The further application of metabolomics in the concert of other “omics” technologies will not only broaden our knowledge, but will also certainly strengthen the base for the biotechnological application of cyanobacteria.Our knowledge on cyanobacterial molecular biology increased tremendously by the application of the “omics” techniques. Only recently, metabolomics was applied systematically to model cyanobacteria. Metabolomics, the quantitative estimation of ideally the complete set of cellular metabolites, is particularly well suited to mirror cellular metabolism and its flexibility under diverse conditions. Traditionally, small sets of metabolites are quantified in targeted metabolome approaches. The development of separation technologies coupled to mass-spectroscopy- or nuclear-magnetic-resonance-based identification of low molecular mass molecules presently allows the profiling of hundreds of metabolites of diverse chemical nature. Metabolome analysis was applied to characterize changes in the cyanobacterial primary metabolism under diverse environmental conditions or in defined mutants. The resulting lists of metabolites and their steady state concentrations in combination with transcriptomics can be used in system biology approaches. The application of stable isotopes in fluxomics,  Metabolet al. ,3. Naturet al. ,5,6,7. Let al. ,9. DespiArabidopsis thaliana were obtained via the cyanobacterial endosymbiont [Here, we will review metabolomic approaches to understand cyanobacterial metabolism. The phylum cyanobacteria comprises photoautotrophic prokaryotes of the domain Bacteria. Ancient cyanobacteria invented oxygenic photosynthesis about three billion years ago, which was later conveyed by an endosymbiotic event into eukaryotes giving rise to the plastids . Therefosymbiont . The cloSynechocystis sp. PCC 6803 (hereafter Synechocystis 6803), was published [http://genome.kazusa.or.jp/cyanobase) [Synechocystis 6803 microarray data are summarized in a specialized database (CyanoExpress 1.2: http://cyanoexpress.sysbiolab.eu/). Recently, the high throughput sequencing of cDNAs (RNAseq) was introduced to study cyanobacterial gene expression. This technique quantifies mRNA abundances as number of sequence reads and also allows the quantitative detection of hundreds of small, non-coding RNAs [Besides easy cultivation, the availability of genetic tools for cyanobacterial model strains supported the analysis of biochemical features. In 1996, the first genome of the most widely used cyanobacterial model strain, ublished . Startinanobase) . Based oanobase) ,15). Theing RNAs ,17,18. Ting RNAs ). Comparing RNAs ,22,23.13C, 15N, 18O or classically radioactive isotopes to reveal the pathways in an organism or cell. High-throughput analyses of metabolite fluxes are summarized under the term fluxomics [According to Fiehn, different metabolomic approaches can be distinguished: (i) metabolite target analysis, (ii) metabolite profiling, (iii) metabolite fingerprinting, (vi) metabolite footprinting, and (v) flux analysis . The metluxomics .The current state of technical developments and examples for the application of metabolomics to cyanobacteria will be summarized and discussed here. The use of metabolomics to understand cyanobacterial metabolism  will nothttp://gmd.mpimp-golm.mpg.de/), which was developed for compounds in complex extracts from plant material [Among the current “omics” technologies for biological systems analyses, metabolomics is perhaps the most generic. DNA, RNA and protein sequences are dependent on the organism of origin. In contrast, the origin of metabolites cannot directly be traced to the organism of origin. This holds true per definition for primary metabolites but is also applicable to many secondary metabolites. As a consequence, most aspects of the current metabolomic technology development are generic. We refer the reader to the wealth of previous reviews on metabolomics specifically its early emergence and application to plants ,35,36,37material ,39,40,41material .in situ metabolome of living cells is preserved within subsequently generated metabolite profiles. This task is challenging in the case of photoautotrophic microorganisms such as cyanobacteria. The flux through the Calvin-Benson cycle and the associated photorespiration responds rapidly to changes of illumination or other environmental factors. As a consequence, metabolic inactivation of photoautotrophic metabolism must be as rapid as possible and attempt to maintain constant illumination and environment throughout sampling. Cyanobacteria are typically grown in liquid culture both for the purpose of laboratory experiments and for biotechnological production. Some cyanobacteria, if not all, are known to leave a metabolic footprint in culture media by release and uptake of metabolites [Adequate sampling technologies are crucial for reliable metabolomic snapshots of biological systems and are largely independent of the subsequently applied analytical technology. The means of inactivating cellular metabolic activities determines how accurate the abolites . As a coChlorella, which culminated in the discovery of the first photosynthetic assimilation product 3-phosphoglycerate (3PGA) [Calvin and Benson came up with an elegant solution in their groundbreaking work on the unicellular alga, e (3PGA) . Algal ce (3PGA) . Howevere (3PGA) . The samAn efficient solution of the combined separation and inactivation problem is fast filtration ,46,47. FMetabolomics efficiently uses multiple analytical technologies, mostly hyphenated technologies that combine high-resolution separation with selective means of detection. Ideally, multiple technologies are combined to extend the - as a rule - limited metabolome coverage of single technologies and to independently verify the analytical results of overlapping metabolites. The advantages and disadvantages of different technological approaches have recently been reviewed . CriteriThe separation technologies currently applied for the analysis of the cyanobacterial metabolome are capillary electrophoresis (CE), liquid chromatography (LC), and gas chromatography (GC). CE is well suited for the separation of charged metabolites from primary metabolism, such as organic acids, phosphates and nucleotides . The limThe means of metabolite annotation are mostly dependent on the type of applied mass spectrometric (MS) technology. CE and LC are typically hyphenated to soft ionization MS technologies, such as electrospray (ESI-MS) or atmospheric pressure chemical ionization (APCI-MS), which typically maintain the molecular ion of a given metabolite. These ionizations are linked to MS systems that provide high mass resolution and accuracy. The recorded exact mass of a metabolite is used as the main criterion for metabolite annotation. Chemical isomers cannot be distinguished, because they have identical molecular formulas and consequently identical molecular masses. In contrast, GC is typically hyphenated to electron impact ionization (EI-MS), which generates highly repeatable and complex patterns of fragments that are suitable for automated matching procedures to mass spectral libraries.1H-NMR is frequently used for metabolome analysis without prior metabolite separation. 1H-NMR is less sensitive than the previously mentioned hyphenated technologies. The essential asset of 1H-NMR together with other NMR technologies is the access to structural information that reveal the position of atoms within novel compounds and the comparative ease of standardization for the absolute quantification of pool sizes. A method combining MS and NMR detection was developed and applied for the analysis of cyanobacterial extracts [MS provides data that support the structural elucidation of novel metabolites, but the gold standard for this purpose is nuclear magnetic resonance spectroscopy (NMR). extracts .In the following, we will highlight aspects, which apply to cyanobacterial metabolomics with a focus on GC-MS-based metabolite profiling technology ,47,51.The general analytical procedure of the GC-MS-based profiling of primary metabolism has previously been described in detail ,53. The Synechocystis 6803, GC-MS-based profiling covers most of central metabolism  and the other in the T-protein subunit of glycine decarboxylase (ΔgcvT). The accumulation of glycolate and glycine, respectively, are linked to the changes in many other metabolites. But only the mapping of data onto the photorespiratory pathway reveals the context of precursor accumulations  into the TCA pathway of Synechocystis 6803  fixation at specific conditions [14C-labeled glucose and the identification of labeled intracellular metabolites also verified the dominating role of the oxidative pentose phosphate (OPP) cycle in sugar breakdown during dark periods, while the TCA cycle did not seem to contribute [Soon after the description of the Calvin-Benson cycle for photosynthetic carbon fixation , the patbstances . These tnditions . Feedingntribute .14C-labelling with several cyanobacteria such as Anacystis nidulans (syn. Synechococcus elongatus PCC 6301), Coccochloris peniocystis or Gloeocapsa alpicola, the 2‑oxoglutarate (2OG) dehydrogenase complex is missing [et al., who showed that labeled 2OG could quickly be converted into succinate in a Synechocystis 6803 mutant defective in fumarase [Synechococcus sp. PCC 7002, which encode the 2OG carboligase and succinic semialdehyde dehydrogenase converting 2OG into succinate [Subsequently, it was shown that the TCA cycle was incomplete among cyanobacteria. According to enzyme measurements and  missing ,66,67. T missing ,69. Rece missing . First hfumarase . But onluccinate .Metabolic target analysis aims to analyze metabolites fulfilling common functions or being involved in defined target processes. Cyanobacteria are not only important models to analyze photosynthetic mechanisms but also produce diverse bioactive substances as secondary metabolites, which can have toxic effects on mammals and are of high concern in environmental and health policies ,74. Becahttp://www.midi-inc.com/pages/mis_libraries.html). Applying such a technology, the species composition of the cyanobacterial genera Aphanizomenon, Microcystis, Nostoc, and Planktothrix was analyzed [et al. used minor fatty acid components like 2-hydroxy or 3-hydroxy fatty acids as chemotaxonomic markers for cyanobacterial strains [Metabolic profiling comprises approaches using metabolite spectra for the differentiation of species or phenotypes. The taxonomy of cyanobacteria is unclear . Cyanobaanalyzed ,87. The  strains .2-neutral and regenerative fuel production [Recently, mechanisms for cyanobacterial lipid and fatty acid biosynthesis as well as the corresponding product analysis received increased attention in the field of biofuel production. Fatty acids can be converted into biodiesel and other fuels of rather high energy density. The photoautotrophic life style of cyanobacteria will allow a COoduction ,90.Synechocystis 6803 under different CO2 concentrations in wild-type cells compared to mutants defective in photorespiratory enzymes [2 amount and/or on the genotype  cellular metabolite spectrum, the metabolome. This technology is currently mostly used to globally characterize metabolic changes under different cultivation  conditions or genetic backgrounds. Among cyanobacteria, metabolic fingerprinting attempts used the separation of low molecular mass metabolites via CE, GC or LC usually coupled to MS detection and identification. These approaches revealed thousands of mass features mostly from unknown metabolites, which could be classified by statistical means (discussed above). For example, we compared the growth of  enzymes . The metgenotype . Unfortui.e., photorespiratory metabolism in Synechocystis 6803 [Nevertheless, these fingerprints are extremely useful for the fast screening of alterations in the cyanobacterial metabolism under different growth conditions or in different genetic backgrounds. The growth and metabolic activities of cyanobacteria are determined and influenced by diverse environmental factors such as light, temperature, salinity, and the availability of different C or N sources ,92. Trantis 6803 ,94. Thes2 and/or by the addition of organic carbon sources such as glucose.Among cyanobacteria, two key factors influencing growth have been investigated in much detail: The availability of carbon by shifts from high to low CO2 availability:CO Cyanobacteria use mainly Ci (CO2 and HCO3−) as primary carbon source. The availability of Ci determines the growth rate and the photosynthetic activity of cyanobacteria. Alterations in the gene expression pattern [Synechocystis 6803 in response to CO2 limitation. These studies revealed the complete set of low CO2-induced genes, among them the well-known transporters for inorganic carbon such as NDH-1, BCT1 and SbtA [ pattern ,94 and m pattern  were repand SbtA . Additioi limitation were investigated in Synechocystis 6803 [2. We could detect significant amounts of 2PG, the by-product of the RubisCO oxygenase reaction. These global metabolic changes were in accordance with changes of gene expression for some of the enzymes involved in the corresponding metabolic routes [2) an increase of the 2OG level while glutamate decreased. The 2OG level was supposed to decrease under lowered Ci due to the decreased C/N ratio. Previously, it has been shown that after a shift from a high to a low N-amount, i.e., an increase in the C/N ratio, 2OG increases [Synechococcus elongatus PCC 7942, showed very similar metabolic alterations including the (transient) increase of the 2OG level when shifted from high to low CO2 [2 starvation. To maintain growth, cells channel less organic carbon to storage and correspondingly more organic carbon to 2OG (and other TCA cycle intermediates) as precursors for amino acid and other biosynthetic processes. At the same time, the cells sharply decrease the uptake of nitrate and the ammonia assimilation via GS/GOGAT, because of the relative increase in N/C ratio after low CO2 shift. Thus, increased channeling of carbon toward the TCA cycle and reduced GS/GOGAT activity result in the accumulation of 2OG. This scenario is supported by the gene expression changes showing decreased expression of nitrate transporters and increased expression of proteins inhibiting GS activity in Synechocystis 6803 [Synechococcus elongatus PCC 7942 [Subsequently, alterations of the metabolome in response to Ctis 6803 . The GC-c routes ,94. Muchncreases . Therefo low CO2 . We intePCC 7942 .2 levels induce a coordinated change in the central C/N-metabolism. The PII protein is known as one of the central regulators for C/N balance in cyanobacterial cells [2 cells was investigated using the PII mutant of Synechococcus elongatus PCC 7942 [i.e. the PII protein seems to be more responsible for the decrease in N-assimilation after shifts to low CO2 than for the changes specifically linked to C-metabolism [Thus, metabolomics reveal that shifts form high to low COal cells . The infPCC 7942 . Indeed,tabolism .Glucose addition:Synechocystis 6803 is one of the few cyanobacteria, which can take up and use external glucose as C-source. Therefore, photoautotrophic and photomixotrophic growth conditions can be directly compared with this strain. Transcriptomic [Synechocystis 6803 have been published during the last 10 years. Metabolomic fingerprints of glucose addition on the primary metabolism were published by Takahashi et al. and recently by Narainsamy et al. [Synechocystis 6803 cells in light induced oxidative stress. Metabolites, which are characteristic for cells exposed to oxidative stress, were identified in the metabolome of cells grown under photoheterotrophic conditions [riptomic ,99 and priptomic ,100 analy et al. ,48. In ay et al. ,48. RemaMetabolomics to characterize selected mutants: The availability of metabolomics also made it possible to analyze the impact of genetic lesions in defined mutants on overall metabolism. For example, Miranda et al. aimed to characterize differences between wild-type and protease-mutant cells by a combined proteomics and metabolomics approach [approach . BecauseSynechocystis 6803 mutants are known, but the basis of the glucose-sensitive phenotype is mostly unknown. The metabolic fingerprint of the glucose-sensitive Synechocystis 6803 mutant ΔpmgA (sll1968) indicated that the absence of PmgA (a protein of unknown function) resulted in failure to coordinate the carbon partition between the Calvin-Benson and OPP cycle. While wild-type cells increased the OPP cycle intermediates after glucose addition, cells of the mutant ΔpmgA activated the OPP to a much smaller extent. Probably, this metabolic imbalance leads to the glucose-sensitivity of the ΔpmgA mutant [Many glucose-sensitive A mutant .et al. have provided evidence that the SigE contributes to the differential expression of genes for enzymes involved in the carbon catabolism of Synechocystis 6803 [Synechocystis 6803 strain over-expressing the sigE was analyzed using transcriptomics, metabolomics and immuno-blotting to quantify the amount of key enzymes in the sugar catabolism. The results clearly indicate that SigE regulates the coordinated expression of sugar catabolism genes, especially those for the OPP cycle and TCA cycle enzymes. Deregulation of these enzymes changes the metabolic acclimation to altered growth conditions, for example, sensitivity towards glucose addition was found for the SigE overexpressing strain [The primary carbon metabolism is regulated on different levels in the cyanobacterial cell. Alternative sigma factors seem to play an important role in the modification of key C- and N-assimilatory routes by changing the expression of genes for key enzymes under changed nutrient conditions. Recently Osani tis 6803 . A Synecg strain .Synechocystis 6803 mutants with impaired glycogen synthesis were constructed to analyze the role of this carbon storage during the acclimation to different growth conditions [i.e. in contrast to wild-type cells, they showed retarded degradation of phycobilisomes and chlorophyll. Obviously, glycogen plays a more important role than just carbon storage in the cyanobacterial metabolism. The metabolome analyses revealed a lack of coordination of metabolism under fluctuating growth conditions when glycogen storage was abolished. Interestingly, the glycogen mutant selectively excreted high amounts of 2OG and pyruvate into the medium [Glycogen represents the main carbon storage in cyanobacteria. Recently, nditions . The mute medium .Synechocystis 6803 also were used to analyze the contribution of specific enzymes toward the CO2-induced alterations in the primary C-metabolism. The metabolome in wild-type cells was compared to that of two photorespiratory mutants, ΔglcD and ΔgcvT. The mutant ΔglcD is defective in one of the two glycolate dehydrogenase genes, while ΔgcvT harbors a mutation in the T-protein of the glycine decarboxylase complex [i.e. ΔglcD accumulated glycolate and an increase of glycine was found in cells of ΔgcvT  cell lysis. As photoautotrophic prokaryotes, cyanobacteria are regarded to use inorganic nutrients and light for the production of organic cell material. However, there are many reports that cyanobacteria can also import and use organic substrates from the environment. A specific glucose transporter allows Synechocystis 6803 to grow with organic carbon instead of CO2 as C-source [Prochlorococcus spp. it also was shown that amino acids could serve as significant N-source [Microorganisms interact permanently with their environment by uptake or release of metabolites. Metabolic footprinting is often used as a term for metabolome analyses of extracellular compounds in growth media. The accumulation of compounds in the surrounding medium can result from their release by exudation, C-source . For oceN-source . DiffereN-source . HoweverN-source ,109.et al. performed the first comprehensive study to investigate the uptake and release of metabolites by axenic cells of Synechococcus sp. PCC 7002 [Synechococcus sp. PCC 7002, many of the cellular metabolites could be imported illustrating the capability of cyanobacteria to avoid or minimize the loss of valuable cellular compounds. The authors also showed that compounds, which were previously not reported as cyanobacterial metabolites such as histidine betaine or γ-glutamylphenylalanine, were not only found in the cell extracts but were also taken up by cells of Synechococcus sp. PCC 7002. Applying stable isotopes made it possible to show that for example adenine and glutamate were not only taken up but also actively incorporated into the cellular metabolism. These data allowed the conclusion that the uptake of diverse metabolites allows Synechococcus sp. PCC 7002 to benefit from the availability of organic nutrients, which are released into the medium by lysis of cells after stress, viral attacks, or predation [Nowadays, metabolic footprinting can be done in a high through put mode combining stable isotope labeling with modern analysis tools like LC-MS or GC-MS. Recently, Baran PCC 7002 . The autredation .The application of radioactive or, in our days, stable isotopes in labeling experiments allows following the carbon or nitrogen flux in an organism. Compared to metabolic fingerprinting, in which changes in the steady state levels of metabolites are detected, flux analysis allows the direct recognition whether or not the synthesis or breakdown of specific intermediates is affected.et al. used 13C-labeled glucose to investigate the metabolic reorganization in Synechocystis 6803 under heterotrophic and mixotrophic growth [2 fixation through the Calvin-Benson cycle exceeded glucose assimilation. Synechocystis 6803 did not only prefer CO2 assimilation via RubisCO under photoheterotrophic conditions, it also employed the PEP carboxylase to increase CO2 assimilation into the TCA cycle and the glycolysis. Based on these findings, Yang et al. postulated the functioning of a C4 pathway in cyanobacteria [Relatively early, Yang c growth . The stubacteria . In an abacteria ,110. Thiet al. and Young et al. used 13C-bicarbonate pulse-labeling to define the major Ci fixation pathways in the cyanobacterium Synechocystis 6803 [13C in selected metabolites was followed up to 60 min after addition of 13C-label. The 13C enrichment kinetics showed that compounds closely linked to the Calvin-Benson cycle became completely saturated by 13C, while other pools such as these of sucrose or TCA cycle intermediates showed relatively slow 13C enrichments.Recently, Huege tis 6803 ,47. The 2fixation was done by RubisCO and the organic carbon was channeled into (i) sucrose via glucose-6-phosphate and (ii) aspartate, malate, citrate and 2OG via PEP [2-acclimated cells increased the second path considerably. Moreover, comparison of labeling kinetics of 3PGA versus 2PG was used to quantify carboxylation/oxygenation ratios. Already at high CO2 a low label of 2PG was found, which increased after shifts to low CO2 conditions amounting to maximal 4% oxygenation. Additional to RubisCO, carboxylation via PEP carboxylase was active, which showed increased contributions to the overall 13C-incorporation in the two photorespiratory mutants ΔgcvT and ΔglcD [13C-atoms into malate over addition of a single 13C-atom by direct 13C-bicarbonate incorporation via PEP carboxylase. The simultaneous addition of two 13C-atoms can be explained by insertion of acetyl-CoA into the TCA pathway of Synechocystis 6803 and subsequent synthesis of malate from the intermediates of this path. If this assumption is true, the labeling pattern of malate could be used as an indicator to analyze the degree of an open versus a more closed TCA cycle under specific conditions. However, the relative contributions of PEP carboxylase and/or the TCA pathway and the exact route of malate synthesis still need to be modeled and explored in more detail.Our experiments revealed two major pathways for the C-assimilation in the light. The main CO via PEP . Low CO2nd ΔglcD . The manet al. already combined stable isotope labeling and a model approach, i.e. they used the isotopically non-stationary metabolic flux analysis (INST-MFA) [2 fixation in cells of Synechocystis 6803. Thus, the authors provided the first kinetic modeling of photoautotrophic growth directly based on measured 13C-fluxes, which was evaluated regarding potential targets of optimization of photosynthetic carbon fixation to support biotechnological applications of cyanobacteria [2 fixation could be related to the incorporation of organic carbon into the biomass. The authors reported that about 25% of initially fixed CO2 was lost during photoautotrophic growth. The parallel investigation of the exo-metabolome revealed that the release of organic carbon into the medium did not play a significant role. Beside CO2 losses due to respiration, the OPP cycle was found to be a significant process leading to diminished carbon use efficiency. Initially, it was thought that the OPP cycle becomes inactivated in light-exposed cyanobacterial cells. At least under the experimental conditions used by Young et al., the OPP pathway was still active in the light possibly to balance redox or precursor needs [2 were also released by the activity of the malic enzyme. However, the malate seemed to be mostly synthesized via C4-like carboxylation of PEP leading to oxaloacetate and then malate, making this pathway CO2 neutral. Most probably the combined action of PEP carboxylase and malic enzyme does not serve as CO2 concentration mechanism as in C4 plants but is rather responsible for an increased synthesis of pyruvate as precursor for amino acids or for other compounds. Photorespiration, i.e. carbon loss due to RubisCO oxygenase activity based 2PG production, was ruled out as a main pathway diminishing the efficiency of photoautotrophic growth of Synechocystis 6803. Under the CO2 and light conditions used by Young et al., oxygenation accounted for only 0.5% of RubisCO carboxylation [Young NST-MFA) . The MFAbacteria . This anor needs . Moreovexylation . Thus, tCyanothece sp. ATCC 51142, an N2-fixing unicellular strain, has recently received much attention as a potential producing host for hydrogen [13C-glycerol feeding. The effect of the organic carbon on primary metabolism and growth was analyzed in the presence of light and other growth conditions [2 fixation in Cyanothece ATCC 51142, i.e., the cells grew mostly heterotrophically in the light when supplemented with organic carbon, while glucose addition had a much lower impact on Synechocystis 6803. Moreover, a detailed analysis of the isotopomer composition of amino acids allowed the discovery of a new isoleucine synthesis pathway among cyanobacteria [Cyanothece sp. ATCC 51149, the authors searched for an alternative isoleucine synthesis. The isotopomer analysis of 13C-labeled isoleucine obtained after 13C-glycerol feeding indicated that this amino acid is synthesized by the citramalate pathway, in which acetyl-CoA and pyruvate are condensed to citramalate as precursor of 2-ketobutyrate, rather than using threonine as precursor. The isoleucine pathway deduced from the isoleucine labeling pattern was supported by the identification of the citramalate synthase gene in the genome of Cyanothece sp. ATCC 51142 [The cyanobacterium hydrogen . In ordenditions . These abacteria . PreviouCC 51142 . BecauseMetabolomics, particularly in combination with the other “omic” tools such as transcriptomics or proteomics, represents a powerful tool for the investigation of basic physiological phenomena. Fiehn described metabolomics as the link between genotype and phenotype . ClearlyCyanothece sp. ATCC 51142, [One of the major challenges for metabolomics will be the future identification of hitherto unknown compounds. A broader coverage of metabolites will not only allow a deeper insight into the cyanobacterial metabolism, it also will certainly lead to the discovery of biochemical routes presently not assigned to the cyanobacterial kingdom. It is noteworthy that each cyanobacterial genome harbors up to 30% of genes for proteins with unknown function, even some enzymes for pathways of the primary C- and N-metabolism are virtually missing . Metaboet al., the evaluation of such labeling experiments needs state of the art modeling approaches [2 using a cyanobacterial cell factory as discussed recently by Nogales et al. [Besides metabolic fingerprinting, fluxomics using labeled precursors promise the best insights into the cyanobacterial metabolism and its regulation. As shown by Young proaches . In the proaches . These ms et al. .


Normothermic machine perfusion has previously been demonstrated to restore damaged warm ischemic livers to transplantable condition in animal models. However, the mechanisms of recovery are unclear, preventing rational optimization of perfusion systems and slowing clinical translation of machine perfusion. In this study, organ recovery time and major perfusate shortcomings were evaluated using a comprehensive metabolic analysis of organ function in perfusion prior to successful transplantation. Two groups, Fresh livers and livers subjected to 1 hr of warm ischemia (WI) received perfusion for a total preservation time of 6 hrs, followed by successful transplantation. 24 metabolic fluxes were directly measured and 38 stoichiometrically-related fluxes were estimated via a mass balance model of the major pathways of energy metabolism. This analysis revealed stable metabolism in Fresh livers throughout perfusion while identifying two distinct metabolic states in WI livers, separated at t = 2 hrs, coinciding with recovery of oxygen uptake rates to Fresh liver values. This finding strongly suggests successful organ resuscitation within 2 hrs of perfusion. Overall perfused livers regulated metabolism of perfusate substrates according to their metabolic needs, despite supraphysiological levels of some metabolites. This study establishes the first integrative metabolic basis for the dynamics of recovery during perfusion treatment of marginal livers. Our initial findings support enhanced oxygen delivery for both timely recovery and long-term sustenance. These results are expected to lead the optimization of the treatment protocols and perfusion media from a metabolic perspective, facilitating translation to clinical use. Transplantation is currently the only treatment option for end-stage liver disease but it is limited by the lack of high quality donor organs. An area of keen investigation is into the use of machine perfusion (MP) as a means of resuscitating presently non-transplantable donor organs after cardiac death (DCD), thereby significantly increasing the donor organ pool While MP is particularly well-suited to dynamic evaluation of organ function, methods for standardized quantitative analysis are only emerging very recently Optimizing the perfusate addresses the optimization of the metabolic state of the liver during MP. During perfusion, ATP levels recover Endeavoring towards a standardized approach to organ recovery and rational perfusion optimization, we employed a comprehensive Metabolic Flux Analysis Male Lewis rats weighing 200–300 g were obtained from Charles River Laboratories  and maintained in accordance with National Research Council guidelines. The Subcommittee on Research Animal Care, Committee on Research at the Massachusetts General Hospital, approved the experimental protocols. All animals were allowed to acclimatize for at least 2 days prior to any experimentation. Procured livers were either Fresh (n = 11) or exposed to an hour of warm ischemia .Full details of the procedure are described elsewhere in vivo influxes (2/21%O2/5%CO2 and 100%O2 to maintain a constant pH. Fresh rat plasma (25% v/v) and erythrocytes (18–20% v/v) were collected earlier 2 membrane area and a 30 kDa nominal molecular cutoff weight  enabled counter-current mixing of perfusate in the primary circuit with a reservoir of WE  in a secondary circuit. Temperature within the system was maintained at 37.5°C. Upon completion of cuffing of Fresh livers (∼5 min) and after the period of warm ischemia for WI livers, they were immersed in perfusate in the perfusion chamber. Livers were perfused at a constant flow rate through the portal vein 2O. The effluent flowed freely from the SHVC and IVC into the surrounding medium. Perfusate and dialysate samples (1 mL) were collected hourly from the liver effluent and reservoir respectively. Perfusate samples were first spun down at 3000 g before storing the supernatant at −80°C. Oxygen delivery and exit rates were determined from samples taken immediately prior to entry to the portal vein and from the IVC. When the recipient hepatectomy was prepared, the liver was disconnected from the circuit, rinsed in a bowl of saline at room temperature, and weighed again before transplantation.The perfusion medium comprised phenol red-free Williams Medium E  chosen for its similarity to influxes . WE was 2O] = (1.39×[Hb]×2HbFO)+0.00314×2pO, which expresses the concentration of oxygen (ml/dL of blood) as the sum of oxygen bound to hemoglobin and free in plasma. [Hb] (g/dL) is the concentration of hemoglobin, FO2Hb is the fraction of oxyhemoglobin present, 1.39 (mlO2/g Hb) is the binding capacity of oxygen to hemoglobin and 0.00314 (mlO2/dL/mmHg) is the solubility coefficient of oxygen in plasma, which is dependent on the partial oxygen tension in the blood, pO2 (mmHg). The rate of oxygen delivery to the liver (ODR) and exit from the liver (OER) is subsequently a product of the oxygen concentration [O2] at the site of entry and departure to the liver, respectively, and the flow rate V (ml/min), normalized to the weight W (g) of the liver. The difference between the two rates provides the hepatic oxygen uptake rate: OUR = ODR-OER.Blood gases were determined immediately using a blood gas analyzer . Oxygen concentration delivered and removed from the liver was calculated using the following equation: [2outlet) released at the outlet of the liver and the total carbon dioxide entering the liver (tCO2inlet), multiplied by the flow rate V (ml/min) and normalized to the weight of the liver, W (g). Total carbon dioxide of perfusate samples taken at the inlet and outlet of the liver was measured via Piccolo Blood Chemistry Analyzer (Abaxis).Similarly, total carbon dioxide release rate (CRR) at each time point was calculated as the difference between total carbon dioxide . Ketone bodies were measured enzymatically by following the appearance of NADH in the conversion to acetoacetate and the disappearance of NADH in the conversion to β-hydroxybutyrate in the presence of β-hydroxybutyrate dehydrogenase 2 values for each time period and experimental group were evaluated to identify the segments of time with highest R2 and minimum variation . This was done in order to identify whether there were multiple distinct metabolic phases during perfusion. This linear-response phase analysis served the dual purpose of establishing times to attain stability or recovery in perfusion, as well as being a necessary prerequisite to calculating fluxes accurately for metabolic flux analysis (see below for details). Box-and-whisker plots of the resulting R2 values despite good fit by visual inspection. These outlier R2 values therefore were not considered as a violation of the linearity assumption above. To avoid the analysis being influenced by such artifacts, the median and quartile analysis, which is more robust against such outliers, was employed as opposed to the mean and standard deviation.Note that all metabolites displayed a generally linear trend line within the identified periods. However, for a few metabolites the concentration profiles were flat, which led to low RFluxes were calculated as the gradient of concentration of each metabolite (i.e. slope of the linear regression curve) An initial outlier analysis was performed for each measurement by plotting box-and-whisker diagrams in MATLAB and eliminating obvious errors . This was followed by a more stringent analysis where for each group any measurement values above/below mean ± 2× inter-quartile range for that group were considered outliers.in vivo results were obtained using fasted rats. WE insulin levels were approximately 10-fold higher than what was observed in fed rats MFA was performed based on a stoichiometric model for the metabolic reaction network developed and tested in more detail previously ij of S is the coefficient of metabolite i in reaction j, and each vj of vector v is the net flux or conversion rate of reaction j.Briefly, in MFA the change in the concentration of intracellular metabolites is assumed to be zero (pseudo steady-state assumption) hence the sum of fluxes of each metabolite's uptake, synthesis and utilization equals zero: S.v = 0. The matrix S contains the stoichiometric coefficients of the incorporated reactions. Each element Sm) and unknown fluxes (vu), as well as the matrices containing stoichiometric coefficients of known (Sm) and unknown reactions (Su): Su.vu = −Sm.vm. The measured fluxes represent rates of uptake or release of extracellular metabolites, and by solving the equation they also give estimates of intracellular fluxes, therefore enabling an intracellular analysis based on extracellular changes. It should be noted that if the number of stoichiometric balances  are equal to the number of unknown fluxes then there is a single solution. Ideally, the unknown fluxes are fewer; in this case the solution becomes a regression problem. Moreover, in this case the consistency of measured fluxes within each other and the model can be validated.The equation S.v = 0 is separated into measured .in vivo values for rats were obtained and reported from another study Procured livers were either Fresh (n = 11) or exposed to an hour of warm ischemia  and then perfused, and metabolic analysis was performed. Note that it was previously demonstrated that in this model of rat DCD livers, 1 hr WI livers are not transplantable , however treatment by the NMP system also employed in this work increases the success to 100% (1 month post-transplant) ex vivo normothermic perfusion of Freshly isolated rat livers results in transplantable organs, and further that the same perfusion system is necessary and sufficient for the restoration of 1 hr warm ischemic rat livers to transplantable condition We previously demonstrated that in vivo conditions , and at the infra-hepatic vena cava to determine oxygen exit rate (OER). Oxygen uptake rate (OUR) was determined as the difference between ODR and OER . ODR andnditions . WI liveConcentration profiles of glucose in both WI and Fresh livers were generally stable at a value slightly above the original perfusate glucose content of 2 g/L .in vivo upper bound value within the first hour . Urea concentration increased similarly and linearly in both groups    may be rSeveral factors have been implicated in the metabolic response to hypoxia, which serve to minimize organ dependence on oxygen by reducing anabolic pathways and increasing catabolic pathways, using anaerobic respiration as the end-point:in vivo. Therefore, the results of MFA generally agree with these expected effects of HIF, suggesting the persistence of hypoxia in both ischemic and Fresh livers.Hypoxia-inducible factor (HIF) AMP-activated protein kinase (AMPK) is also stimulated during hypoxia and ischemia, and functions to suppress the overall metabolic rate and hence energy requirements of cells for self-preservation 2 levels, when cytochrome c oxidase is primarily reduced. NO actively competes with the enzyme and suppresses the respiratory rate, which coincides with the effects of HIF Nitric oxide (NO) has been identified as another important determinant of metabolism during hypoxia, facilitating the distribution of available oxygen both at the mitochondrial and vascular scale Given the importance of adequate oxygenation during perfusion, one possible consideration is to reduce the liver's dependence on oxygen even further by reducing the perfusate temperature, which has also been associated with less post-ischemic vasoconstriction ex vivo liver effectively minimizes the perfusate requirements to keep the liver alive and assess its function. Amino acids may offer the greatest room for further perfusate optimization. Their metabolism in perfused livers helpfully reflected the livers' abilities to restore perfusate content to physiological levels and utilize particular amino acids for specific applications, such as the vasodilatation function of arginine. Fisher and Kerly The reduced metabolic demand on the In summary, this comprehensive metabolic analysis of the performance of Fresh and WI livers in normothermic MP demonstrates sustained organ stability over 5–6 hours of perfusion, and the restoration of WI livers to a likely transplantable state within 2 hours of perfusion.The data presented in this study provide the bases for rational MP optimization. In particular, lipid oxidation was suppressed, likely due to the high insulin levels, while amino acids were extensively metabolized, likely due to supraphysiological perfusate concentrations. Perfused livers did not consume all the available oxygen and were hypoxic independent of ischemic injury suggesting that enhanced microcirculation via vasodilators and anti-thrombolytics might be an effective approach at optimizing the delivery of oxygen to hepatocytes in both groups as early in perfusion as possible.Further, the data and analyses presented here enable the conduct of future meta-analyses, such as Flux Balance Analysis Appendix S1In vivo and Perfusate (WE) Reference Concentrations.(DOCX)Click here for additional data file.Appendix S2Box-and-whisker plots of linear regressions performed on the temporal concentration profiles of 28 metabolites measured for WI and Fresh livers.(DOCX)Click here for additional data file.Appendix S3Metabolic Flux Analysis (µmol/hr/g liver).(DOCX)Click here for additional data file.


An appreciation of the interdependencies between the different measurement values is essential for systems-level understanding. Mathematical modeling has the potential to provide a coherent and quantitative description of the interplay between gene expression, metabolite concentrations, and metabolic fluxes. Escherichia coli undergoes major adaptations in central metabolism when the availability of oxygen changes. Thus, an integrated description of the oxygen response provides a benchmark of our understanding of carbon, energy, and redox metabolism. We present the first comprehensive model of the central metabolism of E. coli that describes steady-state metabolism at different levels of oxygen availability. Variables of the model are metabolite concentrations, gene expression levels, transcription factor activities, metabolic fluxes, and biomass concentration. We analyze the model with respect to the production capabilities of central metabolism of E. coli. In particular, we predict how precursor and biomass concentration are affected by product formation.The efficient redesign of bacteria for biotechnological purposes, such as biofuel production, waste disposal or specific biocatalytic functions, requires a quantitative systems-level understanding of energy supply, carbon, and redox metabolism. The measurement of transcript levels, metabolite concentrations and metabolic fluxes  Escherichia coli is able to utilize a variety of electron and carbon donors, such as glucose or glycerol, and electron acceptors, such as oxygen or nitrate. Energy currencies in form of the proton motive force (pmf) and the ATP/ADP ratio are supplied by either substrate level phosphorylation or by proton translocation against the pmf during membrane-associated electron transport . Even in the absence of extracellular electron acceptors, the electron transport chain can be active, since intracellular electron acceptors, such as fumarate, are available  + R* · T · log(ci/c°) + zi · F · ϕi. For μ″°i we use the transformed Gibbs formation energy of metabolite i. A Legendre transformation is conducted to adapt the Gibbs energy to constant pH and water activity a2OH /(R* · T)) where Ci is the thermokinetic capacity.We use the thermodynamic-kinetic modeling formalism /cjE,) · Jj = Fj(ξ) where Fj(ξ) is the thermokinetic force, Rj(ξ) is the enzyme-specific thermokinetic resistance and cjE, is the enzyme concentration. For example, the thermokinetic force of reaction A + B ⇌ C is F = ξA · ξB − ξC. The above expression reflects three major effects that control metabolic fluxes: the thermokinetic force F describes the influence of reactants and products, the resistance Rj(ξ) describes the specific enzyme activity that may depend on further activators and inhibitors, and the enzyme concentration cjE, describes the influence of the enzyme concentration on the metabolic reaction. For most reactions the thermokinetic resistance Rj(ξ) is assumed to be independent of the metabolite potentials ξ. For some reactions, where enzymatic regulation proved to be important for describing the experimental data according non-constant terms were included in Rj(ξ)  − μ · ciE, where ciE, is the concentration of enzyme i, μ is the dilution rate due to growth and Jisyn, is the synthesis rate of enzyme i that depends on the activities of the transcription factors aTF. The complex dependency of the synthesis rate on the transcription factors is approximated by a phenomenological relationship. For example, the expression of a gene that is activated by transcription factors 1 and 2 and inhibited by transcription factor 3 is modeled by Jisyn, = s · s · s where s = 2k− + (1 − 2k−) · aTF. This means that each transcription factor is able to change the expression of a gene by a factor of 2k and the interaction of different transcription factors is multiplicative. As will be seen, this model allows the description of the measurement data and therefore the use of more complex models allowing for example for additive interactions is not necessary at this stage.Gene expression is described by the equation i define how much precursor is needed to produce 1 gram of dry cell mass  or consumption (negative) rate of metabolite i by the biomass and D is the dilution rate. For the gaseous compounds oxygen and carbon dioxide the equation is modified to ċi = Ji · cX + kiin, − kiout, · ci because this compounds are mainly exchanged via the gas phase, but not the liquid phase. The parameter kiin, describes the supply of the medium by the aeration flow. For oxygen this parameter depends on the aerobiosis value. The parameter kiout, describes the outgasing.With the specific growth rate we get according to the chemostat equations . The resulting differential-algebraic equation system has index 2 but can be simplified to index 1  are scaled with a factor f before comparing them (in a plot) with the respective simulated variables xs. The factor f is calculated such that the quadratic difference ∥1/f*xs − xm∥22 between measured an simulated variables is minimal and can be computed as s and m denote the vectors with corresponding pairs of simulated and measured data points. This means that xs is plotted together not with xm but with f · xm.Relative measurement values E. coli MG1655 were as described in  do not need to be considered. The overall model contains a thermokinetic model of the metabolic network. The model reproduces the effect of reactants, products, important activators and inhibitors, as well as the enzyme concentrations on the metabolic reaction rate by simplified kinetic laws. The metabolic model is complemented by a gene expression model. The synthesis rate of enzymes depends on the activities of transcription factors. The activities of transcription factors depends in turn on their respective metabolic signals. Information about the network structure is based on the EcoCyc database  The stoichiometric parameters and the Gibbs formation energies are largely organism-independent and can be taken from available databases. (2) The thermokinetic resistances and the parameters of gene expression and gene regulation are free and (within bounds) not subject to physical constraints. Their values depend on properties of enzymes, transcription factors, and consensus sequences that may vary between strains. By adjusting the different parameter values for the latter class of parameters, the model can describe different physically feasible behaviors of the cell population. In order to test the model, the free parameters are adapted to a data set describing a steady state chemostat at different levels of aerobiosis. The data set includes metabolite concentrations, gene expression data and uptake and excretion fluxes. The model reproduces the steady state values of most measured variables and predicts the values of many others at several values of aerobiosis. This means that the model is able to describe the steady state response of Despite the complexity and size of the considered system, the use of simplifying assumptions keeps the model tractable. For most reactions a constant thermokinetic resistance is assumed. The resistance is allowed to vary only by integer factors of 10 to restrict search space. Resistances of rapid reactions are assumed to be zero allowing for a reduction of model size and stiffness. Gene expression and gene regulation are described with phenomenological equations. Parameters describing the influence of transcription factors on gene expression are allowed to take only integer values. Growth and maintenance are described by reactions with phenomenological kinetics. The rationale behind these simplifications is that they facilitate modeling and parameter adaptation while still preserving the basic physical and regulatory constraints on the cellular behavior.The following section presents the comparison of model results and experimental data for several aerobiosis values. The subsequent section demonstrates the use of the model by providing predictions of the effects of production pathways on the central metabolism.The model is compared to measurement data of transcripts, metabolites and uptake/excretion fluxes for several values of aerobiosis. Under identical  before the enzymatic reaction catalyzed by glyceraldehyde-3-phosphate dehydrogenase GAPD decrease with oxygen availability. GAPD catalyzes a rapid reaction with NADH as a product. Because the concentration of NADH decreases strongly at high aerobiosis values, the thermodynamic pull of NADH reverses the pattern for 3-phospho-glycerophosphate 13dpg, glycerate 2-phosphate 2pg, 3-phospho-glycerate 3pg, and phosphoenolpyruvate pep. The pattern is again inverted after the essentially irreversible pyruvate kinase PYK such that pyruvate pyr and the metabolites of the fermentation pathways are high for low oxygen availability and low for high oxygen availability. This is consistent with the observed inverse correlation of fermentation product excretion and aerobiosis. As a test to check if the model could predict trends of metabolite concentrations, we computed the steady state solutions for different dilution rates that in a steady-state chemostat are equal to the growth rates. The results are qualitatively consistent with the experimental observation from Schaub and Reuss  of ubiquinones. In the microaerobic range this leads to a seemingly paradoxical increase of the concentration of reduced ubiquinols such that maximal reduction is reached for intermediate aerobiosis levels and not for anaerobic growth.The concentrations of the different quinone species in the electron transport chains are well matched, in particular the non-monotone behavior of the ubiquinone redox state Figure . The NADoaa) and citrate (cit) to succinate (succ) and 2-oxoglutarate akg, whereas in the cyclic mode the 2-oxogluterate dehydrogenase AKGDH couples both branches and flux occurs in the direction from succinate succ to oxaloacetate (oaa). Succinate dehydrogenase SUCDH and fumarate reductase FRD participate in the cyclic and branched modes, respectively. This selectivity is caused by differential gene expression and the different use of quinone species (menaquinone for FRD and ubiquinone for SUCDH). Thus, the model reflects the expected behavior of the citric acid cycle for different oxygen availabilities. The model comprises the anaplerotic reaction phosphoenolpyruvate carboxylase PPC and the reverse phosphoenolpyruvate carboxykinase PPCK as well as the anaplerotic glyoxylate shunt realized by isocitrate lyase ICL and malate synthase MALS. The model is able to fit the data for different distributions of anaplerosis over both possible pathways (PPC/PPCK and ICL/MALS), and the simulation results present only one possibility.Deviations occur for metabolites in the citric acid cycle Figure  and the mgl operon that is activated by CRP. The mgl operon encodes a methyl-β-D-galactoside and galactose ABC transporter. Since this transporter is also able to transport glucose and is active under glucose-limited conditions . The simulated CRP activity is also consistent with the inferred CRP activity from Rolfe et al. (fdp) and CRP activity is controlled by the ratio of phosphoenolpyruvate (pep) to pyruvate (pyr), these predictions follow directly from the distribution of concentrations in glycolysis. The observation that PdhR is more active under aerobic conditions than under anaerobic conditions , we introduce the reaction A + B + 2·ATP → 2·ADP. For every production pathway, we show steady state values of several quantities depending on the enforced biomass-specific production flux Jprod given in mmol/g/h. These quantities are the biomass concentration, concentrations of precursors of the production pathway and the overall productivity per reactor volume qprod in mmol/l/h. The productivity is given by qprod = Jprod · cX where cX is the biomass concentration in g/l. In this way, we can assess the production capabilities independently of the kinetics of the production pathway.We use the above described model to assess the production capabilities of Jprod = 0). Plots of all twelve precursor molecules are shown in the Supplementary Data Sheet Figure g6p) increases with production flux, i.e., the consumption rate of glucose 6-phosphate by the production pathway, under aerobic and anaerobic conditions  and the biomass concentration drop with an increasing consumption of acetyl-CoA  decreases only slightly with a consumption of ATP  from the cell leads to a decrease of the  NADH concentration and the biomass , the AcCoA and NADH concentrations drop rapidly. (2) Ohta et al.  The last example is the production of isoprene via the methylerythritol phosphate (MEP) pathway  drop slightly and the concentrations of pyruvate and glyceraldehyde 3-phosphate (not shown) increase. Since biomass is clearly the limiting resource in this example, supporting biomass production by providing a complex medium may increase productivity.The above examples show that the model provides detailed predictions about the complex effects of production pathways on central metabolism. The synthesis of any biofuel requires a combination of precursor molecules. Figure a et al.  expresseE. coli. We enforce the consumption rate of precursor molecules in stoichiometric relations corresponding to the requirements for the synthesis of biofuel. The analysis of the steady state response of the precursor concentrations, the biomass concentration and the volume-specific productivity gives a detailed picture of the capacity of the metabolism in chemostat conditions. In an ideal situation a high production flux is possible and biomass and precursor concentrations are stable over a wide range of production rates. In real examples, either the decrease in biomass concentration or the decrease in concentration of a precursor limits the productivity of the pathway. The model gives detailed predictions about the responses of metabolism to specific production pathways. For example, the model makes predictions for which biomass-specific production flux the highest volume-specific productivity occurs. The model predicts how precursor concentrations change with varying production rate. For pathways with several precursors, the model shows which precursor concentrations decrease strongly and become limiting. In addition, complex phenomena can be observed. For example, it is possible that, due to autocatalytic effects, the concentration of a precursor increases despite an enforced consumption rate of the precursor. Depending on the kinetics of the production pathway, this may lead to dynamic instabilities that could be manifest as oscillations.The above examples demonstrate the use of the model for assessing the production capabilities of These results are predictions of the model. The approach of dissecting the parameters into an experiment- and organism-independent class and a class that is variable within bounds, assures that the resulting model is generic but can be adapted to specific experimental situations. The validity of model predictions decreases when one departs from the experimental conditions that were used to determine the values of the free parameters. Under changed experimental conditions, other, currently unmodeled, regulatory systems may play a relevant role. Changed experimental conditions may be the introduction of production pathways, as discussed here, the use of different substrates, dilution rates or strains, However, since the model by construction cannot violate the basic mass balance and thermodynamic constraints, the predictions will share the features enforced by these constraints.E. coli. A model of metabolism describing metabolite concentrations and reaction fluxes is complemented with a model of genetic regulation describing gene expression and transcription factor activity. This model of cellular metabolism is embedded into a model of growth describing the requirement of precursor for biomass formation, a model of maintenance describing the non-growth-associated needs for ATP, and a model of the chemostat describing the concentrations of extracellular compounds and biomass. The model provides a thermodynamically consistent view that integrates experimental data at the metabolite, flux, transcript, and regulator levels.To our knowledge, this model presents the first comprehensive, integrated model of steady state metabolism and regulation of the oxygen response of E. coli. The use of the thermokinetic modeling formalism and the availability of the Gibbs formation energies of the metabolites assures that the metabolic model is thermodynamically consistent. This means that the energetic constraints of the cellular metabolism are properly addressed. The mass balances of all relevant metabolites of central metabolism are considered. In particular the model includes also the balances of the biosynthetic precursors and of important energy and redox carriers, as ATP/ADP/AMP, NADH/NAD, and NADPH/NADP. Thus, the model integrates major mass balance constraints with the respective energetic constraints and the respective cellular regulation. The model is able to describe the competition of different pathways for energy and carbon and provides predictions of the effect of the introduction of production pathways on central metabolism.The combined model reflects the major constraints that restrict the behavior of We thank the ERASysbio SysMO (Systems Biology of Microorganisms) initiative for funding the SUMO and SUMO2 consortia. The research was funded by the Biotechnology and Biological Sciences Research Council (BBSRC), the Bundesministerium für Bildung und Forschung (BMBF) and the Nederlandse Organisatie voor Wetenschappelijk Onderzoek (NWO). ME further acknowledges support by the Ministerium für Wissenschaft, Forschung und Kunst Baden-Württemberg within the Ideenwettbewerb Biotechnologie und Medizintechnik Baden-Württemberg.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


BioMog's de novo biomass component lists included, either implicitly or explicitly, up to seventy percent of the components included in the predefined biomass equations, and the resulting de novo biomass equations outperformed the predefined biomass equations at qualitatively predicting mutant growth phenotypes by up to five percent. Additionally, the BioMog procedure can quantify how many experiments support or refute a particular metabolite's essentiality to a cell, and it facilitates the determination of inconsistent experiments and inaccurate reaction and/or gene to reaction associations. To further interrogate metabolite essentiality, the BioMog framework includes an experiment generation algorithm that allows for the design of experiments to test whether a metabolite is essential. Using BioMog, we correct experimental results relating to the essentiality of thyA gene in E. coli, as well as perform knockout experiments supporting the essentiality of protoheme. With these capabilities, BioMog can be a valuable resource for analyzing growth phenotyping data and component of a model developer's toolbox.The success of genome-scale metabolic modeling is contingent on a model's ability to accurately predict growth and metabolic behaviors. To date, little focus has been directed towards developing systematic methods of proposing, modifying and interrogating an organism's biomass requirements that are used in constraint-based models. To address this gap, the biomass modification and generation (BioMog) framework was created and used to generate lists of biomass components  Despite this, while numerous computational methods exist to curate genome-scale models by interrogating, hypothesizing and refining an organism's reaction, gene-reaction and/or transcriptional regulatory networks Biomass Modification and Generation) framework that can, in a scalable, parallelizable and efficient manner, consider all experimental results simultaneously to find a set(s) of biomass components that best matches experimental growth phenotypes. Consequently, BioMog proposes biomass requirements that best match all existing information while avoiding the substantial computational costs associated with existing mixed-integer linear programming (MILP) refinement approaches. Moreover, BioMog can be used to complement existing tools by suggesting other types of model adjustments to improve model predictions.To date, numerous methods have been created and are available to refine constraint-based models using growth phenotyping data . Once this set of new biomass components has been proposed, additional experiments can be designed to interrogate the essentiality of a given metabolite using methods similar to the recently proposed FOCALOnce all such lists have been created, a simple integer program (IP) can be constructed to propose a new biomass Escherichia coli (iJO1366) and Shewanella oneidensis MR-1 (iSO783) using growth phenotype and fitness data for the two organisms de novo and modified biomass requirements. We demonstrate that the new biomass equations outperform the qualitative growth predictions of their predefined counterparts, while providing potential insights into inconsistent experiments and model structures. We subsequently designed and conducted additional experiments to interrogate the essentiality of metabolites for which the existing phenotype data provides no insight or provides conflicting results.Below, we describe the application of BioMog to genome-scale models for de novo or based on an existing biomass equation via the appropriate algorithm. Based on this new biomass and any relevant statistics teased from metabolite lists, BioMog can propose additional growth phenotype experiments that are designed to generate data specific to the essentiality of a particular metabolite to cellular growth. Such experiments complete the BioMog cycle and can be performed iteratively to improve both the quantity and quality of evidence supporting the essentiality of a given metabolite. This entire process is summarized in The BioMog framework uses the concept of blocked metabolites and growth phenotype data to determine potential biomass components that will yield fatal and non-fatal knockout predictions for no growth and growth experimental results, respectively. Once include and exclude metabolite lists have been generated for all experiments, an improved biomass can be computationally proposed either ) is able to produce all metabolites except for Iex and I, which are blocked due to the choice of media components. Iex and I should thus be removed from consideration as possible biomass components since their inclusion would suggest that the wild type would be incapable of growth. For each knockout mutant evaluated experimentally, a list of blocked metabolites  are generated and stored as include/exclude metabolite lists based on the knockout phenotype . Once all include/exclude metabolite lists have been completed, BioMog can propose a de novo biomass or modify the predefined biomass using a simple integer program (IP) such that the agreement between model growth predictions and growth phenotype data is maximized  and Shewanella oneidensis (with ∼1500 fitness experiments) metabolism, resulting in proposed biomass components that were generated de novo or by modifying preexisting biomass equations. BioMog's de novo proposed biomass components are summarized in Tables S1 and S2 in File S1). In proposing these biomasses, metabolites most commonly associated with biomass such as amino and nucleic acids, as well as various currency metabolites, were weighted such that they would be selected preferentially when alternative metabolites were available (see methods for details).In order to demonstrate the efficacy and utility of the BioMog framework, the BioMog framework was applied to models of de novo biomass requirements consistently outperformed their predefined counterparts at predicting the qualitative outcomes of growth phenotype experiments  was incorrectly predicted only by BioMog's proposed biomass. For the 123 commonly mispredicted mutants, just under 90% did not have any blocked metabolites beyond those observed in the wild type network (implying the mutant can only result in a growth phenotype given the existing metabolic network). These mispredicted mutants could be due to experimental errors, incomplete or erroneous GPR associations, enzyme redundancy due to isozymes, metabolic redundancy due to the presence of alternate metabolic pathways which may not be used experimentally, or incomplete pathways that can be filled using methods like GapFill folA (encoding dihydrofolate reductase) in E. coli has been repeatedly reported as an essential gene folM, can compensate for the loss of folA resulting in the model having no new blocked metabolites and an erroneous growth phenotype prediction. Another experimentally essential gene, guaB (encoding IMP dehydrogenase) converts imp (inosine 5-phosphate) into xmp (xanthoine 5-phosphate) in guanine biosynthesis; however, the model has another route to make xmp from imp (imp → inosine → hypoxanthine → xanthine → xmp) so there are no new blocked metabolites in the sucC, (encoding succinyl-CoA synthetase)was classified experimentally as essential; however, other reports indicate that it is non-essential Δb0722 [sdhD], Δb0588 [fepC], Δb0590 [fepD], Δb0592 [fepB],Δb1252 [tonB] and Δb3919 [tpiA]) had Fe-enterobactin (feenter) as a blocked metabolite; however, this was excluded from BioMog's proposed biomass requirements due to numerous conflicting experimental results regarding the metabolite's essentiality . Consequently, to improve the qualitative predictions for these experiments it would require including additional regulatory information, repeating some conflicting experiments, or modifying the reaction network or gene-protein-reaction (GPR) associations.The proposed eriments Figure 3E. coli metabolites with no experimental evidence for or against were excluded to improve visualization) are provided in S. oneidensis . The unique elements of the BioMog biomasses  are critical to improving growth phenotype predictions and are found in both the de novo and biomass modification predictions . This biomass composition data can be used to improve BioMog proposals, such as those for amino acids in S. oneidensis. It is well known that organisms generally require all twenty amino acids for growth; however, based on just the frequency of appearance in include/exclude metabolite lists ; however, now all amino acids and a few additional analytically measured currency metabolites  have been added. Additionally, while the list of items removed from the predefined biomass is largely the same, extracellular crosslinked peptidoglycan . Various experimental proposals involving the use of minimal media lacking essential inorganic compounds, or the deletion of genes essential to metabolite production were generated for E. coli predefined biomass components where BioMog indicated that little to no experimental evidence existed or the experimental evidence was conflicting (see supplementary dataset S1). Experiments testing for the essentiality of protoheme (pheme) and deoxythymidine triphosphate (dttp) were then conducted. The first experiment was designed to test the essentiality of pheme through the double deletion of hemF and hemN. We were unable to construct a viable double knockout mutant – implying the double knockout was lethal in LB medium – indicating this metabolite is likely essential. The second experiment, designed to test for the essentiality of dttp, was one already performed by Orth et al.ΔthyA mutant on glucose M9 medium. However, the positive growth result contradicted findings of other publications ΔthyA experiments, we decided to repeat the results based on the supporting literature. Thus, growth phenotype experiments were performed evaluating the ΔthyA mutant's fitness in LB medium supplemented with thymine, glucose M9 minimal media, and glucose M9 media supplemented with thymine. Our results  suggest that ΔthyA is a thymine auxotroph, incapable of growth in glucose M9 media, in agreement with findings by WeissIn addition, to proposing and modifying biomasses, BioMog is capable of proposing experiments to interrogate the essentiality of any given metabolite  The BioMog framework provides a facile, fast and scalable approach to the task of generating biomass related hypotheses. Such an approach can be used to identify organism specific biomass components and improve a model's predictive power compared to using a predefined biomass equation. Alternatively, future efforts could focus on using growth phenotype data in conjunction with de novo, since the method cannot include metabolites in the biomass when there is no experimental evidence to support (or refute) their inclusion as was seen with S. oneidensis. Presently, there are numerous methods that can be used to generate high-throughput growth and fitness data and to construct higher order genetic knockouts such as TagModule E. coli iJO1366 network, for example, over 50% of the metabolites (after filtering out mutants resulting in large numbers of blocked metabolites) had no direct supporting or refuting evidence for their essentiality when considering experiments only involving single gene deletions. Perhaps more critically, over 40% of the components in the E. coli predefined biomass have no explicit evidence for their inclusion in biomass based on the single knockout datasets considered here Since the quality of BioMog solutions can only be as good as the experimental data used to generate them, it is important to have a large, diverse and accurate experimental dataset. This is especially true when generating biomass components de novo and modified biomass components represent the needs of the organism assuming the model and experiments are an accurate portrayal of the organism's genetic and metabolic capabilities. If known biomass components are not proposed by BioMog then it is possible that the model or experiments are incorrect. This is most readily apparent when BioMog suggests the removal of previously reported essential metabolites such as dttp, Cu2+ and Ca2+ for E. coli or protein_son_aerobic for S. oneidensis. In these instances, it should not immediately be interpreted that these metabolites are inessential for growth  but instead this result should be considered a red flag, indicating additional experiments are needed to verify the accuracy and validity of both the model structure and experiments. In this aspect, BioMog behaves as a hypothesis generation and model interrogation tool with easy traceability .From a mathematical perspective, BioMog works to find the best biomass requirements for the current model structure (reactions and GPR associations) and provided experimental results. As such, the proposed S. oneidensis genome-scale model since the enzyme for producing the precursor pimelyl-ACP was only recently discovered E. coli and S. oneidensis, respectively) that are not biomass candidates due to an inability to synthesize precursors or due to various attached cofactors  for which there is no synthesis pathway included in the model. We should note that when determining net production blocked metabolites sinks are added for all metabolites in the network, so metabolites will only be blocked if their precursors can not be produced (an inability to degrade by-products will not cause a metabolite to be net production blocked by BioMog). If some of these net production blocked metabolites are to be considered as biomass components, the model will first need to be gap filled so the metabolite of interest can be produced using methods like SMILEY and GrowMatch or by adding artificial source reactions  for precursors. Nonetheless, while various structural issues may prevent BioMog from proposing an essential end product used in biomass, it can still capture essential upstream precursors that can be produced by the network. As mentioned above, while biotin is a net production blocked metabolite in S. oneidensis, S-Adenosyl-L-methionine, a biotin precursor, is one of the alternative proposed biomass candidates. Thus, the process of model refinement and automated biomass proposals should be an iterative one where the model is refined, BioMog is used to modify a biomass and any unexpected results investigated and further refined using existing tools.Because BioMog depends on the model structure in order to make biomass proposals, it should be used in conjunction with other model refinement such as GrowMatch E. coli biomass appears to be the direct consequence of an incomplete depiction of the transport systems for these two inorganic compounds. The iJO1366 model includes single calcium and copper transport reactions into the cytoplasm that are associated with zupT (b3040) and yrbG (b3196), respectively. The 2+ or Cu2+ as blocked metabolites and both mutants are viable, suggesting either an incomplete picture of copper and calcium transport across the cell membrane or the non-essentiality of these metabolites. While this may have been difficult to pick out of the entire dataset, using BioMog's blocked metabolite frequencies makes finding areas of metabolism needing model improvement easier. Consequently, even if experimenters are confident in the quality of the defined biomass composition, BioMog can be a valuable component of a model curator's toolbox and supplements existing approaches While valuable as a hypothesis and experimental generation tool, BioMog also has value in identifying experiments with erroneous or contradictory results. Researchers can not only use blocked metabolite lists to assess which experiments support or refute the essentiality of a particular biomass component, but also which experiments appear inconsistent either because of experimental or model inaccuracies. Moreover, BioMog biomass modification proposals can further point out potential structural flaws in an existing genome-scale model's GPR or reaction networks. For example, the recommendation removing calcium and copper from the Accurate knowledge of an organism's essential metabolites is critical to a model's predictive utility. Constraint-based analyses rely on a model's biomass equation to determine whether a particular genetic modification will be fatal. Failure to accurately capture this aspect of a cell can be costly both in terms of time and money when models are used in experimental design, such as strain design in metabolic engineering de novo. Subsequently, experimentalists can use the BioMog framework to recommend additional wet lab experiments to interrogate the essentiality of particular metabolites for which data is lacking or contradictory. This process is accomplished through a series of linear, integer and mixed integer programs , as described in detail in the following sections.The biomass modification and generation (BioMog) framework allows for the identification of required biomass components from high-throughput growth and fitness experiments. The framework enables scientists to systematically make metabolic additions or removals from a predefined biomass or create new biomass equations Escherichia coli (iJO1366) and Shewanella oneidensis MR-1 (iSO783). While iJO1366 was evaluated as is, thirty-five reactions were added to iSO783 to reflect additional knowledge regarding the organism's metabolic capabilities. These modifications are summarized in supplementary dataset S2).BioMog was applied to genome-scale models for BioMog functions by determining which metabolites can be produced and removed from the system by the wild type strain, but not by a particular mutant. Such metabolites are referred to as blocked metabolites. The discovery of blocked metabolites was accomplished using the following MILP and is similar to that used by the GapFind algorithm iz is a binary variable indicating whether a metabolite i can be produced, jv is a free variable for the magnitude of the flux through reaction j, and ix acts as a sink for metabolite i and is used as an indicator of metabolite production. M is the set of all metabolites, R is the set of all reactions, μ is a set of predefined biomass components (if any exist), UL is the set of reactions with non-zero upper limits and LL is the set of reactions with non-zero lower limits. The parameter minv sets a minimum production threshold and was set to 0.0005. Network specific constraints refer to any constraints that are particular to a given genome-scale model. For example, constraints that force reaction fluxes to zero by default, that fix P/O ratios or that set oxidase ratios to a fixed value. Blocked metabolites are those for which iz is 0 and are determined for the mutant and wild type strains used in the experiments. A set difference between the two is then performed and the remaining blocked metabolites are considered potential biomass equation candidates for inclusion or exclusion.Where Eq. 2). As a consequence, the MIP has the unique feature of finding metabolites associated with metabolic dead ends, as well as, metabolites participating in cycles that cannot be depleted from the system without violating conservation of mass. For example, in the S. oneidensis mutant ΔSO4249, succoa is a blocked metabolite even though flux through the metabolite is possible by running the citric acid cycle. This also means that such blocked metabolites may be implicitly essential for growth, but may be removed from the biomass equation.Note that the formulation above is written such that mass is actively removed from the system via sinks (e) and organized into include (In), if mutant does not grow, or exclude (Ex), if the mutant does grow, metabolite lists. NG is the set of experiments where mutants did not grow and G is the set of experiments were mutants grew.Once blocked metabolites have been determined for all experiments, mutants associated with >100 blocked metabolites are filtered from consideration as they are deemed uninformative as these filtered mutants are often due to deletion of an essential early reaction . The set of blocked metabolites for a given mutant are given an experiment number  can be solved to propose, en and eg are binary variables indicating agreement with no growth and growth experiments respectively, iw is a binary variable indicating whether metabolite i is a biomass component, In and Ex are mappings from a blocked metabolite to an experimental and are the include or exclude metabolite lists, respectively, and EM is an optional set containing metabolites that have been experimentally measured within a microbe's biomass. For most analyses EM was left as an empty set, with the exception of the S. oneidensis analysis using analytical biomass measurements. While not done for the purposes of this paper, it is also possible to weight individual experiments in Eq. 7 based on one's confidence of a particular result . Once the maximal experimental agreement is determined, a biomass equation that best agrees with experimental results can be generated. When, multiple possible solutions exist, negative weights can be added to select more common metabolic components by preferentially including them in a new objective (Eq. 11)  or positive weights can be used to find a minimal number of required biomass components .Where W is the set of weighted metabolites for preferential inclusion in the biomass, FixObj, is the objective score obtained from running the LP using Eq. 7–10, and WeightedObj is the new weighted objective function. While not shown, Eq. 8–10 should also be included along with Eq. 11–13 in the weighted LP in order for the problem to be solved correctly.Here, de novo, it is also possible to begin with an existing biomass equation and modify it to include likely biomass components and only remove components that fail to match experimental results. This allows one to include components for which no supporting growth phenotype data exists, but may have been demonstrated to be essential using alternative methods:In addition to generating a biomass equation iy is a binary variable that removes biomass components if experiments disagree with its inclusion, iw is a binary variable that adds biomass components if experiments support their inclusion, and μ is the set of metabolites in the predefined biomass equation.Here Eq. 7). To determine alternative metabolites, iw, all proposed biomass components were fixed to 1 with the except for a single metabolite, k, for which an alternative metabolite is being sought. For this single metabolite, wk was set to 0. The de novo minimal biomass LP is then resolved (Eq. 8–13) using a variant of the objective (Eq. 11) in which no metabolites are preferentially weighted . If a solution exists, then the newly added metabolite is an alternative for metabolite, k. The newly discovered alternative metabolite's indicator value is subsequently fixed to 0, and this process is repeated until the problem is infeasible so that all possible alternatives for that metabolite are exhausted.For the purpose of this paper, alternative metabolites are any compound that can replace a proposed biomass compound without negatively impacting the objective function . To determine precursors, all reaction fluxes, vj, were decoupled into forward and reverse directions such that jv = j,forv−j,revv where j,forv and j,revv are both positive variables. Then, the following formulation was used:Precursor metabolites are defined as upstream compounds that are essential for the production of a biomass metabolite. As such, including a particular metabolite in the biomass implicitly makes that metabolite's precursors essential. Thus, any metabolites within the predefined biomass that are found to be precursors to the Biomass is the set of de novo biomass components and iSink is a binary variable indicating if there is flux through sink xi. For each metabolite not contained within the de novo biomass, the metabolite was individually removed from the system , and the MIP given by Eq. 20–26 was solved. If the cell is only viable when the metabolite is present , then the compound was declared an essential precursor. This process was repeated using the predefined biomass to determine if any de novo components could be found upstream. In this LP, KillForwardRxn and KillBackwardRxn remove consuming reactions associated with the targeted metabolite in the forward and reverse directions respectively, as well, as any forward (reverse) reaction which has an upper (lower) limit equal to zero.Here Eq. 1–6), it is possible that a biomass equation generated only from biomass components proposed using Eq. 8–13 may result in no growth predictions for cases where the model was supposed to predict growth . This occurs when a by-product of a proposed biomass component has no way of being consumed in the network for that experiment (e). The presence of sinks for these by-products in Eq. 2 still allows for the biomass components to be produced when evaluating blocked metabolites. As a result these dead-end by-products need a way of being consumed in the model, which can be done by adding additional consuming reactions to the model or including them in the biomass equation as well. For example, in order to correct erroneous predictions due to dead end by-products, a few metabolites  were added to the iJO1366 BioMog proposed de novo biomass to behave as sinks for these by-products. To determine these dead end by-products, the following formulation was used:Due to the addition of sinks for all metabolites when finding blocked metabolites in the BioMog framework (Biomassv is the flux through the newly proposed de novo biomass equations and Eq. 32) covers any additional constraints specific to the network . To avoid erroneous results due to the predefined biomass behaving as a sink, the predefined biomass equation was removed from the stoichiometric matrix for all subsequent analysis involving the BioMog biomass equation. All non-zero ix, should be considered dead end by-products of required biomass components. These should be fixed by addition of the metabolite to the biomass equation or by adding consuming reactions to the network. This process was repeated for all experiments where the model was supposed to predict growth (eg = 1). Additionally, cuts can be added to determine alternative solutions for any given experiment by fixing a proposed sink to 0.Where de novo biomass requirements, an FBA was solved as described by Feist et al. de novo variant. Since BioMog only makes qualitative proposals, an arbitrary stoichiometric value of −0.0002 was assigned to each essential metabolite proposed. To prevent the predefined biomass from behaving as a sink, both the core and wild type biomasses were removed from the iJO1366 model when evaluating the de novo biomass components.To test the qualitative performance of the E. coli and S. oneidensis were taken from publications by Orth et al. E. coli, the growth and no growth determinations were used as reported by in the paper for four conditions . Additionally, all essential genes from the Keio collection Shewanella, a fitness score of less than −1 indicated no growth and greater than −1 indicated growth. From this dataset, we tested three conditions .Growth phenotyping datasets for E. coli strains used in the experiments were derived from the Keio collection thyA::kan, hemF::kan, and hemN::kan as well as the parental strain, E. coli K-12 BW25113. Two double mutants  were attempted to be made by first removing the kan gene from the single knockouts using FLP recombinase ΔhemN::kan strains into kanomycin sensitive ΔhemN strain and ΔhemF::kan into ΔhemF) and finding robust growth on kanomycin plates following P1 transduction.All 600 measurement was ∼0.05 and cultured for at least 3 days in a Tecan plate reader at 37°C.All strains were grown in triplicate unless otherwise noted. All strains were pre-cultured for approximately 24 hours in 90% (V/V) M9 minimal medium supplemented with 2 g/L glucose and 10% (V/V) LB with 125 µg/mL thymine. Cells were washed twice with M9 minimal media containing no carbon source to remove any residual glucose and LB from precultures. Cells were then resuspended in different media, M9 minimal media with 2 g/L glucose or M9 minimal media with 2 g/L glucose and 125 µg/mL thymine, such that the starting ODDataset S1(XLSX)Click here for additional data file.Dataset S2(XLSX)Click here for additional data file.File S1(DOCX)Click here for additional data file.File S2(ZIP)Click here for additional data file.


Signalling pathways are the cornerstone on understanding cell function and predicting cell behavior. Recently, logical models of canonical pathways have been optimised with high-throughput phosphoproteomic data to construct cell-type specific pathways. However, less is known on how signalling pathways can be linked to a cellular response such as cell growth, death, cytokine secretion, or transcriptional activity.In this work, we measure the signalling activity (phosphorylation levels) and phenotypic behavior (cytokine secretion) of normal and cancer hepatocytes treated with a combination of cytokines and inhibitors. Using the two datasets, we construct "extended" pathways that integrate intracellular activity with cellular responses using a hybrid logical/data-driven computational approach. Boolean logic is used whenever a priori knowledge is accessible , whereas a data-driven approach is used for linking cellular behavior to signalling activity via non-canonical edges. The extended pathway is subsequently optimised to fit signalling and behavioural data using an Integer Linear Programming formulation. As a result, we are able to construct maps of primary and transformed hepatocytes downstream of 7 receptors that are capable of explaining the secretion of 22 cytokines.We developed a method for constructing extended pathways that start at the receptor level and via a complex intracellular signalling pathway identify those mechanisms that drive cellular behaviour. Our results constitute a proof-of-principle for construction of "extended pathways" that are capable of linking pathway activity to diverse responses such as growth, death, differentiation, gene expression, or cytokine secretion. Construction of signalling pathways is a major endeavour in biology. Signalling cascades, starting at the receptor level, orchestrate a variety of normal or pathological responses via a complex network of kinases, adaptor molecules, and other signalling proteins . Severala-priori knowledge of the signalling connectivity and depending on their signal-propagation assumption are classified as physicochemical, fuzzy, or logical. In physicochemical models signalling events are modeled via chemical reactions using ordinary or partial differential equations (ODE or PDE) depending on their ability to model spatial gradients of signalling molecules  as described previously  by takinThe first term penalizes the measurement-prediction mismatch of the key phosphoproteins and removes all edges that contradict the "signalling" dataset. The second term penalizes the measurement-prediction mismatch of the response measurements and prunes non-canonical edges that contradict the response dataset. The third term removes all edges that have no effect on the measurement-prediction error and thus penalizes the map size.The ILP formulation is solved with the state-of-the-art commercial code CPLEX through GAMS ,37. Thisin-silico tests were performed including comparison with a data-driven (regression) model . In contrast, for the map-size reduction term a significantly smaller weight was selected (= 1/20). This weight was chosen based on the longest chain of consecutive reactions, namely 12, with the purpose to force the optimiser not to remove edges if they are essential for satisfying experimental results. For example, consider a chain reaction R1→ R2→...→ R12 that should be kept because experimentally we found the relation "R1 = 1 implies R12 = 1". The reward for the optimiser to satisfy this chain reaction should be more than the penalty that it has to pay for keeping all 12 reactions. Therefore, if by keeping all reactions the map size reduction term increases the objective function by 12 units, then the reward for satisfying a chain of 12 reactions (mismatch term) should be higher than 12. The maximum chain in our pathway is 12 reactions but we choose 20 in case that further refinements in the generic topology increase the maximum chain.The generic map includes a total of 7 receptors, 57 signalling molecules connected with 113 canonical edges, and 352 non-canonical edges that connect the 16 key phosphoproteins to the 22 cytokines. From the 352 non-canonical edges, a large percentage of those have correlation weights close to zero. To minimize the computational cost of the ILP solution, we choose to retain 60% of those weights as explained in the Additional File The presence of cellular response data significantly enhances the optimisation of the signalling topology in two different ways. Firstly, non-canonical edges provide additional pathway information to the ILP formulation. In other words, the optimiser is forced to conserve edges that lead to cytokine nodes but do not affect measured phosphoproteomic signals  . The via consent , as apprHuh7 cells were plated on 96-well plates coated with collagen type I-coated  at approximately 30,000 cells/well in DME medium containing 10% Fetal Bovine Serum (FBS). After an overnight incubation, cells were starved for 4 hours, treated with inhibitors for 40 minutes, and then with stimuli. In each well different experimental conditions were imposed by introducing a combination of stimulus and inhibitor. Kinase inhibitors were used at concentrations sufficient to inhibit at least 95% of the phosphorylation of the nominal target as determined by dose-response assays in previous work . The folApart from the phosphoprotein signals, cytokine release was also measured from supernatants under the same experimental conditions. After incubating the cells with the inhibitors for 45 minutes, the same stimulus/inhibitor combinations were applied and following overnight incubation we removed the supernatant and measured the secretion of 33 cytokines. Among the 33 cytokines, 22 cytokines were shown an activity and were used in the subsequent data analysis. The cytokines used are: bFGF, Eotaxin, GMCSF, IFNγ, IL15, IL17, IL1b, IL1ra, IL2, IL4, IL6, MCP1, MIP1α, MIP1β, IP10, GROa, ICAM, GCSF, RANTES, TNFα, VEGF, SDF1. The 11 cytokines that were excluded are: IL9, IL10, IL12p70, IL13, IL5, IL7, IL8, PDFG, MIF, MIG, VCAM1.Res Y(an m × k matrix representing m response component under k conditions) was then regressed against the signalling matrix Sig X. The computed correlation matrix W is comprised by the correlation coefficients i,jw, where i is the index of response components (i = 1..num_res) and j the index of signals (j = 1..num_sig). The correlation coefficients i,jw, were then used as the stoichiometric weights of the non-signalling reactions in the Boolean framework that originates from a signal j and ends to a response component i .Both signalling and response datasets were organized in data structures in the form of 5-D cubes using the DataRail software ; 4 of thThe ILP formulation first used to optimise Boolean signalling pathways in , is extewhere the summation is only over the relevant terms as described below.res j= {1,...,ns,res} of response species, and the set ires = {1,...,nr,res} of edges linking signalling (j) with response (resj) nodes. The rest of the used symbols are as follows:Major addition to the formulation, compared to the one introduced in , consist- - - - iy, is a variable denoting whether a reaction is possible or not i y= 0 ∨ i y= 1.- res jin experiment k. It equals to the sum of all reactions res ileading to species res jweighted by i that lead to response species j.- The term j) and experimental conditions (k). Note that the summation is only taken over the species j that are measured in experiment k.Therefore, the first term of the objective function resj) and experimental conditions (k). The middle summation is over the response species j that are measured in experiment k. The inner summation is over the reactions i that lead to response species j.The second term The third term tion see . Here weConcerning the term We want to minimize the weighted sum of the absolute differences We can thus scale astoand obtain the desired range, i.e., The above constraints complete the formulation.As aforementioned, the objective function (1) consists of three major terms, namely The error is defined as the deviation between experimental and simulated values using the following formula:The authors declare that they have no competing interests.INM and LGA conceived the project. AM conceived and implemented the ILP formulation and its extension to response measurements. AM wrote the largest portion "Computational Procedure" subsection in the "Materials and Methods" section. LGA and INM wrote the remainder of the manuscript. TSW developed the isolation protocols and provided the hepatocytes. LGA and DEM performed the experiments. INM run the code and analyzed the data. AM and TSW edited the manuscript. All authors approved the final manuscript.Signalling dataset. Dataset in MIACA format (phosphorylation data) that were produced and used in this manuscript .Click here for fileResponse dataset. Dataset in MIACA format that were produced and used in this manuscript .Click here for fileSupplementary Materials and Methods. "Supplementary Materials and Methods" include further information about the proposed methodology, such as, i) data normalisation procedure used, ii) comparison with an alternative 2-step Multiple Linear Regression method, iii) a detailed model assessment and iv) comparison of simulation runs for Huh7 and Normal cells.Click here for file