Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
5.1
2.8
Journals
Processes
Special Issues
In silico metabolic modeling and engineering
share announcement

In silico metabolic modeling and engineering

A special issue of Processes (ISSN 2227-9717). This special issue belongs to the section "Biological Processes and Systems".

Deadline for manuscript submissions: closed (15 July 2019) | Viewed by 34692

Special Issue Editors

Prof. Dr. Dong-Yup Lee

E-Mail
Guest Editor
School of Chemical Engineering, Sungkyunkwan University, Suwon 16419, Korea
Interests: systems and synthetic biotechnology
Dr. Meiyappan Lakshmanan

E-Mail
Guest Editor
Bioprocessing Technology Institute, Agency for Science, Technology and Research (A*STAR), Singapore, Singapore
Interests: systems biological analysis of cellular metabolism; integrative multi-omics data analysis; comparative genomics; bioinformatics and in silico model-driven strain design

Special Issue Information

Dear Colleagues,

Recent advances in sequencing technology have enabled computational system biologists and bioengineers to understand the system behaviors of cells and analyze their metabolisms at the system level. For example, it is now possible to reconstruct the entire metabolic map (called “genome-scale metabolic network” (GSMN)) of a particular organism, with all the metabolic reactions taking place within the cell, thus allowing us to elucidate the intertwined genotype–phenotype relationships in silico. To do so, several computational modeling techniques, including kinetic modeling, flux balance analysis and metabolic pathway analysis, have been successfully applied to postulate design strategies of metabolic engineering, studying microbial interactions, and even identify drug targets. Particularly, GSMNs have been increasingly exploited to derive various successful metabolic engineering targets in the past decade.

This Special Issue on “In Silico Metabolic Modeling and Engineering” focuses on (i) documenting the various approaches to model cellular metabolism, (ii) the possible applications of metabolic modeling, including model-driven strain design, and (iii) provide perspectives addressing the possible challenges. Thus, we welcome research/review/perspective articles on the following topics (but not limited to them):

     i) Mathematical modeling techniques of cellular metabolism/regulation
     ii) Metabolic modeling of cells or microbial community interactions
     iii) Multi-scale and multi-omics data integration and modelling
     iv) Model-guided strain design strategy
     v) Software tools for metabolic modeling and analysis

Prof. Dr. Dong-Yup Lee
Dr. Meiyappan Lakshmanan
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Processes is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Metabolic modeling
  • Genome-scale metabolic models
  • Kinetic modeling
  • Constraint-based flux analysis
  • Multi-omics data
  • Systems Biology

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue polices can be found here.

Published Papers (7 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

17 pages, 3146 KiB  
Article
Workflow for Data Analysis in Experimental and Computational Systems Biology: Using Python as ‘Glue’
by Melinda Badenhorst, Christopher J. Barry, Christiaan J. Swanepoel, Charles Theo van Staden, Julian Wissing and Johann M. Rohwer
Processes 2019, 7(7), 460; https://doi.org/10.3390/pr7070460 - 18 Jul 2019
Cited by 7 | Viewed by 7105
Abstract
Bottom-up systems biology entails the construction of kinetic models of cellular pathways by collecting kinetic information on the pathway components (e.g., enzymes) and collating this into a kinetic model, based for example on ordinary differential equations. This requires integration and data transfer between [...] Read more.
Bottom-up systems biology entails the construction of kinetic models of cellular pathways by collecting kinetic information on the pathway components (e.g., enzymes) and collating this into a kinetic model, based for example on ordinary differential equations. This requires integration and data transfer between a variety of tools, ranging from data acquisition in kinetics experiments, to fitting and parameter estimation, to model construction, evaluation and validation. Here, we present a workflow that uses the Python programming language, specifically the modules from the SciPy stack, to facilitate this task. Starting from raw kinetics data, acquired either from spectrophotometric assays with microtitre plates or from Nuclear Magnetic Resonance (NMR) spectroscopy time-courses, we demonstrate the fitting and construction of a kinetic model using scientific Python tools. The analysis takes place in a Jupyter notebook, which keeps all information related to a particular experiment together in one place and thus serves as an e-labbook, enhancing reproducibility and traceability. The Python programming language serves as an ideal foundation for this framework because it is powerful yet relatively easy to learn for the non-programmer, has a large library of scientific routines and active user community, is open-source and extensible, and many computational systems biology software tools are written in Python or have a Python Application Programming Interface (API). Our workflow thus enables investigators to focus on the scientific problem at hand rather than worrying about data integration between disparate platforms. Full article
(This article belongs to the Special Issue In silico metabolic modeling and engineering)
►▼ Show Figures

Figure 1

14 pages, 2633 KiB  
Article
Uncovering Novel Pathways for Enhancing Hyaluronan Synthesis in Recombinant Lactococcus lactis: Genome-Scale Metabolic Modeling and Experimental Validation
by Abinaya Badri, Karthik Raman and Guhan Jayaraman
Processes 2019, 7(6), 343; https://doi.org/10.3390/pr7060343 - 5 Jun 2019
Cited by 13 | Viewed by 4754
Abstract
Hyaluronan (HA), a glycosaminoglycan with important medical applications, is commercially produced from pathogenic microbial sources. The metabolism of HA-producing recombinant generally regarded as safe (GRAS) systems needs to be more strategically engineered to achieve yields higher than native producers. Here, we use a [...] Read more.
Hyaluronan (HA), a glycosaminoglycan with important medical applications, is commercially produced from pathogenic microbial sources. The metabolism of HA-producing recombinant generally regarded as safe (GRAS) systems needs to be more strategically engineered to achieve yields higher than native producers. Here, we use a genome-scale model (GEM) to account for the entire metabolic network of the cell while predicting strategies to improve HA production. We analyze the metabolic network of Lactococcus lactis adapted to produce HA and identify non-conventional strategies to enhance HA flux. We also show experimental verification of one of the predicted strategies. We thus identified an alternate route for enhancement of HA synthesis, originating from the nucleoside inosine, that can function in parallel with the traditionally known route from glucose. Adopting this strategy resulted in a 2.8-fold increase in HA yield. The strategies identified and the experimental results show that the cell is capable of involving a larger subset of metabolic pathways in HA production. Apart from being the first report to use a nucleoside to improve HA production, we demonstrate the role of experimental validation in model refinement and strategy improvisation. Overall, we point out that well-constructed GEMs could be used to derive efficient strategies to improve the biosynthesis of high-value products. Full article
(This article belongs to the Special Issue In silico metabolic modeling and engineering)
►▼ Show Figures

Figure 1

35 pages, 23843 KiB  
Article
Extended Utilization of Constraint-Based Metabolic Model in a Long-Growing Crop
by Porntip Chiewchankaset, Saowalak Kalapanulak and Treenut Saithong
Processes 2019, 7(5), 259; https://doi.org/10.3390/pr7050259 - 4 May 2019
Viewed by 2966
Abstract
The constraint-based rMeCBM-KU50 model of cassava storage root growth was analyzed to evaluate its sensitivity, with respect to reaction flux distribution and storage root growth rate, to changes in model inputted data and constraints, including sucrose uptake rate-related data—photosynthetic rate, total leaf area, [...] Read more.
The constraint-based rMeCBM-KU50 model of cassava storage root growth was analyzed to evaluate its sensitivity, with respect to reaction flux distribution and storage root growth rate, to changes in model inputted data and constraints, including sucrose uptake rate-related data—photosynthetic rate, total leaf area, total photosynthetic rate, storage root dry weight, and biomass function-related data. These mainly varied within ±90% of the model default values, although exceptions were made for the carbohydrate (−90% to 8%) and starch (−90% to 9%) contents. The results indicated that the predicted storage root growth rate was highly affected by specific sucrose uptake rates through the total photosynthetic rate and storage root dry weight variations; whereas the carbon flux distribution, direction and partitioning inclusive, was more sensitive to the variation in biomass content, particularly the carbohydrate content. This study showed that the specific sucrose uptake rate based on the total photosynthetic rate, storage root dry weight, and carbohydrate content were critical to the constraint-based metabolic modeling and deepened our understanding of the input–output relationship—specifically regarding the rMeCBM-KU50 model—providing a valuable platform for the modeling of plant metabolic systems, especially long-growing crops. Full article
(This article belongs to the Special Issue In silico metabolic modeling and engineering)
►▼ Show Figures

Figure 1

18 pages, 1133 KiB  
Article
Exploring Plant Sesquiterpene Diversity by Generating Chemical Networks
by Waldeyr M. C. da Silva, Jakob L. Andersen, Maristela T. Holanda, Maria Emília M. T. Walter, Marcelo M. Brigido, Peter F. Stadler and Christoph Flamm
Processes 2019, 7(4), 240; https://doi.org/10.3390/pr7040240 - 25 Apr 2019
Cited by 6 | Viewed by 4915
Abstract
Plants produce a diverse portfolio of sesquiterpenes that are important in their response to herbivores and the interaction with other plants. Their biosynthesis from farnesyl diphosphate depends on the sesquiterpene synthases that admit different cyclizations and rearrangements to yield a blend of sesquiterpenes. [...] Read more.
Plants produce a diverse portfolio of sesquiterpenes that are important in their response to herbivores and the interaction with other plants. Their biosynthesis from farnesyl diphosphate depends on the sesquiterpene synthases that admit different cyclizations and rearrangements to yield a blend of sesquiterpenes. Here, we investigate to what extent sesquiterpene biosynthesis metabolic pathways can be reconstructed just from the knowledge of the final product and the reaction mechanisms catalyzed by sesquiterpene synthases. We use the software package MedØlDatschgerl (MØD) to generate chemical networks and to elucidate pathways contained in them. As examples, we successfully consider the reachability of the important plant sesquiterpenes β -caryophyllene, α -humulene, and β -farnesene. We also introduce a graph database to integrate the simulation results with experimental biological evidence for the selected predicted sesquiterpenes biosynthesis. Full article
(This article belongs to the Special Issue In silico metabolic modeling and engineering)
►▼ Show Figures

Figure 1

14 pages, 2630 KiB  
Article
Prediction of N-linked Glycoform Profiles of Monoclonal Antibody with Extracellular Metabolites and Two-Step Intracellular Models
by Sha Sha, Zhuangrong Huang, Cyrus D. Agarabi, Scott C. Lute, Kurt A. Brorson and Seongkyu Yoon
Processes 2019, 7(4), 227; https://doi.org/10.3390/pr7040227 - 23 Apr 2019
Cited by 13 | Viewed by 4858
Abstract
Monoclonal antibodies (mAbs) are commonly glycosylated and show varying levels of galactose attachment. A set of experiments in our work showed that the galactosylation level of mAbs was altered by the culture conditions of hybridoma cells. The uridine diphosphate galactose (UDP-Gal) is one [...] Read more.
Monoclonal antibodies (mAbs) are commonly glycosylated and show varying levels of galactose attachment. A set of experiments in our work showed that the galactosylation level of mAbs was altered by the culture conditions of hybridoma cells. The uridine diphosphate galactose (UDP-Gal) is one of the substrates of galactosylation. Based on that, we proposed a two-step model to predict N-linked glycoform profiles by solely using extracellular metabolites from cell culture. At the first step, the flux level of UDP-Gal in each culture was estimated based on a computational flux balance analysis (FBA); its level was found to be linear with the galactosylation degree on mAbs. At the second step, the glycoform profiles especially for G0F (agalactosylated), G1F (monogalactosylated) and G2F (digalactosylated) were predicted by a kinetic model. The model outputs well matched with the experimental data. Our study demonstrated that the integrated mathematical approach combining FBA and kinetic model is a promising strategy to predict glycoform profiles for mAbs during cell culture processes. Full article
(This article belongs to the Special Issue In silico metabolic modeling and engineering)
►▼ Show Figures

Graphical abstract

15 pages, 2804 KiB  
Article
Strategic Framework for Parameterization of Cell Culture Models
by Pavlos Kotidis and Cleo Kontoravdi
Processes 2019, 7(3), 174; https://doi.org/10.3390/pr7030174 - 26 Mar 2019
Cited by 2 | Viewed by 3729
Abstract
Global Sensitivity Analysis (GSA) is a technique that numerically evaluates the significance of model parameters with the aim of reducing the number of parameters that need to be estimated accurately from experimental data. In the work presented herein, we explore different methods and [...] Read more.
Global Sensitivity Analysis (GSA) is a technique that numerically evaluates the significance of model parameters with the aim of reducing the number of parameters that need to be estimated accurately from experimental data. In the work presented herein, we explore different methods and criteria in the sensitivity analysis of a recently developed mathematical model to describe Chinese hamster ovary (CHO) cell metabolism in order to establish a strategic, transferable framework for parameterizing mechanistic cell culture models. For that reason, several types of GSA employing different sampling methods (Sobol’, Pseudo-random and Scrambled-Sobol’), parameter deviations (10%, 30% and 50%) and sensitivity index significance thresholds (0.05, 0.1 and 0.2) were examined. The results were evaluated according to the goodness of fit between the simulation results and experimental data from fed-batch CHO cell cultures. Then, the predictive capability of the model was tested against four different feeding experiments. Parameter value deviation levels proved not to have a significant effect on the results of the sensitivity analysis, while the Sobol’ and Scrambled-Sobol’ sampling methods and a 0.1 significance threshold were found to be the optimum settings. The resulting framework was finally used to calibrate the model for another CHO cell line, resulting in a good overall fit. The results of this work set the basis for the use of a single mechanistic metabolic model that can be easily adapted through the proposed sensitivity analysis method to the behavior of different cell lines and therefore minimize the experimental cost of model development. Full article
(This article belongs to the Special Issue In silico metabolic modeling and engineering)
►▼ Show Figures

Figure 1

18 pages, 703 KiB  
Article
Modelling and Simulation of Biochemical Processes Using Petri Nets
by Safae Cherdal and Salma Mouline
Processes 2018, 6(8), 97; https://doi.org/10.3390/pr6080097 - 24 Jul 2018
Cited by 10 | Viewed by 5326
Abstract
Systems composed of many components which interact with each other and lead to unpredictable global behaviour, are considered as complex systems. In a biological context, complex systems represent living systems composed of a large number of interacting elements. In order to study these [...] Read more.
Systems composed of many components which interact with each other and lead to unpredictable global behaviour, are considered as complex systems. In a biological context, complex systems represent living systems composed of a large number of interacting elements. In order to study these systems, a precise mathematical modelling was typically used in this context. However, this modelling has limitations in the structural understanding and the behavioural study. In this sense, formal computational modelling is an approach that allows to model and to simulate dynamical properties of these particular systems. In this paper, we use Hybrid Functional Petri Net (HFPN), a Petri net extension dedicated to study and verify biopathways, to model and study the Methionine metabolic pathway. Methionine and its derivatives play significant roles in human bodies. We propose a set of simulations for the purpose of studying and analysing the Methionine pathway’s behaviour. Our simulation results have shown that several important abnormalities in this pathway are related to sever diseases such as Alzheimer’s disease, cardiovascular disease, cancers and others. Full article
(This article belongs to the Special Issue In silico metabolic modeling and engineering)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-7
Back to TopTop