Pichia pastoris (Komagataella phaffii) is currently considered one of the most promising hosts for recombinant protein production (RPP) and metabolites due to the availability of several tools to efficiently regulate the recombinant expression, its ability to perform eukaryotic post-translational modifications and to secrete the product in the extracellular media. The challenge of improving the bioprocess efficiency can be faced from two main approaches: the strain engineering, which includes enhancements in the recombinant expression regulation as well as overcoming potential cell capacity bottlenecks; and the bioprocess engineering, focused on the development of rational-based efficient operational strategies. Understanding the effect of strain and operational improvements in bioprocess efficiency requires to attain a robust knowledge about the metabolic and physiological changes triggered into the cells. For this purpose, a number of studies have revealed chemostat cultures to provide a robust tool for accurate, reliable, and reproducible bioprocess characterization. It should involve the determination of key specific rates, productivities, and yields for different C and N sources, as well as optimizing media formulation and operating conditions. Furthermore, studies along the different levels of systems biology are usually performed also in chemostat cultures. Transcriptomic, proteomic and metabolic flux analysis, using different techniques like differential target gene expression, protein description and 13C-based metabolic flux analysis, are widely described as valued examples in the literature. In this scenario, the main advantage of a continuous operation relies on the quality of the homogeneous samples obtained under steady-state conditions, where both the metabolic and physiological status of the cells remain unaltered in an all-encompassing picture of the cell environment. This contribution aims to provide the state of the art of the different approaches that allow the design of rational strain and bioprocess engineering improvements in Pichia pastoris toward optimizing bioprocesses based on the results obtained in chemostat cultures. Interestingly, continuous cultivation is also currently emerging as an alternative operational mode in industrial biotechnology for implementing continuous process operations.The methylotrophic yeast  In recent years, recombinant protein production (RPP) technology has given rise to a multibillion-dollar market offering products for a wide range of industrial activities such as food, detergent, paper, chemical, cosmetic, and pharmaceutical production. For example, more than 400 RPP-based pharmaceutical products are currently available on the market  cells , Kluyveromyces lactis, Yarrowia lipolytica, or Komagataella phaffii (Pichia pastoris)  has arisen as an efficient and versatile cell factory for obtaining a wide spectrum of biotechnological products including recombinant proteins and metabolites of diverse origins  and the ability to perform post-translational modifications as well as to secrete the products extracellularly , a strong and tightly regulated promoter, was the first used obtaining efficient production rates of several proteins of interest   of the microorganism used  (chemostat or turbidostat-based approaches)  and deceleration-stats (De-stats), which are used to study the increase or decrease the specific growth rate, respectively , where an environmental parameter is continuously changed while the dilution rate is kept constant  studies Bull, , but alsTranscriptomic, proteomic, metabolomics, and fluxomic data, can be jointly integrated into a systems biology approach shown in  in orderTranscriptional analysis is usually the first step in systems biology studies as it is also the first stage in the information flow from genomic data  , which quantify the number of transcripts present in a sample by whole transcriptome sequencing, have arisen as helpful workhorses. In fact, these methods provide a robust overview of all transcripts  was examined by using the RNAseq SOLiD platform. In addition, further RT-PCR analysis showed a constitutive splicing of HAC1 gene transcript across the different phases of RPP induction independent of the lysozyme misfolding level. The importance of this finding is based on the fact that Hac1p corresponds to a key transcription factor in the triggering of UPR response  is one other limiting factor for RPP  improved with increasing specific growth rate (μ), gene transcription had no influence on the latter, indicating a constitutive nature of PTHI11.Furthermore, it has been observed that transcriptional analysis provides useful information for characterizing native and engineered promoters. Thus, Landes et al.  characteG1, corresponding to a high-affinity glucose transporter (GTH1), exhibited strong and tight regulation under carbon-limiting conditions. Also, further characterization of this promoter at variable specific growth rates in chemostat cultures revealed the optimal specific production rate at a dilution rate D = 0.07 h−1.In similar work intended to develop alternatives to the methanol-based expression system, Prielhofer et al.  identifiP. pastoris producer clones growing at controlled specific growth rates in chemostat cultures have also enabled further transcriptional characterization and expansion of existing knowledge about the impact of this key parameter for improvement of the cell factory. Thus, Rebnegger et al. . In order to generate an alternative model for Warburg effect studies, Ata et al.  under AOX1 promoter . As confirmed by ROL transcriptional levels in a ddPCR analysis, optimal specific extracellular lipase activity was obtained with the 2C clone. By contrast, transcriptomic analyses with microarrays revealed constraints with increasing ROL gene dosage on the transcriptional machinery  . As revealed by specific analysis with ddPCR, methanol uptake was found to have a positive effect on the transcriptional response; the effect, however, resulted in no increase in product activity , global stress-related transcription factors and with nutrient responses reveal an adaptation of low substrate availability at near-zero growth rates. This finding reinforces the reduction observed in the maintenance coefficient (ms), in comparison to chemostat cultures growing at higher μ. Otherwise, protein synthesis in retentostat cultures exhibited insubstantial transcriptional regulation, so further research, for example with RP-expressing clones, to determine whether the alternative cultures could be useful with P. pastoris and to outperform S. cerevisiae translational capacity at low μ is proposed.Although most transcriptional analyses have been performed in chemostat cultures, some have been conducted in retentostats. Rebnegger et al.  performeAccording to the central dogma of molecular biology, the protein level falls at the last stage of the information flow . A metabolic phenotype is defined by the actual activities of the metabolic reactions in cells . Because they are rarely constant, reaction rates are usually determined under steady-state or pseudo-steady state conditions and commonly referred to as “metabolic fluxes,” which are thus key indicators of the physiological status of cells.in vivo values for kinetic parameters are usually unavailable. A number of techniques have thus been developed to quantify metabolic fluxes collectively that are known as “metabolic flux analysis” (MFA) or, simply, “fluxomics.” Many of them have been applied to P. pastoris and the most salient are discussed below. Using those techniques for metabolic engineering improves existing knowledge of the physiological properties of biological systems, thus enabling the rational design of cell factories and efficient metabolic engineering cycles.Determining metabolic fluxes remains difficult despite major technological advances in quantifying all components involved. In fact, it is not always possible to determine the concentrations of all relevant components; also, As stated above, MFA is widely used for comprehensive analysis of the physiological effects of environmental changes or genetic engineering modifications. Metabolic fluxes  provide an excellent operational picture of cell operation. A continuous culture provides a simple, convenient tool for maintaining a stable (in steady state) cell metabolism over long periods of time. Comparing metabolic flux distributions under different conditions allows a deeper understanding of the effects resulting from the genetic or environmental changes.13C labeling . Using GEMs in combination with steady-state data should in theory allow one to calculate metabolic fluxes. Unfortunately, the intricate interconnections among metabolites, and the small number of flux measurements typically available , prevent the accurate determination of most of the metabolic fluxes, which requires the combined use of mathematical models, optimization methods and/or experimental techniques such as Genome-scale metabolic models have become essential tools for MFA and rational-based strain engineering. These models are mathematically structured knowledge bases containing descriptions of all biochemical reactions, metabolites, and metabolic genes for a specific organism—a Biochemical, Genetic and Genomic (BiGG) knowledge base  or their mixtures   rate, but also by the methanol fraction in mixed feeds. Experiments using chemostat cultures have shown environmental factors such as oxygen-limited conditions or low temperatures to have a strong impact on MFA  and building block production  while reducing biomass yield  requires a deep physiological understanding of the cell factory. For the topic discussed in this review, such knowledge is expected to eventually allow the rational development of feasible and competitive bioprocesses with shorter times to market. The overall approach is usually not straightforward due to the complexity of the different interactions occurring at many cell physiology levels. Thus, since the interactions between the cell factory performance and the bioprocess conditions define key parameters of interest (KPI) of the production, an empirical determination of those are required. In this sense, to obtain this kind of information accurately, chemostats are considered the most usual continuous cultivation applied  and concretely, batch or fed-batch cultures to determine oxygen requirements  However, currently, continuous cultivations should be considered a preferred option to assess the effect of any culture conditions. The fact of working in steady state conditions in whichever cell population is considered homogeneous allows to obtain very accurate and reliable results. In contrast, dynamic processes such as batch of fed-batch cultures are expected to be less robust and present more deviations due to possible drifts in culture conditions. Furthermore, also parameter determination such as kinetic rates and yields is also considered to be more complex in dynamic processes.Bioprocess performance is influenced by some physical conditions of cultures including temperature (T), pH and dissolved oxygen (DO). The optimum conditions for production may differ from those for growth due to their effect on several factors including protein-specific characteristics, kinetics and sensitivity of products to proteolytic degradation.P. pastoris bioprocesses is about 30°C, above 32°C protein expression is suppressed and cell growth rapidly decays  by P. pastoris in continuous cultures growing at the same μ but at different temperature (22 or 30°C). Overall, the lower temperature led to lower specific productivities irrespective of the co-substrate used. Strikingly, lowering temperature produced an increase in biomass concentration.Berrios et al.  examinedAs a conclusion of the studies focused on the T effect, the efficiency of a low cultivation temperature depends on the target protein, so this effect should be studied for each case.P. pastoris cultures is commonly set at 5.0–6.5  by having the bioreactor operate at a constant agitation rate and using air or oxygen-enriched air as the inlet gas in order to ensure fully aerobic conditions . Chemostat cultures have been used to examine the boosting effect of oxygen-limiting conditions on RPP. For example, Garcia-Ortega et al. . Also, a high osmolarity decreased biomass yield due to the increased energy maintenance requirements.The composition of the cultivation medium also has a direct impact on osmolarity. Dragosits et al.  examinedP. pastoris and constitutive promoters such as PGAP, glycerol and glucose are widely used as carbon sources because they result in high specific growth rates that are usually accompanied by adequate specific production rates  far enough from μ+ strain for PAOX1-driven heterologous production of a sea raven antifreeze protein. The specific product formation rate (qP) and product-based volumetric productivity (QV) increased throughout the range of dilution rates used (0.01–0.10 h−1). Similarly, Boze et al. (V or qP increased relative to the use of methanol as sole carbon source at the same dilution rate (0.01 h−1). However, the substrate concentration ratio was not optimized in either study.D'Anjou and Daugulis  used a se et al.  studied P. pastoris Mut+ strain expressing recombinant avidin at a constant dilution rate D = 0.06 h−1. With methanol fractions higher than 0.6 C-mol C-mol−1, qP was like cultures performed with methanol as sole carbon source.Jungo et al.  studied Rhizopus oryzae lipase (Rol) as a model system to investigate the application of methanol-glycerol feeding mixtures in the P. pastoris Mut+ producer clone. Cultures were grown in a simple chemostat system and response surface methodology was used to evaluate the effects of D and the methanol/glycerol ratio in the feed as experimental variables. The optimum conditions provided qP values similar to, and QV values higher than those of processes using methanol alone as substrate. Furthermore, Berrios et al.  , and product formation (qP) are Monod for cell growth, Pirt's as a maintenance energy model and Luedeking–Piret as linear μ-dependence for substrate uptake and product formation, respectively. However, saturation patterns, non-monotonic behaviors or bell-shaped types have also been observed, especially in substrate-inhibited systems for cell growth and non-constitutive RPP , substrate uptake  usually exhibit different production kinetics. Thus, the PAOX1-driven expression system often exhibits non-monotonic kinetics, possibly as a consequence of the metabolic burden resulting from methanol uptake and/or protein processing, and from secreting limitations due to outstanding expression levels . For other proteins, studies performed in chemostat   and the YP/X values were always higher in continuous mode at similar μ comparing to those obtained in fed-batch cultivation.In terms of absolute values, only in the case of Fab 3H6 and hSA production, the specific production rate (V) has been also included in Additionally, since productivities could be one of the main performance criteria from an industrial point of view, volumetric productivity  including productivities  production with the constitutive promoter PGK in continuous and fed-batch cultures. They found volumetric and specific productivity to peak at the highest μ levels, which indicated a direct correlation between growth and production.de Macedo Robert et al.  comparedProduction kinetic profiles for other proteins have also been obtained in other continuous cultures that were used as references to develop optimal feeding strategies adjusted to cell factory performance. The result was a marked increase in production in terms of volumetric productivity and product titer for some Fab fragments  in chemostat cultures growing under a steady state. Alternatively, it can also be assured with periodic changes in dilution rate or the aeration conditions . Adaptation of the yeast to growth on methanol over 250 generations was examined and observed a complex correlation among carbon source, cell growth and RPP. Increased specific growth rates on rich and minimal growth media was studied at the level of both population and single clone. Selected clones displayed strain-dependent variations for the yield of PAOX1-based recombinant protein expression, one showing up to 2-fold increase in terms product titer.Gasser and Mattanovich  reportedr et al.  investigFrom a bioprocess engineering perspective, process variability often results in issues arising from changes in critical process parameters (CPPs), key parameters of interest (KPIs), or critical material attributes (CMAs), all of which in turn affect critical quality attributes (CQAs). CPPs can change throughout a culture and require using appropriate control strategies to reduce process variability. Advances in process analytical technology (PAT) and quality by design (QbD) approaches have substantially reduced times for process development. According to Hernandez , in the P. pastoris have been described for batch and fed-batch operations. Besides the common sensors used for the monitoring and control of culture conditions and gas analysis, just scarce applications have been reported for continuous mode  was running 6 weeks at μ = 0.14 h−1. In this case, considering the unproductive time spent in setting up, draining out, cleaning and reassembling in fed-batch operation, the overall CalB production was 5.8 times greater than the fed-batch process  was maintained working for 2 weeks reaching similar levels of product titer than at the end of a fed-batch culture. Specifically, QP. pastoris is presented in Finally, a summary including the main items required to address toward the industrial implementation of continuous cultivation with the cell factory P. pastoris is considered an outstanding cell factory alternative for its industrial use in production processes of both metabolites and recombinant proteins. Despite the excellent features of this host, in general, most of the bioprocesses based on fermentation technology are considered still far from achieving the maturity of other industries. In this sense, the present trends toward their optimization are provided by two complementary approaches: Strain development and bioprocess engineering. In order to achieve relevant progresses in the long road to bioprocess optimization it is essential to acquire robust and reliable knowledge of the system used. This knowledge is considered with the objective to be able to design and implement rational developments from any of the both approaches mentioned. In this context, continuous cultivation emerges as an excellent tool to accurately characterize the performance of the cell factory in terms of physiological and production parameters. Furthermore, the steady state conditions achieved during the continuous cultures and the high degree of cell population homogeneity makes this operational mode, an excellent tool toward the development of a wide range of systems biology studies including transcriptomics, proteomics, metabolomics and other cutting edge “-omics” studies.The yeast P. pastoris. It illustrates the different approaches that, based on the results obtained in continuous cultivation (chemostat), allow to achieve rational improvements at both strain and bioprocess engineering level.In P. pastoris has the most relevant challenges with the identification of efficient and methanol-free alternative promoters, the integration of upstream and downstream, reaching short process development times with minimal risks on quality/regulatory requirements through PAT and QbD approaches and, finally, to evaluate the impact of long-term continuous operation on genetic stability and cell physiology.Besides, the current trends of industrial biotechnology is promoting the transition from batch/fed-batch-based processes toward the continuous biomanufacturing due to the numerous advantages that present this operation mode in terms of ensuring a robust and very high product quality as well as, among others, to reduce running costs and minimizing equipment requirements. The potential implementation of biomanufacturing processes with MN-T and XG-O contributed writing the Abstract and the Introduction sections. MN-T and JA wrote the section: Continuous cultivations provide an excellent tool for systems biology research. JM-S and FV wrote the section: Bioprocess characterization with chemostat cultures. XG-O, JM-S, and FV wrote the section: Is continuous cultivation a real alternative for its use in industrial Pichia pastoris processes? XG-O, JM-S, and FV wrote the Conclusions section. All authors read, reviewed, and approved the submitted version.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.


The first discovery of metabolic alterations in cancer cells refers to Otto Warburg’s observations. Cancer metabolism results in alterations in metabolic fluxes that are evident in cancer cells compared with most normal tissue cells. This study applied protein expressions of normal and cancer cells to reconstruct two tissue-specific genome-scale metabolic models. Both models were employed in a tri-level optimization framework to infer oncogenes. Moreover, this study also introduced enzyme pseudo-coding numbers in the gene association expression to avoid performing posterior decision-making that is necessary for the reaction-based method. Colorectal cancer (CRC) was the topic of this case study, and 20 top-ranked oncogenes were determined. Notably, these dysregulated genes were involved in various metabolic subsystems and compartments. We found that the average similarity ratio for each dysregulation is higher than 98%, and the extent of similarity for flux changes is higher than 93%. On the basis of surveys of PubMed and GeneCards, these oncogenes were also investigated in various carcinomas and diseases. Most dysregulated genes connect to  Recent developments in omics data, such as genomics , transcrA better understanding of the genome-scale human metabolic network may enable the identification of disease genes and related pathways, thereby offering better targets for drug development. The release of genome-scale human metabolic networks, such as Recon 1 and 2 ,10, EdinHuman tissues have diverse metabolic functions, which are complex and specialized in different tissues and cell types. Mapping out these tissue-specific metabolisms in GSMMs will advance our understanding of the metabolic basis of various physiological and pathological processes. Nevertheless, for the in-depth study of specific cell phenotypes, the development of tissue- or cell-type-specific metabolic models is necessary. Automated reconstruction algorithms developed to date can be broadly categorized into flux-dependent and pruning methods ,20,21,22The metabolic network reconstruction processes are described in m is the number of metabolites and n is the number of reactions; FBA is a constraint-based modeling approach where the stoichiometry of the underlying biochemical network constrains the solution. The stoichiometric matrix of a typical metabolic system is underdetermined, and infinite solutions are possible. The FBA formulates a metabolic network as a linear programming problem, as shown in Equation , wherein signals ; thus, tFBA assumes that metabolic networks will reach a steady state constrained by the stoichiometry. Even though the stoichiometric constraints lead to an underdetermined system, the bounded solution space of all feasible fluxes can be identified, that is, a large set of alternative flux distributions with an identical objective value. We minimized the squared sum of all internal fluxes for FBA to ensure efficient channeling of all fluxes through all pathways to eliminate numerous flux distributions in Equation . The EucEquation , and ΩInminvf/b∑iWe introduced a tri-level optimization problem (TLOP) to simulate gene screening procedures in a wet lab to infer oncogenes. The flowchart of the in silico experiment is presented in The hierarchical multiple objectives are considered in the outer optimization problem in Equation . The firHere Equation  indicateEquation .The second and third objectives are determining that the similarity ratios of the flux reprogramming are maximized. The similarity ratios provided  to illusThe TLOP framework must decide which genes are selected for mutation. Most optimal strain design methods ,31,32,33The pseudo-enzymes are then used as upper-level variables in Equation  to selecThe candidate genes in Equation  are reprThe TLOP framework in Equation  considerThe metabolic model of Recon 2.2 consists of 5324 species, 7785 reactions, and 1675 associated genes A. Until Total 565 choke-point metabolites (metabolites involve in only one synthesis or degradation reaction) in the normal and cancerous metabolic networks were determined by topology analysis. Due to that different uptakes can lead to a bias of the computational results, same uptakes  for normCAT, G6PD, and CDO1) obtained from the NHDE algorithm are FAP-associated genes. Therefore, the results reveal that the inference optimization framework is a useful computer-aided oncogene detection system.The NHDE algorithm was applied to solve the TLOP, and the top 20 one-hit oncogenes were determined; their average similarity ratios are presented in t-test was used to analyze the RNA-sequencing data, and a p-value less than 0.05  is statistically significant. The result shows that 17 out of the 20 oncogenes are significant as shown in We also applied RNA-sequencing data of the healthy and cancerous colorectal tissues obtained from the TCGA database  on the aCAT, is an essential antioxidant enzyme that catalyzes the decomposition of hydrogen peroxide (H2O2) to water and molecular oxygen. CAT catalyzes three decomposition reactions of H2O2 in the cytoplasm, peroxisome, and mitochondria of the GSMN model. Based on the computation results, we determined that a 78% downregulation of CAT could increase the production of reactive oxygen species (ROS) that exhibits carcinogenic effects [CAT is related to apoptosis, proliferation, and cell growth signaling pathways . However, the reaction-based approach only determined r0249 with average similarity ratio of 0.981 and extent of flux changes of 0.935. The proposed pseudo-enzyme strategy was then applied to regulate both reactions. An average similarity ratio of 0.955 and average flux change ratio of 0.771 were obtained, which were smaller than that obtained using the reaction-based approach. Following the posterior procedures, we observed that the gene HMGCL could obtain an identical result through the reaction-based approach, but it also catalyzed another reaction HMGLx and included the isozyme HMGCLL1. The gene PRODH2 achieved the same result but catalyzed four reactions. Finally, CAT regulated three reactions , but each reaction achieved different results.Furthermore, we applied a reaction-based approach to determine oncogenes in order to illustrate the performance of the proposed pseudo-enzyme strategy. Six top-ranked one-hit reactions are presented in To avoid the solution bias of FBA used in the TLOP problem for evaluation of flux alterations, flux variability analysis (FVA) was applied to compute the minimum and maximum values of each metabolite for the normal model and the mutants. The flux ranges were compared between mutant and normal models, and each flux difference between mutant and normal models was classified into seven categories according to the definition presented in This study introduced a tri-level optimization framework that incorporated protein expressions of normal and tumor tissues for inferring oncogenes. The protein expression data or RAN-sequencing data, input of the optimization framework, was acquired from public databases such as HPA or TCGA. To enable provision of personalized medical treatment, the input of the optimization framework can be replaced with a patient’s protein expressio data. The proposed TLOP is an NP-hard problem, and no commercial software programs could be used to solve the problem. The NHDE algorithm with hierarchical objectives was applied to solve the problem. Moreover, this study introduced enzyme pseudo-coding numbers for expressing gene association to avoid the performing posterior decision-making that is necessary for the reaction-based method. CRC was applied as the case study, and the NHDE algorithm inferred 20 top-ranked oncogenes that cause various carcinomas and diseases based on surveys of PubMed and GeneCards. Notably, the evidence illustrated that the inference optimization framework enables oncogene prediction.


E. coli that exchange molecules with their environment, express the genes required for chemotaxis, swim, grow, and divide. This model is developed incrementally, highlighting cross-compartment mechanisms that link E. coli to its environment, with models for: (1) metabolism and transport, with transport moving nutrients across the membrane boundary and metabolism converting them to useful metabolites, (2) transcription, translation, complexation, and degradation, with stochastic mechanisms that read real gene sequence data and consume base pairs and ATP to make proteins and complexes, and (3) the activity of flagella and chemoreceptors, which together support navigation in the environment.The degree to which we can understand the multi-scale organization of cellular life is tied to how well our models can represent this organization and the processes that drive its evolution. This paper uses Vivarium—an engine for composing heterogeneous computational biology models into integrated, multi-scale simulations. Vivarium’s approach is demonstrated by combining several sub-models of biophysical processes into a model of chemotactic  The fundamental unit of life—the cell—is a compartment of active molecular processes that operate together as an integrated whole. When we look up close, we see dynamics governed by fluxes of matter and energy that drive molecular reactions, reconfigurations, and transport. When we take a step back, we see the cell is embedded in, and interacts with, an environment filled with other cells, chemical gradients, and physical barriers. Cellular life is fundamentally multi-scale—the physiology and behavior observed at the cellular scale (of micrometer length and second/minute duration) are driven by events occurring at spatio-temporal scales that are both greater and smaller. This paper focuses on multi-scaling of bacterial cells —the multTo confront the complexity of cellular biology, we can encode molecular mechanisms into integrated computational models and simulate them to observe how the hypothesized mechanisms operate as a whole. Where the simulation output matches experimental observations, there might be truth to the proposed mechanisms; where the output diverges, we know something is wrong. Cellular phenotypes emerge through diverse mechanisms operating both internally and externally to the cell—to capture this emergence in realistic detail requires a multi-scale modeling approach that decomposes cellular processes into independent sub-systems, defines them with the most appropriate computational representations, and integrates them into unified and executable models.E. coli is an ideal target for multi-scale cell modeling. These minimal organisms—rod-shaped bacteria about 2 microns long—have undergone more scientific scrutiny than any other organism, being the model organism used to discover the genetic code, DNA replication, and genetic regulation, and they are now a cornerstone of modern synthetic biology. This is why, as early as 1973, Francis Crick and Sydney Brenner talked about coordinating a world-wide effort toward “the complete solution of E coli” [E. coli has also been a model organism for the molecular biology of behavior, with every aspect of its chemotaxis—E. coli’s primary mode of navigation—as the focus of intense scientific investigation [ E coli” ,6,7,8,9.tigation . Analysitigation ,12, the tigation , and tratigation . Howevertigation —this is E. coli chemotaxis has, for the most part, been studied in isolated subsystems, with mechanisms proposed for practically every active molecular process. There are models published on the activity of chemoreceptors [E. coli chemotaxis is a highly cross-modal phenotype with many integrated subsystems that coordinate their activities. As a consequence, E. coli can achieve a diverse range of chemotactic behavior as it moves between different physiological states and environments [eceptors , signal eceptors ,18, and eceptors , as welleceptors ,21. In tronments ,23. As aVivarium is a novel simulation engine that addresses computational biology’s dual challenges of model reuse and multi-scale integration by explicitly separating the interface that connects models from the frameworks that implement them. This contrasts with standardized modeling approaches, such as the Systems Biology Markup Language (SMBL), which support reuse through a standardized specification of models . ExistinE. coli chemotaxis. E. coli in three subsections with the progressive integration of new modules:  multi-scale representation of a compartment hierarchy with modular processes, (2) multi-scale simulation with multiple timescales and distributed computation, and (3) multi-scale development of models with modules that can be reused and recombined into increasingly complex computational experiments. This paper’s focus is the application of Vivarium to modeling bigraph  of embedded compartments, based on the writings of Robin Milner [topology is a bipartite graph of processes that connect to stores  a next_update method, which takes the current state and a time-step, runs the model and returns an update; finally, (4) a composite of multiple processes declares a topology that defines how processes are wired together through shared stores. Composites can also accept parameters, which are passed to their processes’ constructors to override the defaults.Any model that can take a state and transform it for an interval of time can be made into a process by implementing a process interface. The interface is designed to be as simple as possible—it requires the following protocol: (1) A constructor that accepts a list of parameters that configure the model—this overrides default parameter values and allows easy access to parameters by users or by learning algorithms; (2) a experiment. A basic simulation cycle has processes view their declared states, run their mechanisms for a set time, and return an update that modifies the states  Custom modules specific to a given biophysical system. These are the easiest way to adapt models from prior publications, and can be used for their single function. This paper demonstrates these with the chemoreceptor and flagella processes. (2) Wrapper modules built around external modeling libraries—if other platforms are available that can be imported to accomplish the modeling task, they should be reused. This paper demonstrates such processes with pymunk  for the Further modularity comes from the separation of functions in different software libraries. The Vivarium engine is available as its own library, which is required for implementing the process interface and running a simulation. Individual modeling projects can release their processes, composites, and data in separate libraries. This streamlines model development, allowing users to (1) write their own processes and composites, (2) import libraries from different projects to access their processes, reconfigure them, and combine them to quickly prototype new models, and to (3) make incremental changes  and iterate on model designs that build upon previous work. Finally, Vivarium is released under the MIT license, permitting open development and reuse. By open-sourcing the software, future scientists can build upon existing models by adding new processes, making new predictions, and testing against new data.E. coli can live in a variety of environments of different molecular compositions, which drive patterns of growth, behavior, and evolution. Their small size, of micron scale, exposes them to very different physics from our own. The low ratio of inertial to viscous forces (a low Reynolds number) overwhelms any inertia they might carry [ht carry . They armulti-body physics and diffusion—these operate on the states of agent bodies and molecular fields  becomes disinhibited and begins to express LacY protein. With sufficient LacY in the membrane, lactose is taken up, the metabolism shifts to lactose metabolism, and growth resumes.Consider glucose–lactose diauxie as an example of this dynamic process ,40. WhenMetabolism: We begin with a dynamic flux balance analysis (dFBA) model of metabolism. Flux balance analysis (FBA) is an optimization-based approach, which takes network reconstructions of biochemical systems and applies linear programming to determine flux distributions in the absence of kinetic parameters. It assumes a steady state, with fluxes across a stoichiometric network of reactions all balanced for each individual molecular species. Constraints are imposed on individual fluxes, setting their upper and lower limits. An objective function is then optimized; the model in this paper optimizes the production of biomass based on known composition of metabolic end-products [products . FBA is products  that caniAF1260b, which comes from the BiGG model database [E. coli strains, Salmonella, and even some human cells. iAF1260b includes 2382 reactions controlled by 1261 genes, which transform 166 metabolites. iAF1260b was selected because it includes the relevant transport reactions and generates the four nucleotides and twenty amino acids required for the gene expression processes in The metabolism process is configurable and can run any properly formatted metabolic network. The chosen network is a genome-scale metabolic model called database . The metWhen a metabolism process is placed in a compartment and run on its own, it takes up metabolites from the environment and grows its internal pools of metabolites, exponentially increasing in mass and reproducing the expected 40 minute doubling time in minimal glucose media a. If metTransport: If we take the perspective of an individual cell in a colony, the influx of nutrients can no longer be explained by the optimization of biomass production. External molecules are brought into the cells by transporters embedded in their membranes, and it takes an optimized composition of transporters to maximize influx—this composition is under complex mechanistic control involving gene regulation networks and competition with other proteins. Glucose is transported into the cell by two primary transport systems [ systems ,44: (1) The transport process uses a generalized form of the Michaelis–Menten kinetic rate laws called convenience kinetics . The minGene expression: To demonstrate the dynamics of kinetic transport, we need to express transporters and have their state control transport flux. A minimal, stochastic ordinary differential equation (ODE) process for gene expression is used for LacY transporters, which is informed by the schematic of the models and parameters described in [ribed in ,46. Thisv), with transport setting the dynamic uptake rates, and metabolism using them as flux constraints on the FBA problem. This works because transport reactions operate on the boundary of the metabolic network, and metabolism is limited to use what is supplied by these flux constraints. Transport and gene expression are coupled through a protein store (p), which holds the transporter proteins, and metabolite store (m), which holds the internal metabolite pools that are built up by metabolism and regulate gene expression. As internal metabolite pools change, this triggers changes to the expression of transporters, which influences the transport rates and, hence, the flux constraints on metabolism.The integrated model can be placed in the environment and run—it grows and divides, with each daughter cell having its own transport kinetics, metabolism, and gene regulation. This results in, for the first time to our knowledge, genome-scale models of colonies that are implemented on an individual cell basis, with heterogeneous cells distributed in a spatial environment—each carrying out its own FBA instance with dynamic flux constraints. This is already a fairly large model compared to a standard agent-based model.The model demonstrates the emergence of glucose/lactose diauxic shifts. We now replace the ODE-based gene expression process of the previous subsection with more detailed, configurable, and stochastic processes for transcription, translation, degradation, and complexation. These processes operate on gene and protein sequence data, transcription unit structure, binding propensities, and other parameters to express mRNA, proteins, and protein complexes. They use a Gillespie algorithm —a discreAfter describing the four processes, they are integrated with the metabolism and transport of the previous subsection and configured to implement a detailed model of flagella expression; in the following section, these flagella will be given a function that enables them to generate forces in the environment.Transcription: E. coli’s genome is organized in operons, in which multiple genes are expressed as transcription units that are transcribed together as mRNA. The synthesis of a transcription unit starts at a promoter sequence in the chromosome, where RNA polymerase (RNAP) binds to begin transcription, and ends at a terminator, where the RNAP unbinds. The RNAP uses the sequence of nucleotides in an operon as a template to synthesize a matching mRNA transcript, itself a sequence of nucleotides. The energy cost of transcription comes from ATP hydrolysis, with one ATP for each nucleotide elongation.The transcription process used here can be configured with any chromosome data that include gene sequence data as well as promoters’ and terminators’ locations. The process operates on the state of RNAPs, nucleotides, ATP, and mRNA. The binding of RNAP is implemented by the Gillespie algorithm, with affinities determined by transcription factors for each promoter. Each promoter has a number of binding sites that can be occupied by a number of different transcription factors . The affinity of the RNAP to bind to a given promoter is modulated by the concentration of transcription factors, which either activate or repress that promoter. The higher these thresholds, the more concentration is required to turn it from an empty state to an occupied state, providing a means to gradually introduce components as determined by changing concentrations of the transcription factor. Once bound to a promoter, the RNAP occludes binding of additional RNAPs—it elongates at a rate of 50 base pairs per second and frees up the promoter region when at a distance of 30 base pairs—which allows a transcription unit to be occupied by many RNAPs at different locations.Translation: The translation process operates on the available mRNAs, amino acids, ATP, and free ribosomes; this process does not include tRNA charging. A ribosome binds to an mRNA and uses it as a template for polymerizing a sequence of amino acids at a rate of 22 amino acids per second, with two ATPs used per amino acid for ATP hydrolysis. The same mRNA can be translated by many ribosomes at once, given a minimum occlusion distance of 50 base pairs between them. Upon termination, the protein and ribosome are released into the appropriate stores, and their counts increased. Each transcript, as a potential operon containing the code for many separate proteins, has a series of separate binding affinities for the gene products it encodes. The affinities of these binding reactions are parameters that can be supplied to the model, and can be targeted by parameter selection algorithms.Complexation: Most protein function comes from the activity of complexes, which are composed of many individual protein monomers. These complexes can be constructed in steps, with intermediate complexes combining to form larger complexes. The stoichiometry of a complexation network describes how monomers combine into complexes, and sub-complexes into larger complexes. The complexation process uses the Gillespie algorithm to determine which monomers or complexes combine and when. The propensities for these reactions are parameters that can be supplied for this model; since these values have not been reported, we used the order-of-magnitude estimate of Degradation: The degradation process uses basic enzyme kinetics (here provided by the enzyme endoRNAse) to reclaim the nucleotide bases from mRNA at a steady rate, with a cost of one ATP per nucleotide degraded. These transcripts are removed from the cytoplasm, and the bases that compose them are returned to the pool of available nucleotides. Proteins are not degraded in the model due to their much longer degradation time in real cells—the loss of protein concentration is due to dilution from growth and division, and this is the loss that has to be countered by ongoing gene expression.The gene expression processes can be configured by providing an expression network that includes all of the parameters described above—gene sequences for the operons, promoter and terminator locations, transcription factors for each promoter with threshold values for RNAP binding, and complexation reaction stoichiometry. To demonstrate the integrated function of these processes, we curated a flagellar expression network from the EcoCyc database , shown iE. coli’s gene expression machinery has been experimentally demonstrated to perform “just-in-time” expression, with proteins synthesized in roughly the order they are required [Flagella are large protein complexes that require the coordinated expression of many individual monomers. To coordinate the expression of these monomers, required , and proThe key to this expression pattern is a multi-gene feedback loop in the network with two transcription factors—FlhDC and FliA—that get expressed and then take over the regulation of additional required genes. The components’ progressive expression is achieved by an increasing sensitivity to the concentration of FlhDC and an inverse sensitivity to FliA, which is itself regulated by FlhDC. When both are present, FlgM binds FliA and prevents its interaction with RNAP, thus inhibiting FliA-dependent transcription. When the flagellum’s basal body is assembled, FlgM is exported from the cell, initiating the next stage with FliA-dependent transcription. This pattern of transcription activation is configured using a binary sum logic gate blueprint and activation coefficients .E. coli [E. coli function. The model shown here does not express the full E. coli genome, so it is initialized with reduced counts of RNAPs and ribosomes to keep flagella from being over-expressed by an entire cell’s worth of polymerases. Based on an assumption of even distribution based on final protein mass, an average of four flagella were estimated to require 20 ribosomes and 10 RNAPs—these polymerases are themselves not expressed in the model, so they are kept constant for all flagellum-expressing cells and reset upon division.Next, polymerases need to be initialized so that they can express the provided flagella genes. There are about 10,000 RNAPs and 20,000 ribosomes in a single E. coli , which iAs shown in E. coli flagellum is a reversible rotary motor that is embedded in the cell membrane and generates thrust in the environment [An ironment . It is pChemotaxis takes this baseline motility and alters it for directed navigation. The key to navigation is chemoreceptors, which sense changing concentrations in the environment and trigger signal cascades that alter the state of the flagellar motor switch, and thus influence the flagellar rotation direction. This elongates the time of runs when moving up an attractant gradient or down a repellent gradient, generating a biased random walk that brings the cell toward favorable and away from adverse conditions.E. coli chemoreceptors—Tar and Tsr for amino acids and other stimuli, and Trg, Tap, and Aer more specifically for sugars, dipeptides, and redox potential. The cell’s chemoreceptors cluster together at its poles—typically one or two clusters in an entire cell. A cluster activates and deactivates together as a single entity; this amplifies detected signals and increases sensitivity [There are several classes of sitivity . ChemoreSignal transduction between chemoreceptors and flagellar motors is mediated by a well-known protein interaction network . CheA isThis subsection takes the flagella expressed in the previous subsection and adds them to the cell as individual sub-compartments with rotational states. Their combined thrust is applied to the agent bodies in the environment, introducing motility. A proton-motive force is integrated to drive their activity, and a chemoreceptor cluster is integrated to sense the environment, trigger signals that control the flagellar rotational states, and generate directed chemotaxis up an attractant gradient.Flagellar activity: Individual flagella synthesized by the gene expression processes are added to the compartment hierarchy as sub-compartments of a cell. The rotational states of each flagellum are modeled as a stochastic bistable system in which the transition rates between CCW and CW are functions of the phosphorylated protein CheY (CheY-P) [(CheY-P) —the resu(CheY-P) .agents’ boundary store , with energy values based on ligand concentration and methylation levels. This results in four energy levels: on without ligand, on with ligand, off without ligand, and off with ligand. Sensory adaptation comes from the methylation of chemoreceptors, which alters the free-energy offset and transition rate to favor the on state; attractant ligand binding favors the off state. Adaptation can be seen in The activity of a chemoreceptor cluster is modeled using a Monod–Wyman–Changeux (MWC) model from  and is sE. coli are well suited to navigate up exponential gradients [radients .The resulting thrust and torque are saved in a boundary store and are read by the environment’s multi-body physics process. These motile forces are applied directly to the agents’ bodies along their outer boundary, which pushes them in the environment. A “run” applies the force at a normal angle to the agent’s back end, and a “tumble” applies the force at a random angle. This combines with forces from the environment, such as the viscous force, which dampens inertia, and jitter force, which applies random impulses that randomize the cell’s orientation. The resulting behavior, shown in 4.2 μm/s ), an ave4.2 μm/s .An insight coming from this integration is that the activities of chemoreceptors, flagella, and the environment are more closely coupled than many internal processes need to be to each other. The timescale for chemoreceptors and flagella to achieve successful chemotaxis is a maximum of 0.01 s—longer timescales make the cell miss important gradient information. Additionally, this required the timescale of the environmental processes to also be set at 0.01 s so that they could apply the generated forces and update the cells’ external ligand concentration at a rate that maintains the cell–environment coupling. This can be compared to internal coupling between metabolism and gene expression, which were safely set at E. coli can move at a velocity 10 body lengths per second. Because of this, significant additional work will be required to analyze the consequences of the described model.The simulation time required to approximate growth is in the timescale of hours—six orders of magnitude higher than the required timescale for chemotaxis. The chemoreceptors and flagellar processes could have achieved this, but in order to have the transport/metabolism system respond to environmental changes, they too would need to be brought to a comparable timescale—at a huge computational cost. Furthermore, the spatial scales of the environment required for simulations of growth are enormous relative to the cellular scale because E. coli cell model with a rich internal state and dynamics, which can grow into colonies of heterogeneous cells and move around within a dynamic, spatial environment. The scope of this model is large, and we only scratched the surface of the phenomena implied by the full set of integrated processes. Metabolism, transport, gene expression, flagellar activity, and chemoreception have many reported cross-dependencies that could be more carefully evaluated with a model such as the one described here. Some interesting examples include: (1) Metabolism and chemoreception are linked by the Aer chemoreceptor, which detects redox changes and is thought to respond to the metabolic state of the cell rather than any particular ligand [This paper took us through a quick tour of the multi-scale development of a chemotactic r ligand . (2) Trar ligand ,64. (3) r ligand . More brr ligand . The oppThis multi-scale model is the first application of the interface provided by Vivarium. Building a complete whole-cell model will require a larger project that harnesses the collective effort of many scientists. Each individual cell type has unique mechanisms that have to be curated, with parameters that need to be reconciled, and with distinct phenotypes that have to be validated. For such projects to reach their full potential, we need to refine our modeling frameworks and collaborative coding practices. Vivarium provides an interface for these efforts—a plug-in system that simplifies the integration of alternate process models into complex hybrid, multi-scale simulations. This will lower the barrier to contribution so that new users can encode new processes and plug them into existing models. With good computational design and the assistance of learning algorithms, such a framework will support incremental progress, which, through collaborative effort, can build up an increasingly accurate representation of cells and will one day make them “whole”.


Mass spectrometry coupled with chromatography separation techniques provides a powerful platform for untargeted metabolomics. Determining the chemical identities of detected compounds however remains a major challenge. Here, we present a novel computational workflow, termed extended metabolic model filtering (EMMF), that aims to engineer a candidate set, a listing of putative chemical identities to be used during annotation, through an extended metabolic model (EMM). An EMM includes not only canonical substrates and products of enzymes already cataloged in a database through a reference metabolic model, but also metabolites that can form due to substrate promiscuity. EMMF aims to strike a balance between discovering previously uncharacterized metabolites and the computational burden of annotation. EMMF was applied to untargeted LC–MS data collected from cultures of Chinese hamster ovary (CHO) cells and murine cecal microbiota. EMM metabolites matched, on average, to 23.92% of measured masses, providing a > 7-fold increase in the candidate set size when compared to a reference metabolic model. Many metabolites suggested by EMMF are not catalogued in PubChem. For the CHO cell, we experimentally confirmed the presence of 4-hydroxyphenyllactate, a metabolite predicted by EMMF that has not been previously documented as part of the CHO cell metabolic model. Metabolomics is an expanding field of research that involves the characterization of small molecules in cells, tissues, and other biological systems. Metabolites are direct products of enzymatic reactions that provide a functional readout of cellular state ,2. Compam/z) and relative intensities for the parent compound and its fragments. Mass spectrometry (MS) techniques coupled with liquid or gas chromatography separation techniques, LC–MS or GC–MS, respectively, have become standard analytical platforms for untargeted metabolomics . The LC 20H22N2O4) [Interpretation of metabolomics data is facilitated by assigning putative chemical identities to the features. Relying on the mass of the ionized parent compound for annotation is problematic, as a particular mass may be associated with many chemical formulas  . The speH22N2O4)  and NISTH22N2O4) , cover oH22N2O4) ,13,14,15In recent years, computational tools have become available to recommend a ranked list of chemical structures that best explain a spectral signature. This ranked list is selected amongst a pre-specified candidate set, a listing of metabolites with formula weights that match the measured masses of parent compounds in the sample. Earlier tools used rule-based approaches to generate fragmentation patterns of candidate metabolites, e.g., . SubsequWe propose a novel annotation workflow for untargeted metabolomics that addresses current limitations regarding spectral database coverage and computational cost of annotation. The goal of this workflow is to engineer a candidate set that can be used for putative identification using database searches or other annotation tools. The key step is to filter the detected masses through a metabolic model that we call an extended metabolic model (EMM). An EMM includes not only the defined substrates and products of enzymes cataloged for the organism(s) associated with the sample, but also additional metabolites reflecting the potential for promiscuous enzymatic activities. The central premise is that an EMM can be used to define a candidate set that is more comprehensive than a standard genome-scale metabolic model, but still enforces a degree of specificity for the system of interest. Our workflow, termed EMMF (EMM-based filtering), broadens the search space for annotation beyond compounds in a reference metabolic model assembled from catalog definitions of enzymatic reactions, thus enhancing discovery while avoiding the computational cost of analyzing every compound in large chemical structural databases. We demonstrated the utility of EMMF on untargeted LC–MS data from cultures of Chinese hamster ovary (CHO) cells and bacterial isolates from murine cecum. We compared the candidate sets from reference metabolic models, EMMs, and a large structural database (PubChem). EMMF suggested biologically relevant chemical identities for almost a quarter of measured features, providing a > 7-fold increase in the candidate set size when compared to using a reference metabolic model. Importantly, EMMF allowed the discovery of novel relevant putative identities that are not currently catalogued in PubChem. Targeted LC–MS experiments confirmed the presence of a predicted CHO cell metabolite that had previously not been cataloged as a Chinese hamster enzyme substrate or product.To describe and evaluate the EMMF workflow, we presented it alongside two other annotation workflows . In descSelecting the candidate set from a large database can potentially enhance annotation by increasing the number of measured masses that have a match in the candidate set B. This dEscherichia coli [Our novel annotation workflow C, EMMF, hia coli . From rehia coli , PROXIMAhia coli  of this m/z), and a set of associated product ion (fragment) masses and their relative intensities, that is, the MS/MS signature. The reference metabolic models for CHO cell and murine cecal microbiota were derived from genomes in the KEGG database. For the CHO cell, we obtained lists of metabolites and reactions cataloged in KEGG that are associated with the organism code cge. The cecal culture is a consortium of many species. We assembled a community-level model based on the taxonomic groups detected in the culture using a previously described procedure [We compared the annotation workflows in rocedure . The numThe EMM for each sample was generated using biotransformation operators derived from each model  matching to a larger number of measured masses, and (b) suggesting a larger set of putative chemical identities. Using these two metrics, we compared the size of the “biologically relevant candidate sets” in the model- and EMMF-based workflows and compared that with the size of the candidate set using PubChem . A smallThe quality of the EMMF candidates with known PubChem or KEGG identities  were evaAs the KEGG database is largely a small subset of PubChem, using all of the KEGG compounds as a candidate set for annotation may not be as computationally prohibitive as using PubChem. Further, using only a biological database such as KEGG for annotation guarantees biological relevance of candidate metabolites. A question that often arises regards the benefits of utilizing a general database for annotation compared to when employing a database that mostly comprises biomolecules. Using the EMMF workflow as a reference and restricting derivatives to those with chemical identities in PubChem, we were able to explore and quantify the benefits. Specifically, we utilized the EMMF workflow to identify candidate sets for our datasets. We then compared the EMM candidates against those obtained using the database-based workflows using KEGG and PubChem . Many caThe table reports the number of EMMF derivatives, percentage of EMMF derivatives that had non-zero CFM-ID scores, and the average score. These results are reported for EMMF candidates that had a matching identity in the KEGG and for PubChem databases.To generate the candidate set as the input to in silico annotation analysis in database-based workflow B, we ide6. When using KEGG as a database for annotation, CFM-ID ranked six of the eight derivatives as the highest ranked candidates, whereas two of the derivatives were not in KEGG  and 4-hydroxyphenyllactate:NADP+ oxidoreductase (R03338) are both catalyzed by D-hydrogenase  and hydroxyphenylpyruvate reductase (E.C. 1.1.1.237). Reaction 3-(4-hydroxyphenyl)lactate hydro-lyase (4-coumarate-forming) (Reaction R08766 in the KEGG database) is associated with an enzyme that has yet to be characterized (E.C. 4.2.1.-). It is unlikely that the source of 4-hydroxyphenyllactate in our sample was exogenous, as our cell culture medium was chemically defined and did not include this metabolite. Further evidence that the metabolite is endogenously derived was provided by a recently updated genome-scale metabolic model (GEM) for the CHO cell in the BiGG database . This GEWe further analyzed the spectral signatures comprising our experimental validation set using the Global Natural Products Social Molecular Networking (GNPS) spectral library . There wWe further explored the annotation of the experimental validation set using the Human Metabolome Database (HMDB) . There wWe report the number of PubChem compounds that matched in mass to the measured spectra , group FOur EMMF workflow addressed the challenge of creating an annotation candidate set that is enzymatically relevant to the sample under study and that includes metabolites beyond what is already catalogued in reference metabolic models. One important contribution of the work is conceptually separating the engineering of the candidate set from annotation, as EMMF creates a biologically relevant candidate set that can be utilized for putative identification. Prior works provided limited engineering of candidate sets. These works focused on exclusions of particular elements, substructures, or compounds , or on iResults from comparing the three workflows emphasized the need for optimizing the engineering of candidate sets. We demonstrated for our two case studies that using candidate sets from large databases is computationally prohibitive, as others have also noted . We alsoEMMF relies on a reference metabolic model for annotation. Other recent studies have also exploited the metabolic network to enhance annotation. One method, iMet, suggests that neighboring metabolites within a metabolic network have similar MS/MS spectra and trains a classifier to predict if two spectra belong to neighboring metabolites . The claPROXIMAL to rank predicted derivatives on the basis of enzyme designations as generalists or specialists [http://hassounlab.cs.tufts.edu/proximal. This work did not evaluate the quality of candidates that did not have a match in PubChem or KEGG. A thorough evaluation of these candidates may have yielded biologically relevant matches. This work presents the first in vivo experimental evidence for a computationally predicted metabolite derived through promiscuous action of an enzyme. Using a chemical standard, we confirmed the presence of 4-hydroxyphenyllactate in a CHO cell culture, even though there is no documented gene associated with the CHO cell metabolism that can catalyze the reaction with 4-hydroxyphenyllactate as product. We were, however, able to confirm only one out of the eight predicted metabolites. This could be due to inaccuracies in the rankings by the annotation tools. Analyses of the tested compounds using the GNPS spectra database, HMDB, CFM-ID, and MetFrag showed significant variations in the annotation results. The low confirmation rate can also be due to the assumption that all enzymes are promiscuous. As an enhancement, we are currently investigating methods to improve cialists  and partcialists . The curIt is possible to utilize other tools or databases to identify metabolites that could occur due to enzyme promiscuity. For example, BioTransfomer utilizes a knowledgebase (MetXBioDB) and a reasoning engine to predict enzyme products . MetXBioDespite limitations due to the underlying potentially incomplete metabolic models and to the accuracy of current annotation tools, EMMF demonstrates utility in creating an expanded, biologically relevant candidate set and in utilizing it to enhance annotation. This utility is demonstrated via the discovery of 4-hydroxyphenylpyruvate and in high annotation scores using CFM-ID for some EMMF derivatives. Importantly, EMMF promises to offer annotation opportunities beyond those possible with metabolic models without the high computational cost of searching large structural databases that contain many non-biological compounds.


Escherichia coli Nissle 1917 (EcN) is an intestinal probiotic that is effective for the treatment of intestinal disorders, such as inflammatory bowel disease and ulcerative colitis. EcN is a representative Gram-negative probiotic in biomedical research and is an intensively studied probiotic. However, to date, its genome-wide metabolic network model has not been developed. Here, we developed a comprehensive and highly curated EcN metabolic model, referred to as iDK1463, based on genome comparison and phenome analysis. The model was improved and validated by comparing the simulation results with experimental results from phenotype microarray tests. iDK1463 comprises 1463 genes, 1313 unique metabolites, and 2984 metabolic reactions. Phenome data of EcN were compared with those of Escherichia coli intestinal commensal K-12 MG1655. iDK1463 was simulated to identify the genetic determinants responsible for the observed phenotypic differences between EcN and K-12. Further, the model was simulated for gene essentiality analysis and utilization of nutrient sources under anaerobic growth conditions. These analyses provided insights into the metabolic mechanisms by which EcN colonizes and persists in the gut. iDK1463 will contribute to the system-level understanding of the functional capacity of gut microbes and their interactions with microbiota and human hosts, as well as the development of live microbial therapeutics. Escherichia coli strain Nissle 1917 (EcN) is among the most studied probiotic bacteria [The nonpathogenic bacteria . Over 10bacteria . EcN is Pseudomonas aeruginosa in Caenorhabditis elegans and mouse infection models [EcN has been genetically engineered to diagnose, prevent, and treat diseases owing to its facile genetics and biosafety profile . Recombin models . EcN cann models ,7,8. EcNn models  and hypen models . With adn models .E. coli strains [A genome-scale metabolic network model is a computational framework that allows the prediction of metabolic flux values for a whole set of metabolic reactions using optimization techniques, such as flux balance analysis (FBA) ,12. Meta strains . In part strains  and BL21 strains . Althoug strains , the proIn this study, we reconstructed a comprehensive and highly curated metabolic network model of EcN based on comparative genome and phenome analyses. A phenotype microarray (PM) test was performed to validate and update the metabolic model, and the reconstruction was extensively manually curated. Combined with results from the phenome analysis, metabolic simulation using the model identified genetic determinants of EcN-specific phenotypes. Further, genetic and metabolic factors responsible for intestinal colonization of EcN were identified.E. coli metabolic models that are the most highly curated K-12 MG1655 (model id: iML1515) [E. coli strains, uropathogenic CFT073 (ic_1306) [E. coli K-12 intestinal commensal strain MG1655 were used as the gold standard for the reconstruction of the EcN metabolic model.The EcN genome (RefSeq accession number NZ_CP007799)  is poorlic_1306)  and asymic_1306) . Genome ic_1306)  and iML1The reconstruction was initiated from the homology searches of the genomes of K-12 MG1655 (NC_000913) , CFT073 There were many missing reactions owing to incorrect, missing, or low-quality reactions listed in CFT073 and ABU83972 reconstructions, which were generated through the automatic pipeline . Thus, tN-acetyl-d-galactosamine, D-arabinose, 2-deoxy-d-ribose, L-glutamic acid, β-hydroxy-butyric acid, γ-hydroxy-butyric acid, D-lactitol, L-proline, D-psicose, D-raffinose, L-sorbose, and D-tagatose) and nine nitrogen sources , whereas it did not grow on five carbon sources  and five nitrogen sources  (We used PM to identify a wide range of digestible and inhibitory substrates through growth behavior . To identhosine) .d-glucose-6-phosphate, and cytidine-3′-monophosphate). Both grew on all the cells of PM5 (nutrient supplements), including that of the negative control. EcN grew on the majority of peptide nitrogen sources (PM6–PM8); however, K-12 could not utilize various valine-containing dipeptides owing to the frameshift mutation of ilvG, which encodes the subunit of acetohydroxy acid synthase II responsible for valine resistance [EcN and K-12 grew similarly on phosphorus and sulfur sources (PM4), with the exception of K-12 growth on three phosphorus sources  at any concentration . Of the 240 different inhibitory compounds tested, EcN and K-12 grew in the presence of 237 and 225 compounds with at least one of four concentrations, respectively. Compared to K-12, only EcN grew in wells containing at least one concentration of 15 compounds . EcN did not grow in the wells containing three antibiotics , whereas both K-12 and CFT073 do , and that the GntII system is primarily involved in the catabolism of L-idonic acid in which D-gluconate is an intermediate [Many of the phenotypic differences between EcN and K-12 could be correlated with the genotypic differences. Genome comparison of EcN, K-12, and CFT073 identified the genomic regions of EcN that differed from those of K-12, possibly responsible for the phenotypic differences . The reltabolism  is intaclization   by insertion element [The comparison of gene clusters for O-antigen biosynthesis revealed that EcN has a nonfunctional ase gene E, which  element . Thus, i element   and 95 nitrogen sources (PM3), differences in the simulated and experimental growths were identified in the utilization of 25 carbon and 19 nitrogen sources , which cN-acetyl-d-glucosamine, N-acetyl-β-d-mannosamine, fructose, sorbitol, mannitol, mannose, and glucosamine, and these are carbohydrates transported by the sugar phosphotransferase system (PTS). Among the genes associated with PTS, we found that ptsI encoding PTS enzyme I was frameshifted in the EcN genome (NZ_CP007799). As PtsI activity is essential for E. coli growth on these PTS sugars [ptsI gene, and its DNA sequence was found to be intact and the same as that of E. coli ABU83972. Thus, we added transport reactions for these PTS sugars. We added one transport reaction and two intracellular reactions associated with the sorEMABFDC operon genes associated with L-sorbose metabolism. We added 13 dipeptidase reactions  to break down 13 dipeptides and one dipeptidase reaction  specifically hydrolyzing dipeptides containing a proline residue at the carboxy-terminal end [The incongruences in the simulated and experimental growths were reflected in the modification of the model . False nS sugars , we sequinal end   were observed for the utilization of 12 substrates  as the sole carbon or nitrogen source. To match the predictions and experimental results, six reactions were removed from the draft model, as no relevant genes were retrieved in the MetaCyc  and/or KpotE) was reported to show weak gene expression levels under normal culture conditions [Eight reactions were reported to be active only under anaerobic , was sequenced to be identical to that in the CFT073 genome. The aceF encodes the subunit of pyruvate dehydrogenase (PDH), catalyzing a preparatory step to the tricarboxylic acid (TCA) cycle by converting pyruvate from glycolysis into acetyl-CoA during aerobic respiration, and thus, it should be intact to explain the observed rapid growth of EcN under aerobic conditions.EcN has fully functional pathways for central carbon metabolism. It is also fully functional in the de novo syntheses of all essential nucleotides, amino acids, fatty acids, and LPS. The growth kinetics of EcN using diverse carbon sources was comparable to that of K-12 , demonstAll the entities of the metabolic model, such as metabolic reactions, genes, metabolites, and gene–protein–reaction rules, were manually curated. The final EcN metabolic model, referred to as iDK1463, comprised 1463 genes, 1313 unique metabolites, and 2984 metabolic reactions . The simargI, can, leuB, and purN) that were not predicted to be essential in K-12, and two of them (can and leuB) were experimentally validated. Ornithine carbamoyltransferase is involved in the L-arginine biosynthetic pathway, which is encoded by two genes in K-12 (argF and argI) and one gene in EcN (argI) [can and cynT) and one gene in EcN (can) [leuB and dmlA) and one gene in EcN (leuB) [purN and purT) and one gene in EcN (purN). A single deletion of purN in K-12 did not lead to purine auxotrophy [purN and purT resulted in purine auxotrophy [FBA was performed to predict the reactions essential for cell growth of EcN and K-12 strains under aerobic conditions using glucose as the sole carbon source. A total of 195 genes were predicted to be essential for each EcN and K-12, of which 191 genes were shared. The predictions were compared with 249 essential genes determined by experimental genome-wide gene-knockout screens performed for K-12 under glucose growth conditions . The preN (argI) . CarbonicN (can) . 3-isoprN (leuB) . Phosphoxotrophy .gltA encoding citrate synthase and icd encoding isocitrate dehydrogenase) were predicted to be essential from FBA using iML1515 of K-12 [lpd, gapA, eno, fbaA, pgk, pfkA, and tpiA) represent false positives . False growth prediction can be used to refine the model when more biochemical information is available [Of the 69 metabolic genes involved in the central catabolic pathways , nine were reported to be essential for K-12 aerobically growing on glucose . Of the N-acetylgalactosamine, N-acetylglucosamine, N-acetylneuraminic acid, L-fucose, D-galactose, and D-gluconate) [d-ribose, L-lactic acid, L-sorbose, and AKG), and D-glucose  supported EcN colonization, while N-acetylglucosamine and D-ribose were unnecessary for the intestinal colonization. For FBA under anaerobic conditions, the upper limits of oxygen and each of the carbon sources were set to zero and 20 mmol gDCW−1 h−1, respectively. Fluxes of six reactions that were deactivated for aerobic FBAs   ,52,53, f-glucose . Glyceroytogenes . From thytogenes , five ofAGMT7pp)  were uncnditions .N-acetylglucosamine, N-acetylneuraminic acid, and L-sorbose), and D-glyceraldehyde 3-phosphate . D-gluconate was metabolized to D-glyceraldehyde 3-phosphate and pyruvate almost exclusively via the Entner–Doudoroff (ED) pathway. L-lactic acid and AKG were subsequently converted to pyruvate. The predicted growth yield, ATP production, and NAD+ regeneration according to the carbon sources are summarized in −1) when N-acetylneuraminic acid was used as the sole carbon source, followed by N-acetylglucosamine (0.554) > D-glucose (0.504) ≈ N-acetylgalactosamine (0.5) > D-galactose (0.455) > D-gluconate (0.407) = L-sorbose (0.407) > 2-deoxy-d-ribose (0.258) ≈ L-fucose (0.251) > glycerol (0.210) > AKG (0.14) > L-lactic acid (0.014)  . The utite lyase A, and th+ regeneration by malate dehydrogenase (MDH). Malate was converted to pyruvate as a part of gluconeogenesis. Pyruvate was converted to acetyl-CoA with concomitant generation of NADH by PDH, whereas the catabolism of the other eight carbon sources employed anaerobically induced pyruvate formate lyase (PFL), which catalyzes the non-oxidative cleavage of pyruvate to acetyl-CoA and formate [+ regeneration resulted in the lowest ATP production for FBA using L-lactate as the sole carbon source.The catabolism of AKG and L-lactic acid did not include glycolysis, thereby yielding the lowest growth rate. Intracellular AKG was converted to citrate by running the TCA cycle in reverse, then to oxaloacetate and acetate by citrate lyase of an anaerobic enzyme . Oxaloac formate . IntraceE. coli strains of Nissle and K-12 in the utilization of diverse nutrient substrates were identified  and two carboxylic acids , which were reported to be present in the gastrointestinal tract [E. coli strains grow predominantly in the mucus layer [N-acetyl-d-galactosamine. Furthermore, it could not grow on 2-deoxy-d-ribose, which promotes pathogenic E. coli strains during host colonization because deoxyribose is derived from DNA degradation and is abundant in the intestine [The phenotypic profiles in this study provide insights into the colonizing capabilities of EcN. Nutrient sources supporting cell growth are important for ntestine . EcN canal tract ,53. Commus layer . They caus layer ,62. Of tntestine .E. coli and absent from commensal E. coli and intestinal pathogenic E. coli [E. coli strains is repressed under anaerobic conditions [E. coli [Mycobacterium tuberculosis in human macrophages [While EcN and K-12 showed high similarity in aerobic respiration, they were different in the anaerobic fermentative pathway. Remarkably, EcN has a large gene cluster for L-lactate biosynthesis, anaerobic utilization of AKG, and their two-component regulatory system F. To dat E. coli . As AKG  E. coli  and actinditions , the ana[E. coli . The int[E. coli , and AKG[E. coli . In addirophages . Furtherrophages , and Lldrophages . Our metE. coli to colonize the mouse intestine as it is the primary route for the catabolism of gluconate and other sugar acids which are available in the mucus layer [E. coli MG1655 to colonize in the mouse intestine [The ED pathway is important for us layer . Especiantestine . In metantestine .E. coli strains cannot synthesize methionine from 5-methylthioribose, which is exported into the culture media as waste [E. coli S88 (O45:K1:H7) [Owing to the incomplete MSP, as waste . Interes5:K1:H7)   . MTRu-1-5:K1:H7) ,72.Coxiella burnetii, Chlamydia trachomatis, Legionella pneumophila, and Listeria monocytogenes [The growth of intracellular pathogens relies on host metabolites and depends on the efficient usage of the nutrients at various spatial locations and stages of intracellular growth . The bipytogenes . Little ytogenes , studiesLactobacillus and Bifidobacterium species [In general, the most common probiotics are Gram-positive strains, such as  species . EcN is  species . EcN has species . In the E. coli strains of K-12 MG1655 (NC_000913) [The complete genome sequence of EcN (RefSeq accession number: NZ_CP007799)  was used_000913) , CFT073 _000913) , and ABU_000913) , the proGenes with potential metabolic roles were identified from the list of genes contained in the metabolic models of K-12 MG1655 (model id: iML1515), CFT073 (ic_1306), and ABU83972 (iECABU_c1320), which were retrieved from the BiGG models database , and theE. coli strain Nissle 1917  was provided by Ardeypharm GmbH, . PM tests were performed as previously described [E. coli K-12 MG1655, which were purchased from American Type Culture Collection (ATCC), were retrieved from our previous study [escribed . The PM us study .Cells were grown overnight at 37 °C on a Biolog universal growth (BUG) + B agar plate. Colonies were picked from the agar surface and suspended in inoculating fluid (IF) containing the indicator dye tetrazolium violet. IF-0 media were used for plates PM1 to PM8 and IF-10 for plates PM9 to PM20. Sodium succinate was added with ferric citrate to the inoculation solution of plates PM3 to PM8. All PM plates were inoculated with cell suspensions at 100 μL/well and incubated in an OmniLog incubator  at 37 °C for 48 h. Four independent PM tests were performed using sodium succinate as the sole carbon source for the plates PM3 to PM8. The PM data were analyzed using the R package opm .−1 h−1 of EcN were used as growth-associated maintenance energy (GAM) and non-growth-associated maintenance energy (NGAM), respectively [−1 h−1, respectively, unless otherwise mentioned.FBA was performed using the COBRApy software . The maxectively . The upp−1 h−1. For PM3 simulations, the maximum uptake rates of succinate and a nitrogen source were set to 10 and 5 mmol gDCW−1 h−1, respectively. A substrate was considered to be not utilized if the growth rate was <5% of the growth objective value calculated for the wild-type strain.For simulation of cell growth on PM plates for testing utilization of carbon (PM1 and PM2) and nitrogen (PM3) sources, the composition of defined media used in the PM plates was retrieved from BioCyc , and theTo identify the reactions that are essential for cell growth, the maximum growth rate was simulated when each reaction was removed from the reconstructed metabolic model using COBRApy . An unreptsI and aceF genes, three sets of primers were prepared for each gene divided into three parts (For DNA sequencing of the ee parts . ConsideptsI and aceF of E. coli Nissle 1917 were deposited in GenBank under accession numbers MW239125 and MW239126, respectively.The sequences of