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      Modeling and Simulating the Aerobic Carbon Metabolism of a Green Microalga Using Petri Nets and New Concepts of VANESA

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          Abstract

          In this work we present new concepts of VANESA, a tool for modeling and simulation in systems biology. We provide a convenient way to handle mathematical expressions and take physical units into account. Simulation and result management has been improved, and syntax and consistency checks, based on physical units, reduce modeling errors. As a proof of concept, essential components of the aerobic carbon metabolism of the green microalga Chlamydomonas reinhardtii are modeled and simulated. The modeling process is based on xHPN Petri net formalism and simulation is performed with OpenModelica, a powerful environment and compiler for Modelica. VANESA, as well as OpenModelica, is open source, free-of-charge for non-commercial use, and is available at: http://agbi.techfak.uni-bielefeld.de/vanesa.

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          Most cited references47

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          Mammalian TOR: a homeostatic ATP sensor.

          P. B. Dennis, A Jaeschke, M Saitoh … (2001)
          The bacterial macrolide rapamycin is an efficacious anticancer agent against solid tumors. In a hypoxic environment, the increase in mass of solid tumors is dependent on the recruitment of mitogens and nutrients. When nutrient concentrations change, particularly those of essential amino acids, the mammalian Target of Rapamycin (mTOR) functions in regulatory pathways that control ribosome biogenesis and cell growth. In bacteria, ribosome biogenesis is independently regulated by amino acids and adenosine triphosphate (ATP). Here we demonstrate that the mTOR pathway is influenced by the intracellular concentration of ATP, independent of the abundance of amino acids, and that mTOR itself is an ATP sensor.
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            Optimizing the distribution of resources between enzymes of carbon metabolism can dramatically increase photosynthetic rate: a numerical simulation using an evolutionary algorithm.

            Eric de Groot, Xin-Guang Zhu, S Long (2007)
            The distribution of resources between enzymes of photosynthetic carbon metabolism might be assumed to have been optimized by natural selection. However, natural selection for survival and fecundity does not necessarily select for maximal photosynthetic productivity. Further, the concentration of a key substrate, atmospheric CO(2), has changed more over the past 100 years than the past 25 million years, with the likelihood that natural selection has had inadequate time to reoptimize resource partitioning for this change. Could photosynthetic rate be increased by altered partitioning of resources among the enzymes of carbon metabolism? This question is addressed using an "evolutionary" algorithm to progressively search for multiple alterations in partitioning that increase photosynthetic rate. To do this, we extended existing metabolic models of C(3) photosynthesis by including the photorespiratory pathway (PCOP) and metabolism to starch and sucrose to develop a complete dynamic model of photosynthetic carbon metabolism. The model consists of linked differential equations, each representing the change of concentration of one metabolite. Initial concentrations of metabolites and maximal activities of enzymes were extracted from the literature. The dynamics of CO(2) fixation and metabolite concentrations were realistically simulated by numerical integration, such that the model could mimic well-established physiological phenomena. For example, a realistic steady-state rate of CO(2) uptake was attained and then reattained after perturbing O(2) concentration. Using an evolutionary algorithm, partitioning of a fixed total amount of protein-nitrogen between enzymes was allowed to vary. The individual with the higher light-saturated photosynthetic rate was selected and used to seed the next generation. After 1,500 generations, photosynthesis was increased substantially. This suggests that the "typical" partitioning in C(3) leaves might be suboptimal for maximizing the light-saturated rate of photosynthesis. An overinvestment in PCOP enzymes and underinvestment in Rubisco, sedoheptulose-1,7-bisphosphatase, and fructose-1,6-bisphosphate aldolase were indicated. Increase in sink capacity, such as increase in ADP-glucose pyrophosphorylase, was also indicated to lead to increased CO(2) uptake rate. These results suggest that manipulation of partitioning could greatly increase carbon gain without any increase in the total protein-nitrogen investment in the apparatus for photosynthetic carbon metabolism.
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              Central carbon metabolism and electron transport in Chlamydomonas reinhardtii: metabolic constraints for carbon partitioning between oil and starch.

              Xenie Johnson, Jean Alric (2013)
              The metabolism of microalgae is so flexible that it is not an easy task to give a comprehensive description of the interplay between the various metabolic pathways. There are, however, constraints that govern central carbon metabolism in Chlamydomonas reinhardtii that are revealed by the compartmentalization and regulation of the pathways and their relation to key cellular processes such as cell motility, division, carbon uptake and partitioning, external and internal rhythms, and nutrient stress. Both photosynthetic and mitochondrial electron transfer provide energy for metabolic processes and how energy transfer impacts metabolism and vice versa is a means of exploring the regulation and function of these pathways. A key example is the specific chloroplast localization of glycolysis/gluconeogenesis and how it impacts the redox poise and ATP budget of the plastid in the dark. To compare starch and lipids as carbon reserves, their value can be calculated in terms of NAD(P)H and ATP. As microalgae are now considered a potential renewable feedstock, we examine current work on the subject and also explore the possibility of rerouting metabolism toward lipid production.
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                Author and article information

                Contributors
                Christoph Brinkrolf
                Journal
                Journal ID (nlm-ta): J Integr Bioinform
                Journal ID (iso-abbrev): J Integr Bioinform
                Journal ID (publisher-id): jib
                Journal ID (doi): jib
                Journal ID (pmc): jib
                Title: Journal of Integrative Bioinformatics
                Publisher: De Gruyter
                ISSN (Electronic): 1613-4516
                Publication date (Electronic): 15 September 2018
                Publication date Collection: September 2018
                Volume: 15
                Issue: 3
                Electronic Location Identifier: 20180018
                Affiliations
                Bielefeld University , deptFaculty of Technology, Bioinformatics Department , Bielefeld, Germany
                Bielefeld University , deptFaculty of Biology and CeBiTec, Genetics of Prokaryotes , Bielefeld, Germany
                Linköping University , deptDepartment of Computer and Information Science , Linköping, Sweden
                Bielefeld University , deptFaculty of Biology and CeBiTec, Genome Research , Bielefeld, Germany
                University of Cambridge , deptDepartment of PlantSciences, Evolution and Diversity , Cambridge, UK
                Bielefeld University , deptFaculty of Biology and CeBiTec, Algae Biotechnology and Bioenergy , Bielefeld, Germany
                Bielefeld University , deptFaculty of Biology and CeBiTec, Proteome and Metabolome Research , Bielefeld, Germany
                Article
                Publisher ID: jib-2018-0018
                DOI: 10.1515/jib-2018-0018
                PMC ID: 6340121
                PubMed ID: 30218605
                SO-VID: f3e854d8-a3dc-4af5-9143-bad120ef88d4
                Copyright © ©2018, Christoph Brinkrolf et al., published by DeGruyter, Berlin/Boston
                License:

                This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

                History
                Date received : 02 March 2018
                Date revision received : 05 August 2018
                Date accepted : 16 August 2018
                Page count
                Figures: 4, Tables: 1, References: 62, Pages: 11
                Categories
                Subject: Research-Article

                Keywords: chlamydomonas reinhardtii,metabolic network,openmodelica,systems biology,xhpn
                Data availability:
                Keywords: chlamydomonas reinhardtii, metabolic network, openmodelica, systems biology, xhpn

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