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Frontiers in Connecting Steady-State and Dynamic Approaches for Modelling Cell Metabolic Behavior

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

Deadline for manuscript submissions: closed (30 November 2022) | Viewed by 24927

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Special Issue Editors

Prof. Dr. Mario Jolicoeur

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Guest Editor

Ecole Polytechnique de Montreal, Department of Chemical Engineering, Montreal, QC, Canada
Interests: development and use of metabolic engineering tools, such as kinetic mathematical models describing cell metabolic behavior, for the enhancement of our understanding of a cell population behavior and the reproducibility of the production of metabolites. Study of metabolic flux regulation by the development of mathematical models that are primarily focusing on cell energetics
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Dr. Sabine Peres

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Guest Editor

1. LRI, Paris-Sud University, CNRS, Paris-Saclay University, 91405 Orsay, France
2. MaIAGE, INRA, University Paris-Saclay, 78350 Jouy-en-Josas, France
3. Laboratory of Biometry and Evolutionary Biology, University Claude Bernard Lyon 1, Villeurbanne, Auvergne-Rhône-Alpes, France
Interests: metabolic pathway analysis; constraints-based modelling; logic-based modelling; cancer metabolism

Special Issue Information

Dear Colleagues,

There are currently two fundamental trends in the analysis of metabolic networks: the constraint-based modelling approach for large-size networks to determine a space of feasible metabolic steady-state flux solution and the kinetic metabolic modelling approach for relatively small-size networks to study the dynamic behavior of a regulated metabolic system. Although kinetic models consider reaction network stoichiometry and flux regulation mechanisms, their resolution is not trivial, because it relies on the determination of a high number of kinetic parameters. On the one hand, these parameters are necessarily biased, accounting for lumped reactions when a subset of a cell reaction network is considered. Furthermore, extensive experimental data are required for extra and intracellular concentrations in metabolites to enable kinetic parameter value estimation. On the other hand, such kinetic models allow real-time simulation of metabolic fluxes and of cell behavior. Indeed, methods are now emerging using the advantages of constraints-based modelling to analyze the time evolution of some interesting variables, integrating or not kinetic descriptions, and we think it is worth compiling a Special Issue representing your perspectives on these themes.

In this Special Issue, our aim is to bring together the steady-state and the kinetic communities into a coherent set of contributions, drawing the synergistic capacity of both approaches. For example, we envision contributions that address, but are not limited to, the following issues:

Use of hybrid methods to study flux regulatory mechanisms within a metabolic network;
Case study-oriented contributions on cell biology, biomedicine, and bioprocesses;
Multi-level studies, connecting signalling, genomics, transcriptomics, interactomics, etc., to the regulation of a metabolic flux or a distributed set of fluxes.

There is no restriction on cell types or biosystems. By biomedicine, we refer to studies on metabolic diseases and on the study of a metabolic target. Approach limitations and future trends are important issues to be discussed in each study.

Prof. Dr. Mario Jolicoeur
Assoc. Prof. Sabine Peres
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

constraint-based modelling approach
kinetic modelling
metabolic network
dynamic metabolic flux analysis
metabolic flux regulation

Published Papers (10 papers)

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Editorial

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2 pages, 152 KiB

Open AccessEditorial

Special Issue on “Frontiers in Connecting Steady-State and Dynamic Approaches for Modelling Cell Metabolic Behavior”

by Sabine Peres and Mario Jolicoeur

Processes 2022, 10(8), 1612; https://0-doi-org.brum.beds.ac.uk/10.3390/pr10081612 - 15 Aug 2022

Viewed by 807

Abstract

Understanding the behaviour of cell metabolism is the crucial key in bioprocess development and optimization, as well as in the development of efficient therapies [...] Full article

(This article belongs to the Special Issue Frontiers in Connecting Steady-State and Dynamic Approaches for Modelling Cell Metabolic Behavior)

Research

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19 pages, 788 KiB

Open AccessFeature PaperArticle

Hybrid Dynamic Models of Bioprocesses Based on Elementary Flux Modes and Multilayer Perceptrons

by Maxime Maton, Philippe Bogaerts and Alain Vande Wouwer

Processes 2022, 10(10), 2084; https://0-doi-org.brum.beds.ac.uk/10.3390/pr10102084 - 14 Oct 2022

Cited by 3 | Viewed by 1025

Abstract

The derivation of minimal bioreaction models is of primary importance to develop monitoring and control strategies of cell/microorganism culture production. These minimal bioreaction models can be obtained based on the selection of a basis of elementary flux modes (EFMs) using an algorithm starting from a relatively large set of EFMs and progressively reducing their numbers based on geometric and least-squares residual criteria. The reaction rates associated with the selected EFMs usually have complex features resulting from the combination of different activation, inhibition and saturation effects from several culture species. Multilayer perceptrons (MLPs) are used in order to undertake the representation of these rates, resulting in a hybrid dynamic model combining the mass-balance equations provided by the EFMs to the rate equations described by the MLPs. To further reduce the number of kinetic parameters of the model, pruning algorithms for the MLPs are also considered. The whole procedure ends up with reduced-order macroscopic models that show promising prediction results, as illustrated with data of perfusion cultures of hybridoma cell line HB-58. Full article

(This article belongs to the Special Issue Frontiers in Connecting Steady-State and Dynamic Approaches for Modelling Cell Metabolic Behavior)

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33 pages, 947 KiB

Open AccessFeature PaperArticle

Regulation of Eukaryote Metabolism: An Abstract Model Explaining the Warburg/Crabtree Effect

by Laetitia Gibart, Rajeev Khoodeeram, Gilles Bernot, Jean-Paul Comet and Jean-Yves Trosset

Processes 2021, 9(9), 1496; https://0-doi-org.brum.beds.ac.uk/10.3390/pr9091496 - 25 Aug 2021

Cited by 3 | Viewed by 2037

Abstract

Adaptation of metabolism is a response of many eukaryotic cells to nutrient heterogeneity in the cell microenvironment. One of these adaptations is the shift from respiratory to fermentative metabolism, also called the Warburg/Crabtree effect. It is a response to a very high nutrient increase in the cell microenvironment, even in the presence of oxygen. Understanding whether this metabolic transition can result from basic regulation signals between components of the central carbon metabolism are the the core question of this work. We use an extension of the René Thomas modeling framework for representing the regulations between the main catabolic and anabolic pathways of eukaryotic cells, and formal methods for confronting models with known biological properties in different microenvironments. The formal model of the regulation of eukaryote metabolism defined and validated here reveals the conditions under which this metabolic phenotype switch occurs. It clearly proves that currently known regulating signals within the main components of central carbon metabolism can be sufficient to bring out the Warburg/Crabtree effect. Moreover, this model offers a general perspective of the regulation of the central carbon metabolism that can be used to study other biological questions. Full article

(This article belongs to the Special Issue Frontiers in Connecting Steady-State and Dynamic Approaches for Modelling Cell Metabolic Behavior)

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11 pages, 1063 KiB

Open AccessFeature PaperArticle

A Theoretical Model of Mitochondrial ATP Synthase Deficiencies. The Role of Mitochondrial Carriers

by Jean-Pierre Mazat, Anne Devin, Edgar Yoboue and Stéphane Ransac

Processes 2021, 9(8), 1424; https://0-doi-org.brum.beds.ac.uk/10.3390/pr9081424 - 17 Aug 2021

Cited by 1 | Viewed by 1945

Abstract

The m.8993T>G mutation of the mitochondrial MT-ATP6 gene is associated with NARP syndrome (neuropathy, ataxia and retinitis pigmentosa). The equivalent point mutation introduced in yeast Saccharomyces cerevisiae mitochondrial DNA considerably reduced the activity of ATP synthase and of cytochrome-c-oxidase, preventing yeast growth on oxidative substrates. The overexpression of the mitochondrial oxodicarboxylate carrier (Odc1p) was able to rescue the growth on the oxidative substrate by increasing the substrate-level phosphorylation of ADP coupled to the conversion of α-ketoglutarate (AKG) into succinate with an increase in Complex IV activity. Previous studies showed that equivalent point mutations in ATP synthase behave similarly and can be rescued by Odc1p overexpression and/or the uncoupling of OXPHOS from ATP synthesis. In order to better understand the mechanism of the ATP synthase mutation bypass, we developed a core model of mitochondrial metabolism based on AKG as a respiratory substrate. We describe the different possible metabolite outputs and the ATP/O ratio values as a function of ATP synthase inhibition. Full article

(This article belongs to the Special Issue Frontiers in Connecting Steady-State and Dynamic Approaches for Modelling Cell Metabolic Behavior)

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14 pages, 1815 KiB

Open AccessFeature PaperArticle

Model Parameterization with Quantitative Proteomics: Case Study with Trehalose Metabolism in Saccharomyces cerevisiae

by Chuan Fu Yap, Manuel Garcia-Albornoz, Andrew F. Jarnuczak, Simon J. Hubbard and Jean-Marc Schwartz

Processes 2021, 9(1), 139; https://0-doi-org.brum.beds.ac.uk/10.3390/pr9010139 - 12 Jan 2021

Cited by 1 | Viewed by 2330

Abstract

When Saccharomyces cerevisiae undergoes heat stress it stimulates several changes that are necessary for its survival, notably in carbon metabolism. Notable changes include increase in trehalose production and glycolytic flux. The increase in glycolytic flux has been postulated to be due to the regulatory effects in upper glycolysis, but this has not been confirmed. Additionally, trehalose is a useful industrial compound for its protective properties. A model of trehalose metabolism in S. cerevisiae was constructed using Convenient Modeller, a software that uses a combination of convenience kinetics and a genetic algorithm. The model was parameterized with quantitative omics under standard conditions and validated using data collected under heat stress conditions. The completed model was used to show that feedforward activation of pyruvate kinase by fructose 1,6-bisphosphate during heat stress contributes to the increase in metabolic flux. We were also able to demonstrate in silico that overexpression of enzymes involved in production and degradation of trehalose can lead to higher trehalose yield in the cell. By integrating quantitative proteomics with metabolic modelling, we were able to confirm that the flux increase in trehalose metabolic pathways during heat stress is due to regulatory effects and not purely changes in enzyme expression. The overexpression of enzymes involved in trehalose metabolism is a potential approach to be exploited for trehalose production without need for increasing temperature. Full article

(This article belongs to the Special Issue Frontiers in Connecting Steady-State and Dynamic Approaches for Modelling Cell Metabolic Behavior)

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17 pages, 567 KiB

Open AccessFeature PaperArticle

Answer Set Programming for Computing Constraints-Based Elementary Flux Modes: Application to Escherichia coli Core Metabolism

by Maxime Mahout, Ross P. Carlson and Sabine Peres

Processes 2020, 8(12), 1649; https://0-doi-org.brum.beds.ac.uk/10.3390/pr8121649 - 14 Dec 2020

Cited by 5 | Viewed by 2412

Abstract

Elementary Flux Modes (EFMs) provide a rigorous basis to systematically characterize the steady state, cellular phenotypes, as well as metabolic network robustness and fragility. However, the number of EFMs typically grows exponentially with the size of the metabolic network, leading to excessive computational demands, and unfortunately, a large fraction of these EFMs are not biologically feasible due to system constraints. This combinatorial explosion often prevents the complete analysis of genome-scale metabolic models. Traditionally, EFMs are computed by the double description method, an efficient algorithm based on matrix calculation; however, only a few constraints can be integrated into this computation. They must be monotonic with regard to the set inclusion of the supports; otherwise, they must be treated in post-processing and thus do not save computational time. We present aspefm, a hybrid computational tool based on Answer Set Programming (ASP) and Linear Programming (LP) that permits the computation of EFMs while implementing many different types of constraints. We apply our methodology to the Escherichia coli core model, which contains

226 \times 10^{6}

EFMs. In considering transcriptional and environmental regulation, thermodynamic constraints, and resource usage considerations, the solution space is reduced to 1118 EFMs that can be computed directly with aspefm. The solution set, for E. coli growth on O₂ gradients spanning fully aerobic to anaerobic, can be further reduced to four optimal EFMs using post-processing and Pareto front analysis. Full article

(This article belongs to the Special Issue Frontiers in Connecting Steady-State and Dynamic Approaches for Modelling Cell Metabolic Behavior)

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15 pages, 811 KiB

Open AccessArticle

An Enhanced Segment Particle Swarm Optimization Algorithm for Kinetic Parameters Estimation of the Main Metabolic Model of Escherichia Coli

by Mohammed Adam Kunna, Tuty Asmawaty Abdul Kadir, Muhammad Akmal Remli, Noorlin Mohd Ali, Kohbalan Moorthy and Noryanti Muhammad

Processes 2020, 8(8), 963; https://0-doi-org.brum.beds.ac.uk/10.3390/pr8080963 - 10 Aug 2020

Cited by 8 | Viewed by 1998

Abstract

Building a biologic model that describes the behavior of a cell in biologic systems is aimed at understanding the physiology of the cell, predicting the production of enzymes and metabolites, and providing a suitable data that is valid for bio-products. In addition, building a kinetic model requires the estimation of the kinetic parameters, but kinetic parameters estimation in kinetic modeling is a difficult task due to the nonlinearity of the model. As a result, kinetic parameters are mostly reported or estimated from different laboratories in different conditions and time consumption. Hence, based on the aforementioned problems, the optimization algorithm methods played an important role in addressing these problems. In this study, an Enhanced Segment Particle Swarm Optimization algorithm (ESe-PSO) was proposed for kinetic parameters estimation. This method was proposed to increase the exploration and the exploitation of the Segment Particle Swarm Optimization algorithm (Se-PSO). The main metabolic model of E. coli was used as a benchmark which contained 172 kinetic parameters distributed in five pathways. Seven kinetic parameters were well estimated based on the distance minimization between the simulation and the experimental results. The results revealed that the proposed method had the ability to deal with kinetic parameters estimation in terms of time consumption and distance minimization. Full article

(This article belongs to the Special Issue Frontiers in Connecting Steady-State and Dynamic Approaches for Modelling Cell Metabolic Behavior)

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13 pages, 2663 KiB

Open AccessFeature PaperArticle

Metabolic Efficiency of Sugar Co-Metabolism and Phenol Degradation in Alicyclobacillus acidocaldarius for Improved Lignocellulose Processing

by Ashley E. Beck

Processes 2020, 8(5), 502; https://0-doi-org.brum.beds.ac.uk/10.3390/pr8050502 - 27 Apr 2020

Cited by 3 | Viewed by 2228

Abstract

Substrate availability plays a key role in dictating metabolic strategies. Most microorganisms consume carbon/energy sources in a sequential, preferential order. The presented study investigates metabolic strategies of Alicyclobacillus acidocaldarius, a thermoacidophilic bacterium that has been shown to co-utilize glucose and xylose, as well as degrade phenolic compounds. An existing metabolic model was expanded to include phenol degradation and was analyzed with both metabolic pathway and constraint-based analysis methods. Elementary flux mode analysis was used in conjunction with resource allocation theory to investigate ecologically optimal metabolic pathways for different carbon substrate combinations. Additionally, a dynamic version of flux balance analysis was used to generate time-resolved simulations of growth on phenol and xylose. Results showed that availability of xylose along with glucose did not predict enhanced growth efficiency beyond that of glucose alone, but did predict some differences in pathway utilization and byproduct profiles. In contrast, addition of phenol as a co-substrate with xylose predicted lower growth efficiency. Dynamic simulations predicted co-consumption of xylose and phenol in a similar pattern as previously reported experiments. Altogether, this work serves as a case study for combining both elementary flux mode and flux balance analyses to probe unique metabolic features, and also demonstrates the versatility of A. acidocaldarius for lignocellulosic biomass processing applications. Full article

(This article belongs to the Special Issue Frontiers in Connecting Steady-State and Dynamic Approaches for Modelling Cell Metabolic Behavior)

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Review

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23 pages, 1644 KiB

Open AccessFeature PaperReview

Combining Kinetic and Constraint-Based Modelling to Better Understand Metabolism Dynamics

by Cecile Moulin, Laurent Tournier and Sabine Peres

Processes 2021, 9(10), 1701; https://0-doi-org.brum.beds.ac.uk/10.3390/pr9101701 - 23 Sep 2021

Cited by 8 | Viewed by 2406

Abstract

To understand the phenotypic capabilities of organisms, it is useful to characterise cellular metabolism through the analysis of its pathways. Dynamic mathematical modelling of metabolic networks is of high interest as it provides the time evolution of the metabolic components. However, it also has limitations, such as the necessary mechanistic details and kinetic parameters are not always available. On the other hand, large metabolic networks exhibit a complex topological structure which can be studied rather efficiently in their stationary regime by constraint-based methods. These methods produce useful predictions on pathway operations. In this review, we present both modelling techniques and we show how they bring complementary views of metabolism. In particular, we show on a simple example how both approaches can be used in conjunction to shed some light on the dynamics of metabolic networks. Full article

(This article belongs to the Special Issue Frontiers in Connecting Steady-State and Dynamic Approaches for Modelling Cell Metabolic Behavior)

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38 pages, 2754 KiB

Open AccessFeature PaperReview

Modelling Cell Metabolism: A Review on Constraint-Based Steady-State and Kinetic Approaches

by Mohammadreza Yasemi and Mario Jolicoeur

Processes 2021, 9(2), 322; https://0-doi-org.brum.beds.ac.uk/10.3390/pr9020322 - 09 Feb 2021

Cited by 34 | Viewed by 6891

Abstract

Studying cell metabolism serves a plethora of objectives such as the enhancement of bioprocess performance, and advancement in the understanding of cell biology, of drug target discovery, and in metabolic therapy. Remarkable successes in these fields emerged from heuristics approaches, for instance, with the introduction of effective strategies for genetic modifications, drug developments and optimization of bioprocess management. However, heuristics approaches have showed significant shortcomings, such as to describe regulation of metabolic pathways and to extrapolate experimental conditions. In the specific case of bioprocess management, such shortcomings limit their capacity to increase product quality, while maintaining desirable productivity and reproducibility levels. For instance, since heuristics approaches are not capable of prediction of the cellular functions under varying experimental conditions, they may lead to sub-optimal processes. Also, such approaches used for bioprocess control often fail in regulating a process under unexpected variations of external conditions. Therefore, methodologies inspired by the systematic mathematical formulation of cell metabolism have been used to address such drawbacks and achieve robust reproducible results. Mathematical modelling approaches are effective for both the characterization of the cell physiology, and the estimation of metabolic pathways utilization, thus allowing to characterize a cell population metabolic behavior. In this article, we present a review on methodology used and promising mathematical modelling approaches, focusing primarily to investigate metabolic events and regulation. Proceeding from a topological representation of the metabolic networks, we first present the metabolic modelling approaches that investigate cell metabolism at steady state, complying to the constraints imposed by mass conservation law and thermodynamics of reactions reversibility. Constraint-based models (CBMs) are reviewed highlighting the set of assumed optimality functions for reaction pathways. We explore models simulating cell growth dynamics, by expanding flux balance models developed at steady state. Then, discussing a change of metabolic modelling paradigm, we describe dynamic kinetic models that are based on the mathematical representation of the mechanistic description of nonlinear enzyme activities. In such approaches metabolic pathway regulations are considered explicitly as a function of the activity of other components of metabolic networks and possibly far from the metabolic steady state. We have also assessed the significance of metabolic model parameterization in kinetic models, summarizing a standard parameter estimation procedure frequently employed in kinetic metabolic modelling literature. Finally, some optimization practices used for the parameter estimation are reviewed. Full article

(This article belongs to the Special Issue Frontiers in Connecting Steady-State and Dynamic Approaches for Modelling Cell Metabolic Behavior)

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