolomics Facility, Institute of Biotechnology, Cornell University, 139 Biotechnology Building, 526 Campus Road, Ithaca, NY, 14853, USA.
| |
Collapse
|
17
|
Lo-Thong-Viramoutou O, Charton P, Cadet XF, Grondin-Perez B, Saavedra E, Damour C, Cadet F. Non-linearity of Metabolic Pathways Critically Influences the Choice of Machine Learning Model. Front Artif Intell 2022; 5:744755. [PMID: 35757298 PMCID: PMC9226554 DOI: 10.3389/frai.2022.744755] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2021] [Accepted: 04/29/2022] [Indexed: 11/13/2022] Open
Abstract
The use of machine learning (ML) in life sciences has gained wide interest over the past years, as it speeds up the development of high performing models. Important modeling tools in biology have proven their worth for pathway design, such as mechanistic models and metabolic networks, as they allow better understanding of mechanisms involved in the functioning of organisms. However, little has been done on the use of ML to model metabolic pathways, and the degree of non-linearity associated with them is not clear. Here, we report the construction of different metabolic pathways with several linear and non-linear ML models. Different types of data are used; they lead to the prediction of important biological data, such as pathway flux and final product concentration. A comparison reveals that the data features impact model performance and highlight the effectiveness of non-linear models (e.g., QRF: RMSE = 0.021 nmol·min-1 and R2 = 1 vs. Bayesian GLM: RMSE = 1.379 nmol·min-1 R2 = 0.823). It turns out that the greater the degree of non-linearity of the pathway, the better suited a non-linear model will be. Therefore, a decision-making support for pathway modeling is established. These findings generally support the hypothesis that non-linear aspects predominate within the metabolic pathways. This must be taken into account when devising possible applications of these pathways for the identification of biomarkers of diseases (e.g., infections, cancer, neurodegenerative diseases) or the optimization of industrial production processes.
Collapse
Affiliation(s)
- Ophélie Lo-Thong-Viramoutou
- University of Paris, BIGR—Biologie Intégrée du Globule Rouge, Inserm, UMR_S1134, Paris, France
- Laboratory of Excellence GR-Ex, Paris, France
- Laboratory DSIMB, UMR_S1134, BIGR, Inserm, Faculty of Sciences and Technology, University of La Reunion, Saint-Denis, France
| | - Philippe Charton
- University of Paris, BIGR—Biologie Intégrée du Globule Rouge, Inserm, UMR_S1134, Paris, France
- Laboratory of Excellence GR-Ex, Paris, France
- Laboratory DSIMB, UMR_S1134, BIGR, Inserm, Faculty of Sciences and Technology, University of La Reunion, Saint-Denis, France
| | | | - Brigitte Grondin-Perez
- EnergyLab, EA 4079, Faculty of Sciences and Technology, University of La Reunion, Saint-Denis, France
| | - Emma Saavedra
- Departamento de Bioquímica, Instituto Nacional de Cardiología Ignacio Chávez, Mexico City, Mexico
| | - Cédric Damour
- EnergyLab, EA 4079, Faculty of Sciences and Technology, University of La Reunion, Saint-Denis, France
| | - Frédéric Cadet
- University of Paris, BIGR—Biologie Intégrée du Globule Rouge, Inserm, UMR_S1134, Paris, France
- Laboratory of Excellence GR-Ex, Paris, France
- Laboratory DSIMB, UMR_S1134, BIGR, Inserm, Faculty of Sciences and Technology, University of La Reunion, Saint-Denis, France
| |
Collapse
|
18
|
Liao X, Ma H, Tang YJ. Artificial intelligence: a solution to involution of design–build–test–learn cycle. Curr Opin Biotechnol 2022; 75:102712. [DOI: 10.1016/j.copbio.2022.102712] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2021] [Revised: 02/05/2022] [Accepted: 03/01/2022] [Indexed: 01/08/2023]
|
19
|
Bi X, Liu Y, Li J, Du G, Lv X, Liu L. Construction of Multiscale Genome-Scale Metabolic Models: Frameworks and Challenges. Biomolecules 2022; 12:biom12050721. [PMID: 35625648 PMCID: PMC9139095 DOI: 10.3390/biom12050721] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2022] [Revised: 05/15/2022] [Accepted: 05/16/2022] [Indexed: 12/04/2022] Open
Abstract
Genome-scale metabolic models (GEMs) are effective tools for metabolic engineering and have been widely used to guide cell metabolic regulation. However, the single gene–protein-reaction data type in GEMs limits the understanding of biological complexity. As a result, multiscale models that add constraints or integrate omics data based on GEMs have been developed to more accurately predict phenotype from genotype. This review summarized the recent advances in the development of multiscale GEMs, including multiconstraint, multiomic, and whole-cell models, and outlined machine learning applications in GEM construction. This review focused on the frameworks, toolkits, and algorithms for constructing multiscale GEMs. The challenges and perspectives of multiscale GEM development are also discussed.
Collapse
Affiliation(s)
- Xinyu Bi
- Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, Jiangnan University, Wuxi 214122, China; (X.B.); (Y.L.); (J.L.); (G.D.); (X.L.)
- Science Center for Future Foods, Ministry of Education, Jiangnan University, Wuxi 214122, China
| | - Yanfeng Liu
- Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, Jiangnan University, Wuxi 214122, China; (X.B.); (Y.L.); (J.L.); (G.D.); (X.L.)
- Science Center for Future Foods, Ministry of Education, Jiangnan University, Wuxi 214122, China
| | - Jianghua Li
- Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, Jiangnan University, Wuxi 214122, China; (X.B.); (Y.L.); (J.L.); (G.D.); (X.L.)
- Science Center for Future Foods, Ministry of Education, Jiangnan University, Wuxi 214122, China
| | - Guocheng Du
- Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, Jiangnan University, Wuxi 214122, China; (X.B.); (Y.L.); (J.L.); (G.D.); (X.L.)
- Science Center for Future Foods, Ministry of Education, Jiangnan University, Wuxi 214122, China
| | - Xueqin Lv
- Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, Jiangnan University, Wuxi 214122, China; (X.B.); (Y.L.); (J.L.); (G.D.); (X.L.)
- Science Center for Future Foods, Ministry of Education, Jiangnan University, Wuxi 214122, China
| | - Long Liu
- Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, Jiangnan University, Wuxi 214122, China; (X.B.); (Y.L.); (J.L.); (G.D.); (X.L.)
- Science Center for Future Foods, Ministry of Education, Jiangnan University, Wuxi 214122, China
- Correspondence: ; Tel.: +86-0510-8591-8312; Fax: +86-0510-8591-8309
| |
Collapse
|
20
|
Sampaio M, Rocha M, Dias O. Exploring synergies between plant metabolic modelling and machine learning. Comput Struct Biotechnol J 2022; 20:1885-1900. [PMID: 35521559 PMCID: PMC9052043 DOI: 10.1016/j.csbj.2022.04.016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2022] [Revised: 04/08/2022] [Accepted: 04/11/2022] [Indexed: 11/03/2022] Open
|
21
|
Wu C, Yu J, Guarnieri M, Xiong W. Computational Framework for Machine-Learning-Enabled 13C Fluxomics. ACS Synth Biol 2022; 11:103-115. [PMID: 34705423 DOI: 10.1021/acssynbio.1c00189] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
13C metabolic flux analysis (MFA) has emerged as a powerful tool for synthetic biology. This optimization-based approach suffers long computation time and unstable solutions depending on the initial guess. Here, we develop a machine-learning-based framework for 13C fluxomics. Specifically, training and test data sets are generated by metabolic network decomposition and flux sampling, in which flux ratios at metabolic nodes and simulated labeling patterns of metabolites are used as training targets and features, respectively. To improve prediction accuracy and simplify the model, automated processes are developed for flux ratio selection based on solvability and feature screening based on importance. We found that predictive performance can be significantly improved using both amino acids and central carbon metabolites in comparison with amino acids alone. Together with measured external fluxes, the predicted flux ratios determine the mass balance system, yielding global flux distributions. This approach is validated by flux estimation using both simulated and experimental data in comparison with canonical 13C MFA. The approach represents a reliable fluxomics method readily applicable to high-throughput metabolic phenotyping, which highlights the advances of intelligent learning algorithms in synthetic biology, specifically in the Test and Learn stage of the Design-Build-Test-Learn cycle.
Collapse
Affiliation(s)
- Chao Wu
- Biosciences Center, National Renewable Energy Laboratory, Golden, Colorado 80401, United States
| | - Jianping Yu
- Biosciences Center, National Renewable Energy Laboratory, Golden, Colorado 80401, United States
| | - Michael Guarnieri
- Biosciences Center, National Renewable Energy Laboratory, Golden, Colorado 80401, United States
| | - Wei Xiong
- Biosciences Center, National Renewable Energy Laboratory, Golden, Colorado 80401, United States
| |
Collapse
|
22
|
Khaleghi MK, Savizi ISP, Lewis NE, Shojaosadati SA. Synergisms of machine learning and constraint-based modeling of metabolism for analysis and optimization of fermentation parameters. Biotechnol J 2021; 16:e2100212. [PMID: 34390201 DOI: 10.1002/biot.202100212] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2021] [Revised: 08/10/2021] [Accepted: 08/11/2021] [Indexed: 11/06/2022]
Abstract
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 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. This article is protected by copyright. All rights reserved.
Collapse
Affiliation(s)
- Mohammad Karim Khaleghi
- Biotechnology Department, Faculty of Chemical Engineering, Tarbiat Modares University, Tehran, Iran
| | - Iman Shahidi Pour Savizi
- Biotechnology Department, Faculty of Chemical Engineering, Tarbiat Modares University, Tehran, Iran
| | - Nathan E Lewis
- Department of Bioengineering, University of California, San Diego, USA.,Department of Pediatrics, University of California, San Diego, USA
| | - Seyed Abbas Shojaosadati
- Biotechnology Department, Faculty of Chemical Engineering, Tarbiat Modares University, Tehran, Iran
| |
Collapse
|
23
|
Mowbray M, Savage T, Wu C, Song Z, Cho BA, Del Rio-Chanona EA, Zhang D. Machine learning for biochemical engineering: A review. Biochem Eng J 2021. [DOI: 10.1016/j.bej.2021.108054] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
|
24
|
Integrated knowledge mining, genome-scale modeling, and machine learning for predicting Yarrowia lipolytica bioproduction. Metab Eng 2021; 67:227-236. [PMID: 34242777 DOI: 10.1016/j.ymben.2021.07.003] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2021] [Revised: 06/17/2021] [Accepted: 07/05/2021] [Indexed: 01/14/2023]
Abstract
Predicting bioproduction titers from microbial hosts has been challenging due to complex interactions between microbial regulatory networks, stress responses, and suboptimal cultivation conditions. This study integrated knowledge mining, feature extraction, genome-scale modeling (GSM), and machine learning (ML) to develop a model for predicting Yarrowia lipolytica chemical titers (i.e., organic acids, terpenoids, etc.). First, Y. lipolytica production data, including cultivation conditions, genetic engineering strategies, and product information, was manually collected from literature (~100 papers) and stored as either numerical (e.g., substrate concentrations) or categorical (e.g., bioreactor modes) variables. For each case recorded, central pathway fluxes were estimated using GSMs and flux balance analysis (FBA) to provide metabolic features. Second, a ML ensemble learner was trained to predict strain production titers. Accurate predictions on the test data were obtained for instances with production titers >1 g/L (R2 = 0.87). However, the model had reduced predictability for low performance strains (0.01-1 g/L, R2 = 0.29) potentially due to biosynthesis bottlenecks not captured in the features. Feature ranking indicated that the FBA fluxes, the number of enzyme steps, the substrate inputs, and thermodynamic barriers (i.e., Gibbs free energy of reaction) were the most influential factors. Third, the model was evaluated on other oleaginous yeasts and indicated there were conserved features for some hosts that can be potentially exploited by transfer learning. The platform was also designed to assist computational strain design tools (such as OptKnock) to screen genetic targets for improved microbial production in light of experimental conditions.
Collapse
|
25
|
Xu Y, Wu Y, Lv X, Sun G, Zhang H, Chen T, Du G, Li J, Liu L. Design and construction of novel biocatalyst for bioprocessing: Recent advances and future outlook. BIORESOURCE TECHNOLOGY 2021; 332:125071. [PMID: 33826982 DOI: 10.1016/j.biortech.2021.125071] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/17/2021] [Revised: 03/19/2021] [Accepted: 03/23/2021] [Indexed: 06/12/2023]
Abstract
Bioprocess, a biocatalysis-based technology, is becoming popular in many research fields and widely applied in industrial manufacturing. However, low bioconversion, low productivity, and high costs during industrial processes are usually the limitation in bioprocess. Therefore, many biocatalyst strategies have been developed to meet these challenges in recent years. In this review, we firstly discuss protein engineering strategies, which are emerged for improving the biocatalysis activity of biocatalysts. Then, we summarize metabolic engineering strategies that are promoting the development of microbial cell factories. Next, we illustrate the necessity of using the combining strategy of protein engineering and metabolic engineering for efficient biocatalysts. Lastly, future perspectives about the development and application of novel biocatalyst strategies are discussed. This review provides theoretical guidance for the development of efficient, sustainable, and economical bioprocesses mediated by novel biocatalysts.
Collapse
Affiliation(s)
- Yameng Xu
- Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, Jiangnan University, Wuxi 214122, PR China; Science Center for Future Foods, Jiangnan University, Wuxi 214122, PR China
| | - Yaokang Wu
- Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, Jiangnan University, Wuxi 214122, PR China; Science Center for Future Foods, Jiangnan University, Wuxi 214122, PR China
| | - Xueqin Lv
- Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, Jiangnan University, Wuxi 214122, PR China; Science Center for Future Foods, Jiangnan University, Wuxi 214122, PR China
| | - Guoyun Sun
- Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, Jiangnan University, Wuxi 214122, PR China; Science Center for Future Foods, Jiangnan University, Wuxi 214122, PR China
| | - Hongzhi Zhang
- Shandong Runde Biotechnology Co., Ltd., Tai'an 271000, PR China
| | - Taichi Chen
- Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, Jiangnan University, Wuxi 214122, PR China; Science Center for Future Foods, Jiangnan University, Wuxi 214122, PR China
| | - Guocheng Du
- Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, Jiangnan University, Wuxi 214122, PR China; Science Center for Future Foods, Jiangnan University, Wuxi 214122, PR China
| | - Jianghua Li
- Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, Jiangnan University, Wuxi 214122, PR China; Science Center for Future Foods, Jiangnan University, Wuxi 214122, PR China
| | - Long Liu
- Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, Jiangnan University, Wuxi 214122, PR China; Science Center for Future Foods, Jiangnan University, Wuxi 214122, PR China.
| |
Collapse
|
26
|
Cakmak A, Celik MH. Personalized Metabolic Analysis of Diseases. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2021; 18:1014-1025. [PMID: 32750887 DOI: 10.1109/tcbb.2020.3008196] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
The metabolic wiring of patient cells is altered drastically in many diseases, including cancer. Understanding the nature of such changes may pave the way for new therapeutic opportunities as well as the development of personalized treatment strategies for patients. In this paper, we propose an algorithm called Metabolitics, which allows systems-level analysis of changes in the biochemical network of cells in disease states. It enables the study of a disease at both reaction- and pathway-level granularities for a detailed and summarized view of disease etiology. Metabolitics employs flux variability analysis with a dynamically built objective function based on biofluid metabolomics measurements in a personalized manner. Moreover, Metabolitics builds supervised classification models to discriminate between patients and healthy subjects based on the computed metabolic network changes. The use of Metabolitics is demonstrated for three distinct diseases, namely, breast cancer, Crohn's disease, and colorectal cancer. Our results show that the constructed supervised learning models successfully differentiate patients from healthy individuals by an average f1-score of 88 percent. Besides, in addition to the confirmation of previously reported breast cancer-associated pathways, we discovered that Biotin Metabolism along with Arginine and Proline Metabolism is subject to a significant increase in flux capacity, which have not been reported before.
Collapse
|
27
|
Helmy M, Smith D, Selvarajoo K. Systems biology approaches integrated with artificial intelligence for optimized metabolic engineering. Metab Eng Commun 2020; 11:e00149. [PMID: 33072513 PMCID: PMC7546651 DOI: 10.1016/j.mec.2020.e00149] [Citation(s) in RCA: 32] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2020] [Revised: 10/01/2020] [Accepted: 10/07/2020] [Indexed: 12/05/2022] Open
Abstract
Metabolic engineering aims to maximize the production of bio-economically important substances (compounds, enzymes, or other proteins) through the optimization of the genetics, cellular processes and growth conditions of microorganisms. This requires detailed understanding of underlying metabolic pathways involved in the production of the targeted substances, and how the cellular processes or growth conditions are regulated by the engineering. To achieve this goal, a large system of experimental techniques, compound libraries, computational methods and data resources, including multi-omics data, are used. The recent advent of multi-omics systems biology approaches significantly impacted the field by opening new avenues to perform dynamic and large-scale analyses that deepen our knowledge on the manipulations. However, with the enormous transcriptomics, proteomics and metabolomics available, it is a daunting task to integrate the data for a more holistic understanding. Novel data mining and analytics approaches, including Artificial Intelligence (AI), can provide breakthroughs where traditional low-throughput experiment-alone methods cannot easily achieve. Here, we review the latest attempts of combining systems biology and AI in metabolic engineering research, and highlight how this alliance can help overcome the current challenges facing industrial biotechnology, especially for food-related substances and compounds using microorganisms.
Collapse
Affiliation(s)
- Mohamed Helmy
- Singapore Institute of Food and Biotechnology Innovation (SIFBI), Agency for Science, Technology and Research (A∗STAR), Singapore, Singapore
| | - Derek Smith
- Singapore Institute of Food and Biotechnology Innovation (SIFBI), Agency for Science, Technology and Research (A∗STAR), Singapore, Singapore
| | - Kumar Selvarajoo
- Singapore Institute of Food and Biotechnology Innovation (SIFBI), Agency for Science, Technology and Research (A∗STAR), Singapore, Singapore
- Synthetic Biology for Clinical and Technological Innovation (SynCTI), National University of Singapore (NUS), Singapore, Singapore
| |
Collapse
|
28
|
Lawson CE, Martí JM, Radivojevic T, Jonnalagadda SVR, Gentz R, Hillson NJ, Peisert S, Kim J, Simmons BA, Petzold CJ, Singer SW, Mukhopadhyay A, Tanjore D, Dunn JG, Garcia Martin H. Machine learning for metabolic engineering: A review. Metab Eng 2020; 63:34-60. [PMID: 33221420 DOI: 10.1016/j.ymben.2020.10.005] [Citation(s) in RCA: 86] [Impact Index Per Article: 21.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2020] [Revised: 10/22/2020] [Accepted: 10/31/2020] [Indexed: 12/14/2022]
Abstract
Machine learning provides researchers a unique opportunity to make metabolic engineering more predictable. In this review, we offer an introduction to this discipline in terms that are relatable to metabolic engineers, as well as providing in-depth illustrative examples leveraging omics data and improving production. We also include practical advice for the practitioner in terms of data management, algorithm libraries, computational resources, and important non-technical issues. A variety of applications ranging from pathway construction and optimization, to genetic editing optimization, cell factory testing, and production scale-up are discussed. Moreover, the promising relationship between machine learning and mechanistic models is thoroughly reviewed. Finally, the future perspectives and most promising directions for this combination of disciplines are examined.
Collapse
Affiliation(s)
- Christopher E Lawson
- Biological Systems and Engineering, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA; Joint BioEnergy Institute, Emeryville, CA, 94608, USA
| | - Jose Manuel Martí
- Biological Systems and Engineering, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA; Joint BioEnergy Institute, Emeryville, CA, 94608, USA; DOE Agile BioFoundry, Emeryville, CA, 94608, USA
| | - Tijana Radivojevic
- Biological Systems and Engineering, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA; Joint BioEnergy Institute, Emeryville, CA, 94608, USA; DOE Agile BioFoundry, Emeryville, CA, 94608, USA
| | - Sai Vamshi R Jonnalagadda
- Biological Systems and Engineering, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA; Joint BioEnergy Institute, Emeryville, CA, 94608, USA; DOE Agile BioFoundry, Emeryville, CA, 94608, USA
| | - Reinhard Gentz
- Biological Systems and Engineering, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA; Joint BioEnergy Institute, Emeryville, CA, 94608, USA; Computational Research Division, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA
| | - Nathan J Hillson
- Biological Systems and Engineering, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA; Joint BioEnergy Institute, Emeryville, CA, 94608, USA; DOE Agile BioFoundry, Emeryville, CA, 94608, USA
| | - Sean Peisert
- Biological Systems and Engineering, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA; Computational Research Division, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA; University of California Davis, Davis, CA, 95616, USA
| | - Joonhoon Kim
- Joint BioEnergy Institute, Emeryville, CA, 94608, USA; Pacific Northwest National Laboratory, Richland, 99354, WA, USA
| | - Blake A Simmons
- Biological Systems and Engineering, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA; Joint BioEnergy Institute, Emeryville, CA, 94608, USA; DOE Agile BioFoundry, Emeryville, CA, 94608, USA
| | - Christopher J Petzold
- Biological Systems and Engineering, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA; Joint BioEnergy Institute, Emeryville, CA, 94608, USA; DOE Agile BioFoundry, Emeryville, CA, 94608, USA
| | - Steven W Singer
- Biological Systems and Engineering, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA; Joint BioEnergy Institute, Emeryville, CA, 94608, USA
| | - Aindrila Mukhopadhyay
- Biological Systems and Engineering, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA; Joint BioEnergy Institute, Emeryville, CA, 94608, USA; Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, USA
| | - Deepti Tanjore
- Biological Systems and Engineering, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA; Advanced Biofuels and Bioproducts Process Development Unit, Emeryville, CA, 94608, USA
| | | | - Hector Garcia Martin
- Biological Systems and Engineering, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA; Joint BioEnergy Institute, Emeryville, CA, 94608, USA; DOE Agile BioFoundry, Emeryville, CA, 94608, USA; Basque Center for Applied Mathematics, 48009, Bilbao, Spain; Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, USA.
| |
Collapse
|
29
|
Antonakoudis A, Barbosa R, Kotidis P, Kontoravdi C. The era of big data: Genome-scale modelling meets machine learning. Comput Struct Biotechnol J 2020; 18:3287-3300. [PMID: 33240470 PMCID: PMC7663219 DOI: 10.1016/j.csbj.2020.10.011] [Citation(s) in RCA: 40] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2020] [Revised: 10/07/2020] [Accepted: 10/08/2020] [Indexed: 12/15/2022] Open
Abstract
With omics data being generated at an unprecedented rate, genome-scale modelling has become pivotal in its organisation and analysis. However, machine learning methods have been gaining ground in cases where knowledge is insufficient to represent the mechanisms underlying such data or as a means for data curation prior to attempting mechanistic modelling. We discuss the latest advances in genome-scale modelling and the development of optimisation algorithms for network and error reduction, intracellular constraining and applications to strain design. We further review applications of supervised and unsupervised machine learning methods to omics datasets from microbial and mammalian cell systems and present efforts to harness the potential of both modelling approaches through hybrid modelling.
Collapse
Affiliation(s)
| | | | | | - Cleo Kontoravdi
- Department of Chemical Engineering, Imperial College London, London SW7 2AZ, United Kingdom
| |
Collapse
|
30
|
Rana P, Berry C, Ghosh P, Fong SS. Recent advances on constraint-based models by integrating machine learning. Curr Opin Biotechnol 2020; 64:85-91. [DOI: 10.1016/j.copbio.2019.11.007] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2019] [Revised: 11/04/2019] [Accepted: 11/06/2019] [Indexed: 01/06/2023]
|
31
|
Wu C, Cano M, Gao X, Lo J, Maness P, Xiong W. A quantitative lens on anaerobic life: leveraging the state-of-the-art fluxomics approach to explore clostridial metabolism. Curr Opin Biotechnol 2020; 64:47-54. [DOI: 10.1016/j.copbio.2019.09.012] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2019] [Revised: 09/02/2019] [Accepted: 09/12/2019] [Indexed: 10/25/2022]
|
32
|
Volk MJ, Lourentzou I, Mishra S, Vo LT, Zhai C, Zhao H. Biosystems Design by Machine Learning. ACS Synth Biol 2020; 9:1514-1533. [PMID: 32485108 DOI: 10.1021/acssynbio.0c00129] [Citation(s) in RCA: 52] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
Biosystems such as enzymes, pathways, and whole cells have been increasingly explored for biotechnological applications. However, the intricate connectivity and resulting complexity of biosystems poses a major hurdle in designing biosystems with desirable features. As -omics and other high throughput technologies have been rapidly developed, the promise of applying machine learning (ML) techniques in biosystems design has started to become a reality. ML models enable the identification of patterns within complicated biological data across multiple scales of analysis and can augment biosystems design applications by predicting new candidates for optimized performance. ML is being used at every stage of biosystems design to help find nonobvious engineering solutions with fewer design iterations. In this review, we first describe commonly used models and modeling paradigms within ML. We then discuss some applications of these models that have already shown success in biotechnological applications. Moreover, we discuss successful applications at all scales of biosystems design, including nucleic acids, genetic circuits, proteins, pathways, genomes, and bioprocesses. Finally, we discuss some limitations of these methods and potential solutions as well as prospects of the combination of ML and biosystems design.
Collapse
|
33
|
Liebal UW, Phan ANT, Sudhakar M, Raman K, Blank LM. Machine Learning Applications for Mass Spectrometry-Based Metabolomics. Metabolites 2020; 10:E243. [PMID: 32545768 PMCID: PMC7345470 DOI: 10.3390/metabo10060243] [Citation(s) in RCA: 133] [Impact Index Per Article: 33.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2020] [Revised: 06/09/2020] [Accepted: 06/11/2020] [Indexed: 12/20/2022] Open
Abstract
The metabolome of an organism depends on environmental factors and intracellular regulation and provides information about the physiological conditions. Metabolomics helps to understand disease progression in clinical settings or estimate metabolite overproduction for metabolic engineering. The most popular analytical metabolomics platform is mass spectrometry (MS). However, MS metabolome data analysis is complicated, since metabolites interact nonlinearly, and the data structures themselves are complex. Machine learning methods have become immensely popular for statistical analysis due to the inherent nonlinear data representation and the ability to process large and heterogeneous data rapidly. In this review, we address recent developments in using machine learning for processing MS spectra and show how machine learning generates new biological insights. In particular, supervised machine learning has great potential in metabolomics research because of the ability to supply quantitative predictions. We review here commonly used tools, such as random forest, support vector machines, artificial neural networks, and genetic algorithms. During processing steps, the supervised machine learning methods help peak picking, normalization, and missing data imputation. For knowledge-driven analysis, machine learning contributes to biomarker detection, classification and regression, biochemical pathway identification, and carbon flux determination. Of important relevance is the combination of different omics data to identify the contributions of the various regulatory levels. Our overview of the recent publications also highlights that data quality determines analysis quality, but also adds to the challenge of choosing the right model for the data. Machine learning methods applied to MS-based metabolomics ease data analysis and can support clinical decisions, guide metabolic engineering, and stimulate fundamental biological discoveries.
Collapse
Affiliation(s)
- Ulf W. Liebal
- Institute of Applied Microbiology, Aachen Biology and Biotechnology, RWTH Aachen University, Worringer Weg 1, 52074 Aachen, Germany;
| | - An N. T. Phan
- Institute of Applied Microbiology, Aachen Biology and Biotechnology, RWTH Aachen University, Worringer Weg 1, 52074 Aachen, Germany;
| | - Malvika Sudhakar
- Department of Biotechnology, Bhupat and Juoti Mehta School of Biosciences, Indian Institute of Technology (IIT) Madras, Chennai 600 036, India; (M.S.); (K.R.)
- Initiative for Biological Systems Engineering, IIT Madras, Chennai 600 036, India
- Robert Bosch Centre for Data Science and Artificial Intelligence (RBCDSAI), IIT Madras, Chennai 600 036, India
| | - Karthik Raman
- Department of Biotechnology, Bhupat and Juoti Mehta School of Biosciences, Indian Institute of Technology (IIT) Madras, Chennai 600 036, India; (M.S.); (K.R.)
- Initiative for Biological Systems Engineering, IIT Madras, Chennai 600 036, India
- Robert Bosch Centre for Data Science and Artificial Intelligence (RBCDSAI), IIT Madras, Chennai 600 036, India
| | - Lars M. Blank
- Institute of Applied Microbiology, Aachen Biology and Biotechnology, RWTH Aachen University, Worringer Weg 1, 52074 Aachen, Germany;
| |
Collapse
|
34
|
Blanco-Míguez A, Fdez-Riverola F, Sánchez B, Lourenço A. Resources and tools for the high-throughput, multi-omic study of intestinal microbiota. Brief Bioinform 2020; 20:1032-1056. [PMID: 29186315 DOI: 10.1093/bib/bbx156] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2017] [Revised: 10/23/2017] [Indexed: 12/18/2022] Open
Abstract
The human gut microbiome impacts several aspects of human health and disease, including digestion, drug metabolism and the propensity to develop various inflammatory, autoimmune and metabolic diseases. Many of the molecular processes that play a role in the activity and dynamics of the microbiota go beyond species and genic composition and thus, their understanding requires advanced bioinformatics support. This article aims to provide an up-to-date view of the resources and software tools that are being developed and used in human gut microbiome research, in particular data integration and systems-level analysis efforts. These efforts demonstrate the power of standardized and reproducible computational workflows for integrating and analysing varied omics data and gaining deeper insights into microbe community structure and function as well as host-microbe interactions.
Collapse
Affiliation(s)
| | | | | | - Anália Lourenço
- Dpto. de Informática - Universidade de Vigo, ESEI - Escuela Superior de Ingeniería Informática, Edificio politécnico, Campus Universitario As Lagoas s/n, 32004 Ourense, Spain
| |
Collapse
|
35
|
Predicting Microbial Species in a River Based on Physicochemical Properties by Bio-Inspired Metaheuristic Optimized Machine Learning. SUSTAINABILITY 2019. [DOI: 10.3390/su11246889] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
Abstract
The main goal of the analysis of microbial ecology is to understand the relationship between Earth’s microbial community and their functions in the environment. This paper presents a proof-of-concept research to develop a bioclimatic modeling approach that leverages artificial intelligence techniques to identify the microbial species in a river as a function of physicochemical parameters. Feature reduction and selection are both utilized in the data preprocessing owing to the scarce of available data points collected and missing values of physicochemical attributes from a river in Southeast China. A bio-inspired metaheuristic optimized machine learner, which supports the adjustment to the multiple-output prediction form, is used in bioclimatic modeling. The accuracy of prediction and applicability of the model can help microbiologists and ecologists in quantifying the predicted microbial species for further experimental planning with minimal expenditure, which is become one of the most serious issues when facing dramatic changes of environmental conditions caused by global warming. This work demonstrates a neoteric approach for potential use in predicting preliminary microbial structures in the environment.
Collapse
|
36
|
Zampieri G, Vijayakumar S, Yaneske E, Angione C. Machine and deep learning meet genome-scale metabolic modeling. PLoS Comput Biol 2019; 15:e1007084. [PMID: 31295267 PMCID: PMC6622478 DOI: 10.1371/journal.pcbi.1007084] [Citation(s) in RCA: 150] [Impact Index Per Article: 30.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022] Open
Abstract
Omic data analysis is steadily growing as a driver of basic and applied molecular biology research. Core to the interpretation of complex and heterogeneous biological phenotypes are computational approaches in the fields of statistics and machine learning. In parallel, constraint-based metabolic modeling has established itself as the main tool to investigate large-scale relationships between genotype, phenotype, and environment. The development and application of these methodological frameworks have occurred independently for the most part, whereas the potential of their integration for biological, biomedical, and biotechnological research is less known. Here, we describe how machine learning and constraint-based modeling can be combined, reviewing recent works at the intersection of both domains and discussing the mathematical and practical aspects involved. We overlap systematic classifications from both frameworks, making them accessible to nonexperts. Finally, we delineate potential future scenarios, propose new joint theoretical frameworks, and suggest concrete points of investigation for this joint subfield. A multiview approach merging experimental and knowledge-driven omic data through machine learning methods can incorporate key mechanistic information in an otherwise biologically-agnostic learning process.
Collapse
Affiliation(s)
- Guido Zampieri
- Department of Computer Science and Information Systems, Teesside University, Middlesbrough, United Kingdom
| | - Supreeta Vijayakumar
- Department of Computer Science and Information Systems, Teesside University, Middlesbrough, United Kingdom
| | - Elisabeth Yaneske
- Department of Computer Science and Information Systems, Teesside University, Middlesbrough, United Kingdom
| | - Claudio Angione
- Department of Computer Science and Information Systems, Teesside University, Middlesbrough, United Kingdom
- Healthcare Innovation Centre, Teesside University, Middlesbrough, United Kingdom
| |
Collapse
|
37
|
Human Systems Biology and Metabolic Modelling: A Review-From Disease Metabolism to Precision Medicine. BIOMED RESEARCH INTERNATIONAL 2019; 2019:8304260. [PMID: 31281846 PMCID: PMC6590590 DOI: 10.1155/2019/8304260] [Citation(s) in RCA: 42] [Impact Index Per Article: 8.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/01/2018] [Revised: 02/07/2019] [Accepted: 05/20/2019] [Indexed: 01/06/2023]
Abstract
In cell and molecular biology, metabolism is the only system that can be fully simulated at genome scale. Metabolic systems biology offers powerful abstraction tools to simulate all known metabolic reactions in a cell, therefore providing a snapshot that is close to its observable phenotype. In this review, we cover the 15 years of human metabolic modelling. We show that, although the past five years have not experienced large improvements in the size of the gene and metabolite sets in human metabolic models, their accuracy is rapidly increasing. We also describe how condition-, tissue-, and patient-specific metabolic models shed light on cell-specific changes occurring in the metabolic network, therefore predicting biomarkers of disease metabolism. We finally discuss current challenges and future promising directions for this research field, including machine/deep learning and precision medicine. In the omics era, profiling patients and biological processes from a multiomic point of view is becoming more common and less expensive. Starting from multiomic data collected from patients and N-of-1 trials where individual patients constitute different case studies, methods for model-building and data integration are being used to generate patient-specific models. Coupled with state-of-the-art machine learning methods, this will allow characterizing each patient's disease phenotype and delivering precision medicine solutions, therefore leading to preventative medicine, reduced treatment, and in silico clinical trials.
Collapse
|
38
|
Presnell KV, Alper HS. Systems Metabolic Engineering Meets Machine Learning: A New Era for Data-Driven Metabolic Engineering. Biotechnol J 2019; 14:e1800416. [PMID: 30927499 DOI: 10.1002/biot.201800416] [Citation(s) in RCA: 31] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2019] [Revised: 02/20/2019] [Indexed: 12/30/2022]
Abstract
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.
Collapse
Affiliation(s)
- Kristin V Presnell
- McKetta Department of Chemical Engineering, The University of Texas at Austin, 200 E Dean Keeton St. Stop C0400, Austin, TX, 78712, USA
| | - Hal S Alper
- McKetta Department of Chemical Engineering, The University of Texas at Austin, 200 E Dean Keeton St. Stop C0400, Austin, TX, 78712, USA.,Institute for Cellular and Molecular Biology, The University of Texas at Austin, 100 E 24 St., Austin, TX, 78712, USA
| |
Collapse
|
39
|
Oyetunde T, Liu D, Martin HG, Tang YJ. Machine learning framework for assessment of microbial factory performance. PLoS One 2019; 14:e0210558. [PMID: 30645629 PMCID: PMC6333410 DOI: 10.1371/journal.pone.0210558] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2018] [Accepted: 12/27/2018] [Indexed: 01/01/2023] Open
Abstract
Metabolic models can estimate intrinsic product yields for microbial factories, but such frameworks struggle to predict cell performance (including product titer or rate) under suboptimal metabolism and complex bioprocess conditions. On the other hand, machine learning, complementary to metabolic modeling necessitates large amounts of data. Building such a database for metabolic engineering designs requires significant manpower and is prone to human errors and bias. We propose an approach to integrate data-driven methods with genome scale metabolic model for assessment of microbial bio-production (yield, titer and rate). Using engineered E. coli as an example, we manually extracted and curated a data set comprising about 1200 experimentally realized cell factories from ~100 papers. We furthermore augmented the key design features (e.g., genetic modifications and bioprocess variables) extracted from literature with additional features derived from running the genome-scale metabolic model iML1515 simulations with constraints that match the experimental data. Then, data augmentation and ensemble learning (e.g., support vector machines, gradient boosted trees, and neural networks in a stacked regressor model) are employed to alleviate the challenges of sparse, non-standardized, and incomplete data sets, while multiple correspondence analysis/principal component analysis are used to rank influential factors on bio-production. The hybrid framework demonstrates a reasonably high cross-validation accuracy for prediction of E.coli factory performance metrics under presumed bioprocess and pathway conditions (Pearson correlation coefficients between 0.8 and 0.93 on new data not seen by the model).
Collapse
Affiliation(s)
- Tolutola Oyetunde
- Department of Energy, Environmental and Chemical Engineering, Washington University, Saint Louis, Missouri, United States of America
| | - Di Liu
- Department of Energy, Environmental and Chemical Engineering, Washington University, Saint Louis, Missouri, United States of America
| | - Hector Garcia Martin
- DOE Joint BioEnergy Institute, Emeryville, California, United States of America
- DOE Agile BioFoundry, Emeryville, California, United States of America
- Biological Systems and Engineering Division, Lawrence Berkeley National Lab, Berkeley, California, United States of America
- BCAM, Basque Center for Applied Mathematics, Bilbao, Spain
| | - Yinjie J. Tang
- Department of Energy, Environmental and Chemical Engineering, Washington University, Saint Louis, Missouri, United States of America
| |
Collapse
|
40
|
Heckmann D, Lloyd CJ, Mih N, Ha Y, Zielinski DC, Haiman ZB, Desouki AA, Lercher MJ, Palsson BO. Machine learning applied to enzyme turnover numbers reveals protein structural correlates and improves metabolic models. Nat Commun 2018; 9:5252. [PMID: 30531987 PMCID: PMC6286351 DOI: 10.1038/s41467-018-07652-6] [Citation(s) in RCA: 100] [Impact Index Per Article: 16.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2018] [Accepted: 11/15/2018] [Indexed: 11/09/2022] Open
Abstract
Knowing the catalytic turnover numbers of enzymes is essential for understanding the growth rate, proteome composition, and physiology of organisms, but experimental data on enzyme turnover numbers is sparse and noisy. Here, we demonstrate that machine learning can successfully predict catalytic turnover numbers in Escherichia coli based on integrated data on enzyme biochemistry, protein structure, and network context. We identify a diverse set of features that are consistently predictive for both in vivo and in vitro enzyme turnover rates, revealing novel protein structural correlates of catalytic turnover. We use our predictions to parameterize two mechanistic genome-scale modelling frameworks for proteome-limited metabolism, leading to significantly higher accuracy in the prediction of quantitative proteome data than previous approaches. The presented machine learning models thus provide a valuable tool for understanding metabolism and the proteome at the genome scale, and elucidate structural, biochemical, and network properties that underlie enzyme kinetics.
Collapse
Affiliation(s)
- David Heckmann
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, 92093-0412, USA.
| | - Colton J Lloyd
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, 92093-0412, USA
| | - Nathan Mih
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, 92093-0412, USA
| | - Yuanchi Ha
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, 92093-0412, USA
| | - Daniel C Zielinski
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, 92093-0412, USA
| | - Zachary B Haiman
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, 92093-0412, USA
| | - Abdelmoneim Amer Desouki
- Institute for Computer Science and Department of Biology, Heinrich Heine University, 40225, Düsseldorf, Germany
| | - Martin J Lercher
- Institute for Computer Science and Department of Biology, Heinrich Heine University, 40225, Düsseldorf, Germany
| | - Bernhard O Palsson
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, 92093-0412, USA.
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800, Lyngby, Denmark.
| |
Collapse
|
41
|
Oyetunde T, Bao FS, Chen JW, Martin HG, Tang YJ. Leveraging knowledge engineering and machine learning for microbial bio-manufacturing. Biotechnol Adv 2018; 36:1308-1315. [DOI: 10.1016/j.biotechadv.2018.04.008] [Citation(s) in RCA: 42] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2017] [Revised: 02/27/2018] [Accepted: 04/26/2018] [Indexed: 12/21/2022]
|
42
|
Abernathy MH, He L, Tang YJ. Channeling in native microbial pathways: Implications and challenges for metabolic engineering. Biotechnol Adv 2017. [DOI: 10.1016/j.biotechadv.2017.06.004] [Citation(s) in RCA: 42] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
|
43
|
Long CP, Gonzalez JE, Feist AM, Palsson BO, Antoniewicz MR. Fast growth phenotype of E. coli K-12 from adaptive laboratory evolution does not require intracellular flux rewiring. Metab Eng 2017; 44:100-107. [PMID: 28951266 DOI: 10.1016/j.ymben.2017.09.012] [Citation(s) in RCA: 48] [Impact Index Per Article: 6.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2017] [Revised: 08/12/2017] [Accepted: 09/19/2017] [Indexed: 11/30/2022]
Abstract
Adaptive laboratory evolution (ALE) is a widely-used method for improving the fitness of microorganisms in selected environmental conditions. It has been applied previously to Escherichia coli K-12 MG1655 during aerobic exponential growth on glucose minimal media, a frequently used model organism and growth condition, to probe the limits of E. coli growth rate and gain insights into fast growth phenotypes. Previous studies have described up to 1.6-fold increases in growth rate following ALE, and have identified key causal genetic mutations and changes in transcriptional patterns. Here, we report for the first time intracellular metabolic fluxes for six such adaptively evolved strains, as determined by high-resolution 13C-metabolic flux analysis. Interestingly, we found that intracellular metabolic pathway usage changed very little following adaptive evolution. Instead, at the level of central carbon metabolism the faster growth was facilitated by proportional increases in glucose uptake and all intracellular rates. Of the six evolved strains studied here, only one strain showed a small degree of flux rewiring, and this was also the strain with unique genetic mutations. A comparison of fluxes with two other wild-type (unevolved) E. coli strains, BW25113 and BL21, showed that inter-strain differences are greater than differences between the parental and evolved strains. Principal component analysis highlighted that nearly all flux differences (95%) between the nine strains were captured by only two principal components. The distance between measured and flux balance analysis predicted fluxes was also investigated. It suggested a relatively wide range of similar stoichiometric optima, which opens new questions about the path-dependency of adaptive evolution.
Collapse
Affiliation(s)
- Christopher P Long
- Department of Chemical and Biomolecular Engineering, Metabolic Engineering and Systems Biology Laboratory, University of Delaware, Newark, DE 19716, USA
| | - Jacqueline E Gonzalez
- Department of Chemical and Biomolecular Engineering, Metabolic Engineering and Systems Biology Laboratory, University of Delaware, Newark, DE 19716, USA
| | - Adam M Feist
- Department of Bioengineering, University of California, San Diego, CA 92093, USA; Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800 Lyngby, Denmark
| | - Bernhard O Palsson
- Department of Bioengineering, University of California, San Diego, CA 92093, USA; Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800 Lyngby, Denmark
| | - Maciek R Antoniewicz
- Department of Chemical and Biomolecular Engineering, Metabolic Engineering and Systems Biology Laboratory, University of Delaware, Newark, DE 19716, USA.
| |
Collapse
|
44
|
Literature mining supports a next-generation modeling approach to predict cellular byproduct secretion. Metab Eng 2016; 39:220-227. [PMID: 27986597 DOI: 10.1016/j.ymben.2016.12.004] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2016] [Revised: 10/19/2016] [Accepted: 12/07/2016] [Indexed: 11/21/2022]
Abstract
The metabolic byproducts secreted by growing cells can be easily measured and provide a window into the state of a cell; they have been essential to the development of microbiology, cancer biology, and biotechnology. Progress in computational modeling of cells has made it possible to predict metabolic byproduct secretion with bottom-up reconstructions of metabolic networks. However, owing to a lack of data, it has not been possible to validate these predictions across a wide range of strains and conditions. Through literature mining, we were able to generate a database of Escherichia coli strains and their experimentally measured byproduct secretions. We simulated these strains in six historical genome-scale models of E. coli, and we report that the predictive power of the models has increased as they have expanded in size and scope. The latest genome-scale model of metabolism correctly predicts byproduct secretion for 35/89 (39%) of designs. The next-generation genome-scale model of metabolism and gene expression (ME-model) correctly predicts byproduct secretion for 40/89 (45%) of designs, and we show that ME-model predictions could be further improved through kinetic parameterization. We analyze the failure modes of these simulations and discuss opportunities to improve prediction of byproduct secretion.
Collapse
|
45
|
He L, Wu SG, Zhang M, Chen Y, Tang YJ. WUFlux: an open-source platform for 13C metabolic flux analysis of bacterial metabolism. BMC Bioinformatics 2016; 17:444. [PMID: 27814681 PMCID: PMC5096001 DOI: 10.1186/s12859-016-1314-0] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2016] [Accepted: 10/26/2016] [Indexed: 12/21/2022] Open
Abstract
Background Flux analyses, including flux balance analysis (FBA) and 13C-metabolic flux analysis (13C-MFA), offer direct insights into cell metabolism, and have been widely used to characterize model and non-model microbial species. Nonetheless, constructing the 13C-MFA model and performing flux calculation are demanding for new learners, because they require knowledge of metabolic networks, carbon transitions, and computer programming. To facilitate and standardize the 13C-MFA modeling work, we set out to publish a user-friendly and programming-free platform (WUFlux) for flux calculations in MATLAB®. Results We constructed an open-source platform for steady-state 13C-MFA. Using GUIDE (graphical user interface design environment) in MATLAB, we built a user interface that allows users to modify models based on their own experimental conditions. WUFlux is capable of directly correcting mass spectrum data of TBDMS (N-tert-butyldimethylsilyl-N-methyltrifluoroacetamide)-derivatized proteinogenic amino acids by removing background noise. To simplify 13C-MFA of different prokaryotic species, the software provides several metabolic network templates, including those for chemoheterotrophic bacteria and mixotrophic cyanobacteria. Users can modify the network and constraints, and then analyze the microbial carbon and energy metabolisms of various carbon substrates (e.g., glucose, pyruvate/lactate, acetate, xylose, and glycerol). WUFlux also offers several ways of visualizing the flux results with respect to the constructed network. To validate our model’s applicability, we have compared and discussed the flux results obtained from WUFlux and other MFA software. We have also illustrated how model constraints of cofactor and ATP balances influence fluxome results. Conclusion Open-source software for 13C-MFA, WUFlux, with a user-friendly interface and easy-to-modify templates, is now available at http://www.13cmfa.org/or (http://tang.eece.wustl.edu/ToolDevelopment.htm). We will continue documenting curated models of non-model microbial species and improving WUFlux performance. Electronic supplementary material The online version of this article (doi:10.1186/s12859-016-1314-0) contains supplementary material, which is available to authorized users.
Collapse
Affiliation(s)
- Lian He
- Department of Energy, Environmental and Chemical Engineering, Washington University, St. Louis, MO, 63130, USA.
| | - Stephen G Wu
- Department of Energy, Environmental and Chemical Engineering, Washington University, St. Louis, MO, 63130, USA
| | - Muhan Zhang
- Department of Computer Science and Engineering, Washington University, St. Louis, MO, 63130, USA
| | - Yixin Chen
- Department of Computer Science and Engineering, Washington University, St. Louis, MO, 63130, USA
| | - Yinjie J Tang
- Department of Energy, Environmental and Chemical Engineering, Washington University, St. Louis, MO, 63130, USA.
| |
Collapse
|
46
|
Liu D, Wan N, Zhang F, Tang YJ, Wu SG. Enhancing fatty acid production in
Escherichia coli
by
Vitreoscilla
hemoglobin overexpression. Biotechnol Bioeng 2016; 114:463-467. [DOI: 10.1002/bit.26067] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2016] [Revised: 06/02/2016] [Accepted: 08/01/2016] [Indexed: 01/06/2023]
Affiliation(s)
- Di Liu
- Department of Energy, Environmental and Chemical EngineeringWashington University in St. LouisOne Brookings DriveSt. LouisMissouri63130
| | - Ni Wan
- Department of Energy, Environmental and Chemical EngineeringWashington University in St. LouisOne Brookings DriveSt. LouisMissouri63130
| | - Fuzhong Zhang
- Department of Energy, Environmental and Chemical EngineeringWashington University in St. LouisOne Brookings DriveSt. LouisMissouri63130
| | - Yinjie J. Tang
- Department of Energy, Environmental and Chemical EngineeringWashington University in St. LouisOne Brookings DriveSt. LouisMissouri63130
| | - Stephen G. Wu
- Department of Energy, Environmental and Chemical EngineeringWashington University in St. LouisOne Brookings DriveSt. LouisMissouri63130
| |
Collapse
|