A model of optimal protein allocation during phototrophic growth

Faizi, Marjan; Zavřel, Tomáš; Loureiro, Cristina; Červený, Jan; Steuer, Ralf

doi:10.1016/j.biosystems.2018.02.004

Cited by 27 publications

(108 citation statements)

References 56 publications

(76 reference statements)

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“…Using proteomics, we show that ∼ 57% (779 out of 1356 identified proteins) proteins change their abundance in dependence of growth rate, whereas the rest was independent of growth rate. A detailed analysis of changes in individual protein fractions revealed phototrophic "growth laws": abundances of proteins associated with light harvesting decreased with increasing light intensity and growth rate, whereas abundances of proteins associated with translation and biosynthesis increased with increasing light intensity and growth rate-in good agreement with recent computational models of cyanobacterial resource allocation (Burnap, 2015;Rügen et al, 2015;Mueller et al, 2017;Reimers et al, 2017;Faizi et al, 2018).…”

Section: Introductionsupporting

confidence: 80%

“…The sharp upregulation of ribosomes at high light intensities in the model is due to the increased turnover of proteins subject to photodamage. As already noted in (Faizi et al, 2018), the model is likely to overestimate this effect, due to the fact that within the model photodamage is exclusively related to protein turnover. We expect that in cells other repair mechanisms are active as well, resulting in a less pronounced upregulation of ribosomes and energy useage elsewhere.…”

Section: Proteome Allocation With Growth Ratementioning

confidence: 96%

“…A coarse-grained model provides insight into proteome allocation To interpret the experimental results on cyanobacterial physiology, we made use of a semi-quantitative resource allocation model of cyanobacterial phototrophic growth. The model was adopted from Faizi et al (2018) and is shown in Figure 5. In brief, the model includes coarse-grained proteome fractions for cellular processes related to growth, including carbon uptake T , metabolism M , photosynthesis P , and ribosomes R, as well as a growth-independent protein fraction Q.…”

Section: Gene Ontologymentioning

confidence: 99%

“…Following Faizi et al (2018), all kinetic parameters were sourced from the primary literature, except the parameters for the photosynthetic cross section, photosynthetic turnover rate, and the rate constant for photoinhibition (see Materials and Methods). These 3 parameters were fitted numerically, such that the predicted maximal growth rate µ ( Figure 1C-D) matched the experimental values ( Figure 5B).…”

Section: Gene Ontologymentioning

confidence: 99%

“…Recently, high light was reported to promote the transcription of genes associated with central metabolic pathways, repression of phycobilisome genes, and accelerated glycogen accumulation rates (Tan et al, 2018) While these studies point to strain-specific differences and are important for characterizing non-model microbial metabolism (Abernathy et al, 2017), the general principles of resource allocation in photoautotrophic metabolism and the laws of phototrophic growth are still poorly understood. Therefore, the aim of this study is to provide a consistent quantitative dataset of cyanobacterial physiology and protein abundance for a range of different light intensities and growth rates-and put the data in the context of published values obtained by a comprehensive literature search as well as in the context of a recent model of photosynthetic resource allocation (Faizi et al, 2018). To this end, we chose the widely used model strain Synechocystis sp.…”

Section: Introductionmentioning

confidence: 99%

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Quantitative insights into the cyanobacterial cell economy

Zavřel

Faizi

Loureiro

et al. 2018

Preprint

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Phototrophic microorganisms are promising resources for green biotechnology. 13 Compared to heterotrophic microorganisms, however, the cellular economy of phototrophic 14 growth is still insufficiently understood. We provide a quantitative analysis of light-limited, 15 light-saturated, and light-inhibited growth of the cyanobacterium Synechocystis sp. PCC 6803 using 16 a reproducible cultivation setup. We report key physiological parameters, including growth rate, cell 17 size, and photosynthetic activity over a wide range of light intensities. Intracellular proteins were 18 quantified to monitor proteome allocation as a function of growth rate. Among other physiological 19 adaptations, we identify an upregulation of the translational machinery and downregulation of light 20 harvesting components with increasing light intensity and growth rate. The resulting growth laws 21 are discussed in the context of a coarse-grained model of phototrophic growth and available data 22 obtained by a comprehensive literature search. Our insights into quantitative aspects of 23 cyanobacterial adaptations to different growth rates have implications to understand and optimize 24 photosynthetic productivity. 25 26 2014). While quantitative insight into the cellular economy of phototrophic microorganisms is 36 still scarce, the cellular economy of heterotrophic growth has been studied extensively-starting 37 with the seminal works of Monod, Neidhardt, and others (Neidhardt et al., 1990; Neidhardt, 1999; 38 Jun et al., 2018) to more recent quantitative studies of microbial resource allocation (Molenaar 39 et al.40 Maitra and Dill, 2015; Weiße et al., 2015). In response to changing environments, heterotrophic 41 1 of 28 Manuscript submitted to eLife microorganisms are known to differentially allocate their resources: with increasing growth rate, 42 heterotrophic microorganisms typically exhibit upregulation of ribosomes and other proteins 43 related to translation and protein synthesis (Scott et al., 2010; Molenaar et al., 2009; Peebo et al., 44 2015), exhibit complex changes in transcription profiles, e.g. (Klumpp et al., 2009; Matsumoto et al., 45 2013), and increase cell size (Kafri et al., 2016). The molecular limits of heterotrophic growth have 46 been described thoroughly (Kafri et al., 2016; Erickson et al., 2017; Scott et al., 2014; Metzl-Raz 47 et al., 2017; Klumpp et al., 2013). 48 In contrast, only few studies so far have addressed the limits of cyanobacterial growth from an 49 experimental perspective (Bernstein et al., 2016; Yu et al., 2015; Abernathy et al., 2017; Ungerer 50 et al., 2018; Jahn et al., 2018). Of particular interest were the adaptations that enable fast pho-51 toautotrophic growth (Bernstein et al., 2016; Yu et al., 2015; Abernathy et al., 2017; Ungerer et al., 52 2018). The cyanobacterium with the highest known photoautotrophic growth rate, growing with a 53 doubling time of up to ∼ 1.5h, is the strain Synechococcus elongatus UTEX 2973 (Ungerer et al., 54 2018). Compared to its c...

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Section: Introductionsupporting

confidence: 80%

Section: Proteome Allocation With Growth Ratementioning

confidence: 96%

Section: Gene Ontologymentioning

confidence: 99%

Section: Gene Ontologymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Quantitative insights into the cyanobacterial cell economy

Zavřel

Faizi

Loureiro

et al. 2018

Preprint

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Formalizing Metabolic-Regulatory Networks by Hybrid Automata

Liu

Bockmayr

2019

Acta Biotheor

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Computational approaches in systems biology have become a powerful tool for understanding the fundamental mechanisms of cellular metabolism and regulation. However, the interplay between the regulatory and the metabolic system is still poorly understood. In particular, there is a need for formal mathematical frameworks that allow analyzing metabolism together with dynamic enzyme resources and regulatory events. Here, we introduce a metabolicregulatory network model (MRN) that allows integrating metabolism with transcriptional regulation, macromolecule production and enzyme resources. Using this model, we show that the dynamic interplay between these different cellular processes can be formalized by a hybrid automaton, combining continuous dynamics and discrete control.

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