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Gilmour KM, Daley MA, Egginton S, Kelber A, McHenry MJ, Patek SN, Sane SP, Schulte PM, Terblanche JS, Wright PA, Franklin CE. Through the looking glass: attempting to predict future opportunities and challenges in experimental biology. J Exp Biol 2023; 226:jeb246921. [PMID: 38059428 DOI: 10.1242/jeb.246921] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/08/2023]
Abstract
To celebrate its centenary year, Journal of Experimental Biology (JEB) commissioned a collection of articles examining the past, present and future of experimental biology. This Commentary closes the collection by considering the important research opportunities and challenges that await us in the future. We expect that researchers will harness the power of technological advances, such as '-omics' and gene editing, to probe resistance and resilience to environmental change as well as other organismal responses. The capacity to handle large data sets will allow high-resolution data to be collected for individual animals and to understand population, species and community responses. The availability of large data sets will also place greater emphasis on approaches such as modeling and simulations. Finally, the increasing sophistication of biologgers will allow more comprehensive data to be collected for individual animals in the wild. Collectively, these approaches will provide an unprecedented understanding of 'how animals work' as well as keys to safeguarding animals at a time when anthropogenic activities are degrading the natural environment.
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Affiliation(s)
| | - Monica A Daley
- Department of Ecology and Evolutionary Biology, University of California, Irvine, Irvine, CA 92697, USA
| | - Stuart Egginton
- School of Biomedical Sciences, University of Leeds, Leeds LS2 9JT, UK
| | - Almut Kelber
- Department of Biology, Lund University, 22362 Lund, Sweden
| | - Matthew J McHenry
- Department of Ecology and Evolutionary Biology, University of California, Irvine, Irvine, CA 92697, USA
| | - Sheila N Patek
- Biology Department, Duke University, Durham, NC 27708, USA
| | - Sanjay P Sane
- National Centre for Biological Sciences, Tata Institute of Fundamental Research, GKVK Campus, Bellary Road, Bangalore, Karnataka 560065, India
| | - Patricia M Schulte
- Department of Zoology, University of British Columbia, Vancouver, BC V6T 1Z4, Canada
| | - John S Terblanche
- Center for Invasion Biology, Department of Conservation Ecology & Entomology, Stellenbosch University, Stellenbosch 7602, South Africa
| | - Patricia A Wright
- Department of Integrative Biology, University of Guelph, Guelph, ON N1G 2W1, Canada
| | - Craig E Franklin
- School of the Environment, The University of Queensland, St. Lucia, Brisbane 4072, Australia
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2
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Villaverde AF, Pathirana D, Fröhlich F, Hasenauer J, Banga JR. A protocol for dynamic model calibration. Brief Bioinform 2022; 23:bbab387. [PMID: 34619769 PMCID: PMC8769694 DOI: 10.1093/bib/bbab387] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2021] [Revised: 08/06/2021] [Accepted: 08/29/2021] [Indexed: 12/23/2022] Open
Abstract
Ordinary differential equation models are nowadays widely used for the mechanistic description of biological processes and their temporal evolution. These models typically have many unknown and nonmeasurable parameters, which have to be determined by fitting the model to experimental data. In order to perform this task, known as parameter estimation or model calibration, the modeller faces challenges such as poor parameter identifiability, lack of sufficiently informative experimental data and the existence of local minima in the objective function landscape. These issues tend to worsen with larger model sizes, increasing the computational complexity and the number of unknown parameters. An incorrectly calibrated model is problematic because it may result in inaccurate predictions and misleading conclusions. For nonexpert users, there are a large number of potential pitfalls. Here, we provide a protocol that guides the user through all the steps involved in the calibration of dynamic models. We illustrate the methodology with two models and provide all the code required to reproduce the results and perform the same analysis on new models. Our protocol provides practitioners and researchers in biological modelling with a one-stop guide that is at the same time compact and sufficiently comprehensive to cover all aspects of the problem.
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Affiliation(s)
- Alejandro F Villaverde
- Universidade de Vigo, Department of Systems Engineering & Control, Vigo 36310, Galicia, Spain
| | - Dilan Pathirana
- Faculty of Mathematics and Natural Sciences, University of Bonn, Bonn 53115, Germany
| | - Fabian Fröhlich
- Institute of Computational Biology, Helmholtz Zentrum München, Neuherberg 85764, Germany
| | - Jan Hasenauer
- Center for Mathematics, Technische Universität München, Garching 85748, Germany
- Harvard Medical School, Cambridge, MA 02115, USA
| | - Julio R Banga
- Bioprocess Engineering Group, IIM-CSIC, Vigo 36208, Galicia, Spain
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3
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Ahamed F, Song HS, Ho YK. Modeling coordinated enzymatic control of saccharification and fermentation by Clostridium thermocellum during consolidated bioprocessing of cellulose. Biotechnol Bioeng 2021; 118:1898-1912. [PMID: 33547803 DOI: 10.1002/bit.27705] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2020] [Revised: 02/03/2021] [Accepted: 02/04/2021] [Indexed: 11/09/2022]
Abstract
Consolidated bioprocessing (CBP) of cellulose is a cost-effective route to produce valuable biochemicals by integrating saccharification, fermentation and cellulase synthesis in a single step. However, the lack of understanding of governing factors of interdependent saccharification and fermentation in CBP eludes reliable process optimization. Here, we propose a new framework that synergistically couples population balances (to simulate cellulose depolymerization) and cybernetic models (to model enzymatic regulation of fermentation) to enable improved understanding of CBP. The resulting framework, named the unified cybernetic-population balance model (UC-PBM), enables simulation of CBP driven by coordinated control of enzyme synthesis through closed-loop interactions. UC-PBM considers two key aspects in controlling CBP: (1) heterogeneity in cellulose properties and (2) cellular regulation of competing cell growth and cellulase secretion. In a case study on Clostridium thermocellum, UC-PBM not only provides a decent fit with various exometabolomic data, but also reveals that: (i) growth-decoupled cellulase-secreting pathways are only activated during famine conditions to promote the production of growth substrates, and (ii) starting cellulose concentration has a strong influence on the overall flux distribution. Equipped with mechanisms of cellulose degradation and fermentative regulations, UC-PBM is practical to explore phenotypic functions for primary evaluation of microorganisms' potential for metabolic engineering and optimal design of bioprocess.
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Affiliation(s)
- Firnaaz Ahamed
- Chemical Engineering Discipline, School of Engineering, Monash University Malaysia, Jalan Lagoon Selatan, Bandar Sunway, Selangor, Malaysia
| | - Hyun-Seob Song
- Department of Biological Systems Engineering, University of Nebraska-Lincoln, Lincoln, Nebraska, USA.,Department of Food Science and Technology, Nebraska Food for Health Center, University of Nebraska-Lincoln, Lincoln, Nebraska, USA
| | - Yong Kuen Ho
- Chemical Engineering Discipline, School of Engineering, Monash University Malaysia, Jalan Lagoon Selatan, Bandar Sunway, Selangor, Malaysia.,Monash-Industry Palm Oil Education and Research Platform, Monash University Malaysia, Jalan Lagoon Selatan, Bandar Sunway, Selangor, Malaysia
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4
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Remli MA, Mohamad MS, Deris S, Sinnott R, Napis S. An Improved Scatter Search Algorithm for Parameter Estimation in Large-Scale Kinetic Models of Biochemical Systems. CURR PROTEOMICS 2019. [DOI: 10.2174/1570164616666190401203128] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Abstract
Background:
Mathematical models play a central role in facilitating researchers to better
understand and comprehensively analyze various processes in biochemical systems. Their usage is
beneficial in metabolic engineering as they help predict and improve desired products. However, one
of the primary challenges in model building is parameter estimation. It is the process to find nearoptimal
values of kinetic parameters which may culminate in the best fit of model prediction to experimental
data.
Methods:
This paper proposes an improved scatter search algorithm to address the challenging parameter
estimation problem. The improved algorithm is based on hybridization of quasi opposition-based
learning in enhanced scatter search (QOBLESS) method. The algorithm is tested using a large-scale
metabolic model of Chinese Hamster Ovary (CHO) cells.
Results:
The experimental result shows that the proposed algorithm performs better than other algorithms
in terms of convergence speed and the minimum value of the objective function (loglikelihood).
The estimated parameters from the experiment produce a better model by means of obtaining
a reasonable good fit of model prediction to the experimental data.
Conclusion:
The kinetic parameters’ value obtained from our work was able to result in a reasonable
best fit of model prediction to the experimental data, which contributes to a better understanding and
produced more accurate model. Based on the results, the QOBLESS method can be used as an efficient
parameter estimation method in large-scale kinetic model building.
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Affiliation(s)
- Muhammad Akmal Remli
- Faculty of Computer Systems & Software Engineering, Universiti Malaysia Pahang, Kuantan, Pahang 26300, Malaysia
| | - Mohd Saberi Mohamad
- Institute for Artificial Intelligence and Big Data, Universiti Malaysia Kelantan, City Campus, Pengkalan Chepa, 16100 Kota Bharu, Kelantan, Malaysia
| | - Safaai Deris
- Institute for Artificial Intelligence and Big Data, Universiti Malaysia Kelantan, City Campus, Pengkalan Chepa, 16100 Kota Bharu, Kelantan, Malaysia
| | - Richard Sinnott
- Department of Computing and Information Systems, University of Melbourne, Victoria, 3010, Australia
| | - Suhaimi Napis
- Department of Cell and Molecular Biology, Faculty of Biotechnology and Biomolecular Sciences, Universiti Putra Malaysia, 43400 UPM, Serdang, Selangor, Malaysia
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5
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Dutta D, Saini S. Phenomenological models as effective tools to discover cellular design principles. Arch Microbiol 2019; 201:283-293. [PMID: 30826848 DOI: 10.1007/s00203-019-01641-z] [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: 11/05/2018] [Revised: 02/20/2019] [Accepted: 02/21/2019] [Indexed: 11/28/2022]
Abstract
Microbes have proved useful to us in many different ways. To utilize microbes, we have mostly focused on maximizing growth, to improve yield of chemicals derived from the microbes. However, to truly tap into their potential, we should also aim to understand microbial physiology. We present a historical perspective of the developments in the field of Microbial Biotechnology, focusing on how the growth-modelling approaches have changed. Starting from simple empirical growth models, we have evolved towards mechanistic and phenomenological models which use molecular and physiological details to drastically improve prediction power of these models. Lastly, we explore the as of yet unsolved questions in microbial physiology, and discuss how the ability to monitor microbial growth at single cell resolution using the lab-on-a-chip technologies is uncovering previously unobservable causal principles underlying microbial growth.
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Affiliation(s)
- Dibyendu Dutta
- Department of Chemical Engineering, Indian Institute of Technology Bombay, Mumbai, India
| | - Supreet Saini
- Department of Chemical Engineering, Indian Institute of Technology Bombay, Mumbai, India.
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6
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Luna MF, Martínez EC. Optimal design of dynamic experiments in the development of cybernetic models for bioreactors. Chem Eng Res Des 2018. [DOI: 10.1016/j.cherd.2018.05.036] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
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7
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Effect of cosubstrate on xylitol production by Debaryomyces nepalensis NCYC 3413: A cybernetic modelling approach. Process Biochem 2018. [DOI: 10.1016/j.procbio.2018.03.023] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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8
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Song HS, Goldberg N, Mahajan A, Ramkrishna D. Sequential computation of elementary modes and minimal cut sets in genome-scale metabolic networks using alternate integer linear programming. Bioinformatics 2018; 33:2345-2353. [PMID: 28369193 DOI: 10.1093/bioinformatics/btx171] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2016] [Accepted: 03/23/2017] [Indexed: 01/22/2023] Open
Abstract
Motivation Elementary (flux) modes (EMs) have served as a valuable tool for investigating structural and functional properties of metabolic networks. Identification of the full set of EMs in genome-scale networks remains challenging due to combinatorial explosion of EMs in complex networks. It is often, however, that only a small subset of relevant EMs needs to be known, for which optimization-based sequential computation is a useful alternative. Most of the currently available methods along this line are based on the iterative use of mixed integer linear programming (MILP), the effectiveness of which significantly deteriorates as the number of iterations builds up. To alleviate the computational burden associated with the MILP implementation, we here present a novel optimization algorithm termed alternate integer linear programming (AILP). Results Our algorithm was designed to iteratively solve a pair of integer programming (IP) and linear programming (LP) to compute EMs in a sequential manner. In each step, the IP identifies a minimal subset of reactions, the deletion of which disables all previously identified EMs. Thus, a subsequent LP solution subject to this reaction deletion constraint becomes a distinct EM. In cases where no feasible LP solution is available, IP-derived reaction deletion sets represent minimal cut sets (MCSs). Despite the additional computation of MCSs, AILP achieved significant time reduction in computing EMs by orders of magnitude. The proposed AILP algorithm not only offers a computational advantage in the EM analysis of genome-scale networks, but also improves the understanding of the linkage between EMs and MCSs. Availability and Implementation The software is implemented in Matlab, and is provided as supplementary information . Contact hyunseob.song@pnnl.gov. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Hyun-Seob Song
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99352, USA
| | - Noam Goldberg
- Department of Management, Bar-Ilan University, Ramat Gan 52900, Israel
| | - Ashutosh Mahajan
- Industrial Engineering and Operations Research, IIT Bombay, Powai, Mumbai 400076, India
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9
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Kyriakopoulos S, Ang KS, Lakshmanan M, Huang Z, Yoon S, Gunawan R, Lee DY. Kinetic Modeling of Mammalian Cell Culture Bioprocessing: The Quest to Advance Biomanufacturing. Biotechnol J 2017; 13:e1700229. [DOI: 10.1002/biot.201700229] [Citation(s) in RCA: 41] [Impact Index Per Article: 5.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2017] [Revised: 09/27/2017] [Accepted: 10/11/2017] [Indexed: 12/15/2022]
Affiliation(s)
- Sarantos Kyriakopoulos
- Bioprocessing Technology Institute, Agency for Science; Technology and Research (A*STAR); Singapore
| | - Kok Siong Ang
- Bioprocessing Technology Institute, Agency for Science; Technology and Research (A*STAR); Singapore
| | - Meiyappan Lakshmanan
- Bioprocessing Technology Institute, Agency for Science; Technology and Research (A*STAR); Singapore
| | - Zhuangrong Huang
- Department of Chemical Engineering; University of Massachusetts Lowell; Lowell MA USA
| | - Seongkyu Yoon
- Department of Chemical Engineering; University of Massachusetts Lowell; Lowell MA USA
| | - Rudiyanto Gunawan
- Institute for Chemical and Bioengineering; ETH Zurich; Zurich Switzerland
| | - Dong-Yup Lee
- Bioprocessing Technology Institute, Agency for Science; Technology and Research (A*STAR); Singapore
- Department of Chemical and Biomolecular Engineering; National University of Singapore; Singapore
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10
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Song HS, Thomas DG, Stegen JC, Li M, Liu C, Song X, Chen X, Fredrickson JK, Zachara JM, Scheibe TD. Regulation-Structured Dynamic Metabolic Model Provides a Potential Mechanism for Delayed Enzyme Response in Denitrification Process. Front Microbiol 2017; 8:1866. [PMID: 29046664 PMCID: PMC5627231 DOI: 10.3389/fmicb.2017.01866] [Citation(s) in RCA: 32] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2017] [Accepted: 09/12/2017] [Indexed: 11/20/2022] Open
Abstract
In a recent study of denitrification dynamics in hyporheic zone sediments, we observed a significant time lag (up to several days) in enzymatic response to the changes in substrate concentration. To explore an underlying mechanism and understand the interactive dynamics between enzymes and nutrients, we developed a trait-based model that associates a community's traits with functional enzymes, instead of typically used species guilds (or functional guilds). This enzyme-based formulation allows to collectively describe biogeochemical functions of microbial communities without directly parameterizing the dynamics of species guilds, therefore being scalable to complex communities. As a key component of modeling, we accounted for microbial regulation occurring through transcriptional and translational processes, the dynamics of which was parameterized based on the temporal profiles of enzyme concentrations measured using a new signature peptide-based method. The simulation results using the resulting model showed several days of a time lag in enzymatic responses as observed in experiments. Further, the model showed that the delayed enzymatic reactions could be primarily controlled by transcriptional responses and that the dynamics of transcripts and enzymes are closely correlated. The developed model can serve as a useful tool for predicting biogeochemical processes in natural environments, either independently or through integration with hydrologic flow simulators.
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Affiliation(s)
- Hyun-Seob Song
- Pacific Northwest National Laboratory, Richland, WA, United States
| | - Dennis G Thomas
- Pacific Northwest National Laboratory, Richland, WA, United States
| | - James C Stegen
- Pacific Northwest National Laboratory, Richland, WA, United States
| | - Minjing Li
- Pacific Northwest National Laboratory, Richland, WA, United States
| | - Chongxuan Liu
- Pacific Northwest National Laboratory, Richland, WA, United States
| | - Xuehang Song
- Pacific Northwest National Laboratory, Richland, WA, United States
| | - Xingyuan Chen
- Pacific Northwest National Laboratory, Richland, WA, United States
| | | | - John M Zachara
- Pacific Northwest National Laboratory, Richland, WA, United States
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11
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Farzan P, Ierapetritou MG. Integrated modeling to capture the interaction of physiology and fluid dynamics in biopharmaceutical bioreactors. Comput Chem Eng 2017. [DOI: 10.1016/j.compchemeng.2016.11.037] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
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12
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Review of the important challenges and opportunities related to modeling of mammalian cell bioreactors. AIChE J 2016. [DOI: 10.1002/aic.15442] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
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13
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Mangan NM, Brunton SL, Proctor JL, Kutz JN. Inferring Biological Networks by Sparse Identification of Nonlinear Dynamics. ACTA ACUST UNITED AC 2016. [DOI: 10.1109/tmbmc.2016.2633265] [Citation(s) in RCA: 172] [Impact Index Per Article: 21.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
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14
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Optimization of microalgal photobioreactor system using model predictive control with experimental validation. Bioprocess Biosyst Eng 2016; 39:1235-46. [DOI: 10.1007/s00449-016-1602-0] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2015] [Accepted: 03/31/2016] [Indexed: 11/26/2022]
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15
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Villaverde AF, Bongard S, Mauch K, Balsa-Canto E, Banga JR. Metabolic engineering with multi-objective optimization of kinetic models. J Biotechnol 2016; 222:1-8. [PMID: 26826510 DOI: 10.1016/j.jbiotec.2016.01.005] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2015] [Revised: 12/30/2015] [Accepted: 01/11/2016] [Indexed: 10/22/2022]
Abstract
Kinetic models have a great potential for metabolic engineering applications. They can be used for testing which genetic and regulatory modifications can increase the production of metabolites of interest, while simultaneously monitoring other key functions of the host organism. This work presents a methodology for increasing productivity in biotechnological processes exploiting dynamic models. It uses multi-objective dynamic optimization to identify the combination of targets (enzymatic modifications) and the degree of up- or down-regulation that must be performed in order to optimize a set of pre-defined performance metrics subject to process constraints. The capabilities of the approach are demonstrated on a realistic and computationally challenging application: a large-scale metabolic model of Chinese Hamster Ovary cells (CHO), which are used for antibody production in a fed-batch process. The proposed methodology manages to provide a sustained and robust growth in CHO cells, increasing productivity while simultaneously increasing biomass production, product titer, and keeping the concentrations of lactate and ammonia at low values. The approach presented here can be used for optimizing metabolic models by finding the best combination of targets and their optimal level of up/down-regulation. Furthermore, it can accommodate additional trade-offs and constraints with great flexibility.
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Affiliation(s)
- Alejandro F Villaverde
- Bioprocess Engineering Group, IIM-CSIC, Eduardo Cabello 6, 36208 Vigo, Spain; Centre of Biological Engineering, Universidade do Minho, Campus de Gualtar, 4710-057 Braga, Portugal; Department of Systems and Control Engineering, Universidade de Vigo, Rua Maxwell, 36310 Vigo, Spain
| | - Sophia Bongard
- Insilico Biotechnology AG, Meitnerstraße 9, 70563 Stuttgart, Germany
| | - Klaus Mauch
- Insilico Biotechnology AG, Meitnerstraße 9, 70563 Stuttgart, Germany
| | - Eva Balsa-Canto
- Bioprocess Engineering Group, IIM-CSIC, Eduardo Cabello 6, 36208 Vigo, Spain
| | - Julio R Banga
- Bioprocess Engineering Group, IIM-CSIC, Eduardo Cabello 6, 36208 Vigo, Spain
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16
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17
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Song HS, Liu C. Dynamic Metabolic Modeling of Denitrifying Bacterial Growth: The Cybernetic Approach. Ind Eng Chem Res 2015. [DOI: 10.1021/acs.iecr.5b01615] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Hyun-Seob Song
- Pacific Northwest National Laboratory, Richland, Washington 99352, United States
| | - Chongxuan Liu
- Pacific Northwest National Laboratory, Richland, Washington 99352, United States
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18
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BioPreDyn-bench: a suite of benchmark problems for dynamic modelling in systems biology. BMC SYSTEMS BIOLOGY 2015; 9:8. [PMID: 25880925 PMCID: PMC4342829 DOI: 10.1186/s12918-015-0144-4] [Citation(s) in RCA: 52] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/21/2014] [Accepted: 01/15/2015] [Indexed: 11/21/2022]
Abstract
Background Dynamic modelling is one of the cornerstones of systems biology. Many research efforts are currently being invested in the development and exploitation of large-scale kinetic models. The associated problems of parameter estimation (model calibration) and optimal experimental design are particularly challenging. The community has already developed many methods and software packages which aim to facilitate these tasks. However, there is a lack of suitable benchmark problems which allow a fair and systematic evaluation and comparison of these contributions. Results Here we present BioPreDyn-bench, a set of challenging parameter estimation problems which aspire to serve as reference test cases in this area. This set comprises six problems including medium and large-scale kinetic models of the bacterium E. coli, baker’s yeast S. cerevisiae, the vinegar fly D. melanogaster, Chinese Hamster Ovary cells, and a generic signal transduction network. The level of description includes metabolism, transcription, signal transduction, and development. For each problem we provide (i) a basic description and formulation, (ii) implementations ready-to-run in several formats, (iii) computational results obtained with specific solvers, (iv) a basic analysis and interpretation. Conclusions This suite of benchmark problems can be readily used to evaluate and compare parameter estimation methods. Further, it can also be used to build test problems for sensitivity and identifiability analysis, model reduction and optimal experimental design methods. The suite, including codes and documentation, can be freely downloaded from the BioPreDyn-bench website, https://sites.google.com/site/biopredynbenchmarks/. Electronic supplementary material The online version of this article (doi:10.1186/s12918-015-0144-4) contains supplementary material, which is available to authorized users.
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19
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Mathematical Modeling of Microbial Community Dynamics: A Methodological Review. Processes (Basel) 2014. [DOI: 10.3390/pr2040711] [Citation(s) in RCA: 91] [Impact Index Per Article: 9.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022] Open
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20
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Yoo SJ, Kim JH, Lee JM. Dynamic modelling of mixotrophic microalgal photobioreactor systems with time-varying yield coefficient for the lipid consumption. BIORESOURCE TECHNOLOGY 2014; 162:228-235. [PMID: 24755320 DOI: 10.1016/j.biortech.2014.03.128] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/12/2014] [Revised: 03/19/2014] [Accepted: 03/23/2014] [Indexed: 06/03/2023]
Abstract
Microalgae have been suggested as a promising feedstock for producing biofuel because of their potential for lipid production. In this study, microalgal photobioreactor systems under mixotrophic conditions were investigated, for the purpose of developing a mathematical model that predicts biomass and lipid production. The model was developed based on the Droop model, and the optimal input design using D-optimality criterion was performed to compute the system input profile, to estimate parameters more accurately. From the experimental observations, the newly defined yield coefficient was suggested to represent the consumption of lipid and nitrogen within the cell, which reduces the number of parameters with more accurate prediction. Furthermore, the lipid consumption rate was introduced to reflect the experimental results that lipid consumption is related to carbon source concentration. The model was validated with experiments designed with different initial conditions of nutrients and input changes, and showed good agreement with experimental observations.
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Affiliation(s)
- Sung Jin Yoo
- School of Chemical and Biological Engineering, Institute of Chemical Processes, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 151-744, Republic of Korea
| | - Jung Hun Kim
- School of Chemical and Biological Engineering, Institute of Chemical Processes, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 151-744, Republic of Korea
| | - Jong Min Lee
- School of Chemical and Biological Engineering, Institute of Chemical Processes, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 151-744, Republic of Korea.
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21
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Zomorrodi AR, Islam MM, Maranas CD. d-OptCom: Dynamic multi-level and multi-objective metabolic modeling of microbial communities. ACS Synth Biol 2014; 3:247-57. [PMID: 24742179 DOI: 10.1021/sb4001307] [Citation(s) in RCA: 121] [Impact Index Per Article: 12.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
Most microbial communities change with time in response to changes and/or perturbations in environmental conditions. Temporal variations in interspecies metabolic interactions within these communities can significantly affect their structure and function. Here, we introduce d-OptCom, an extension of the OptCom procedure, for the dynamic metabolic modeling of microbial communities. It enables capturing the temporal dynamics of biomass concentration of the community members and extracellular concentration of the shared metabolites, while integrating species- and community-level fitness functions. The applicability of d-OptCom was demonstrated by modeling the dynamic co-growth of auxotrophic mutant pairs of E. coli and by computationally assessing the dynamics and composition of a uranium-reducing community comprised of Geobacter sulfurreducens, Rhodoferax ferrireducens, and Shewanella oneidensis. d-OptCom was also employed to examine the impact of lactate vs acetate addition on the relative abundance of uranium-reducing species. These studies highlight the importance of simultaneously accounting for both species- and community-level fitness functions when modeling microbial communities, and demonstrate that the incorporation of uptake kinetic information can substantially improve the prediction of interspecies flux trafficking. Overall, this study paves the way for the dynamic multi-level and multi-objective analysis of microbial ecosystems.
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Affiliation(s)
- Ali R. Zomorrodi
- Department of Chemical Engineering, Pennsylvania State University, University Park, Pennsylvania 16802, United States
| | - Mohammad Mazharul Islam
- Department of Chemical Engineering, Pennsylvania State University, University Park, Pennsylvania 16802, United States
| | - Costas D. Maranas
- Department of Chemical Engineering, Pennsylvania State University, University Park, Pennsylvania 16802, United States
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Complex Nonlinear Behavior in Metabolic Processes: Global Bifurcation Analysis of Escherichia coli Growth on Multiple Substrates. Processes (Basel) 2013. [DOI: 10.3390/pr1030263] [Citation(s) in RCA: 28] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
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