The minimum energy required to build a cell

Abstract

Understanding the energy requirements for cell synthesis accurately and comprehensively has been a longstanding challenge. We introduce a computational model that estimates the minimum energy necessary to build any cell from its constituent parts. This method combines omics and internal cell compositions from various sources to calculate the Gibbs Free Energy of biosynthesis independently of specific metabolic pathways. Our public tool, Synercell, can be used with other models for minumum species-specific energy estimations in any well-sequenced species. The energy for synthesising the genome, transcriptome, proteome, and lipid bilayer of four cell types: Escherichia coli, Saccharomyces cerevisiae, an average mammalian cell and JCVI-syn3A were estimated. Their modelled minimum synthesis energies at 298 K were $9.54\times {10}^{-11}$ J/cell, $4.99\times {10}^{-9}$ J/cell, $3.71\times {10}^{-7}$ J/cell and $3.69\times {10}^{-12}$ respectively. Gram-for-gram synthesis of lipid bilayers requires the most energy, followed by the proteome, genome, and transcriptome. The average per gram cost of biomass synthesis is in the 300s of J/g for all four cells. Implications for the generalisability of cell construction and applications to biogeosciences, cellular biology, biotechnology, and astrobiology are discussed.

Keywords: Biosynthesis; Gibbs energy; Group Contribution Algorithm (GCA); Virtual cell.

Figure 1

Comparative analysis of minimum energetic costs for cellular synthesis across different organisms at different temperatures calculated using synercell. The panels display the energy cost (horizontal axes) vs. temperature (vertical axes). Values noted below are at 298 K: (A) E. coli Mass-Specific Synthesis Displays the energetic cost in Joules per gram for E. coli’s DNA, RNA, proteins, and phospholipids. The energy to synthesise one gram of E. coli cells is 331 Joules. (B) E. coli Cell-Specific Synthesis Shows the synthesis energy of one E. coli cell, including the contributions from its genome, transcriptome, proteome, and lipid bilayer. The energy necessary to synthesise one E. coli cell is $9.54\times {10}^{-11}$ J. (C) E. coli approximate millimolar amount of ATP required for syntheses in Panel B, assuming 35,000 J per mole of ATP. (D) S. cerevisiae Cell-Specific Synthesis The energetic costs in building S. cerevisiae and its cellular components. The energy necessary to synthesise one cell is $4.99\times {10}^{-9}$ J. (E) Average Mammalian Cell Synthesis The energetic costs in building an average mammalian cell and its cellular components. The energy necessary to synthesise one average mammalian cell is $3.71\times {10}^{-7}$ J. (F) JCVI-syn3A Cell Synthesis The energetic costs in building a JCVI-syn3A cell and its cellular components. The energy necessary to synthesise one JCVI-syn3A cell is $3.69\times {10}^{-12}$ J. Cell compositions represent averaged values derived from various sources (Supplementary Table S1), aiming to capture a general representation across diverse growth phases. The average composition represents a generalised perspective of cell energetics, mainly reflecting conditions akin to the natural environmental lag phase.

Figure 2

Workflow summary of Synercell. The tool will first require data input, including the type of cell (bacterial, yeast, mammalian or JCVI-syn3A), a genome sequence, a protein sequence and the temperature at which the user wants to calculate the energy. The tool creates a virtual cell with the omics data input, transcribing the genome and adjusting the concentration pool according to the cell type. Using the GCA, the tool uses tailored models for each biomolecule type to estimate its $\mathrm{\Delta }{G}_f^{\circ }$ at the chosen temperature. Next, the tool obtains the $\mathrm{\Delta }{G}_r^{\circ }$ following its stoichiometry. Finally, it calculates $\mathrm{\Delta }{G}_r$ combining experimental and theoretical concentrations data for each constituent.