Mycoplasma mycoides capri, we assembled a near-complete metabolic network with 98% of enzymatic reactions supported by annotation or experiment. The model agrees well with genome-scale in vivo transposon mutagenesis experiments, showing a Matthews correlation coefficient of 0.59. The genes in the reconstruction have a high in vivo essentiality or quasi-essentiality of 92% , compared to 79% in silico essentiality. This coherent model of the minimal metabolism in JCVI-syn3A at the same time also points toward specific open questions regarding the minimal genome of JCVI-syn3A, which still contains many genes of generic or completely unclear function. In particular, the model, its comparison to in vivo essentiality and proteomics data yield specific hypotheses on gene functions and metabolic capabilities; and provide suggestions for several further gene removals. In this way, the model and its accompanying data guide future investigations of the minimal cell. Finally, the identification of 30 essential genes with unclear function will motivate the search for new biological mechanisms beyond metabolism.JCVI-syn3A, a robust minimal cell with a 543 kbp genome and 493 genes, provides a versatile platform to study the basics of life. Using the vast amount of experimental information available on its precursor,  One way that researchers can test whether they understand a biological system is to see if they can accurately recreate it as a computer model. The more they learn about living things, the more the researchers can improve their models and the closer the models become to simulating the original. In this approach, it is best to start by trying to model a simple system.Biologists have previously succeeded in creating ‘minimal bacterial cells’. These synthetic cells contain fewer genes than almost all other living things and they are believed to be among the simplest possible forms of life that can grow on their own. The minimal cells can produce all the chemicals that they need to survive – in other words, they have a metabolism. Accurately recreating one of these cells in a computer is a key first step towards simulating a complete living system.Breuer et al. have developed a computer model to simulate the network of the biochemical reactions going on inside a minimal cell with just 493 genes. By altering the parameters of their model and comparing the results to experimental data, Breuer et al. explored the accuracy of their model. Overall, the model reproduces experimental results, but it is not yet perfect. The differences between the model and the experiments suggest new questions and tests that could advance our understanding of biology. In particular, Breuer et al. identified 30 genes that are essential for life in these cells but that currently have no known purpose.Continuing to develop and expand models like these to reproduce more complex living systems provides a tool to test current knowledge of biology. These models may become so advanced that they could predict how living things will respond to changing situations. This would allow scientists to test ideas sooner and make much faster progress in understanding life on Earth. Ultimately, these models could one day help to accelerate medical and industrial processes to save lives and enhance productivity. Establishing the core requirements of cellular life is a fundamental challenge of biology. The question of the minimal set of biochemical functions necessary for a cell to grow and replicate has been studied from a number of angles for more than 20 years. It has long been suggested  that a mMycoplasma genitalium , sequenced in 1995  yieldedfluenzae ; a subsefluenzae . A limitfluenzae  avoid thssential .Escherichia coli and Bacillus subtilis , with tMycoplasma mycoides capri serovar LC GM12, a minimal genome of 531 kbp was designed and constructed containing 473 genes (438 protein-coding genes and 35 genes for RNAs) , using the available genetic manipulation tools to assemble the genome as a plasmid inside the yeast cell (https://www.ncbi.nlm.nih.gov/nuccore/CP002027.1)  . The res014940.1 ), has a reported , transfoast cell . These m02027.1) . This waI-syn3.0 .JCVI-syn3.0 contains all the genes of JCVI-syn1.0 that are required for growth. This includes both essential and quasi-essential genes. Individually non-essential genes were removed in the design for JCVI-syn3.0 to the greatest extent possible without causing synthetic lethality or a major growth disadvantage from simultaneous knockouts. However, in a few cases, genes that appear to be non-essential were retained for ease of genome design and construction. Intriguingly, the role of a considerable fraction of the minimal genome of JCVI-syn3.0 could not be elucidated in spite of extensive bioinformatic analyses. At the time of publication of the minimal cell, 149 of the genes (∼31% of the genome) could not be assigned a completely specific biological function. Assignment to a broad functional category could not even be made for a subset of 79 genes. These genes of unknown or poorly defined function potentially point toward required features of cellular life yet to be discovered.https://www.ncbi.nlm.nih.gov/nuccore/CP016816.2.  function compared to 311 and 1780 for M. pneumoniae and E. coli, respectively  model . FBA yieeumoniae  (which i(E. coli ) and othThis FBA model for JCVI-syn3A allows for the analysis of the properties of minimized metabolism in JCVI-syn3A. In particular, gene essentiality can be compared between the model and experimental transposon mutagenesis data. Random gene disruption by bombardment with transposon insertions  was usedM. mycoides capri has been studied in depth, which informed all aspects of the metabolic model. To refer to genes in the JCVI-syn3A genome, we use the locus names of the form MMSYN1_xxxx as used in the annotation of JCVI-syn1.0 ; however, SecA is certainly essential because it is a necessary component of the protein translocase, which inserts proteins such as transporters into the membrane. While the absence of gene products for genes carrying transposon inserts has not been confirmed experimentally, genes with relatively high insertion counts are more likely to be functionally disrupted and thus non-essential. Genes that are not required by the organism to grow in rich media will contain many transposon insertions , whereas genes required for cell viability will be sparsely hit by transposon insertions. To identify genes whose disruption might not be immediately detrimental but might cause a growth defect apparent later on, sequencing of the transposon mutagenesis library was performed on Transposon insertion mutagenesis studies were performed in order to probe the dispensability of individual genes in JCVI-syn3A . In this experiment, transposons are randomly inserted into the chromosomes of a population of cells that is then plated under selection for a drug resistance gene carried by the transposon . After tA Poisson mixture model was used to partition the coding regions into two sets of genes based on the transposon insertion density. By comparing the assignment of genes into classes of sparse and dense transposon insertions between rpsl/0637), L27 (rpmA/0499), and L31 (rpmE/0137) were manually assigned as essential since they are necessary to construct a functional ribosome. The gene secA/0095 could not be automatically classified since the mixture model predicted the gene to be more heavily hit with insertions in trxA/0065) was assumed to be essential since its associated reductase (trx/0819) was predicted to be essential by the mixture model. Only one gene was misclassified: the ATP synthase subunit atpC/0789), initially classified as non-essential, was manually reassigned to essential since all other ATPase subunits (atpD/0790 through atpB/0796) were essential according to the mixture model. The majority of transposon insertions in atpC/0789 are found in the secA. However, it is possible that the M. pneumoniae, transposon insertions into the atpC (MPN597) lead to viable cells with decreased cytadherence activity . The fact that ∼15% of the genes in JCVI-syn3A are individually non-essential is not inconsistent with the near-minimality of the genome as a whole: it is not possible to remove all non-essential genes without vastly decreasing the growth rate or outright killing the cell. Furthermore, a genome comprised only of essential and quasi-essential genes is non-viable as well, since the removal of a non-essential gene can cause a previously quasi-essential gene to become non-essential in the new construct. As JCVI-syn3A grows more slowly than JCVI-syn1.0 (2 hr doubling time vs. 1 hr), a gene disruption that in JCVI-syn1.0 led to outgrowth by unaffected competitor cells might still survive through passage four in JCVI-syn3A. As a result, genes that were classified as quasi-essential in JCVI-syn1.0 can appear non-essential in JCVI-syn3A, and could in principle be removed as well—for the price of some gradual further decrease in growth rate. This lack of a clear cutoff again underscores the ‘trade-off between genome size and growth rate’ taking place during genome minimization .The cellular components of JCVI-syn3A fall into three categories: macromolecules, lipids and capsule, and small molecules and ions. M. mycoides capri (M. mycoides capri substrain). The DNA fraction is slightly increased from the reported 5% to 5.5%, which corresponds to exactly one chromosome in JCVI-syn3A. Assuming almost all RNA to be present as ribosomal RNA (rRNA) and around 4600 bases per ribosome yields an upper limit of ∼670 ribosomes per average cell. This number would correspond to ∼20,000 ribosomes in E. coli when scaled by cell volume, which is within the observed growth dependent range of 8000–73,000 for E. coli (M. mycoides capri serovar LC (M. mycoides capri LC) strain Y  and dUTPase (dut/0447). ACP carries a 4′-phosphopantetheine moiety in its holo form, and including the holoenzyme in the biomass equation enforces flux through the corresponding prosthetic group attachment reaction. dUTPase is included for technical reasons discussed in Section 'Nucleotide metabolism'. We use cellular abundances of 138 (ACP) and 10 (dUTPase), derived from the proteomics experiments. The resulting mass fractions are then subtracted from the total protein mass fraction.In addition to a generic protein species (describing the average JCVI-syn3A protein), two specific proteins are included: acyl carrier protein  is known to produce a galactan polysaccharide  ; while iccharide , the JCVM. mycoides capri LC Y , PtsH (ptsH/0694), Crr  and PtsG (ptsG/0779). The phosphate-transfer reaction chain from phosphoenolpyruvate (PEP) to glucose (manA/0435) and NagB  suggests possible utilization of mannose and glucosamine as well, which is supported by mutation studies suggesting that in M. mycoides mycoides strain T1, PtsG is involved in the uptake of all three sugars (nagA operon (0493 through 0495) suggests possible uptake of N-acetylmannosamine or N-acetylneuraminate . Therefore, an uptake reaction for N-acetylmannosamine is included, noting that this is probably not imported through the glucose PTS system, as concomitant phosphorylation would render the putative N-acetylmannosamine kinase NagC (0495) redundant. Lacking more information, we assume that this import reaction is mediated by an ATP-binding cassette (ABC) transporter instead.A schematic diagram of central metabolism in JCVI-syn3A is provided in  glucose  is lumpee sugars . Thus PTM. pneumoniae  has been annotated; however, it seems likely that this gene forms part of an operon together with its two adjacent genes. The RAST annotation pipeline (Four possible sugar sources are assumed to feed into glycolysis via fructose-6-phosphate (F6P): glucose, mannose, glucosamine and N-acetylmannosamine. Only the putative N-acetylmannosamine-6-phosphate epimerase NanE (pipeline  suggestsM. mycoides capri LC Y (M. mycoides capri LC Y cell extracts (-1 g cell protein-1) and the level of background noise in that study is not known. Thus, this reaction is included in the model but we note that the evidence seems ambiguous.Starting from fructose-6-phosphate, the annotation of JCVI-syn3A contains a complete glycolytic pathway as well as the non-oxidative branch of the pentose phosphate pathway up to the nucleotide precursor phosphoribosylpyrophosphate (PRPP), in line with experimental studies on pri LC Y . As in opri LC Y , the genpri LC Y . In partextracts , albeit ldh/0475) is possible, several genes from the acetate fermentation branch have been deleted, namely the E1 subunit of pyruvate dehydrogenase  and NADH oxidase . However, the remaining subunits of the PDH complex , as well as the path from acetyl-CoA to acetate, are still present in the genome. NAD regeneration could possibly be carried out by one of the remaining oxidoreductases of unclear functionality. Until further information becomes available, the remaining PDH complex is assumed to act on pyruvate to yield acetyl-CoA and finally acetate.While the completion of the glycolytic pathway via lactate dehydrogenase . A strong candidate for phosphopentomutase activity is the putative phosphoglucomutase (PGMT) gene deoB/0733. It is preceded by the deoxyribose phosphate aldolase gene (deoC/0732) and  succeeded by the pyrimidine nucleoside phosphorylase MMSYN1_0734, that is it is neighboring two genes responsible for dR1P production and dR5P breakdown, respectively. At the same time, it shows some similarity (21% sequence identity) to the phosphopentomutase TK1777 from the archaeon Thermococcus kodakaraensis. TK1777 showed activity mainly against d1RP, but also weaker activity against glucose 1-phosphate (G1P) . Phosphopentomutase (PPM) and deoxyribose phosphate aldolase (DRPA) activity have been experimentally observed in pri LC Y , enablinte (G1P) . Thus, dM. mycoides; with mutational data on M. mycoides mycoides T1 , which is assumed to import all nucleosides (ribo- and deoxyribo-), but no individual bases or free ribose, in accordance with the experimental characterization of the ribonucleoside ABC importer in Streptococcus mutans .The genome of JCVI-syn3A contains three deoxynucleoside kinases: tmk/0045, gmk/0203, cmk/0347, pyrH/0537, and adk/0651) that can phosphorylate all (deoxy-)mononucleotides except for dUMP (ndk). Instead, the glycolytic enzymes phosphoglycerate kinase (PGK) and pyruvate kinase (PYK) have been found to phosphorylate other dinucleotides besides ADP in several mycoplasmas . All deoxytrinucleotides except dTTP can be obtained either from their deoxynucleosides or from the corresponding ribodinucleotide through the action of ribonucleotide diphosphate reductase .The genome of JCVI-syn3A contains several mononucleotide kinases . PNP is assumed to also act on purine deoxynucleosides, as this activity has been demonstrated in M. capricolum and M. gallisepticum . We note the presence of several hydrolases of unclear function that may possibly carry out the other reactions. Thus, all these hydrolysis reactions are included in the network, without the assignment of a specific gene. Experimental studies suggest a common enzyme for all deoxymononucleotidase reactions, but separate enzymes for dUTP and dCTP hydrolysis , a dinucleotide (dCDP) and several trinucleotides  has been experimentally observed . A putatdrolysis .M. mycoides capri does not itself imply that this activity has to be present in JCVI-syn3A, possible DNA incorporation of dUTP (formed from UDP through RDNR and subsequent phosphorylation) is a problem all organisms must contend with, as exemplified by the essentiality of dUTPase in E. coli . The second possibility would be the export of either dUMP or deoxyuridine. We note that the possibility to recycle deoxyuridine through pyrimidine nucleoside phosphorylase in the natural M. mycoides capri  renders an additional dedicated export system for either metabolite unlikely, but side activity of some other system would be possible.The breakdown of dUTP to dUMP, however, raises the question of the downstream degradation. The uptake requirement of  thymine , in spit thymine ; NTD1 woM. mycoides capri on external thymine/thymidine rules out any thymidylate synthase activity high enough to meet cellular dTTP needs, such activity has been reported for M. mycoides mycoides SC (dctD/0515) enables the conversion of dCMP to dUMP (which is used in wild-type M. mycoides capri to ultimately convert thymine to thymidine (pyrG/0129), which catalyzes the conversion of UTP to CTP, spends ATP and uses glutamine as an amino donor, which suggests that DctD catalyzing the amination without ATP and from free ammonia is unlikely. Instead, the question arises whether CTPS in JCVI-syn3A may have a relaxed substrate specificity. The E. coli CTPS was found to not act on UMP ecfT/0641, ecfA/0642 and ecfA/0643 and four ecfS genes spread throughout the genome . Folate, riboflavin, coenzyme A, nicotinate, and pyridoxal are all imported through the ECF system. We note that the absence of related salvage enzymes in JCVI-syn3A necessitates the uptake of complete coenzyme A; this is in accordance with the experimental requirement of M. mycoides capri LC Y for coenzyme A rather than coenzyme A precursors , FolD (folD/0684) and Fmt (fmt/0390), together with the repair enzyme YgfA (ygfA/0443/FTHFCL). YgfA is not only required to utilize imported 5-formyl-THF, but also to recycle the 5-formyl-THF produced from the hydrolysis of 5,10-methenyl-THF  is unclear, they are tentatively assigned to nicotinate, pyridoxal phosphate, and coenzyme A respectively. For downstream conversion, the genome contains pathways for NAD(P) and FMN/FAD formation from nicotinate and riboflavin.For the case of folate, ervation  and its on . The RA of GlyA  suggestspotC/0195 through potA/0197 and thiB/0706 through 0708, respectively). For thiamine, the deletion of the corresponding diphosphokinase (MMSYN1_0261) suggests that thiamine diphosphate (ThDP) must be taken up directly. Sequence and structural information suggest this to be possible (see Appendix 1 and M. mycoides capri LC Y (M. pneumoniae (pdhC/0227). Instead, simultaneous import and transamidation of lipoate from lipoyllysine onto PdhC, catalyzed by the peptidase 0401, is tentatively assumed in the model (see Appendix 1).Spermine and thiamine have their own uptake systems  production pathways. JCVI-syn3A shares the fatty acid utilization pathway from Staphylococcus aureus (plsY/0117) and acyl carrier protein . Holo-ACP is formed from apo-ACP and coenzyme A by ACP synthase (acpS/0513), releasing adenosine ytqI/0139 is 30% identical to the experimentally confirmed bifunctional oligoribonuclease/pAp phosphatase NrnA from M. pneumoniae (MPN140)  . Thus, iS. aureus contain two copies of FakB, and these have been demonstrated in Staphylococcus aureus to display distinct substrate specificities, one acting preferably on unsaturated fatty acids and the other on saturated fatty acids. Assuming similar specificities in JCVI-syn3A would be consistent with the assumed prevailing fatty acids . No annotation for FakA exists in JCVI-syn3A, however, (fakA gene in M. pneumoniae (MPN547), which shows 33% sequence identity to fakA/0420 and shares the same genomic context (located upstream of plsX). Thus, it is assumed that fakA/0420 is the missing FakA subunit, completing the lipid assembly pathway from free fatty acids to cardiolipin.Fatty acid kinase consists of a kinase FakA and a fatty acid-binding protein FakB. Both JCVI-syn3A and however,  reports M. genitalium, DAG is produced from phosphatidate via phosphatidate phosphatase (PAPA). While no such enzyme is annotated in JCVI-syn3A, and a BLAST search with the M. genitalium gene (MPN455) against JCVI-syn3A scores no hits, we note that the presence of a number of unassigned phosphatases in the genome of JCVI-syn3A makes it plausible that one of them could act on phosphatidate. An alternative possibility might be phosphatidate phosphatase side activity by phosphatidylglycerophosphatase (pgpA/0214), which has been reported for phosphatidylglycerophosphatase B (PgpB) in E. coli  and the galactan lipopolysaccharide. In  E. coli . With noepsG/0113 and cps/0114, see Section 'Biomass composition and reaction') and Gal-DAG. Lacking further evidence, Gal-DAG is assumed to be formed by either of the two glycosyltransferases, cps/0114 and 0697.The product DAG serves as the lipid moiety for the synthesis of lipogalactan ; however, as lcholine , we assuoppB/0165 through oppA/0169). In Lactococcus lactis, the Opp system has been found to import peptides of four to at least 35 amino acids with little dependence of uptake rates on peptide length or amino acid composition  can split these peptides into individual amino acids.Schematics of the macromolecular and amino acid reaction networks are provided in position . For theposition . The modgltP/0886, as well as two amino acid permeases of unknown specificity (0876 and 0878) have been identified in JCVI-syn3A. The substrate specificities of these two amino acid permeases are not known. However, M. mycoides capri LC Y has been found capable of taking up all amino acids in their free form  are included that consume amino acids and nucleotides according to the assumed macromolecular compositions. These reactions produce representative DNA, RNA and protein species that enter the biomass reaction according to the mass fractions in E. coli  are assugatB/0687 through gatC/0689) using glutamine or asparagine as amino donor. Translation is assumed to be initiated with formylated methionyl initiator tRNA (fMet-tRNAfMet). While the natural M. mycoides capri has been found to be able to initiate protein synthesis with the unformylated species (fMet formyltransferase fmt/0390) is quasi-essential in JCVI-syn3A and the assumption is hence made that formylation cannot be omitted in JCVI-syn3A. Translation reactions for both apo-ACP and the generic protein species include deformylation and methionine cleavage, in line with ca. 80% of proteins in a proteome assumed to have the initial methionine cleaved  and dUTPase (dut/0447), only deformylation is considered, as the phenylalanine and isoleucine in second position of their sequences are not favorable for methionine cleavage  is transamidated to glutaminyl-tRNA(Gln) via an amidotransferase  and (putatively) FtsH (ftsH/0039). Lon has been found to degrade ssrA-tagged peptides in place of ClpXP in Mesoplasma florum . The RNA degradation constraint corresponds to an mRNA half-life time of around 1 min, assuming mRNA to account for ∼5% of all cellular RNA , the prl growth . The proular RNA  (and asssepticum . While RmetK/0432), which produces the methyl donor mtnN/0381) to homocysteine. As there is no obvious way for further breakdown of homocysteine in JCVI-syn3A, it is assumed to be secreted for the time being. As a precedent, secretion—rather than further conversion—of homocysteine has been suspected in Plasmodium falciparum  and phosphate ; corA/0879 could import calcium as well based on its similarity to the B. subtilis calcium importer YloB (corA/0879 to YloB (CAB31439) is lower than between YloB and the deleted calcium importer MMSYN1_0246 (22% vs. 30%), we note that the putative calcium-binding sites in YloB . Based on this, an ATP-consuming K+/Na+ antiport reaction is catalyzed by KtrAB (natA/0685 and trkA/0686) in the model, and an Na+/H+ antiport reaction is included as well without a specific gene assignment. Import systems have not been identified for sodium or chloride, but chloride has been proposed to freely permeate the membrane of M. mycoides capri  is imposed to ensure non-zero flux through this reaction. Reconversion of 5-formyl-THF to 5,10-methenyl-THF via YgfA consumes 1 ATP per 5-formyl-THF and thus requires 0.01 mmol gDW−1 h-1 ATP. Another damage preemption/repair enzyme included in the model is dUTPase (dut/0447), which hydrolyses dUTP and prevents its erroneous incorporation into DNA. Rather than enforcing a minimal flux through this reaction, dUTPase is included directly in the biomass for technical reasons (see Section 'Nucleotide metabolism').On top of the enzymes involved in required metabolic reactions, we note the presence of possible damage reactions and repair enzymes to repair this damage. As discussed, 5,10-methenyl-THF is hydrolyzed to 5-formyl-THF as a side reaction of GlyA  and YgfAIn order to account for cellular energy expenses, the growth- and non-growth-associated maintenance costs (GAM and NGAM) need to be included in the model. The GAM describes the energy cost associated with cellular growth, and therefore enters the biomass reaction as a certain amount of ATP spent per unit biomass production. The NGAM describes the basic, growth rate-independent cellular energy requirements and is therefore frequently implemented as a lower constraint on a separate ATP hydrolysis reaction . Both pa−1 ATP. For the non-quantifiable part, 25 mmol gDW−1 ATP is used following the previously published M. pneumoniae model ; the ATP costs in protein and RNA synthesis; and the associated costs for tRNA charging and nucleotide phosphorylation, respectively. Specifically, a constant part of the protein/RNA synthesis fluxes  is routed through protein/RNA degradation, and only the surplus beyond this constant flux actually contributes to model growth. These turnover-associated ATP costs amount to 2.73 mmol gDW−1 h-1 in the model for JCVI-syn3A. The maintenance of a transmembrane pH gradient in M. mycoides capri PG3 has been experimentally demonstrated . This value is hence implemented as a lower bound on the model ATPase reaction. The resulting total NGAM in the model for JCVI-syn3A is then 3.30 mmol gDW−1 h-1, which comes out similar to the iJO1366 model for E. coli (3.15 mmol gDW−1 h-1)  .dt = 2.02 hr  in M. mycoides capri PG3 . However, the model does qualitatively reproduce the further splitting between galactan and Gal-DAG production, which gives a ratio of ∼22:1 , compared to a ratio of ∼64:1 from 3H labeling , which produces the lipid intermediate CDP-diacylglycerol; ACPS (acpS/0513), which attaches the hptA/0216), which produces GMP from guanine. DASYN has a -1) and one of the lowest protein abundances (just one copy per cell per our proteomics data). If either value turned out to be not accurate, this could easily raise the M. mycoides capri  . The pros (kcat)   and tRNA charging (subsystem ‘Amino acid metabolism’), respectively. Analogously, ‘Nucleotide metabolism’ only includes ATP expenses beyond RNA turnover (i.e. NTP production for growth-associated nucleic acid synthesis and nucleotide usage in other subsystems). ‘GAMNonquant’ denotes the non-quantifiable fraction of the GAM. M. pneumoniae, for which non-growth associated maintenance accounts for 71–88% of total cellular ATP consumption   metabolic processes . The vast majority of energy (75%) is spent directly on growth, that is macromolecular synthesis and tRNA charging and the non-quantifiable contribution to GAM. A modest fraction of ∼16% of cellular energy expenses falls to the NGAM (macromolecular turnover and ATPase). These numbers stand in striking difference to urnover) . This cond 20 hr , that isgltP/0886) using proton symport reactions, the ATP expense on transport does not increase significantly (only to ∼5%) when forcing amino acid uptake through the ATP-consuming Opp peptide importer. This illustrates how JCVI-syn3A can maintain a relatively fast growth rate in spite of its extreme genome minimization and reliance on fermentative ATP production: By importing pre-formed precursors or recovering them through salvage reactions, the cell expends a minimal amount of energy to obtain the final macromolecular precursors and passes this savings in energy along to the production of biomass.The ATP breakdown also reveals that in spite of the minimal cell’s heavy reliance on uptake of pre-formed precursors, transport processes only account for ∼3% of ATP consumption. While the optimal FBA solution only takes up amino acids through the permeases  and, in tiny amounts, by FolD . The only consumer of NADPH in the model is ribodinucleotide reductase . In vivo, however, NADPH is expected to also be needed for expenses not captured by the model, including RNA modification (dihydrouridine synthesis) and response to oxidative stress: The reduction of protein disulfide bonds formed by oxidative stress is mediated by thioredoxin (cdr/0887) serves to reduce coenzyme A disulfide dimers to the free thiol-carrying monomers. NADPH production through GAPDP diverts flux from the ATP-producing GAPD/PGK branch in glycolysis, effectively incurring an ATP cost for NADPH production. In order to probe the cellular capacity for NADPH production, 2, introduced for testing purposes). Within a considerable margin, the doubling time rises shallowly with NADPH consumption: for example, at 3.5 mmol gDW−1 h-1 , the model doubling time only rises by 25% to ∼2.5 hr. This suggests that even though NADPH usage is not fully captured by the model, the cell should be able to accommodate a considerable amount of NADPH demand without strong impact on the growth rate .The other important currency in the cell are reduction equivalents in the form of NADPH, which in JCVI-syn3A is produced by GapN , which can be compared to the corresponding in silico acid secretion (which in turn is connected to ATP production). The measurements were performed in a saline buffer containing glucose but no other nutrients for growth. Under these conditions, the cell is not able to grow . In addition, a possibly significant energy sink not covered yet by the model are metabolite repair functions, of which thus far only two are included in the model, namely 5-formyl-THF cyclo-ligase (ygfA/0443) and dUTPase (dut/0447). Metabolite repair usually consumes energy , it shos energy , and it metK/0432  and mtnN/0381  is non-essential in silico, the downstream peptidases have to be non-essential as well. A further analysis of in silico essentialities is featured in Sections 'Interpretation of individual gene essentialities', 'The role of folate metabolism', and 'A partial bypass to the pentose phosphate pathway'.An individual breakdown of in silico gene essentialities is provided in These in silico essentialities can be compared to the experimental transposon data . Two limiting cases of interest are treating all quasi-essential genes as either in vivo essential (1) or in vivo non-essential (2). Given that the identification of quasi-essential genes was crucial for the successful genome minimization in JCVI-syn3.0 , treatinM. mycoides capri LC Y (M. pneumoniae (potC/0195 through potA/0197) essential in the model, they are only quasi-essential in vivo , two amino acid permeases (0878 and gltP/0886) and three of the four peptidases . As further discussed in Section 'Interpretation of individual gene essentialities', the in vivo essentiality of these genes is likely affected by their exact substrate profiles and maximal uptake rates. If all 12 genes related to amino acid utilization (i.e. the genes above plus ietS/0133 and 0876) are excluded from the prediction comparison, the right confusion matrix in Similarly, it is of interest to consider one set of genes whose functionality is difficult to capture precisely based on the currently available information, namely the genes pertaining to uptake and utilization of amino acids (in free or peptide form): As can be seen in In summary, this analysis demonstrates an overall good agreement between model and experiment, which is mainly impacted by the in vivo quasi-essential genes, whose essentiality is inherently difficult to capture in an FBA model. The disagreements observed  are discussed in detail in Sections 'Interpretation of individual gene essentialities', 'The role of folate metabolism', and 'A partial bypass to the pentose phosphate pathway'. Some of them can be rationalized, while others lead to new hypotheses.Finally, performing in silico double knockouts  yields jAbsolute cellular abundances (molecules per average cell) of JCVI-syn3A proteins were obtained from mass spectrometry based proteomics and the assumed protein dry mass fraction. Relative and absolute protein abundances were used in the reconstruction of the JCVI-syn3A biomass composition (see Section 'Biomass composition and reaction') and estimates of the Comparing the overall JCVI-syn3A proteomics breakdown in M. mycoides capri and assembled a metabolic reconstruction and FBA model for the minimal cell. The majority of reactions in this model are supported by experimental evidence on the parent organism and related mycoplasmas. The model is near-complete with regard to accounting for the biomass components, describes cellular energy expenses well, shows good agreement with experimental transposon insertion data, and importantly has relatively few non-essential metabolic genes. It thus provides a foundation to study the features of the minimal metabolic network.The creation of the first minimal bacterial cell JCVI-syn3.0 in 2016 provided a powerful platform for understanding the basics of life. As a first step along this road, we have combined the genetic information of JCVI-syn3A with the extensive amount of experimental information available for the natural +/H+ antiporter remains to be identified. Central and nucleotide metabolism display a number of potential redundancies  and contains no redundant features (except for one more glycosyltransferase than required by the current reconstruction). The only remaining gap in lipid metabolism is the missing gene for phosphatidate phosphatase. In cofactor metabolism, the remaining questions are the substrate specificities of the EcfS transporter subunits, and the proposed lipoate uptake mechanism. Amongst the ion transport reactions, a gene for the Nacies see , and sevM. pneumoniae'). Furthermore, from these 21 gap fills, only four are not supported by experimental evidence. Hence, 98% of all non-pseudo reactions are justified through gene assignments and/or experimental evidence, or are assumed to be passive. Even so, we obtain a number of gap-filled reactions of only 21—a fraction of 6% of all model reactions, or 9% of the 244 ‘non-pseudo’ reactions. . These 21 gap fills are obtained from the total number of 35 non-pseudo reactions without assigned gene after subtraction of 14 passive transport reactions assumed to take place without protein mediation (see e.g. discussion on passive permeative glycerol uptake in Appendix 1). This number of gap fills is considerably lower than in comparable models  essentiality. While a number of genes can be discussed individually , two pathways need to be discussed as a whole: the folate cycle (see Section 'The role of folate metabolism') and the pentose phosphate pathway . Overall, the analysis suggests a few new hypotheses and even yields suggestions for some genes or groups of genes that could still be removed from the genome of JCVI-syn3A to minimize the genome even further. In this way, it complements the transposon mutagenesis data that can only probe individual essentialities—and simultaneous knockouts prove challenging in experiment .pta/0229 and ackA/0230) in central metabolism and dCMP deaminase (dctD/0515) in nucleotide metabolism. The former two are essential in vivo while the remaining pyruvate dehydrogenase subunits (pdhC/0227 and pdhD/0228) are not. The non-essentiality of pdhC/D implies that JCVI-syn3A can grow without acetate fermentation, as also predicted by the model. This however raises the question what other essential function phosphate acetyltransferase and acetate kinase perform. A conceivable possibility could be that knockout of pta/0229 would lead to sequestration of coenzyme A as acetyl-coenzyme A if pyruvate dehydrogenase itself was still active. This could impede any function that coenzyme A could have as a cellular redox buffer. Similarly, knocking out ackA/0230 with the remainder of the pathway still active could lead to accumulation of acetyl phosphate, whose capacity as a nonenzymatic protein acetylation agent essentiality is challenging to rationalize, and is therefore mysterious. These are the last two enzymes of the acetate branch  is essential in vivo but non-essential in silico—suggesting that the residual capacity of pyruvate dehydrogenase is not sufficient to sustain cell growth on its own, or that the truncated complex is non-functional entirely. Furthermore, it suggests that pyruvate cannot be secreted at the same rate as lactate and/or that the assumed residual NADH oxidase activity would not be high enough to regenerate NAD+ for the GAPD/PGK branch in lower glycolysis . The idea of a lower pyruvate transport rate is supported by reports of M. mycoides oxidizing external pyruvate 2–3 times slower than lactate (punA/0747) is quasi-essential in vivo, which suggests that either the nucleobase uptake capacities in JCVI-syn3A do not suffice to cover purine base demand; or that the flux through the pentose phosphate pathway on its own does not suffice to provide all required PRPP, and additional synthesis starting from R1P released by PNP is necessary . In lipid metabolism, the second glycosyltransferase, 0697, assumed to be redundant with cps/0114 for synthesis of Gal-DAG , is quasi-essential in vivo—suggesting that either the activity of cps/0114 is not high enough to cover both Gal-DAG and capsule production, or that cps/0114 only catalyzes capsule production and is not involved in Gal-DAG synthesis at all. In amino acid metabolism, 10 genes related to amino acid uptake and utilization are non-essential in silico but quasi-essential in vivo: namely, all genes of the Opp peptide importer (oppB/0165 through oppA/0169), three of the peptidases , the glutamate/aspartate permease GltP (gltP/0886) and one of the amino acid permeases (0878). The quasi-essentiality of Opp  is consistent with M. mycoides capri LC Y requiring alanine in peptide form for optimal growth  occur in central, nucleotide, lipid and amino acid metabolism. In central metabolism, the assumed N-acetylglucosamine-6-phosphate deacetylase, 0493, is the only in vivo quasi-essential gene along the entire N-acetylmannosamine utilization pathway , which strongly suggests some other  function for this gene. Lactate dehydrogenase  is only quasi-essential in vivo, but it is not apparent how the cell would produce dCTP in its absence, or deal with CMP (produced from RNA breakdown). The most obvious hypothesis would be relaxed substrate specificity of uridylate kinase  to also act on CMP and dCMP (possibly with weaker activity), as is the case for the eukaryotic enzyme. Guanine phosphoribosyltransferase  is the only phosphoribosyltransferase that is only quasi-essential. This suggests either some small but not negligible guanosine kinase activity or some capability to import intact GMP. Unfortunately, M. mycoides capri, so that the possibility of GMP uptake seems conceivable but is not directly supported by experiment. However, it is noteworthy that GUAPRT is one of the few reactions whose Three ‘weak’ false positives  suggest relaxed substrate specificities or additional functionalities. In lipid metabolism, the quasi-essentiality of . aureus   is non-essential in the model (with RNDR as the only consumer of NADPH) but essential in vivo. As discussed in Section 'Energy usage', there are most likely other cellular NADPH demands not captured yet by the model, which would explain the in vivo essentiality. In nucleotide and lipid metabolism, the genes nrdE/0771 and nrdF/0773 (subunits of RNDR) and pgpA/0214 (PGPP) are essential in the FBA model but only quasi-essential in the transposon data. However, the RNDR maintenance gene nrdI (nrdI/0772) is essential in vivo, in line with the in silico essentiality of RNDR. Similarly, the gene downstream from pgpA in the lipid synthesis pathway, cardiolipin synthase (clsA/0147), is essential, just as the entire pathway is in silico. This suggests that nrdE/F and pgpA might also be essential but not identified as such by the transposon mutagenesis analysis, possibly because their knockouts do not become lethal immediately . In Appendix 1, we provide a possible rationalization for these genes in terms of enzyme dilution and required fluxes. Finally, the genes for spermine uptake (potC/0195 through potA/0197), ytqI/0139) and the peptidase Lon (lon/0394) are all quasi-essential in vivo, suggesting that lack of nucleic acid stabilization by spermine, lack of protein turnover or buildup of The remaining discrepancies of individual genes can be rationalized by genetic context or biological interpretation. In central metabolism, the NADPH-producing GapN .folT/0822, the folylpolyglutamate synthase folC/0823 and all genes of the folate cycle) in cofactor metabolism are essential in silico, they are only quasi-essential in vivo, with the exception of the gene for 5-formyl-THF cyclo-ligase . The in vivo essentiality of YgfA could mean inhibitory actions of YgfA’s substrate 5-formyl-THF outside folate metabolism (see Appendix 1). The in silico essentiality of all folate genes arises per construction in the model since formylation of Met-tRNAfMet for translation is assumed, and a THF derivative is included in the biomass.While all folate-related genes .  It is generally known that translational mistakes, such as those introduced by enzyme knockouts, can sum up and become detrimental once a certain threshold is crossed. In the genome of JCVI-syn3A, there already seems to be a precedent for this threshold scenario: Thiouridine tRNA modification through IscS and MnmA (iscS/0441 and mnmA/0387) appears to be non-essential in M. mycoides capri LC Y, which can grow without a precursor for the IscS cofactor pyridoxal phosphate , which converts ribose 5-phosphate (R5P) to the pathway end product PRPP. This is noteworthy because with deoB/0733 (PGMT/PPM), there is an alternative route to R5P, bypassing the rest of the pentose phosphate pathway by providing pentose sugars from nucleoside breakdown. We note that PRPP itself is still needed, as is also evidenced by the essentiality of its utilizing enzymes encoded by the genes apt/0413 (ADPT), upp/0798 (UPPRT), and 0164 (NCTPPRT) in nucleotide and cofactor metabolism. However, the fact that the remainder of the pathway is only quasi-essential in vivo suggests that the demand on R5P could partially be covered by R1P from purine nucleoside phosphorylase .While all genes from the pentose phosphate pathway are essential in silico, they are only quasi-essential in vivo—except for the final gene, For every equivalent of R1P released, PNP will also yield as the other product an equivalent of free purine base, which, in order to be utilized, would consume again the equivalent of PRPP generated from the R1P released. In this way, PNP would allow to exactly meet the PRPP demand for AMP and GMP synthesis; in the absence of pyrimidine nucleoside phosphorylase in JCVI-syn3A however (see Section 'Nucleotide metabolism'), the PRPP demand of uracil would still not be met . For PNP alone to cover the R1P/PRPP demand, it would need to degrade more purine nucleosides and release purine bases than can be used in the cell, and the cell would have to secrete purine bases at the same rate as uracil is taken up and phosphoribosylated.punA/0747) becomes essential in silico under these circumstances. Furthermore, the model secretes about as much purine bases as it takes up uracil .This conclusion is supported by an in silico test: Setting the base symport reactions in the model to reversible for testing purposes indeed renders Tkt, Rpi and Rpe non-essential in silico—but not Prs. For example, upon knocking out the first reaction of the pathway (TKT2), we observe that the flux through PRPPS equals that through PPM (providing R5P), which in turn equals the sum of the fluxes through PUNP1 and PUNP3 (providing R1P from nucleoside breakdown). Accordingly, PNP R1P into glycolysis, which renders PGI and the PTS system non-essential in silico even if fluxes through the pentose phosphate pathway and DRPA are constrained with the folT/0822 in the last two suggested experiments, all listed genes can be removed simultaneously in silico, as any interdependencies between them do not pertain to essential functions. The resulting in silico doubling time is 3.2 hr, that is the same doubling time observed by knocking out any single gene along the acetate branch.The 12 true negatives from the essentiality comparison  are strong candidates for attempts to remove more genes, and hence minimize the genome of JCVI-syn3A even further. Suggested gene removal experiments are listed in manA/0435 (mannose 6-phosphate isomerase) and deoC/0732  could be removed individually. The whole acetylmannosamine branch  could be removed, with the exception of the proposed N-acetylglucosamine 6-phosphate deacetylase 0493, which per the transposon essentiality data seems to have some other/additional function. The remaining subunits of pyruvate dehydrogenase (pdhC/0227 and pdhD/0228) and the proposed lipoylpeptide importer 0401 could also be removed together per our reconstruction and transposon data. The remaining individual functions include the two deoxyadenosine kinases (dak1/0330 and dak2/0382), a peptidase (ietS/0133) and an amino acid permease (0876). The experimental non-essentiality of the peptidase and the amino acid permease supports the model assumption of broad substrate profiles and hence some redundancy among peptidases and amino acid permeases. The individual non-essentiality of the two deoxynucleoside kinases is also consistent with the assumed overlapping substrate profiles (see Appendix 1). However, it is not known whether the ribodinucleotide reductase  on its own could supply all dNTPs if both deoxyadenosine kinases were removed. Thus, simultaneous removal of dak1/0330 and dak2/0382 might incur a synthetic lethality or growth defect.All the true negatives are either individual genes or belong to short pathways. The genes ygfA/0443, as the metabolic source of 5-formyl-THF would be gone; the remaining THF-dependent RNA modification enzymes ; and the peptide deformylase def /0201, as nascent peptides would not be formylated anymore to begin with. Thus, a removal of 10 genes might be possible if JCVI-syn3A could be shown to grow without folate usage, or be enabled to do so (e.g. by reintroducing translation-related gene(s) whose removal might have raised the importance of Met-tRNAfMet formylation; see Section 'The role of folate metabolism').Furthermore, the observed quasi-essentiality of folate metabolism, in conjunction with the experimental observations of folate-free growth , raises folT knockout vs. the wild type for both JCVI-syn3A and JCVI-syn1.0. If folate non-usage only occurs a significant fitness cost in JCVI-syn3A but not in JCVI-syn1.0, this would imply that JCVI-syn3A could be re-enabled to grow without folate as well.To probe the fitness cost of folate non-usage in JCVI-syn3A more specifically and study whether this fitness cost is affected by the genome minimization, it would be of interest to carry out a specific competition study of a As presented in the preceding sections, the comparison between in silico and in vivo essentiality yielded a number of hypotheses and suggested several possible gene removal experiments . SimilarM. pneumoniae is an important systems biology model organism that has been extensively studied  of 6% out of all model reactions, compared to 25% in the M. pneumoniae model iJW145; or 9% for JCVI-syn3A vs. 32%, if exchange, macromolecular and biomass reactions are excluded from the total number of reactions in each model. The JCVI-syn3A model yields a higher degree of in silico essentiality (79% vs. 56% for the 131 ‘metabolic proteins’ in the M. pneumoniae model ( studied  so a com studied ) that inae model )—reflectM. pneumoniae has 116 reactions not present in JCVI-syn3A, which mainly includes uptake and utilization of additional sugar sources, further nucleotide conversions, more extensive cofactor salvage reactions, and additional lipid-related reactions. Some of these reactions were present in JCVI-syn1.0 but were removed during minimization of the genome to JCVI-syn3A. Furthermore, some of the differences are technical in nature, for example the choice to model amino acid uptake as ABC import reactions in M. pneumoniae, or the decision to include amino acid secretion reactions there. Interestingly, in spite of the much smaller genome of JCVI-syn3A, its reconstruction still contains 120 reactions not present in the M. pneumoniae model. While a number of these arise from a more detailed description of various transport processes , we note the presence of some functionalities not present or known in M. pneumoniae. These include the production of a polysaccharide capsule , some alternative sugar sources specific to JCVI-syn3A and also specific nucleotide conversion and breakdown reactions, perhaps most notably the presence of the essential damage preemption enzyme dUTPase . The reconstruction of the metabolic network began with the curated annotation published for JCVI-syn3.0 . The biomass composition of JCVI-syn3A was assembled based on experimental information available for I-syn3.0  .Flux constraints for certain reactions were based on in vivo measurements, other models or physicochemical parameters. Reaction reversibilities were based on information from MetaCyc  and eQui−1 h-1 was used for all reactions and default lower bounds of −1000 mmol gDW−1 h-1 and 0 mmol gDW−1 h-1 were used for reversible and irreversible reactions, respectively. Specific constraints were chosen to account for uptake, secretion and ATP consumption restrictions. An optimal flux vector or set of flux vectors within the constrained solution space is then found by maximizing a particular objective function by means of linear programming. We picked biomass production as our objective function, so that the optimal flux vector describes the optimal growth under the chosen constraints.Model assembly and flux-balance analysis  were carAs the solution to the flux optimization may not be unique, parsimonious FBA (pFBA)  is emploIn order to analyze the energy consumption in the metabolic model for JCVI-syn3A, the consumption of ATP equivalents per subsystem was calculated. The term ‘ATP equivalent’ is used to account for the fact that phosphorylation of all dinucleotides in JCVI-syn3A is assumed to be carried out by the glycolytic enzymes phosphoglycerate kinase and pyruvate kinase, so that the phosphate donors are 1,3-diphosphoglycerate  and phosphoenolpyruvate (PEP) instead of ATP (whose role in dinucleotide phosphorylation is effectively bypassed). For all model reactions not involving 1,3-DPG or PEP, the production or consumption of ATP equivalents was calculated from the number of phosphate bonds formed or broken in each reaction producing or consuming ATP multiplied by the flux through that reaction in the FBA solution. Interconversion of ATP and ADP produces/consumes one phosphate bond. Hydrolysis of ATP to AMP (e.g. in tRNA charging) was counted as consuming two phosphate bonds, since the free pyrophosphate can only be hydrolyzed further to two individual phosphates. Consumption of the ATP moiety as a whole (e.g. in NAD+ synthesis) was also counted as consuming two phosphate bonds, accounting for the phosphorylation steps from AMP to ATP; the energy spent in AMP is already accounted for in other reactions (nucleoside uptake and PRPP synthesis for adenine phosphoribosylation). The flux through adenylate kinase (ADK1) phosphorylating AMP to ADP is already accounted for by counting ATP–>AMP hydrolysis as two phosphate bonds; it is thus ignored to avoid double-counting. To properly account for 1,3-DPG and PEP as phosphate donors for trinucleotide production, the fluxes for the ATP-producing PGK and PYK reactions were set equal to the sum of all PGK or PYK fluxes, respectively, in order to obtain the total number of ATP equivalents produced. In turn, the PGK and PYK model reactions phosphorylating dinucleotides other than ADP were counted as consuming one ATP equivalent each. Accuracy and correct accounting of the calculated ATP equivalent creation and consumption fluxes were verified by confirming that all individual fluxes thus calculated added up to zero.Turnover’ was introduced to include all energy expenses attributable to protein and RNA turnover. This includes the ATP spent on protein degradation itself, as well as the fractions of protein synthesis, RNA synthesis, tRNA charging and phosphorylation of mononucleotides to trinucleotides that produce protein and RNA for turnover only (as determined from the protein and RNA degradation reaction constraints). The remainder of the protein and RNA synthesis fluxes then produces protein and RNA to be consumed in the biomass equation; hence, the associated energy consumption is part of the quantifiable fraction of the growth-associated maintenance (GAM) cost, and is hence included in ‘GAMMacromolecules’ in tRNA charging’ is the fraction of energy expense in tRNA charging attributable to growth-associated protein synthesis. The consumption of the ATP moiety in RNA and biomass production was included in nucleotide metabolism, in order to stay consistent with the definition of the GAM to only include the ATP hydrolyzed for growth (including macromolecular synthesis), but not the consumption of the ATP moiety as a precursor  is part of central metabolism in the model but as a reaction is independent from energy production in glycolysis. It was hence assigned its own subsystem (‘Pentose phosphate pathway’) for the purposes of energy usage breakdown.To obtain the total ATP equivalent consumption percentage per category in see also . AccordiM. pneumoniae (iJW145)  was constructed. The KEGG API was used to search for a C number based on the substrate description in the SBML files. When a C number was not found for a particular substrate, the mapping was created by hand. The name to compound map was verified manually by comparing the name given in the model to the name given in the KEGG database to that C number.Reactions between the models for JCVI-syn3A and for (iJW145)  were comA reaction in the JCVI-syn3A model was determined to be equivalent to a reaction in the iJW145 model if the metabolite sets associated with each reaction were equal. The resulting grouping of reactions into common or model-specific reactions was then manually curated to distinguish reactions where different directionalities/reversibilities between models arose from different roles of these reactions in the model .Growth and rate measurements of minimized synthetic cells have been described in detail elsewhere . BrieflyRates were measured from log–linear portions of the growth curve. To avoid minor variables such as batch differences among medium preparations and temperature fluctuations, strains with different genomes were compared under identical conditions and within a single experiment. The accuracy and reproducibility of the measurements (reflected in the observed We used the procedure described in 0) and a 45 µl sample was added to 45 ml SP4 media containing puromycin 2 µg/ml and grown for 24 hr . A 45-µl sample of P1 culture was added to 45 ml of fresh SP4 media (P2) and grown for 48 hr. Two more passages (P3 and P4) were done.An estimated 92,000 colonies were harvested from the plates in 20 ml of SP4 media  and that the number of insertions in a particular gene is described by one of two possible distributions. The two distributions separate the genes into groups with few and many insertions. The probability to observe mization .The probability that a particular observation of Comparing passage 1 and passage 4, the classification probabilities areA gene is assigned to a category if the classification probability is greater than 0.5. Genes where the classification probabilities are all less than 0.5 With the objective of studying protein expression changes along the growth curve (unrelated to the current study), mass spectrometry with tandem mass tag labeling was carried out on JCVI-syn3A samples from different time points along a growth curve. In the current study, we use the data from the first time point (logarithmic phase). JCVI-syn3A cells used for proteomic analysis were grown as described previously  and in ‘Cells were lysed in a buffer comprised of 3% SDS, 75 mM NaCl, 1 mM NaF, 1 mM , pH 8.5 . Lysates, pH 8.5  and prec, pH 8.5  before r, pH 8.5 . Dried pLyophilized peptides were re-suspended in 30% dry acetonitrile in 200 mM HEPES, pH 8.5 and 8 µL of the appropriate tandem mass tag (TMT) reagent was added to each sample, incubated for 1 hr at room temperature, and quenched with 5% hydroxylamine. Labeled samples were then acidified with 1% trifluoroacetic acid. Differentially labeled samples were pooled into multiplex experiments and then desalted via solid-phase extraction and lyophilized. Samples were fractionated by basic pH reverse-phase liquid chromatography, using a 4.6 mm d manner , lyophilAll LC-MS2/MS3 experiments were performed on an Orbitrap Fusion mass spectrometer with an in-line Easy-nLC 1000 with chilled autosampler (Thermo Fisher Scientific). Peptides were separated on columns that were packed with C4 resin , followed by C18 resin  and then to a final length of 30 cm with C18 . Peptides were eluted with a linear gradient from 11% to 30% acetonitrile in 0.125% formic acid over 165 min at a flow rate of 300 nL/min and heating the column to 60 °C. Electrospray ionization was achieved by applying 2000 V through a stainless-steel T-junction. Mass spectrometer settings were as previously described .ftp://massive.ucsd.edu/MSV000081687). The ProteomeXchange accession number is PXD008159. (Data were processed using the ProteomeDiscoverer 2.1.0.81 software package (Thermo Fisher Scientific). The built-in version of SequestHT  was utilD008159. .M. pneumoniae (T00006) and E. coli (T00944). A mapping between genes and KO identifiers for JCVI-syn3A was derived from the locus tag/KO map for M. mycoides capri LC str. 95010 (T01478) by matching M. mycoides capri genes to JCVI-syn3A genes using a reciprocal best hit BLASTp search .Thank you for submitting your article "Essential Metabolism for a Minimal Cell" for consideration by The reviewers have discussed the reviews with one another and the Reviewing Editor has drafted this decision to help you prepare a revised submission.Summary:The manuscript by Breuer et al., addresses the important question of a minimal cell and provides a valuable resource in the field of synthetic biology. The extensive manual curation, the reconstruction of the metabolic network, and its characterization in silico and with usage of proteomics data provide a valuable foundation for studying the features of a minimal cell. The experiments and hypotheses suggested in the discussion are particularly appreciated. While the presented work provides a knowledge base resource to the community, the predictions formulated by the metabolic model should be described in a more nuanced way.Essential revisions:One of the main concerns deals with the lack of organism-specific incidental to the model predictions, namely: biomass composition, substrate uptake rates and secretion rates. While it is challenging to obtain such level of detail for a minimal organism, and the employed data pertain to the parental strain, it is important to explain how the model represents Syn3.0A and not Syn1.0. Although a comparison of the proposed and existing models has been attempted, the authors should provide a direct comparison of the metabolic capabilities between the original Syn1.0 strain and the reduced Syn3.0A to better delineate the latter from the former. The authors should clearly describe the approach (automated (how) or manual) used in comparing JCVI-syn3.0A model and iJW145.A related concern is the approach used to infer internal composition from the growth media, since the media composition is not representative of the cytosol concentration. The authors should clarify the logic and approach used; if already published, the followed procedure should be appropriately referenced.A third concern is the support of the claim that the model correctly predicts gene essentiality and could help further reduce the model namely through double knock-out simulations. Since Table S6 describes model accuracy, it should be moved to the main text. Given the availability of the data on categorization of the genes in 3 categories , the authors should calculate the Matthews Correlation Coefficient: (1) assuming the quasi-essential genes to be essential and (2) assuming quasi-essential genes to be non-essential. The obtained metric should be compared to other Mollicute metabolic models.The authors should verify the way in which the sensitivity analysis is conducted. A reasonable way to do the sensitivity analysis is to investigate what percentage of growth rate change is found for a given percentage of change in a parameter. Such an analysis may imply very strong variation in contrast to what is stated by the authors. In particular, the effect of the non-growth associated maintenance (NGAM) should be particularly re-investigated since the assumption that the NGAM would be strictly limited to the ATP efflux seems likely to be an under-estimation as many cellular processes may not be described.The authors should use correct terminology when describing predictions from flux balance analysis throughout the manuscript, as it predicts yield; the conversion to growth rate is dictated by what the authors assume for uptake rate which if not measured but is a gross proxy, as indicated above.eLife. Your revised article has been favorably evaluated by Naama Barkai (Senior Editor), a Reviewing Editor, and two reviewers.Thank you for resubmitting your work entitled "Essential Metabolism for a Minimal Cell" for further consideration at The manuscript has been improved but there are some remaining issues that need to be addressed before acceptance, as outlined below:Reviewer #2:We wish to thank the authors for taking time to address our previous comments. The addition of the whole metabolic map for JCVI Syn3.0 is integrative and should help readers understand the metabolism of the minimal cell. We appreciate that the model predictions were nuanced according to the available data . While these comments were properly addressed, a second view of the (lengthy) paper yielded new comments:Abstract:The Abstract is overly concise for a paper that spans 76 pages including references. In general, the conclusions exposed in the abstract should be extended and "98% of enzymatic reactions supported by annotation." This seems like a wrong and inflated number that does not reflect the uncertainty in the network. Excluding exchange reactions, biomass, growth-associated maintenance and gas diffusion (O2 and CO2) 36 reactions remain orphan, including many transports. This correction brings the percentage down to 85.6% when applying a denominator of 250 (again excluding the mentioned reactions above)."The model agrees well with genome-scale in vivo transposon mutagenesis experiments" Perhaps provide a quantitative statement rather than a qualitative one."The genes in the reconstruction have a high in vivo essentiality or quasi-essentiality of 92% , compared to 79% in silico essentiality." While the results presented in supplementary table 2 are consistent with these two numbers, we find 76% in silico essentiality when testing the model provided for single gene deletions. Also, the in vivo essentiality of 92% is obtained when assuming quasi-essential genes as essential and should be specified or the number with only essential genes (68%) should be included or at least mentioned."The reconstruction is the starting point for studying the evolution of metabolic subsystems and analyzing the effects of introducing alternative pathways." If claiming this in the abstract perhaps include subsystems to the reactions in the model."Finally, the identification of 30 essential genes with unknown function will motivate the search for new biological mechanisms beyond metabolism." After reading through the entire paper multiple times I still cannot see where this number is fetched from, so I cannot imagine how a reader could get to it.Rather than pointing to a very accurate model of Syn3.0, the abstract should demonstrate that despite being the smallest organism known to date, Syn3.0 has many unknown elements. While the amount of information provided in the paper is highly valuable for the community, presenting the reconstruction as a way to clearly identify the grey areas of the genome is key for the development and substantial efforts have been made in that sense through 1- a detailed manual curation process, 2- proteomic validation of enzyme expression, 3- in vivo gene essentiality. This sentence of the abstract should be of prime importance: "This comparison together with proteomics data yields new hypotheses on gene functions as well as suggestions for several further gene removals". Summary:The manuscript by Breuer et al., addresses the important question of a minimal cell and provides a valuable resource in the field of synthetic biology. The extensive manual curation, the reconstruction of the metabolic network, and its characterization in silico and with usage of proteomics data provide a valuable foundation for studying the features of a minimal cell. The experiments and hypotheses suggested in the discussion are particularly appreciated. While the presented work provides a knowledge base resource to the community, the predictions formulated by the metabolic model should be described in a more nuanced way.We appreciate that the comprehensive nature of the work on a minimal bacterial cell  was acknowledged and have endeavored to formulate the predictions from the metabolic model in a more nuanced way.Essential revisions:1) One of the main concerns deals with the lack of organism-specific incidental to the model predictions, namely: biomass composition, substrate uptake rates and secretion rates. While it is challenging to obtain such level of detail for a minimal organism, and the employed data pertain to the parental strain, it is important to explain how the model represents Syn3.0A and not Syn1.0. Although a comparison of the proposed and existing models has been attempted, the authors should provide a direct comparison of the metabolic capabilities between the original Syn1.0 strain and the reduced Syn3.0A to better delineate the latter from the former. The authors should clearly describe the approach (automated (how) or manual) used in comparing JCVI-syn3.0A model and iJW145.We should have made the point more clearly that in preparing the metabolic model for JCVI-syn3A the authors did use as much of the existing information on the new organism as possible at the time, especially with regard to its DNA genome size, proteomics data for amino acid composition in the biomass equation and its reduced metabolic capabilities. Unfortunately, this information was distributed throughout the manuscript and was not concentrated in one section. We have tried to make the point more clearly with a paragraph summarizing the differences between JCVI-syn1.0 and JCVI-syn3A in the Supplementary Text. The details are provided below. We do intend to continue to characterize more completely the biomass composition and determine uptake/secretion rates in the future. We thank the reviewers for acknowledging that this can be challenging for a minimal organism. We were planning to do a careful comparison once the defined media have been determined for both strains. At the moment we only have a semi-defined medium for JCVI-syn1.0.M. mycoides capri in the current model and justify the adoption or modifications of this information at the appropriate places in the manuscript. We will eventually measure the cellular composition of JCVI-syn3A experimentally once a defined media for JCVI-syn3A is developed.We would like to stress that experiments to accurately determine cellular composition and dry weight are far from trivial . We thus capitalize on the extensive experimental information available for Metabolic capabilities:M. pneumoniae' is between JCVI-syn3A and Mycoplasma pneumoniae, another important model mycoplasma. To quantify the metabolic differences between JCVI-syn1.0 and JCVI-syn3A, we added a Supplementary Spreadsheet with metabolic reactions that were removed in the minimization from JCVI-syn1.0 to JCVI-syn3A. Many of the genes removed during the minimization process have unknown function, but through the work presented in this manuscript we have been able to assign essentiality scores to the 91 remaining genes of unknown function in JCVI-syn3A. To our knowledge, there is no metabolic reconstruction for JCVI-syn1.0; the model comparison in Section 'Comparison to The spreadsheet contains 53 reactions connected to 48 removed genes of metabolic function: 20 genes (17 reactions) pertain to uptake of alternative sugars and conversion to glycolytic intermediates, demonstrating the wider scope of carbon sources JCVI-syn1.0 can use to generate energy. The removal of five genes in nucleotide metabolism further tightened the nucleotide precursor requirements in JCVI-syn3A, which cannot utilize uridine and thymine anymore and also requires adenine (in free base or nucleoside form) since guanine cannot serve as precursor for all purines anymore. Seven genes in JCVI-syn1.0 enable the degradation of certain amino acids as further ATP sources. This paragraph about comparison of metabolic capabilities has been added to the Supplementary Text of the manuscript.Depending on the information available, some of the reactions in the sheet are described in a generic fashion.  The sheet is in the same format as Supplementary file 3 .M. mycoides capri to evade the immune system of its host. M. mycoides capri is a pathogen of goats.We would like to stress that there may be genes of metabolic function but unknown substrates not only among the 91 unknown genes retained in JCVI-syn3A, but also among the genes removed from JCVI-syn1.0. As such, there could be additional metabolic functions among the deleted genes that currently cannot be precisely described yet. As outlined in Hutchison et al., 2016, the majority of the ~400 genes removed from JCVI-syn1.0 to JCVI-syn3.0  were lipoproteins of unclear function, mobile elements/DNA restriction genes, or completely unassigned altogether. The deleted non-metabolic genes include amongst others e.g. a large number of genes used by Biomass composition:M. mycoides capri (the species to which JCVI-syn1.0 belongs) and several other mycoplasmas provided the starting point to estimate the biomass equation and coefficients for JCVI-syn3A.In spite of the differences in metabolic capabilities between JCVI-syn1.0 and JCVI-syn3A, there are reasons to believe (as outlined below) that the overall biomass composition should be similar between the two organisms. The published experimental data and biomass composition for M. mycoides capri composition, while the main text describes the biomass reconstruction in a summary fashion, the Supplementary Text already featured several sections on the topic of how certain features of the macromolecular composition of M. mycoides capri and other mycoplasmas were adapted to JCVI-syn3A . We have re-worded some of the text to make the approximations and justifications more clear. The most important aspects are the expected conservation of the protein dry mass fraction, which is the largest dry mass fraction among the macromolecules, the retention of specific genes deemed relevant for certain biomass components (in particular the polysaccharide capsule), accounting for the “ions and small molecules” dry mass fraction, and re-scaling of the DNA mass fraction to account for the shorter genome in JCVI-syn3A. Finally, we would like to emphasize that the average protein composition (average length of 386 aa and corresponding amino acid composition) in the model is directly based on JCVI-syn3A proteomics data.Regarding the applicability of the E. coli . In particular, we note that Acholeplasma laidlawii PG8, in spite of having a 1.5-times larger genome than M. mycoides capri but a comparable cell size, shows a similar protein dry mass fraction (55% ) to M. mycoides capri (58% ). This suggests that the protein content in M. mycoides capri should provide a very good initial approximation for JCVI-syn3A as well.A protein dry mass fraction of 40-60% is generally observed in bacteria, e.g. in different mycoplasmas  or The JCVI-syn3A proteomics data also gave an abundance estimate of the ribosomal proteins (average 340) which seemed reasonably consistent with the expected upper limit on ribosome number (670) coming from the RNA dry mass fraction of ~16% assuming all the RNA to be ribosomal. The presence of the same number of rRNA operons (2) in both JCVI-syn1.0 and JCVI-syn3A is also consistent with the assumption of comparable rRNA contents in the two organisms.M. mycoides capri comes out at approximately one chromosome in JCVI-syn3A .We note that the DNA content adopted from the natural M. mycoides capri. We used the direct measurement of potassium and sodium ions per cellular protein by Leblanc et al., (1979), the estimates of the pools of ATP and other nucleotides from Mitchell and Finch, 1979, and the estimate of the total amino acid pool  from the M. pneumoniae study by Wodke et al., 2013. We feel that the final values are within the expected range and acceptable until the biomass composition can be measured directly for JCVI-syn1.0 and JCVI-syn3A.The largest contributions to the small molecule/ion mass fraction come from potassium, phosphate, chloride, ATP and sodium, some of which have been measured directly in strains of Uptake/secretion rates:E. coli and other bacteria has been measured by Fuhrer et al., 2005. For E. coli with a two hour doubling time, they reported a value of 7.8 mmol gDW-1h-1. We decided to use the value 7.4 mmol gDW-1h-1for the glucose uptake in JCVI-syn3A as it was taken from the Mycoplasma pneumoniae study by Wodke et al., 2013 which measured the uptake in rich medium; and M. pneumoniae had a comparable number of PtsG proteins based on the JCVI-syn3A proteomics data. Again, once the defined media have been determined for JCVI-syn1.0 and JCVI-syn3A, the uptake rates will be determined experimentally. A cautionary note has been added to the manuscript about the use of inherited uptake rates even when the high affinity glucose transporters are similar.A range of glucose uptake rates in iJW145 model comparison:We added a subsection 'Model comparison'. In brief, a script was used for a first round of automatic comparison, followed by manual curation to catch common or distinct reactions the script could not correctly classify.2) A related concern is the approach used to infer internal composition from the growth media, since the media composition is not representative of the cytosol concentration. The authors should clarify the logic and approach used; if already published, the followed procedure should be appropriately referenced.We have revised subsection 'Small molecules and Ions'  to clarify the approach taken. The media composition is not taken to be representative of cytosolic concentrations. Rather, the cited minimal media are used to infer what compounds are required by the cell in the first place: Any compound present in a minimal medium is  required by the cell, and thus this compound (or its downstream product(s)) need to be included in the biomass. As had already been stated at the end of that subsection, the cellular dry mass fractions (and thus cytosolic concentrations) were taken from separate measurements or other models. The revised subsection should convey the procedure more clearly.3) A third concern is the support of the claim that the model correctly predicts gene essentiality and could help further reduce the model namely through double knock-out simulations. Since Table S6 describes model accuracy, it should be moved to the main text. Given the availability of the data on categorization of the genes in 3 categories , the authors should calculate the Matthews Correlation Coefficient: (1) assuming the quasi-essential genes to be essential and (2) assuming quasi-essential genes to be non-essential. The obtained metric should be compared to other Mollicute metabolic models.We moved Supplementary file 6 to the main text  and also introduced another table with performance metrics . We also merged the subsection 'Mapping in silico to in vivo essential genes' in the Supplementary Text into an expanded subsection, 'In silico gene knockouts and mapping to in vivo essentiality' in the main text.As suggested by the reviewers, we calculated the Matthews Correlation Coefficient (MCC) assuming the in vivo quasi-essential genes (1) to be essential and (2) to be non-essential. As the identification of quasi-essential genes was crucial for the successful genome minimization in JCVI-syn3.0 , the former assumption might be biologically more relevant. For our model, the MCC comes out at ~0.59 in either case.Table 1: Naming convention for calculation of Matthews Correlation Coefficient.Table 2: Confusion matrix for JCVI-syn3A model .While this does not amount to perfect agreement, we note that the quasi-essentials giving “weak false [positives/negatives]” in Table 2 above actually encompass the vast majority of false model predictions. Thus, in addition to the two limiting cases presented above, it is also instructive to consider the MCC for only those genes that can be classified as essential or non-essential in vivo, i.e. those genes that can be compared to the model classification without further assumptions. In that case, we get an MCC of 0.85, in line with only 4 genes being incorrectly predicted by the model . This demonstrates that the lower MCC otherwise obtained really arises from the large number of quasi-essential genes included in the model, that are inherently difficult to describe in an FBA model. Similarly, it is of interest to consider one set of genes whose functionality is difficult to capture precisely based on the currently available information, namely the genes pertaining to uptake and utilization of amino acids (in free or peptide form). As already discussed in the manuscript, the in vivo essentiality of these genes is likely affected by their exact substrate profiles and maximal uptake rates. In line with this, 10 out of 14 weak false negatives in the model are accounted for by genes from amino acid utilization (uptake and peptidases). If we hence exclude all 12 related genes including these 10 weak disagreements, the MCC for case (1) increases to 0.72. (In case (2) the MCC decreases to 0.46, as the removed quasi-essentials would have been classified as true negatives here – so the agreement worsens by excluding "true negatives" that in the actual data are weak false negatives. The MCC increase in case (1) thus seems more informative.)Table 3: Confusion matrix for the M. genitalium metabolic submodel in Karr et al., 2012, assuming any in silico knockout with nonzero growth rate to be nonessential. Treating knockouts yielding < 0.01*WT growth rate as essential leads to reassignment of two false negatives as true positives.Mycoplasma genitalium is the only one for which comprehensive experimental essentiality data has been published to our knowledge . Comparing in silico gene essentiality of the M. genitalium metabolic submodel used in the whole-cell model of Karr et al., 2012) to the Glass et al., 2006 data set yields an MCC of 0.35 . Our MCCs calculated above thus compare very favorably to the M. genitalium model.Among other Mollicutes with published metabolic reconstructions, M. pneumoniae transposon mutagenesis data set is comprehensive enough to be used as a whole organism reference for gene essentiality. The numbers reported for essentiality prediction accuracy by the M. pneumoniae metabolic model  yield MCCs of 0.84 and 0.91 but are based on ortholog-based comparison to the M. genitalium data cited above; with an M. pneumoniae transposon library only screened in case the M. genitalium data was in disagreement with the model.We note that to date, no single published Finally, we would like to stress that the suggestions for gene removal experiments in subsection 'Targeted gene removal experiments' only involve the subset of true negative predictions, i.e. genes found to be non-essential in both model and experiment. These suggestions are therefore not affected by any mispredictions. The role of the model in this context is not to predict possible removals independently from the experimental data, but rather to suggest gene knockouts that might be possible simultaneously: While the transposon mutagenesis study could only probe individual gene essentialities, all of the true negative predictions can be knocked out simultaneously in silico with limited impact on growth rate. We would like to clarify that the in silico double knockouts reported at the end of subsection 'In silico gene knockouts and mapping to in vivo essentiality' were not meant to yield suggestions for gene removal experiments, but were meant to probe model behavior in general; they were performed for all in silico non-essential genes, not just for the true negative predictions discussed in subsection 'Targeted gene removal experiments' The addition of a pyruvate secretion reaction to the model mentioned above changed the double knockout results with respect to the original submission: Double knockouts of lactate dehydrogenase (MMSYN1_0475) and any gene along the acetate fermentation branch are not lethal anymore. This leaves the previously reported double knockout of both amino acid permeases as the only synthetic lethality in the model.4) The authors should verify the way in which the sensitivity analysis is conducted. A reasonable way to do the sensitivity analysis is to investigate what percentage of growth rate change is found for a given percentage of change in a parameter. Such an analysis may imply very strong variation in contrast to what is stated by the authors. In particular, the effect of the non-growth associated maintenance (NGAM) should be particularly re-investigated since the assumption that the NGAM would be strictly limited to the ATP efflux seems likely to be an under-estimation as many cellular processes may not be described.We have done the sensitivity analysis in a more rigorous fashion by calculating elasticities, as suggested by Ron Milo, in subsection 'Macromolecules and amino acids', which is equivalent to the ratio of percentage changes in model growth rate and changed parameter. This does yield a high elasticity of 0.8 for the glucose uptake rate, i.e. a change in glucose uptake rate by 10% would yield a growth rate change of 8%. In light of this, we have revised the beginning of subsection 'Steady state fluxes' (results for steady state fluxes) to emphasize that while the chosen constraints happen to reproduce the experimental growth rate, this agreement is sensitive to the model parameters (in particular glucose uptake rate) and hence, the growth rate prediction should be seen as provisional. As also noted in that revised paragraph, our subsequent gene essentiality analysis is not affected by the uncertainty in the predicted growth rate.Rather than qualifying every reference in the manuscript to the model reproducing the experimental growth rate, we removed most of these references as this is not a main aspect of the work anyway.Regarding the NGAM, we would like to note that the growth rate-independent energy expenses in the model not only encompass the proton efflux through ATPase but also turnover of protein and RNA. The lower bounds on protein and RNA degradation imply that a constant part of the protein/RNA synthesis flux is routed through protein/RNA degradation, and only the surplus beyond this constant flux actually contributes to model growth. We had opted to only refer to the single contribution of the ATPase as "NGAM" in our model for simplicity, as these other constant expenses are spread across various reactions. However, we realized that the same holds for the quantifiable part of the GAM; thus, we changed our language in the manuscript to refer to both ATPase- and turnover-related ATP expenses as components of the overall NGAM. We have calculated the growth rate elasticities with respect to each of these three constraints and they all come out rather low: 0.1 (RNA degradation), 0.04 (protein degradation) and 0.03 (ATPase).5) The authors should use correct terminology when describing predictions from flux balance analysis throughout the manuscript, as it predicts yield; the conversion to growth rate is dictated by what the authors assume for uptake rate which if not measured but is a gross proxy, as indicated above.E. coli with a two hour doubling time  which also has the high affinity glucose transporter PtsG as in JCVI-syn3A.While uptake rates might not be available in many cases as also noted by Ron Milo in his comments, our glucose uptake rate is actually taken from a measurement in another mycoplasma  and is in the range measured for As to the conversion from yield to growth rate, we revised the corresponding paragraph in the Introduction to emphasize or clarify:- that the conversion from yield (growth rate per uptake rate) to growth rate requires an uptake rate;Mycoplasma pneumoniae and E. coli for a similar high affinity glucose transporter;- that while no such values are available for JCVI-syn3A itself, measurements are available for - that adopting related uptake rates and not experimentally determined ones leads to a provisional growth rate prediction;- that when model growth rates are stated henceforth in the manuscript, it comes with the understanding that they are provisional and have a degree of uncertainty due to the sensitivity with regard to the assumed uptake rate .Reviewer #2:We wish to thank the authors for taking time to address our previous comments. The addition of the whole metabolic map for JCVI Syn3.0 is integrative and should help readers understand the metabolism of the minimal cell. We appreciate that the model predictions were nuanced according to the available data . While these comments were properly addressed, a second view of the (lengthy) paper yielded new comments:Abstract:1) The Abstract is overly concise for a paper that spans 76 pages including references. In general, the conclusions exposed in the abstract should be extended and "98% of enzymatic reactions supported by annotation." This seems like a wrong and inflated number that does not reflect the uncertainty in the network. Excluding exchange reactions, biomass, growth-associated maintenance and gas diffusion (O2 and CO2) 36 reactions remain orphan, including many transports. This correction brings the percentage down to 85.6% when applying a denominator of 250 (again excluding the mentioned reactions above).The full claim in the abstract reads: "98% of enzymatic reactions supported by annotation or experiment." In summary, most of our gap fills are supported by experimental observations; and our number of gap fills itself is actually smaller than stated by the reviewer, as many transport reactions are assumed to be passive, thus not requiring a gene product in the first place.Table 6 in the main text shows that there are indeed 21 gap fills in the model (please see below for how we arrive at that number); however, 17 of these are supported by experimental evidence, and only four are gap fills based on the network only.  As biochemical evidence provides strong support for inclusion of a reaction (with the caveat that the evidence is based on the parent species), this means that 240 out of our 244 "non-pseudo reactions" (see below for definition), or 98% , are well supported.As our accounting yields different numbers than those reported by the reviewer, we would like to lay out how we count reactions. As mentioned in the explanation to Table 6, we only consider "non-pseudo reactions", i.e. only reactions that correspond to individual chemical or transport reactions. This excludes exchange, biomass and growth-associated maintenance reactions (in agreement with the reviewer); however, we also exclude the lumped reactions pertaining to macromolecule metabolism. E.g. DNA polymerization has no metabolic genes assigned and would therefore constitute a "gap fill", which would be misleading. We do not exclude any transport reactions, and therefore also include gas transport. By this account, there are 244 non-pseudo reactions: 339 model reactions – 85 exchange reactions – 2 biomass/GAM reactions – 8 macromolecule reactions.2, CO2, NH3 and H2O among the passive reactions in Table 6; instead, they were accidentally included in "Annotation-supported", thus not changing the number of gap fills however. They have been included in the passive reactions now.)From these 244 non-pseudo reactions, there are 35 reactions without an associated gene; however, as outlined in the metabolic reconstruction results, several transport reactions are assumed to be passive, and therefore assumed to not require a gene product in the first place (see e.g. discussion on passive glycerol uptake). Subtracting these 14 passive transport reactions yields the 21 gap fills listed in Table 6. . It also would yield his stated 36 gap fill reactions (35 reactions without associated gene in our count + 3 “gap fill” macromolecule reactions – 2 transport reactions for O2 and CO2). As outlined above, the inclusion of macromolecule and passive transport reactions however leads to an overestimation of the actual number of gap fills.It appears the reviewer might have included macromolecule reactions in his count, three of which have no gene assigned. This would yield his stated 250 reactions if OWe hope this explains and justifies how we arrive at a count of 98% of non-pseudo reactions supported by annotation or experiment. We have also slightly expanded the discussion around Table 6 to lay out our counting of reactions more clearly. The abstract refers to enzymatic reactions, which technically are a subset of the set of non-pseudo reactions (obtained by omitting the few passive reactions); but the annotation/experiment coverage comes out at 98% (rounded) in either case so we refrain from using the expression "98% of non-pseudo reactions" in the abstract, as the meaning might not be apparent without the definition provided in the main text.2) "The model agrees well with genome-scale in vivo transposon mutagenesis experiments" Perhaps provide a quantitative statement rather than a qualitative one.We have added the MCC as quantitative information."The genes in the reconstruction have a high in vivo essentiality or quasi-essentiality of 92% , compared to 79% in silico essentiality." While the results presented in supplementary table 2 are consistent with these two numbers, we find 76% in silico essentiality when testing the model provided for single gene deletions. Also, the in vivo essentiality of 92% is obtained when assuming quasi-essential genes as essential and should be specified or the number with only essential genes (68%) should be included or at least mentioned.The reported apparent discrepancy of nine genes  arises from the eight genes for ATPase (MMSYN1_0789-0796) and the one gene for Lon (MMSYN1_0394); these genes are connected to model reactions carrying a lower flux bound due to their assumed biological importance (maintaining transmembrane PMF/protein degradation). Manually setting the upper bounds on these reaction fluxes to zero in COBRApy 0.4 renders the FBA problem infeasible as expected (due to inconsistent flux bounds). However, the single gene deletion routine in COBRApy sets both flux bounds (upper and lower) to zero, which therefore removes the pre-imposed lower flux bounds.  As the ATPase and Lon reactions do not produce any required biomass precursors, they therefore show up as “non-essential” in the COBRApy routine. The reported in silico essentiality is therefore based on the manual essentiality check for the mentioned genes. Regarding the in vivo essentiality of model-included genes, the sentence in the abstract actually states: "in vivo essentiality or quasi-essentiality of 92% ", clarifying that the 142 genes in question are not all essential. We now also added the subset of 68% essential genes however.3) "The reconstruction is the starting point for studying the evolution of metabolic subsystems and analyzing the effects of introducing alternative pathways." If claiming this in the abstract perhaps include subsystems to the reactions in the model.The revised json model file contains subsystems, KEGG identifiers and InChI keys (SBML does not store the "subsystems" and "notes" attributes).4) "Finally, the identification of 30 essential genes with unknown function will motivate the search for new biological mechanisms beyond metabolism." After reading through the entire paper multiple times I still cannot see where this number is fetched from, so I cannot imagine how a reader could get to it.This number is taken from Table 1, Subtotal of "Unclear" genes that are essential; this number and its potential pertinence to new biological mechanisms are also mentioned in subsection 'Transposon mutagenesis experiments probe in vivo gene essentiality' on in vivo gene essentiality. For consistency, we have changed the language in the abstract from “unknown function” to “unclear function”.5) Rather than pointing to a very accurate model of Syn3.0, the abstract should demonstrate that despite being the smallest organism known to date, Syn3.0 has many unknown elements. While the amount of information provided in the paper is highly valuable for the community, presenting the reconstruction as a way to clearly identify the grey areas of the genome is key for the development and substantial efforts have been made in that sense through 1- a detailed manual curation process, 2- proteomic validation of enzyme expression, 3- in vivo gene essentiality. This sentence of the abstract should be of prime importance: "This comparison together with proteomics data yields new hypotheses on gene functions as well as suggestions for several further gene removals".We thank the reviewer for this remark. We have slightly expanded the abstract to more clearly emphasize how the presented coherent model of the metabolic network at the same time points toward specific questions, and how the model, its comparison to in vivo essentiality and proteomics data guide future work through the specific hypotheses provided.