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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.
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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.
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2
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Yeo HC, Selvarajoo K. Machine learning alternative to systems biology should not solely depend on data. Brief Bioinform 2022; 23:6731718. [PMID: 36184188 PMCID: PMC9677488 DOI: 10.1093/bib/bbac436] [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: 05/06/2022] [Revised: 08/24/2022] [Accepted: 09/09/2022] [Indexed: 12/14/2022] Open
Abstract
In recent years, artificial intelligence (AI)/machine learning has emerged as a plausible alternative to systems biology for the elucidation of biological phenomena and in attaining specified design objective in synthetic biology. Although considered highly disruptive with numerous notable successes so far, we seek to bring attention to both the fundamental and practical pitfalls of their usage, especially in illuminating emergent behaviors from chaotic or stochastic systems in biology. Without deliberating on their suitability and the required data qualities and pre-processing approaches beforehand, the research and development community could experience similar 'AI winters' that had plagued other fields. Instead, we anticipate the integration or combination of the two approaches, where appropriate, moving forward.
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Affiliation(s)
- Hock Chuan Yeo
- Bioinformatics Institute (BII), Agency for Science, Technology and Research (A*STAR), Singapore
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3
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Lapin A, Perfahl H, Jain HV, Reuss M. Integrating a dynamic central metabolism model of cancer cells with a hybrid 3D multiscale model for vascular hepatocellular carcinoma growth. Sci Rep 2022; 12:12373. [PMID: 35858953 PMCID: PMC9300625 DOI: 10.1038/s41598-022-15767-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2022] [Accepted: 06/29/2022] [Indexed: 11/09/2022] Open
Abstract
We develop here a novel modelling approach with the aim of closing the conceptual gap between tumour-level metabolic processes and the metabolic processes occurring in individual cancer cells. In particular, the metabolism in hepatocellular carcinoma derived cell lines (HEPG2 cells) has been well characterized but implementations of multiscale models integrating this known metabolism have not been previously reported. We therefore extend a previously published multiscale model of vascular tumour growth, and integrate it with an experimentally verified network of central metabolism in HEPG2 cells. This resultant combined model links spatially heterogeneous vascular tumour growth with known metabolic networks within tumour cells and accounts for blood flow, angiogenesis, vascular remodelling and nutrient/growth factor transport within a growing tumour, as well as the movement of, and interactions between normal and cancer cells. Model simulations report for the first time, predictions of spatially resolved time courses of core metabolites in HEPG2 cells. These simulations can be performed at a sufficient scale to incorporate clinically relevant features of different tumour systems using reasonable computational resources. Our results predict larger than expected temporal and spatial heterogeneity in the intracellular concentrations of glucose, oxygen, lactate pyruvate, f16bp and Acetyl-CoA. The integrated multiscale model developed here provides an ideal quantitative framework in which to study the relationship between dosage, timing, and scheduling of anti-neoplastic agents and the physiological effects of tumour metabolism at the cellular level. Such models, therefore, have the potential to inform treatment decisions when drug response is dependent on the metabolic state of individual cancer cells.
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Affiliation(s)
- Alexey Lapin
- Stuttgart Research Center Systems Biology, University Stuttgart, Stuttgart, Germany.,Institute of Chemical Process Engineering, University Stuttgart, Stuttgart, Germany
| | - Holger Perfahl
- Stuttgart Research Center Systems Biology, University Stuttgart, Stuttgart, Germany
| | - Harsh Vardhan Jain
- Department of Mathematics and Statistics, University of Minnesota Duluth, Duluth, MN, USA
| | - Matthias Reuss
- Stuttgart Research Center Systems Biology, University Stuttgart, Stuttgart, Germany.
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4
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Hybrid model to predict the effect of complex media changes in mammalian cell cultures. Biochem Eng J 2022. [DOI: 10.1016/j.bej.2022.108560] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
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5
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Tryptophan Production Maximization in a Fed-Batch Bioreactor with Modified E. coli Cells, by Optimizing Its Operating Policy Based on an Extended Structured Cell Kinetic Model. Bioengineering (Basel) 2021; 8:bioengineering8120210. [PMID: 34940363 PMCID: PMC8698263 DOI: 10.3390/bioengineering8120210] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2021] [Revised: 12/04/2021] [Accepted: 12/06/2021] [Indexed: 11/17/2022] Open
Abstract
Hybrid kinetic models, linking structured cell metabolic processes to the dynamics of macroscopic variables of the bioreactor, are more and more used in engineering evaluations to derive more precise predictions of the process dynamics under variable operating conditions. Depending on the cell model complexity, such a math tool can be used to evaluate the metabolic fluxes in relation to the bioreactor operating conditions, thus suggesting ways to genetically modify the microorganism for certain purposes. Even if development of such an extended dynamic model requires more experimental and computational efforts, its use is advantageous. The approached probative example refers to a model simulating the dynamics of nanoscale variables from several pathways of the central carbon metabolism (CCM) of Escherichia coli cells, linked to the macroscopic state variables of a fed-batch bioreactor (FBR) used for the tryptophan (TRP) production. The used E. coli strain was modified to replace the PTS system for glucose (GLC) uptake with a more efficient one. The study presents multiple elements of novelty: (i) the experimentally validated modular model itself, and (ii) its efficiency in computationally deriving an optimal operation policy of the FBR.
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6
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Mirsalami SM, Alihosseini A. Selection of the most effective kinetic model of lactase hydrolysis by immobilized Aspergillus niger and free β-galactosidase. JOURNAL OF SAUDI CHEMICAL SOCIETY 2021. [DOI: 10.1016/j.jscs.2021.101395] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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7
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MARIA G. A CCM-based modular and hybrid kinetic model to simulate the tryptophan synthesis in a fed-batch bioreactor using modified E. coli cells. Comput Chem Eng 2021. [DOI: 10.1016/j.compchemeng.2021.107450] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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8
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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.
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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.
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9
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Maria G. In silico Determination of Some Conditions Leading to Glycolytic Oscillations and Their Interference With Some Other Processes in E. coli Cells. Front Chem 2020; 8:526679. [PMID: 33195042 PMCID: PMC7655968 DOI: 10.3389/fchem.2020.526679] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2020] [Accepted: 09/23/2020] [Indexed: 01/05/2023] Open
Abstract
Autonomous oscillations of species levels in the glycolysis express the self-control of this essential cellular pathway belonging to the central carbon metabolism (CCM), and this phenomenon takes place in a large number of bacteria. Oscillations of glycolytic intermediates in living cells occur according to the environmental conditions and to the cell characteristics, especially the adenosine triphosphate (ATP) recovery system. Determining the conditions that lead to the occurrence and maintenance of the glycolytic oscillations can present immediate practical applications. Such a model-based analysis allows in silico (model-based) design of genetically modified microorganisms (GMO) with certain characteristics of interest for the biosynthesis industry, medicine, etc. Based on our kinetic model validated in previous works, this paper aims to in silico identify operating parameters and cell factors leading to the occurrence of stable glycolytic oscillations in the Escherichia coli cells. As long as most of the glycolytic intermediates are involved in various cellular metabolic pathways belonging to the CCM, evaluation of the dynamics and average level of its intermediates is of high importance for further applicative analyses. As an example, by using a lumped kinetic model for tryptophan (TRP) synthesis from literature, and its own kinetic model for the oscillatory glycolysis, this paper highlights the influence of glycolytic oscillations on the oscillatory TRP synthesis through the PEP (phosphoenolpyruvate) glycolytic node shared by the two oscillatory processes. The numerical analysis allows further TRP production maximization in a fed-batch bioreactor (FBR).
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Affiliation(s)
- Gheorghe Maria
- Department of Chemical and Biochemical Engineering, University POLITEHNICA of Bucharest, Bucharest, Romania.,Chemical Sciences Section, Romanian Academy, Bucharest, Romania
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10
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Abstract
Abstract
Living organisms in analogy with chemical factories use simple molecules such as sugars to produce a variety of compounds which are necessary for sustaining life and some of which are also commercially valuable. The metabolisms of simple (such as bacteria) and higher organisms (such as plants) alike can be exploited to convert low value inputs into high value outputs. Unlike conventional chemical factories, microbial production chassis are not necessarily tuned for a single product overproduction. Despite the same end goal, metabolic and industrial engineers rely on different techniques for achieving productivity goals. Metabolic engineers cannot affect reaction rates by manipulating pressure and temperature, instead they have at their disposal a range of enzymes and transcriptional and translational processes to optimize accordingly. In this review, we first highlight how various analytical approaches used in metabolic engineering and synthetic biology are related to concepts developed in systems and control engineering. Specifically, how algorithmic concepts derived in operations research can help explain the structure and organization of metabolic networks. Finally, we consider the future directions and challenges faced by the field of metabolic network modeling and the possible contributions of concepts drawn from the classical fields of chemical and control engineering. The aim of the review is to offer a current perspective of metabolic engineering and all that it entails without requiring specialized knowledge of bioinformatics or systems biology.
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11
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In silico optimization of a bioreactor with an E. coli culture for tryptophan production by using a structured model coupling the oscillating glycolysis and tryptophan synthesis. Chem Eng Res Des 2018. [DOI: 10.1016/j.cherd.2018.05.011] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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12
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Portela RMC, von Stosch M, Oliveira R. Hybrid semiparametric systems for quantitative sequence-activity modeling of synthetic biological parts. Synth Biol (Oxf) 2018; 3:ysy010. [PMID: 32995518 PMCID: PMC7513808 DOI: 10.1093/synbio/ysy010] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2018] [Revised: 05/21/2018] [Accepted: 06/11/2018] [Indexed: 12/20/2022] Open
Abstract
Predicting the activity of modified biological parts is difficult due to the typically large size of nucleotide sequences, resulting in combinatorial designs that suffer from the "curse of dimensionality" problem. Mechanistic design methods are often limited by knowledge availability. Empirical methods typically require large data sets, which are difficult and/or costly to obtain. In this study, we explore for the first time the combination of both approaches within a formal hybrid semiparametric framework in an attempt to overcome the limitations of the current approaches. Protein translation as a function of the 5' untranslated region sequence in Escherichia coli is taken as case study. Thermodynamic modeling, partial least squares (PLS) and hybrid parallel combinations thereof are compared for different data sets and data partitioning scenarios. The results suggest a significant and systematic reduction of both calibration and prediction errors by the hybrid approach in comparison to standalone thermodynamic or PLS modeling. Although with different magnitudes, improvements are observed irrespective of sample size and partitioning method. All in all the results suggest an increase of predictive power by the hybrid method potentially leading to a more efficient design of biological parts.
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Affiliation(s)
- Rui M C Portela
- REQUIMTE/LAQV, Departamento de Química, Faculdade de Ciências e Tecnologia Universidade Nova de Lisboa, Caparica, Portugal
| | - Moritz von Stosch
- CEAM Faculty of Science, Agriculture and Engineering, Newcastle University, Newcastle upon Tyne, UK
| | - Rui Oliveira
- REQUIMTE/LAQV, Departamento de Química, Faculdade de Ciências e Tecnologia Universidade Nova de Lisboa, Caparica, Portugal
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13
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Costello Z, Martin HG. A machine learning approach to predict metabolic pathway dynamics from time-series multiomics data. NPJ Syst Biol Appl 2018; 4:19. [PMID: 29872542 PMCID: PMC5974308 DOI: 10.1038/s41540-018-0054-3] [Citation(s) in RCA: 109] [Impact Index Per Article: 18.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2017] [Revised: 04/11/2018] [Accepted: 04/20/2018] [Indexed: 02/01/2023] Open
Abstract
New synthetic biology capabilities hold the promise of dramatically improving our ability to engineer biological systems. However, a fundamental hurdle in realizing this potential is our inability to accurately predict biological behavior after modifying the corresponding genotype. Kinetic models have traditionally been used to predict pathway dynamics in bioengineered systems, but they take significant time to develop, and rely heavily on domain expertise. Here, we show that the combination of machine learning and abundant multiomics data (proteomics and metabolomics) can be used to effectively predict pathway dynamics in an automated fashion. The new method outperforms a classical kinetic model, and produces qualitative and quantitative predictions that can be used to productively guide bioengineering efforts. This method systematically leverages arbitrary amounts of new data to improve predictions, and does not assume any particular interactions, but rather implicitly chooses the most predictive ones.
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Affiliation(s)
- Zak Costello
- 1Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA USA.,DOE Agile Biofoundry, Emeryville, CA USA.,3DOE Joint BioEnergy Institute, Emeryville, CA USA
| | - Hector Garcia Martin
- 1Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA USA.,DOE Agile Biofoundry, Emeryville, CA USA.,3DOE Joint BioEnergy Institute, Emeryville, CA USA.,4BCAM, Basque Center for Applied Mathematics, Bilbao, Spain
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14
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Improved kinetic model of Escherichia coli central carbon metabolism in batch and continuous cultures. J Biosci Bioeng 2018; 125:251-257. [DOI: 10.1016/j.jbiosc.2017.09.005] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2017] [Revised: 09/01/2017] [Accepted: 09/16/2017] [Indexed: 11/21/2022]
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15
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Maria G, Gijiu CL, Maria C, Tociu C. Interference of the oscillating glycolysis with the oscillating tryptophan synthesis in the E. coli cells. Comput Chem Eng 2018. [DOI: 10.1016/j.compchemeng.2017.10.003] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022]
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16
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Chen X, Zhang C, Zou R, Stephanopoulos G, Too HP. In Vitro Metabolic Engineering of Amorpha-4,11-diene Biosynthesis at Enhanced Rate and Specific Yield of Production. ACS Synth Biol 2017; 6:1691-1700. [PMID: 28520394 DOI: 10.1021/acssynbio.6b00377] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
In vitro metabolic engineering is an alternative approach to cell-based biosynthesis. It offers unprecedented flexibility for the study of biochemical pathways, thus providing useful information for the rational design and assembly of reaction modules. Herein, we took the advantage of in vitro metabolic engineering to initially gain insight into the regulatory network of a reconstituted amorpha-4,11-diene (AD) synthetic pathway. Guided by lin-log approximation, we rapidly identified the hitherto unrecognized inhibition of adenosine triphosphate (ATP) on both farnesyl pyrophosphate synthase (FPPS) and amorpha-4,11-diene synthase (ADS). Furthermore, the byproduct, pyrophosphate (PPi), potently inhibits ADS, but not FPPS. To lower the inhibition, an ATP recycling system and pyrophosphatase were used and resulted in a significant (∼3-fold) enhancement in the rate of AD production (∼5.7 μmol L-1 min-1). A coimmobilized multienzyme reaction system was then developed to recycle the enzymes. When inhibitory metabolites concentrations were reduced, the specific enzymatic yield of AD was further enhanced (>6-fold). This study demonstrated that mitigating the accumulation of inhibitory metabolites can result in higher yields of AD production by in vitro multienzymatic reactions.
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Affiliation(s)
- Xixian Chen
- Chemical
and Pharmaceutical Engineering, Singapore-MIT Alliance, Singapore 138602
- Biotransformation
Innovation Platform, Agency for Science Technology and Research, Singapore 138632
| | - Congqiang Zhang
- Chemical
and Pharmaceutical Engineering, Singapore-MIT Alliance, Singapore 138602
- Biotransformation
Innovation Platform, Agency for Science Technology and Research, Singapore 138632
| | - Ruiyang Zou
- Chemical
and Pharmaceutical Engineering, Singapore-MIT Alliance, Singapore 138602
| | - Gregory Stephanopoulos
- Chemical
and Pharmaceutical Engineering, Singapore-MIT Alliance, Singapore 138602
- Department
of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States of America
| | - Heng-Phon Too
- Chemical
and Pharmaceutical Engineering, Singapore-MIT Alliance, Singapore 138602
- Department
of Biochemistry, National University of Singapore, Singapore 119077
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17
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Current state and challenges for dynamic metabolic modeling. Curr Opin Microbiol 2016; 33:97-104. [PMID: 27472025 DOI: 10.1016/j.mib.2016.07.008] [Citation(s) in RCA: 32] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2016] [Revised: 06/28/2016] [Accepted: 07/06/2016] [Indexed: 01/06/2023]
Abstract
While the stoichiometry of metabolism is probably the best studied cellular level, the dynamics in metabolism can still not be well described, predicted and, thus, engineered. Unknowns in the metabolic flux behavior arise from kinetic interactions, especially allosteric control mechanisms. While the stoichiometry of enzymes is preserved in vitro, their activity and kinetic behavior differs from the in vivo situation. Next to this challenge, it is infeasible to test the interaction of each enzyme with each intracellular metabolite in vitro exhaustively. As a consequence, the whole interacting metabolome has to be studied in vivo to identify the relevant enzymes properties. In this review we discuss current approaches for in vivo perturbation experiments, that is, stimulus response experiments using different setups and quantitative analytical approaches, including dynamic carbon tracing. Next to reliable and informative data, advanced modeling approaches and computational tools are required to identify kinetic mechanisms and their parameters.
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18
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Srinivasan S, Cluett WR, Mahadevan R. Constructing kinetic models of metabolism at genome-scales: A review. Biotechnol J 2016; 10:1345-59. [PMID: 26332243 DOI: 10.1002/biot.201400522] [Citation(s) in RCA: 64] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2014] [Revised: 04/01/2015] [Accepted: 07/08/2015] [Indexed: 11/08/2022]
Abstract
Constraint-based modeling of biological networks (metabolism, transcription and signal transduction), although used successfully in many applications, suffer from specific limitations such as the lack of representation of metabolite concentrations and enzymatic regulation, which are necessary for a complete physiologically relevant model. Kinetic models conversely overcome these shortcomings and enable dynamic analysis of biological systems for enhanced in silico hypothesis generation. Nonetheless, kinetic models also have limitations for modeling at genome-scales chiefly due to: (i) model non-linearity; (ii) computational tractability; (iii) parameter identifiability; (iv) estimability; and (v) uncertainty. In order to support further development of kinetic models as viable alternatives to constraint-based models, this review presents a brief description of the existing obstacles towards building genome-scale kinetic models. Specific kinetic modeling frameworks capable of overcoming these obstacles are covered in this review. The tractability and physiological feasibility of these models are discussed with the objective of using available in vivo experimental observations to define the model parameter space. Among the different methods discussed, Monte Carlo kinetic models of metabolism stand out as potentially tractable methods to model genome scale networks while also addressing in vivo parameter uncertainty.
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Affiliation(s)
- Shyam Srinivasan
- Department of Chemical Engineering and Applied Chemistry, University of Toronto, Toronto, ON, Canada
| | - William R Cluett
- Department of Chemical Engineering and Applied Chemistry, University of Toronto, Toronto, ON, Canada
| | - Radhakrishnan Mahadevan
- Department of Chemical Engineering and Applied Chemistry, University of Toronto, Toronto, ON, Canada. .,Institute of Biomaterials and Biomedical Engineering, University of Toronto, Toronto, ON, Canada.
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19
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Costa RS, Hartmann A, Vinga S. Kinetic modeling of cell metabolism for microbial production. J Biotechnol 2015; 219:126-41. [PMID: 26724578 DOI: 10.1016/j.jbiotec.2015.12.023] [Citation(s) in RCA: 29] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2015] [Revised: 11/25/2015] [Accepted: 12/15/2015] [Indexed: 12/20/2022]
Abstract
Kinetic models of cellular metabolism are important tools for the rational design of metabolic engineering strategies and to explain properties of complex biological systems. The recent developments in high-throughput experimental data are leading to new computational approaches for building kinetic models of metabolism. Herein, we briefly survey the available databases, standards and software tools that can be applied for kinetic models of metabolism. In addition, we give an overview about recently developed ordinary differential equations (ODE)-based kinetic models of metabolism and some of the main applications of such models are illustrated in guiding metabolic engineering design. Finally, we review the kinetic modeling approaches of large-scale networks that are emerging, discussing their main advantages, challenges and limitations.
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Affiliation(s)
- Rafael S Costa
- IDMEC, Instituto Superior Técnico, Universidade de Lisboa, Av. Rovisco Pais 1, 1049-001 Lisboa, Portugal.
| | - Andras Hartmann
- IDMEC, Instituto Superior Técnico, Universidade de Lisboa, Av. Rovisco Pais 1, 1049-001 Lisboa, Portugal
| | - Susana Vinga
- IDMEC, Instituto Superior Técnico, Universidade de Lisboa, Av. Rovisco Pais 1, 1049-001 Lisboa, Portugal
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Matsuoka Y, Shimizu K. Current status and future perspectives of kinetic modeling for the cell metabolism with incorporation of the metabolic regulation mechanism. BIORESOUR BIOPROCESS 2015. [DOI: 10.1186/s40643-014-0031-7] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
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21
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Khodayari A, Zomorrodi AR, Liao JC, Maranas CD. A kinetic model of Escherichia coli core metabolism satisfying multiple sets of mutant flux data. Metab Eng 2014; 25:50-62. [DOI: 10.1016/j.ymben.2014.05.014] [Citation(s) in RCA: 145] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2014] [Revised: 04/17/2014] [Accepted: 05/28/2014] [Indexed: 01/27/2023]
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22
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Almquist J, Cvijovic M, Hatzimanikatis V, Nielsen J, Jirstrand M. Kinetic models in industrial biotechnology - Improving cell factory performance. Metab Eng 2014; 24:38-60. [PMID: 24747045 DOI: 10.1016/j.ymben.2014.03.007] [Citation(s) in RCA: 158] [Impact Index Per Article: 15.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2013] [Revised: 03/07/2014] [Accepted: 03/09/2014] [Indexed: 11/16/2022]
Abstract
An increasing number of industrial bioprocesses capitalize on living cells by using them as cell factories that convert sugars into chemicals. These processes range from the production of bulk chemicals in yeasts and bacteria to the synthesis of therapeutic proteins in mammalian cell lines. One of the tools in the continuous search for improved performance of such production systems is the development and application of mathematical models. To be of value for industrial biotechnology, mathematical models should be able to assist in the rational design of cell factory properties or in the production processes in which they are utilized. Kinetic models are particularly suitable towards this end because they are capable of representing the complex biochemistry of cells in a more complete way compared to most other types of models. They can, at least in principle, be used to in detail understand, predict, and evaluate the effects of adding, removing, or modifying molecular components of a cell factory and for supporting the design of the bioreactor or fermentation process. However, several challenges still remain before kinetic modeling will reach the degree of maturity required for routine application in industry. Here we review the current status of kinetic cell factory modeling. Emphasis is on modeling methodology concepts, including model network structure, kinetic rate expressions, parameter estimation, optimization methods, identifiability analysis, model reduction, and model validation, but several applications of kinetic models for the improvement of cell factories are also discussed.
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Affiliation(s)
- Joachim Almquist
- Fraunhofer-Chalmers Centre, Chalmers Science Park, SE-412 88 Göteborg, Sweden; Systems and Synthetic Biology, Department of Chemical and Biological Engineering, Chalmers University of Technology, SE-412 96 Göteborg, Sweden.
| | - Marija Cvijovic
- Mathematical Sciences, Chalmers University of Technology and University of Gothenburg, SE-412 96 Göteborg, Sweden; Mathematical Sciences, University of Gothenburg, SE-412 96 Göteborg, Sweden
| | - Vassily Hatzimanikatis
- Laboratory of Computational Systems Biotechnology, Ecole Polytechnique Federale de Lausanne, CH 1015 Lausanne, Switzerland
| | - Jens Nielsen
- Systems and Synthetic Biology, Department of Chemical and Biological Engineering, Chalmers University of Technology, SE-412 96 Göteborg, Sweden
| | - Mats Jirstrand
- Fraunhofer-Chalmers Centre, Chalmers Science Park, SE-412 88 Göteborg, Sweden
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23
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k-OptForce: integrating kinetics with flux balance analysis for strain design. PLoS Comput Biol 2014; 10:e1003487. [PMID: 24586136 PMCID: PMC3930495 DOI: 10.1371/journal.pcbi.1003487] [Citation(s) in RCA: 104] [Impact Index Per Article: 10.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2013] [Accepted: 01/10/2014] [Indexed: 11/19/2022] Open
Abstract
Computational strain design protocols aim at the system-wide identification of intervention strategies for the enhanced production of biochemicals in microorganisms. Existing approaches relying solely on stoichiometry and rudimentary constraint-based regulation overlook the effects of metabolite concentrations and substrate-level enzyme regulation while identifying metabolic interventions. In this paper, we introduce k-OptForce, which integrates the available kinetic descriptions of metabolic steps with stoichiometric models to sharpen the prediction of intervention strategies for improving the bio-production of a chemical of interest. It enables identification of a minimal set of interventions comprised of both enzymatic parameter changes (for reactions with available kinetics) and reaction flux changes (for reactions with only stoichiometric information). Application of k-OptForce to the overproduction of L-serine in E. coli and triacetic acid lactone (TAL) in S. cerevisiae revealed that the identified interventions tend to cause less dramatic rearrangements of the flux distribution so as not to violate concentration bounds. In some cases the incorporation of kinetic information leads to the need for additional interventions as kinetic expressions render stoichiometry-only derived interventions infeasible by violating concentration bounds, whereas in other cases the kinetic expressions impart flux changes that favor the overproduction of the target product thereby requiring fewer direct interventions. A sensitivity analysis on metabolite concentrations shows that the required number of interventions can be significantly affected by changing the imposed bounds on metabolite concentrations. Furthermore, k-OptForce was capable of finding non-intuitive interventions aiming at alleviating the substrate-level inhibition of key enzymes in order to enhance the flux towards the product of interest, which cannot be captured by stoichiometry-alone analysis. This study paves the way for the integrated analysis of kinetic and stoichiometric models and enables elucidating system-wide metabolic interventions while capturing regulatory and kinetic effects.
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24
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Wang CCN, Chang PC, Ng KL, Chang CM, Sheu PCY, Tsai JJP. A model comparison study of the flowering time regulatory network in Arabidopsis. BMC SYSTEMS BIOLOGY 2014; 8:15. [PMID: 24513114 PMCID: PMC3938817 DOI: 10.1186/1752-0509-8-15] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/26/2013] [Accepted: 01/15/2014] [Indexed: 11/12/2022]
Abstract
Background Several dynamic models of a gene regulatory network of the light-induced floral transition process in Arabidopsis have been developed to capture the behavior of gene transcription and infer predictions based on experimental observations. It has been proven that the models can make accurate and novel predictions, which generate testable hypotheses. Two major issues were addressed in this study. First, construction of dynamic models for gene regulatory networks requires the use of mathematic modeling that comprises equations of a large number of parameters. Second, the binding mechanism of the transcription factor with DNA is another factor that requires detailed modeling. The first issue was tackled by adopting an optimization algorithm, and the second was addressed by comparing the performance of three alternative modeling approaches, namely the S-system, the Michaelis-Menten model and the Mass-action model. The efficiencies of parameter estimation and modeling performance were calculated based on least square error (O(p)), mean relative error (MRE) and Akaike Information Criterion (AIC). Results We compared three models to describe gene regulation of the flowering transition process in Arabidopsis. The Mass-action model is the simplest and has the least parameters. It is therefore less computation-intensive with the smallest AIC value. The disadvantage, however, is that it assumes the system is simply a second order reaction which is not the case in our study. The Michaelis-Menten model also assumes the system is homogeneous and ignores the intracellular protein transport process. The S-system model has the best performance and it does describe the diffusion effects. A disadvantage of the S-system is that it involves the most parameters. The largest AIC value also implies an over-fitting may occur in parameter estimation. Conclusions Three dynamic models were adopted to describe the dynamics of the gene regulatory network of the flowering transition process in Arabidopsis. Based on MRE, the least square error and global sensitivity analysis, the S-system has the best performance. However, the fact that it has the highest AIC suggests an over-fitting may occur in parameter estimation. The result of this study may need to be applied carefully when modeling complex gene regulatory networks.
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Affiliation(s)
- Charles C N Wang
- Department of Biomedical Informatics, Asia University, Taichung, Taiwan.
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25
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Shestov AA, Barker B, Gu Z, Locasale JW. Computational approaches for understanding energy metabolism. WILEY INTERDISCIPLINARY REVIEWS. SYSTEMS BIOLOGY AND MEDICINE 2013; 5:733-50. [PMID: 23897661 PMCID: PMC3906216 DOI: 10.1002/wsbm.1238] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
There has been a surge of interest in understanding the regulation of metabolic networks involved in disease in recent years. Quantitative models are increasingly being used to interrogate the metabolic pathways that are contained within this complex disease biology. At the core of this effort is the mathematical modeling of central carbon metabolism involving glycolysis and the citric acid cycle (referred to as energy metabolism). Here, we discuss several approaches used to quantitatively model metabolic pathways relating to energy metabolism and discuss their formalisms, successes, and limitations.
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Affiliation(s)
| | - Brandon Barker
- Division of Nutritional Sciences, Cornell University, Ithaca NY 14850
- Tri-Institutional Field of Computational Biology and Medicine, Cornell University, Ithaca NY 14850
| | - Zhenglong Gu
- Division of Nutritional Sciences, Cornell University, Ithaca NY 14850
- Tri-Institutional Field of Computational Biology and Medicine, Cornell University, Ithaca NY 14850
| | - Jason W Locasale
- Division of Nutritional Sciences, Cornell University, Ithaca NY 14850
- Tri-Institutional Field of Computational Biology and Medicine, Cornell University, Ithaca NY 14850
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26
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Zomorrodi AR, Lafontaine Rivera JG, Liao JC, Maranas CD. Optimization-driven identification of genetic perturbations accelerates the convergence of model parameters in ensemble modeling of metabolic networks. Biotechnol J 2013; 8:1090-104. [DOI: 10.1002/biot.201200270] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2012] [Revised: 01/22/2013] [Accepted: 02/28/2013] [Indexed: 11/08/2022]
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27
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Egbert MD. Bacterial chemotaxis: introverted or extroverted? A comparison of the advantages and disadvantages of basic forms of metabolism-based and metabolism-independent behavior using a computational model. PLoS One 2013; 8:e63617. [PMID: 23717454 PMCID: PMC3661575 DOI: 10.1371/journal.pone.0063617] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2013] [Accepted: 04/04/2013] [Indexed: 01/03/2023] Open
Abstract
Using a minimal model of metabolism, we examine the limitations of behavior that is (a) solely in response to environmental phenomena or (b) solely in response to metabolic dynamics, showing that basic forms of each of these kinds of behavior are incapable of driving survival-prolonging behavior in certain situations. Inspired by experimental evidence of concurrent metabolism-based and metabolism-independent chemotactic mechanisms in Escherichia coli and Rhodobacter sphaeroides, we then investigate how metabolism-independent and metabolism-based sensitivities can be integrated into a single behavioral response, demonstrating that a simple switching mechanism can be sufficient to effectively integrate metabolism-based and metabolism-independent behaviors. Finally, we use a spatial simulation of bacteria to show that the investigated forms of behavior produce different spatio-temporal patterns that are influenced by the metabolic-history of the bacteria. We suggest that these patterns could be a way to experimentally derive insight into the relationship between metabolism and chemotaxis in real bacteria.
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Affiliation(s)
- Matthew D Egbert
- Biosystems Analysis Group, Friedrich Schiller University, Jena, Germany.
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28
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Abstract
Biochemical systems theory (BST) is the foundation for a set of analytical andmodeling tools that facilitate the analysis of dynamic biological systems. This paper depicts major developments in BST up to the current state of the art in 2012. It discusses its rationale, describes the typical strategies and methods of designing, diagnosing, analyzing, and utilizing BST models, and reviews areas of application. The paper is intended as a guide for investigators entering the fascinating field of biological systems analysis and as a resource for practitioners and experts.
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29
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Tohsato Y, Ikuta K, Shionoya A, Mazaki Y, Ito M. Parameter optimization and sensitivity analysis for large kinetic models using a real-coded genetic algorithm. Gene 2012; 518:84-90. [PMID: 23274652 DOI: 10.1016/j.gene.2012.11.080] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2012] [Accepted: 11/27/2012] [Indexed: 10/27/2022]
Abstract
Dynamic modeling is a powerful tool for predicting changes in metabolic regulation. However, a large number of input parameters, including kinetic constants and initial metabolite concentrations, are required to construct a kinetic model. Therefore, it is important not only to optimize the kinetic parameters, but also to investigate the effects of their perturbations on the overall system. We investigated the efficiency of the use of a real-coded genetic algorithm (RCGA) for parameter optimization and sensitivity analysis in the case of a large kinetic model involving glycolysis and the pentose phosphate pathway in Escherichia coli K-12. Sensitivity analysis of the kinetic model using an RCGA demonstrated that the input parameter values had different effects on model outputs. The results showed highly influential parameters in the model and their allowable ranges for maintaining metabolite-level stability. Furthermore, it was revealed that changes in these influential parameters may complement one another. This study presents an efficient approach based on the use of an RCGA for optimizing and analyzing parameters in large kinetic models.
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Affiliation(s)
- Yukako Tohsato
- Department of Bioinformatics, College of Life Sciences, Ritsumeikan University, Kusatsu, Shig 525-8577, Japan.
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30
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Systematic applications of metabolomics in metabolic engineering. Metabolites 2012; 2:1090-122. [PMID: 24957776 PMCID: PMC3901235 DOI: 10.3390/metabo2041090] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2012] [Revised: 11/29/2012] [Accepted: 12/10/2012] [Indexed: 02/05/2023] Open
Abstract
The goals of metabolic engineering are well-served by the biological information provided by metabolomics: information on how the cell is currently using its biochemical resources is perhaps one of the best ways to inform strategies to engineer a cell to produce a target compound. Using the analysis of extracellular or intracellular levels of the target compound (or a few closely related molecules) to drive metabolic engineering is quite common. However, there is surprisingly little systematic use of metabolomics datasets, which simultaneously measure hundreds of metabolites rather than just a few, for that same purpose. Here, we review the most common systematic approaches to integrating metabolite data with metabolic engineering, with emphasis on existing efforts to use whole-metabolome datasets. We then review some of the most common approaches for computational modeling of cell-wide metabolism, including constraint-based models, and discuss current computational approaches that explicitly use metabolomics data. We conclude with discussion of the broader potential of computational approaches that systematically use metabolomics data to drive metabolic engineering.
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31
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González-Díaz H, Riera-Fernández P. New Markov-Autocorrelation Indices for Re-evaluation of Links in Chemical and Biological Complex Networks used in Metabolomics, Parasitology, Neurosciences, and Epidemiology. J Chem Inf Model 2012; 52:3331-40. [DOI: 10.1021/ci300321f] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Affiliation(s)
- Humberto González-Díaz
- Department of Microbiology
and Parasitology,
Faculty of Pharmacy, University of Santiago de Compostela (USC), 15782 Santiago de Compostela, Spain
| | - Pablo Riera-Fernández
- Department of Microbiology
and Parasitology,
Faculty of Pharmacy, University of Santiago de Compostela (USC), 15782 Santiago de Compostela, Spain
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32
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Jouhten P. Metabolic modelling in the development of cell factories by synthetic biology. Comput Struct Biotechnol J 2012; 3:e201210009. [PMID: 24688669 PMCID: PMC3962133 DOI: 10.5936/csbj.201210009] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2012] [Revised: 11/05/2012] [Accepted: 11/07/2012] [Indexed: 11/22/2022] Open
Abstract
Cell factories are commonly microbial organisms utilized for bioconversion of renewable resources to bulk or high value chemicals. Introduction of novel production pathways in chassis strains is the core of the development of cell factories by synthetic biology. Synthetic biology aims to create novel biological functions and systems not found in nature by combining biology with engineering. The workflow of the development of novel cell factories with synthetic biology is ideally linear which will be attainable with the quantitative engineering approach, high-quality predictive models, and libraries of well-characterized parts. Different types of metabolic models, mathematical representations of metabolism and its components, enzymes and metabolites, are useful in particular phases of the synthetic biology workflow. In this minireview, the role of metabolic modelling in synthetic biology will be discussed with a review of current status of compatible methods and models for the in silico design and quantitative evaluation of a cell factory.
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Affiliation(s)
- Paula Jouhten
- VTT Technical Research Centre of Finland, Tietotie 2, 02044 VTT, Espoo, Finland
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33
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Riera-Fernández P, Munteanu CR, Escobar M, Prado-Prado F, Martín-Romalde R, Pereira D, Villalba K, Duardo-Sánchez A, González-Díaz H. New Markov–Shannon Entropy models to assess connectivity quality in complex networks: From molecular to cellular pathway, Parasite–Host, Neural, Industry, and Legal–Social networks. J Theor Biol 2012; 293:174-88. [DOI: 10.1016/j.jtbi.2011.10.016] [Citation(s) in RCA: 30] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2011] [Revised: 10/09/2011] [Accepted: 10/14/2011] [Indexed: 11/25/2022]
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34
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Machado D, Costa RS, Rocha M, Ferreira EC, Tidor B, Rocha I. Modeling formalisms in Systems Biology. AMB Express 2011; 1:45. [PMID: 22141422 PMCID: PMC3285092 DOI: 10.1186/2191-0855-1-45] [Citation(s) in RCA: 118] [Impact Index Per Article: 9.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2011] [Accepted: 12/05/2011] [Indexed: 12/18/2022] Open
Abstract
Systems Biology has taken advantage of computational tools and high-throughput experimental data to model several biological processes. These include signaling, gene regulatory, and metabolic networks. However, most of these models are specific to each kind of network. Their interconnection demands a whole-cell modeling framework for a complete understanding of cellular systems. We describe the features required by an integrated framework for modeling, analyzing and simulating biological processes, and review several modeling formalisms that have been used in Systems Biology including Boolean networks, Bayesian networks, Petri nets, process algebras, constraint-based models, differential equations, rule-based models, interacting state machines, cellular automata, and agent-based models. We compare the features provided by different formalisms, and discuss recent approaches in the integration of these formalisms, as well as possible directions for the future.
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Affiliation(s)
- Daniel Machado
- IBB-Institute for Biotechnology and Bioengineering/Centre of Biological Engineering, University of Minho, Campus de Gualtar, 4710-057 Braga, Portugal
| | - Rafael S Costa
- IBB-Institute for Biotechnology and Bioengineering/Centre of Biological Engineering, University of Minho, Campus de Gualtar, 4710-057 Braga, Portugal
| | - Miguel Rocha
- Department of Informatics/CCTC, University of Minho, Campus de Gualtar, 4710-057 Braga, Portugal
| | - Eugénio C Ferreira
- IBB-Institute for Biotechnology and Bioengineering/Centre of Biological Engineering, University of Minho, Campus de Gualtar, 4710-057 Braga, Portugal
| | - Bruce Tidor
- Department of Biological Engineering/Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Isabel Rocha
- IBB-Institute for Biotechnology and Bioengineering/Centre of Biological Engineering, University of Minho, Campus de Gualtar, 4710-057 Braga, Portugal
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35
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Berthoumieux S, Brilli M, de Jong H, Kahn D, Cinquemani E. Identification of metabolic network models from incomplete high-throughput datasets. ACTA ACUST UNITED AC 2011; 27:i186-95. [PMID: 21685069 PMCID: PMC3117355 DOI: 10.1093/bioinformatics/btr225] [Citation(s) in RCA: 20] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
Motivation: High-throughput measurement techniques for metabolism and gene expression provide a wealth of information for the identification of metabolic network models. Yet, missing observations scattered over the dataset restrict the number of effectively available datapoints and make classical regression techniques inaccurate or inapplicable. Thorough exploitation of the data by identification techniques that explicitly cope with missing observations is therefore of major importance. Results: We develop a maximum-likelihood approach for the estimation of unknown parameters of metabolic network models that relies on the integration of statistical priors to compensate for the missing data. In the context of the linlog metabolic modeling framework, we implement the identification method by an Expectation-Maximization (EM) algorithm and by a simpler direct numerical optimization method. We evaluate performance of our methods by comparison to existing approaches, and show that our EM method provides the best results over a variety of simulated scenarios. We then apply the EM algorithm to a real problem, the identification of a model for the Escherichia coli central carbon metabolism, based on challenging experimental data from the literature. This leads to promising results and allows us to highlight critical identification issues. Contact:sara.berthoumieux@inria.fr; eugenio.cinquemani@inria.fr Supplementary information:Supplementary data are available at Bioinformatics online.
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36
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Costa R, Rocha I, Ferreira E, Machado D. Critical perspective on the consequences of the limited availability of kinetic data in metabolic dynamic modelling. IET Syst Biol 2011; 5:157-63. [DOI: 10.1049/iet-syb.2009.0058] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022] Open
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37
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An in vivo data-driven framework for classification and quantification of enzyme kinetics and determination of apparent thermodynamic data. Metab Eng 2011; 13:294-306. [DOI: 10.1016/j.ymben.2011.02.005] [Citation(s) in RCA: 80] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2010] [Revised: 01/10/2011] [Accepted: 02/15/2011] [Indexed: 01/21/2023]
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38
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Abstract
Recent interest in modeling biochemical networks raises questions about the relationship between often complex mathematical models and familiar arithmetic concepts from classical enzymology, and also about connections between modeling and experimental data. This review addresses both topics by familiarizing readers with key concepts (and terminology) in the construction, validation, and application of deterministic biochemical models, with particular emphasis on a simple enzyme-catalyzed reaction. Networks of coupled ordinary differential equations (ODEs) are the natural language for describing enzyme kinetics in a mass action approximation. We illustrate this point by showing how the familiar Briggs-Haldane formulation of Michaelis-Menten kinetics derives from the outer (or quasi-steady-state) solution of a dynamical system of ODEs describing a simple reaction under special conditions. We discuss how parameters in the Michaelis-Menten approximation and in the underlying ODE network can be estimated from experimental data, with a special emphasis on the origins of uncertainty. Finally, we extrapolate from a simple reaction to complex models of multiprotein biochemical networks. The concepts described in this review, hitherto of interest primarily to practitioners, are likely to become important for a much broader community of cellular and molecular biologists attempting to understand the promise and challenges of "systems biology" as applied to biochemical mechanisms.
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Affiliation(s)
- William W. Chen
- Center for Cell Decision Processes, Department of Systems Biology, Harvard Medical School, Boston, Massachusetts 02115, USA
| | - Mario Niepel
- Center for Cell Decision Processes, Department of Systems Biology, Harvard Medical School, Boston, Massachusetts 02115, USA
| | - Peter K. Sorger
- Center for Cell Decision Processes, Department of Systems Biology, Harvard Medical School, Boston, Massachusetts 02115, USA
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