Resource allocation modeling for autonomous prediction of plant cell phenotypes

IF 6.8 1区 生物学 Q1 BIOTECHNOLOGY & APPLIED MICROBIOLOGY
Anne Goelzer , Loïc Rajjou , Fabien Chardon , Olivier Loudet , Vincent Fromion
{"title":"Resource allocation modeling for autonomous prediction of plant cell phenotypes","authors":"Anne Goelzer ,&nbsp;Loïc Rajjou ,&nbsp;Fabien Chardon ,&nbsp;Olivier Loudet ,&nbsp;Vincent Fromion","doi":"10.1016/j.ymben.2024.03.009","DOIUrl":null,"url":null,"abstract":"<div><p>Predicting the plant cell response in complex environmental conditions is a challenge in plant biology. Here we developed a resource allocation model of cellular and molecular scale for the leaf photosynthetic cell of <em>Arabidopsis thaliana</em>, based on the Resource Balance Analysis (RBA) constraint-based modeling framework. The RBA model contains the metabolic network and the major macromolecular processes involved in the plant cell growth and survival and localized in cellular compartments. We simulated the model for varying environmental conditions of temperature, irradiance, partial pressure of CO<sub>2</sub> and O<sub>2</sub>, and compared RBA predictions to known resource distributions and quantitative phenotypic traits such as the relative growth rate, the C:N ratio, and finally to the empirical characteristics of CO<sub>2</sub> fixation given by the well-established Farquhar model. In comparison to other standard constraint-based modeling methods like Flux Balance Analysis, the RBA model makes accurate quantitative predictions without the need for empirical constraints. Altogether, we show that RBA significantly improves the autonomous prediction of plant cell phenotypes in complex environmental conditions, and provides mechanistic links between the genotype and the phenotype of the plant cell.</p></div>","PeriodicalId":18483,"journal":{"name":"Metabolic engineering","volume":"83 ","pages":"Pages 86-101"},"PeriodicalIF":6.8000,"publicationDate":"2024-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Metabolic engineering","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1096717624000545","RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"BIOTECHNOLOGY & APPLIED MICROBIOLOGY","Score":null,"Total":0}
引用次数: 0

Abstract

Predicting the plant cell response in complex environmental conditions is a challenge in plant biology. Here we developed a resource allocation model of cellular and molecular scale for the leaf photosynthetic cell of Arabidopsis thaliana, based on the Resource Balance Analysis (RBA) constraint-based modeling framework. The RBA model contains the metabolic network and the major macromolecular processes involved in the plant cell growth and survival and localized in cellular compartments. We simulated the model for varying environmental conditions of temperature, irradiance, partial pressure of CO2 and O2, and compared RBA predictions to known resource distributions and quantitative phenotypic traits such as the relative growth rate, the C:N ratio, and finally to the empirical characteristics of CO2 fixation given by the well-established Farquhar model. In comparison to other standard constraint-based modeling methods like Flux Balance Analysis, the RBA model makes accurate quantitative predictions without the need for empirical constraints. Altogether, we show that RBA significantly improves the autonomous prediction of plant cell phenotypes in complex environmental conditions, and provides mechanistic links between the genotype and the phenotype of the plant cell.

自主预测植物细胞表型的资源分配模型。
预测植物细胞在复杂环境条件下的反应是植物生物学的一项挑战。在此,我们基于资源平衡分析(RBA)约束建模框架,为拟南芥叶片光合细胞建立了细胞和分子尺度的资源分配模型。RBA 模型包含植物细胞生长和存活过程中的代谢网络和主要大分子过程,并将其定位在细胞区室中。我们模拟了温度、辐照度、二氧化碳和氧气分压等不同环境条件下的模型,并将 RBA 预测与已知的资源分布和定量表型特征(如相对生长速率、C:N 比值)进行了比较,最后将 RBA 预测与成熟的 Farquhar 模型给出的二氧化碳固定经验特征进行了比较。与通量平衡分析等其他基于标准约束的建模方法相比,RBA 模型无需经验约束即可做出准确的定量预测。总之,我们的研究表明,RBA 能显著提高复杂环境条件下植物细胞表型的自主预测能力,并提供植物细胞基因型与表型之间的机理联系。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
Metabolic engineering
Metabolic engineering 工程技术-生物工程与应用微生物
CiteScore
15.60
自引率
6.00%
发文量
140
审稿时长
44 days
期刊介绍: Metabolic Engineering (MBE) is a journal that focuses on publishing original research papers on the directed modulation of metabolic pathways for metabolite overproduction or the enhancement of cellular properties. It welcomes papers that describe the engineering of native pathways and the synthesis of heterologous pathways to convert microorganisms into microbial cell factories. The journal covers experimental, computational, and modeling approaches for understanding metabolic pathways and manipulating them through genetic, media, or environmental means. Effective exploration of metabolic pathways necessitates the use of molecular biology and biochemistry methods, as well as engineering techniques for modeling and data analysis. MBE serves as a platform for interdisciplinary research in fields such as biochemistry, molecular biology, applied microbiology, cellular physiology, cellular nutrition in health and disease, and biochemical engineering. The journal publishes various types of papers, including original research papers and review papers. It is indexed and abstracted in databases such as Scopus, Embase, EMBiology, Current Contents - Life Sciences and Clinical Medicine, Science Citation Index, PubMed/Medline, CAS and Biotechnology Citation Index.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信