In biotechnology, the emergence of high-throughput technologies challenges the interpretation of large datasets. One way to identify meaningful outcomes impacting process and product attributes from large datasets is using systems biology tools such as metabolic models. However, these tools are still not fully exploited for this purpose in industrial context due to gaps in our knowledge and technical limitations. In this paper, key aspects restraining the routine implementation of these tools are highlighted in three research fields: monitoring, network science and hybrid modeling. Advances in these fields could expand the current state of systems biology applications in biopharmaceutical industry to address existing challenges in bioprocess development and improvement. This shifting paradigm created considerable challenges for the interpretation of large datasets and the generation of meaningful outcomes impacting process and product attributes. Among the numerous computational methods developed in this context, metabolic modeling tools allowed the coherent organization of large datasets into biological networks providing nonintuitive insights on biological systems that in vivo experiments alone cannot provide2. These model-based approaches have been proven to be invaluable at the level of preclinical research. For example, these approaches have been used for the design of new drugs by informing target selection and for the engineering of cells by rewiring metabolism towards the production of a product of interest4.The emergence of high-throughput technologies is elevating the biotechnology field to the era of big data8. However, model-based methods can be used for much more than this. For instance, these methods could also be applied at the industrial level in the field of process design, monitoring and control to lower the experimental effort and increase the process robustness. Indeed, model-based methods would allow a more rational design of bioprocesses but also the objective identification of the variables to monitor and control. Therefore, the routine implementation of these methods can be expected to greatly facilitate the implementation of regulatory requirements related to the Quality by Design paradigm  and Process Analytical Technologies initiative . Numerous modeling methods exist to describe and quantify the reactions occurring in a cell under specific environments, from deterministic kinetic models to stochastic and statistical models. While kinetic modeling has been widely used for small-scale metabolic models, these mechanistic models are for the moment not scalable and suitable for genome-wide approaches due to challenges in acquiring the required kinetic parameters  and computational problems associated with such complex parametric systems 10. On the other hand, while statistical modeling is very useful for the manipulation of large complex datasets, these black-box approaches have limited capacity to generate biologically relevant information for bioprocess improvement. Therefore, the lack of clear workflows to set up specific process models based on genome-wide information have limited the potential scope of computational biology applications in industry.Pharmaceutical companies are already investing substantially in computational approaches to guide drug discovery and cell design. While these important applications are out of the scope of this paper, we refer the interested readers to detailed reviews on the usage of metabolic engineering and synthetic biology in industrial context13, their practical implementation in biopharmaceutical research and development facilities still seems in its infancy. Actually, bioprocess improvement performance is still mainly achieved by semi-empirical media and bioprocess optimization 14. In this paper, gaps in current knowledge and technical limitations restraining the widespread usage of metabolic modeling tools for process development  in biopharmaceutical industry are highlighted. Specifically, research topics in three fields  are identified as key drivers for evolving the current state of some systems biology tools in industrial process engineering applications. Advances in these fields will be invaluable to address existing challenges in bioprocess development and improvement.However, the systems biology field is in continuous development and genome-scale metabolic networks are now available for most of the industrially relevant organisms. Therefore, we should be able to systematically predict cell metabolic behavior under different specific complex media compositions and process conditions. These types of predictions should be used to objectively determine the optimal operating conditions with respect to the desired cell phenotype and related production criteria. Unfortunately, while numerous studies have successfully used genome-scale models to rationally guide the culture process design16.Real-time monitoring is a key issue for effective bioprocess operation. Indeed, one needs first to recognize and analyze cellular physiological state in function of environmental conditions to be further able to adequately manipulate operating conditions. Therefore, real-time monitoring technologies development is a critical aspect of the bioprocess automatization. Previously, only a small number of so-called “process variables” were commonly measured online in bioreactors . The scope of available real-time probes has now been significantly expanded thanks to the recent advances in analytical technologies and the adoption of Process Analytical Technologies initiative17. Unfortunately, some of these experimental measurements cannot currently be achieved online in situ and, doing so, limit our capacity to have a live snapshot of the cell metabolism during the culture process. However, continuous improvements in analytical technologies and the emergence of the single-cell experiments open new perspectives for gaining insights into cell metabolism.In the context of bioprocesses, real-time measurements of the metabolites and cell concentrations in the cell culture medium are key elements to enable the prediction of cell phenotype with respect to operating conditions 19. However, the capacity to systematically extract accurate quantitative data is, for the moment, limited to a handful number of metabolites . We refer the reader to the publications of Li et al.20 and Ryder21 for detailed comparison of the performance and limitations of existing spectroscopic methods. In this context, chemometric modeling has been a state-of-art technique to effectively extract the maximum of information from spectroscopic spectra. It is worth to highlight that spectroscopic sensors can in this sense be embedded in the concept of soft sensor22 as they are used to compensate the lack of specific measurement by reconstructing the missing signals based on available measurements using data analysis models23. This type of hybrid approach will therefore continue to greatly benefit from the flourishing evolution of artificial intelligence tools and certainly allows to overcome the current limitations of spectroscopic monitoring methods24.Spectroscopic methods  have attracted an increasing interest for their potential capacity of simultaneous in situ measurement of nutrients, metabolites and cell concentrations. These techniques have been successfully integrated into real-time pharmaceutical manufacturing to provide a “molecular fingerprint” of samples enabling in-process correction needed to ensure the acquisition of a specific quality product25. These “single-cell approaches” in combination with adequate analytical methods could help unravel the impact of operating conditions on the cellular physiology. Specifically, the new generation of high-throughput-omics technologies allow to envision in a close future a near real-time measurements of cell transcriptome and proteome28. Interestingly, very few examples of interfacing flow cytometry with a bioreactor and its use for effective bioreactor control can be found in the literature29.Parallelly, the rise of online single‐cell probing opens the door for the specific investigation of cell metabolism and bioenergetics at a definition level never captured before12, the complexity of these large networks also hinders their utility in various practical applications. Actually, metabolic networks involving, most of the time, more reactions than metabolites, the associated system presents, generally, a multitude of solutions . Therefore, metabolic network structures are also too complex to be handled for the development of utilitarian tools .A genome-scale metabolic model is a network connecting all metabolic reactions that can occur in a specific organism with their associated metabolites, proteins and genes. While these models have been proven to be powerful tools for in silico simulation of cell metabolism30 or Ataman et al.31 allow an easy-to-implement systematic reduction of genome-scale networks into core models. However, these methods rely on the definition by the user of the part of the model that should be “protected” during the reduction  which is far from easy task. On the other hand, numerous algorithms have been developed to tailor genome-scale metabolic networks by integrating diverse types of -omics data, including transcriptomics, proteomics and metabolomics35. Actually, genome-scale models are used to link genes, enzymes, and metabolism through the use of gene-protein-reactions (GPR). Therefore, the knowledge of GPR is used by researchers to explain the metabolic state of a cell, based on the expression of metabolic genes and/or specific proteins37. The most common “integration algorithms” use gene expression data to recapitulate the metabolism of an organism under a specific condition  by only extracting the subset of active reactions from the genome-scale model. These methods have been proven to be valuable for enhancing the accuracy of model-predicted growth rates and gene essentiality37. However, since currently no quantitative description of the GPR relationship exists in genome-scale metabolic models, the integration of gene expression data requires the use of strong assumptions to link the GPR expression and the metabolic reaction activity, which could lead to an oversimplification of the complex relation existing between fluxes, enzymes and genes38.Different approaches have been proposed for tailoring these metabolic networks based on a priori knowledge and/or available experimental data. For example, algorithms like the one presented in Erdrich et al.39) will always be required to achieve an instantaneous picture of the flux distributions in the cell. A recent research in this field presented a new approach using biochemical thermodynamic constraints  to shape the solution space of potential flux distributions40. The promising results obtained in this study pave a new way towards the understanding of the mechanisms that governs the definition of metabolic pathway usage across organisms and conditions. Further development of these computational methods will be crucial to obtain a holistic and integrated picture of the cell metabolism in function of specific environmental conditions while considering uncertainty associated with experiments, measurements and modeling.Finally, it is important to highlight that these “network tailoring” approaches generally do not completely leverage the problem of system underdetermination related to network complexity. Therefore, the choice of adequate strategy to solve the system  with data-driven methods to describe the unknown parts. This type of models provides an attractive method for modeling biochemical processes as much of the underlying complexity can be lumped in the data-driven part. Therefore, these approaches can be used to effectively establish the link between metabolism and operating conditions in a way that can be used to implement control and optimization strategies.46, the first application of hybrid modeling in systems biology only arose in 201047 and few have been reported ever since53. One reason might be the absence of dedicated software that would allow for a straightforward development of these type of models. Another reason might be the broad interdisciplinarity of competences required to implement these approaches . Finally, there is still several major open questions that need to be investigated to allow a better combined usage of hybrid modeling and metabolic modeling tools54. From the effective generation of sufficiently informative experimental data to the joint parameter identification of the mechanistic and data-driven parts of the model, the assessment of these gaps will be critical for the development of a systematic workflow for integrating knowledge from systems biology into model structures enabling a next-generation technology for the life-sciences and biotechnology sectors, besides others .Process intensification through continuous processing has been adopted for decades in industries such as chemical and petrochemical. In the context of biopharmaceutical production, the move from batch to continuous or semi-continuous mode of production has been slowed down mainly due to the more conservative approach of this industry resulting from the stringent quality/regulatory requirements. Therefore, innovation in biotechnology has become more product- than process-centered57.The proper implementation of continuous manufacturing requires a thorough understanding of the process to achieve and maintain steady-state conditions . Furthermore, the in-process quality should be evaluated and ensured by the real-time analysis of process data. To this end, in-line process monitoring system linked to robust model-based control will be crucial technologies to support this real-time release testing14.Despite their advantages, the potential of metabolic modeling tools is still not fully exploited to address these challenges in bioprocess development and improvement. This is mainly due to the upfront time and effort investment required to implement these approaches and gain in process understanding as critical gaps remain to set these methods up in clear workflows. Empirical process development techniques will continue to play an important role in addressing these challenges. However, systems biology-driven approaches are likely to be more impactful to provide insights into the cellular responses to process conditions changes59. In this context, computational tools will be invaluable to systematically identify system limitations and areas of improvement for production efficiency, as demonstrated in the work of Vilkhovoy et al.60.Finally, the research topics, highlighted in this review, in the field of monitoring, network science and hybrid modeling, will also be cutting-edge technologies to improve new emergent biopharmaceutical production platforms such as cell-free systems. Actually, cell-free protein synthesis has been emerging as a flexible and powerful platform to address challenges in biomanufacturing by overcoming inherent limitations related to the use of living cells. Cell-free systems offer the ability to design metabolic pathways towards the production of desired products but also to synthesize complex proteins with unnatural amino acids and to buildup artificial cells. Despite these promising features, challenges remain such as the proper control of post-translational modifications and the expansion of the genetic code for unnatural amino acids incorporation. To address these challenges, targeted gene editing and addition of adequate exogenous substances to control reaction conditions will be needed to optimally regulate the transcription and translation4. Taken together, these technologies could facilitate the switch to continuous processing in biopharmaceutical industry but also foresee needs for improvements in emergent biomanufacturing platforms such as cell-free systems.We highlighted gaps in our knowledge and technical limitations restraining the effective application of metabolic modeling tools throughout the lifecycle of a biopharmaceutical process. Specifically, advances in real-time monitoring of bioprocesses, biological network modeling and their combination with data-driven approaches will be the key drivers to lower the time and cost associated with the development of new drugsReporting Sum


GSE138379), metabolomics and proteomics (available via ProteomeXchange with identifier PXD015518) data, which we obtained from healthy controls and RD patient fibroblasts incubated with phytol, a precursor of phytanic acid. Our model correctly represents the metabolism of phytanate and displays fibroblast‐specific metabolic functions. Using this model, we investigated the metabolic phenotype of RD at the genome scale, and we studied the effect of phytanate on cell metabolism. We identified 53 metabolites that were predicted to discriminate between healthy and RD patients, several of which with a link to amino acid metabolism. Ultimately, these insights in metabolic changes may provide leads for pathophysiology and therapy.Refsum disease (RD) is an inborn error of metabolism that is characterised by a defect in peroxisomal α‐oxidation of the branched‐chain fatty acid phytanic acid. The disorder presents with late‐onset progressive retinitis pigmentosa and polyneuropathy and can be diagnosed biochemically by elevated levels of phytanate in plasma and tissues of patients. To date, no cure exists for RD, but phytanate levels in patients can be reduced by plasmapheresis and a strict diet. In this study, we reconstructed a fibroblast‐specific genome‐scale model based on the recently published, FAD‐curated model, based on Recon3D reconstruction. We used transcriptomics  is an inborn error of metabolism that is characterised by a defect in peroxisomal α‐oxidation of the branched‐chain fatty acid phytanic acid. In this study, we used various omics obtained from healthy controls and RD patient fibroblasts to reconstruct a fibroblast‐specific genome‐scale model. Using this model, we identified 53 metabolites that discriminated between healthy and RD patients, several of which with a link to amino acid metabolism. They perform both anabolic and catabolic processes, including the α‐ and β‐oxidation of very‐long‐chain fatty acids, dicarboxylic acids and methyl‐branched‐chain fatty acids . FurtherRefsum disease (RD) is a rare inborn error of peroxisomal metabolism with an unknown incidence. It probably remains highly unrecognised since the awareness of inborn errors of metabolism is low among ophthalmologists. RD is caused by biallelic mutations in the gene encoding phytanoyl‐CoA 2‐hydroxylase (PHYH), resulting in defective α‐oxidation of the branched‐chain fatty acid phytanate  . The endRecently, computational models have become valuable tools to study the complex behaviour of metabolic networks. One type of computational models is genome‐scale models of metabolism, which contain all currently known stoichiometric information of metabolic reactions, together with enzyme and pathway annotation . These mIn the last decade, a paradigm shift occurred in the field of inborn errors of metabolism. Today, they are no longer viewed according to the ‘one gene, one disease’ paradigm as proposed more than 100 years ago, but recognised to be complex diseases . Howeverin vivo situation in fibroblasts and demonstrates the physiological effects of a defective α‐oxidation. Ultimately, such insights in metabolic changes may provide leads for pathophysiology and therapy.In this study, we aim to investigate the metabolic phenotype of RD at the genome scale and to study the effect of phytanate on cell metabolism. Cultured fibroblasts contain most metabolic functions present in the human body, and biochemical and functional studies in cultured skin fibroblasts are important tools for the diagnosis of patients with a peroxisomal disorder . TherefoFor this study, we used an updated version of the Recon3D model in which flavoprotein‐related metabolism was curated . This adhttps://github.com/WegrzynAB/Papers).To optimise the model, we added 25 reactions involved in the ω‐oxidation and the subsequent β‐oxidation of phytanate. Furthermore, 17 reactions involved in phytanate metabolism were deleted, because they were duplicates of other reactions in the model. Lastly, we examined the import/export reaction boundaries and blocked the flux of several drug metabolism pathways, such as those of statins, ibuprofen, paracetamol and antibiotics. These pathways were not relevant to this study but could play a role in the model outcome. All changes to the model are summarised in Table n = 6) and RD patient‐derived fibroblasts with a defect in α‐oxidation (n = 5) under standardised conditions, and harvested cells after 96 h to isolate RNA and protein. The cells were either incubated with phytol, a precursor of phytanate, or with the solvent ethanol , published transcriptomics and proteomics data obtained from fibroblasts , deleted reactions [ Recon3D_del], added metabolites [Added_mets], and information about the media constraints [HAM'sF10].Table S2. Fibroblast specific genes.Table S3. Detailed information about the ATP yields for Figs 3 and 4.Table S4. Genes and proteins significantly changed between RD and CTRL fibroblasts.Table S5. Detailed results of the metabolic tasks analysis in the fibroblast model.Table S6. Comparison between the exchanged amino acids in model prediction and in the in vitro experiments (CTRL + phyt group). For a full list of predicted uptake and secretion rates of metabolites see Table S7.Table S7. Detailed comparison at the level of secretion and uptake reactions between healthy and Refsum models forced to take up phytanate.Fig. S1. Additional data on amino acid uptake and secretion rates in the fibroblast CTRL  and RD  cultures exposed to phytol for 96 h (shown as mean ± SD). Rates were calculated based on the fresh medium measurements.Fig. S2. Additional experimental data of (A) amino acids and (B) glucose determinations in the medium of the fibroblast CTRL (n = 6) and RD (n = 5) cells after incubation at indicated time points (shown as mean ± SD). For details, see Fig. 6C. (C) Protein concentrations of cell pellets after incubation at indicated time points. (D) Growth curves of attached cells for the indicated time points (left panel), and statistical analysis of the total area under the curve per cell line after 7 days of incubation (right panel). Data are shown as bar plots.Click here for additional data file.


Step-by-step procedures explain how to characterise and categorise the nutritional input and to successfully apply it to constraint-based metabolic models. In parallel, we illustrate the proposed procedure with a case study of the growth of Escherichia coli in a complex nutritional medium and show that an accurate representation of the medium is crucial for physiological predictions. The proposed framework will assist researchers to expand their existing metabolic models of their microbial systems of interest with detailed representations of the nutritional environment, which allows more accurate and reproducible predictions of microbial metabolic processes.The reconstruction and application of genome-scale metabolic network models is a central topic in the field of systems biology with numerous applications in biotechnology, ecology, and medicine. However, there is no agreed upon standard for the definition of the nutritional environment for these models. The objective of this article is to provide a guideline and a clear paradigm on how to translate nutritional information into an  A key approach in Systems Biology is the elucidation of the metabolic potential of an organism and the transformation of this information into networks of metabolic reactions. Such networks are called genome-scale metabolic models and can be used in computer simulations of metabolism of cells and cellular communities. For instance, models for various eukaryotic and prokaryotic organisms and cell types have been reconstructed (e.g. as reviewed in ) and appBy mathematical approaches, such as the application of Flux Balance Analysis (FBA), we are able to optimise a predefined objective function, which is based on nutritional constraints that represent the chemical environment of cells . A widelThus, it is a common issue that there is no unanimous approach on how to model the nutritional input in constraint-based modelling. For instance, it is usually not well-defined how to proceed in representing various compounds  in the models and how to proceed in calculating their inflow values (i.e. constraints) starting from their concentrations. Researchers often unintentionally fail to provide information on their exact methodology how nutritional constraints were calculated and in numerous instances arbitrary numbers are used for nutritional constraints . As a rein-silico) that contains, besides water and NaCl, the chemically complex components tryptone and yeast extract which provide a broad range of nutrients for microbial growth. Based on the example we emphasise the importance for rationally designed nutritional constraints in FBA models in order to obtain meaningful physiological predictions.Here, we provide clear guidelines on how to design a realistic computational representation of the nutritional input for metabolic models for environments, which are initially only defined on the level of chemically complex components such as yeast extract, tryptone or milk powder. Therefore, a step-by-step procedure is proposed that incorporates publicly available databases and resources and facilitates reproducible calculations from chemically complex components to the quantities of specific molecules that are presumably available for uptake by the cell types of interest. The procedure is illustrated on the example of Lysogeny Broth (LB) medium, a commonly used microbial growth medium  Reviewers' comments:Reviewer's Responses to QuestionsComments to the Author1. Is the manuscript technically sound, and do the data support the conclusions?The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented. Reviewer #1: YesReviewer #2: Yes**********2. Has the statistical analysis been performed appropriately and rigorously? Reviewer #1: YesReviewer #2: Yes**********3. Have the authors made all data underlying the findings in their manuscript fully available?PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.The Reviewer #1: YesReviewer #2: Yes**********4. Is the manuscript presented in an intelligible fashion and written in standard English?PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.Reviewer #1: YesReviewer #2: Yes**********5. Review Comments to the AuthorPlease use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. Reviewer #1: The paper is a systematic review and guide to correctly setting up the environment (through exchange fluxes) in genome scale models of metabolic networks, with a clear focus on FBA or FBA-like models. In my view the article is clear (didactic) and comprehensively covers the topic. The presentation is completely consistent with the scope of the paper and could indeed be useful for many applications of FBA in biotechnology. I therefore have no major criticism to raise. I would however ask authors to address the following points, all of which are in my view important (despite being related to one another).1) In the example they use throughout the paper (E coli growth on LB), the fact that something is wrong is reflected in the growth rate being nil (we know that E coli grows on LB). I understand that by adding a specific compound to the medium one cures this problem. My guess however is that one could cure this problem in a few other ways . My question is: when are exchange fluxes the most reasonable variables to adjust in such cases? This point somehow lies on top of the issues discussed in the paper but I believe it would be important to address it.2) Besides adding compounds, one could solve the zero growth problem by removing and/or replacing compounds from the medium or by changing the availability of existing compounds. For instance, because there is a lower bound on ATP hydrolysis, growth in FBA is impaired when the max glucose uptake is too low. So adding compounds does not seem like a general recipe to adjust the medium composition. The authors should address this point.3) I understand the review is focused on FBA-like models. Still in principle some of the ideas presented here could be important in other theoretical frameworks . The authors should try to highlight which parts of their recipes are general (model-independent) and which are instead specifically thought for FBA.Reviewer #2: Please see attached document. **********what does this mean?). If published, this will include your full peer review and any attached files.6. PLOS authors have the option to publish the peer review history of their article  digital diagnostic tool, Attachmentref_p1.pdfSubmitted filename: Click here for additional data file. 14 Jul 2020Response to Reviewers:REVIEWER #1"The paper is a systematic review and guide to correctly setting up the environment (through exchange fluxes) in genome scale models of metabolic networks, with a clear focus on FBA or FBA-like models. In my view the article is clear (didactic) and comprehensively covers the topic. The presentation is completely consistent with the scope of the paper and could indeed be useful for many applications of FBA in biotechnology. I therefore have no major criticism to raise. I would however ask authors to address the following points, all of which are in my view important (despite being related to one another).1) In the example they use throughout the paper (E coli growth on LB), the fact that something is wrong is reflected in the growth rate being nil (we know that E coli grows on LB). I understand that by adding a specific compound to the medium one cures this problem. My guess however is that one could cure this problem in a few other ways . My question is: when are exchange fluxes the most reasonable variables to adjust in such cases? This point somehow lies on top of the issues discussed in the paper but I believe it would be important to address it."=> We thank the reviewer for this comment and agree that adding compounds to the modelled media should not be considered as universal solution to facilitate growth of a model. We argue that this approach to achieve a desired model property (e.g. growth) should be considered as option in cases where (i) the metabolic network model underwent prior substantial curation efforts and (ii) where uncertainty exists, if a specific compound is part of the focal growth medium (e.g. molybdate in LB medium). We have added these points to the manuscript in the section for step 6 (‘troubleshooting’) on lines 332-344. "2) Besides adding compounds, one could solve the zero growth problem by removing and/or replacing compounds from the medium or by changing the availability of existing compounds. For instance, because there is a lower bound on ATP hydrolysis, growth in FBA is impaired when the max glucose uptake is too low. So adding compounds does not seem like a general recipe to adjust the medium composition. The authors should address this point."=> Yes, zero-growth can also be caused by too low maximum uptake rates. In such cases, the approach to analyse the reduced costs that are associated to the FBA solution can also point towards compounds, which are already part of the modelled medium but at too low availability. We thank the reviewer for pointing out this theoretical scenario and addressed it in the revised manuscript on lines 326-331."3) I understand the review is focused on FBA-like models. Still in principle some of the ideas presented here could be important in other theoretical frameworks . The authors should try to highlight which parts of their recipes are general (model-independent) and which are instead specifically thought for FBA."=> We agree that most steps in our proposed procedure to model the nutritional environment for metabolic modelling approaches are not limited to FBA-models. We have added a new subsection in the discussion about the limitations of our proposed procedure and outlook by highlighting the potential applicability especially to dynamic-kinetic models of cellular metabolism (lines 498-531).REVIEWER #2"In this draft, G. Marinos et al. address the problem of modeling complex, possibly undefined nutritional media for their use as input for constraint based models of metabolic networks. They propose a guideline based on 6 steps to translate computationally the nutritional information for the model and show its application to the test case of the Lysogeny broth for E. Coli, where they found it improved much more the plausibility of the FBA solution both in terms of matching experimental growth physiology and overall analysis on supplementation. Although it lacks a bit of generality, the article gives an original (to the best of my knowledge) contribution towards more rigorous applications of metabolic computational modeling and thus shall deserve publication in PLOS ONE but I would suggest the author to afford the following concerns:• It is pointed out by the authors that their aim is to make metabolic modeling more rigorous and quantitative, but then the authors apply and discuss their framework to FBA optimal solutions based on objective functions. The FBA approach is a very useful and wide spread approach eg to provide qualitative estimates of gene KO and/or medium growth feasibility but it misses sistematically even simple phenotypic (not even quantitative) predictions. For instance even for ideal E. coli cultures in minimal M9 medium  FBA fails to account for low : 46446-46451) and high (Microbiol. Mol. Biol. Rev. 69.1 (2005): 12-50) growth rate phenotypes (as well as intermediate in non-linear way: Appl. Environ. Microbiol. 72.2 (2006): 1164-1172). In the range of growth rate explored by the authors there shall be acetate excretion, even in rich medium (Nature 528.7580 (2015): 99-104.). Is this simple prediction verified in the authors’ framework? My general question is then why the authors limit themself to FBA? In principle their framework is more general and could be more useful in morequantitative computational modeling approaches, from dynamical kinetic models to Metabolic Flux Analysis. Although an application to these more quantitative approaches would add substantial value to their work, I invite the authors to at least acknowledge the limitations of their approach within the context of FBA."=>We thank the reviewer for pointing out the limitations of our proposed procedure in the context of FBA-models and the potential benefit for other metabolic modelling techniques.Following the suggestions to check for acetate production, we re-analysed the model-predictions and can confirm that the genome-scale E. coli model (iJO1366) as well as the core model predicted acetate production under aerobic conditions and on the basis of the modelled LB-medium. We have added these results and their discussion on lines 373-376 and lines 436-438, respectively.In addition, we extended the discussion in the revised manuscript as suggested with a new subsection for the limitations of our proposed procedure to model the nutritional environment for metabolic models and an outlook for the potential impact of the procedure for other metabolic modelling approaches, namely kinetic models of cellular metabolism (lines 498-531)."• A related issue is: how does the approach applies to smaller networks, like the core metabolism of the model iJO1366? The latter are the networks usually used for truly quantitative approaches like metabolic flux analysis. Probably the issue is non trivial since one shall reliably estimate precursors concentrations."=> We agree with the reviewer that smaller metabolic networks (e.g. of core metabolism) are powerful frameworks for quantitative and dynamic modelling approaches. Although FBA certainly is limited in possibilities to make quantitative predictions, we have applied our modelled LB-medium to the core model of E. coli to compare predictions with the genome-scale model (see lines 365-372). While also the core model, as mentioned above, can predict acetate production alongside cell growth on LB, it lacks the capabilities to utilize a large number of nutrients that are part of this chemically complex medium. Thus, we argue that our proposed procedure for in silico nutritional environment representation will have the most impact on small-scale networks for metabolic flux analysis techniques, which include the uptake/transport reactions and nutrient utilisation pathways for several resource compounds from complex growth media. We have added this consideration and outlook to the revised version of out manuscript (lines 506-524)."• The authors could spend few words more in the discussion on applications to eucaryotic growth media, that are the most complex and for which the impact of their framework could be bigger."=> The complexity of eukaryotic growth media is indeed an important topic. We added a brief discussion for the potential impact of our proposed procedure to define the nutritional environment for metabolic models of eukaryotic cell metabolism (lines 525-531). "• Minorsline 62: ions → inorganic ions line 63: prediction of biomass production → estimation of maximal biomass production line 71: the accuracy of FBA ...depends.... → I would add that the accuracy on FBA could depend, most crucially, on the extent that cell metabolism is well described by linear objective functions.line 329-332 “When the nutritional input of the models is defined as molecular quantities and not as fluxes the lower bound of each exchange reaction can be filled with the respective concentration of the metabolite”. I find this unclear. How is the conversion made? That in principle depends in non trivial way on the mechanism of uptake that could be different for each compound, with different kinetic constants, etc (eg Monod equation for glucose)."=> All points were corrected for the revised version of our manuscript. We also rephrased and clarified the mentioned sentence by elaborating on the details for the conversion of concentrations and exchange reaction bounds (lines 282-289).Attachmentresponse_to_reviewers.pdfSubmitted filename: Click here for additional data file. 16 Jul 2020Defining the nutritional input for genome-scale metabolic models: a roadmapPONE-D-20-13622R1Dear Dr. Waschina,We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.http://www.editorialmanager.com/pone/, click the 'Update My Information' link at the top of the page, and double check that your user information is up-to-date. 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Pathogens alter the key metabolic processes in infected host, ultimately the objective of these integrative constraint-based context-specific metabolic models is to provide novel insights toward understanding the metabolic basis of the acute and chronic processes of infection, revealing cellular mechanisms of pathogenesis, identifying strain-specific biomarkers and developing new therapeutic approaches including the combination drugs. The reaction rates predicted during different time points of pathogen development enable us to predict active pathways and those that only occur during certain stages of infection, and thus point out the putative drug targets. Among others, fatty acid and lipid syntheses reactions are recent targets of new antimicrobial drugs. Genome-scale metabolic models provide an improved understanding of how intracellular pathogens utilize the existing microenvironment of the host. Here, we reviewed the current knowledge of genome-scale metabolic modeling in pathogen cells as well as pathogen host interaction systems and the promising applications in the extension of curative strategies against pathogens for global preventative healthcare.Pathogens give rise to a wide range of diseases threatening global health and hence drawing public health agencies' attention to establish preventative and curative solutions. Genome-scale metabolic modeling is ever increasingly used tool for biomedical applications including the elucidation of antibiotic resistance, virulence, single pathogen mechanisms and pathogen-host interaction systems. With this approach, the sophisticated cellular system of metabolic reactions inside the pathogens as well as between pathogen and host cells are represented in conjunction with their corresponding genes and enzymes. Along with essential metabolic reactions, alternate pathways and fluxes are predicted by performing computational flux analyses for the growth of pathogens in a very short time. The genes or enzymes responsible for the essential metabolic reactions in pathogen growth are regarded as potential drug targets, as  Mycobacterium tuberculosis takes the global priority.Pathogens give rise to a wide range of diseases threatening global health and drawing public health agencies' attention to establish preventative and curative solutions Sweileh, . As antiHaemophilus influenzae Rd in 1995, the advances in experimental and computational technologies have accelerated context-specific understanding of complex biological networks and the emergence of a new field of science, called systems biology  play crucial roles in GEMs. When hundreds or thousands of metabolic reactions occurring inside the pathogenic cells and their literature-based distribution among different compartments are taken into consideration, the reconstruction process is extensively time-consuming and needs to be standardized. This reconstruction process was described by various studies  of the metabolic reactions in the GEM. At steady state, a system of linear equations obtained from the metabolic network, is given by:Flux balance analysis (FBA) is a frequently used constraint-based modeling approach to represent the possible behavior of microbial metabolism and plays an irreplaceable role in genome-scale metabolic modeling  determines the possible range of flux quantities which is allowable with the given objective value  in the GEM holds a great importance  lowering agents or inhibitors of glycosylceramide synthesis can be considered as new opportunities to prevent fungal infections  lowering antifungal agents have an important potential to control and prevent infections. Acyhydrazones have been identified as the inhibitor of glucosylceramide synthase enzyme. When the glycosylceramide synthesis gene is deleted in in silico killing strategies of pathogenic bacteria is the main goal of the pathogen-specific GEMs; however, the maximum amount of production of valuable metabolites takes great importance in industrial microorganisms' GEMs with the minimum cost. Depending on the application of the GEMs, various approaches are also available in the literature. Among them, dynamic FBA (dFBA) was suggested for the study of the metabolic network dynamics since the classical FBA is used for steady state systems  and metabolomics data  by the same research group for comparative analysis of these two strains for understanding the pathogenicity  to provide insight into the relationship between virulence factor production and growth. These two in silico models included 112 and 108 virulence-associated genes, respectively. The genes only critical for virulence factor production, growth and both were analyzed and thus non-obvious links between virulence factor production and growth were observed. iMO1056 and Opt208964, an automatically generated GEM by Model SEED, were used as initial drafts in the development of iPAO1, which is then extensively curated using the literature and databases toward the study of polymyxin treatment in P. aeruginosa metabolism at the systems level  in fatty acid and phospholipid metabolism were predicted to be essential for growth and production of at least eight virulence factors. Growth-essential genes in aromatic amino acid synthesis  were predicted to be essential for the synthesis of six virulence factors which are chorismate, pyocyanin, 1-carboxyphenazine, salicylate, dihydroaeruginoic acid and pyochelin. Moreover, the interconnectivity for each knockout was computationally researched by plotting virulence factor vs. growth inhibition compared to wild-type simulations. Uncertain connections between growth and virulence factor production were determined from the distribution of data points on plots. Several growth-essential genes partially hindered virulence factor synthesis. The virulence factor-related essential genes were significantly altered and not correlated with pathway complexity.The essential genes in P. aeruginosa to polymyxin B treatment unlocked its effect on bacterial metabolism with the help of genome-scale metabolic model iPAO1  for E. coli was reconstructed for K-12 MG1655 commensal and common laboratory strain , two uropathogenic (UPEC) and two commensal strains) by means of pangenome and core GEM. In addition to several pathogen specific reaction deletions, eight new reactions unique to EHEC strains were added, which are fructose synthetase, gentisate 1,2,-dioxygenase, perosamine synthetase, salicylate hydroxylase, sucrose transport, urease, tellurite reduction and UDP-N-acetylglucosamine 4-epimerase reactions. UPEC strains included only one addition of propionate CoA-transferase reaction in common. Furthermore, one unique reaction was added to each UPEC strain; hydroxy-pyruvate reaction for UTI89 strain and galactose isomerase reaction for CFT073 strain. The comperative simulations of strain-specific E. coli models resulted in variations of biomass yields on glucose due to the strain-specific metabolic reactions.Due to its genetic simplicity as well as its history in a variety of infections, r et al.  developeE. coli strains were broadened ; however, increased number of reactions in the core metabolism to 1,065 from 885. This increase in the number of the reactions in the core metabolism among E. coli strains indicates a conserved metabolism in E. coli. The reactions absent from Shigella core metabolism were distributed in various pathways including D-allose degradation, phenylethylamine and phenylacetate degradation, and biosynthesis pathways related amino acids, nucleotides and fatty acids. Another strain-specific GEM was built for 55 strains of E. coli and Shigella to shine light on adaptations to diverse environments  were predicted significantly higher than those of experimental results. After adaptive evolution of K. pneumoniae to myo-inositol by serial passage, the experimental growth rate increased and the rate of growth error decreased from 80% to 24%. Hence, GEMs can also give remarkable clues toward uncovering adaptive evolution mechanism. In another study, the in silico iYL1228 was used as a platform to reconstruct GEMs for 22 K. pneumoniae strains  was identified as the highest-ranked putative drug target satisfying virulence, druggability and broad-spectrum criteria and ZINC95543764 was suggested as a potential Klebsiella inhibitor in gene-centric approach. The enzymes related to essential metabolites were suggested as putative drug targets in metabolite-centric approach. These are enoyl-(acyl carrier protein) reductase (FabI) in fatty acid metabolism, riboflavin synthase subunit α (RibC) and riboflavin synthase subunit β (RibH) in riboflavin metabolism, and penicillin-binding protein 1A-C  in the peptidoglycan biosynthesis. PBP 1A-C were found to be synthetic lethal and the remaining three were essential.The screening of putative drug targets in Salmonella differ in their antimicrobial host specificity, resistance profiles, and virulence phenotypes. For example, S. typhimurium is the leading cause of human gastroenteritis and have more than 2000 serovars. An in silico strain-specific metabolic reconstruction was performed for 410 Salmonella strains and the metabolic capabilities on minimal media with more than 500 different growth-supporting nutrition sources including carbon, nitrogen, phosphorous, and sulfur were compared across the Salmonella genus in aerobic and anaerobic conditions . A total of 21 catabolic pathways like the utilization of D-tagatose, L-xylulose, D-xylose, deoxy-D-ribose, L-idonate, D-glyceraldehyde, and allantoin, that form part of the host's diet and/or exist in the intestinal environment, contributed to Salmonella fitness during intestinal infection. The compositional differences in the intestinal vs. extraintestinal milieu of the host may reflect differences in pathogenicity of different Salmonella types.Salmonellae are gram-negative bacterial pathogens with a wide host range, causing millions of human infections and hundreds of thousands of deaths per year worldwide. The serovars of in silico model of S. typhimurium (iRR1083) provided a suitable platform to analyze the role of reactions important in infection and pathogenesis, such as those required for drug efflux, proton pumps, inhibitory effects of antibiotics, mechanisms for reactive nitrogen species (RNS) and reactive oxygen species (ROS) resistance  and virulent (can grow) in the host cells, respectively. Of the 24 in vivo essential genes from the literature, the in silico analysis correctly predicted 22 virulence characterizations. For example, AceA, which was predicted as non-essential and mutants defective in AceA are virulent. AceA is required only for chronic and not for acute Salmonella infections  responsible in acetyl phosphate formation lead to virulent strain. The fatty acid degradation genes fadA, fadD/fadE genes have been implicated for virulence in chronic Salmonella infection, however the model predicted the presence of glucose or pyruvate but not acetate or short chain fatty acids during the early stages of infection  was reported suggestive of an aerobic condition in vivo, during the early stages of infection  predictions were consistent with the literature  mutants and super oxide dismutase mutants (sodA and sodCII) are virulent, confirming the pathogens have multiple defenses to oxidative stresses. On the other hand, the model predicted that mutants defective in aromatic amino acid biosynthesis are avirulent.The S. aureus strains were reconstructed by using genome annotation, functional pathway analysis and comparative genomics approaches  were present in all strains. All these genes, exluding fabI, were determined as essential. The analysis of single unconditionally essential enzymes  resulted in 70 essential enzymes in one or more of 13 strains and 44 in all S. aureus strains. Of the 44 shared essential enzymes, minimum six of them were experimentally supported in S. aureus including transketolase, hydroxyl-methylbilane synthase, methionine adenosyltransferase, UDP-N-acetyl-glucosamine 1-carboxyvinyltransferase, protein N (pi)-phospho-histidine-sugar phosphortransferase and acetyl-CoA carboxylase  and B3 (niacin) were additionally required to grow in glucose minimal media. The thiamin and niacin auxotrophy are due to absence of the pathways converting tyrosine to thiamin and nicotinate-nucleotide diphosphorylase, respectively. The prediction of growth capabilities on alternative sources  indicated 238 nutrients including glucose and glycerol as carbon sources and arginine as nitrogen source are used by all 64 in silico S. aureus strains. Among other strain-specific nutrients, both uracil and thymidine were predicted as nitrogen source for 42 strains, and dulcose and inosine as carbon source for 42 and 13 strains, respectively. The simulations of growth resulted in the presence of two virulence factors (staphylokinase and IgG binding protein A precursor) and the ability to catabolize maltotriose distinguished human-associated strains from livestock-associated strains. Thus, the simulations based on genome-scale metabolic modeling allow the classification of S. aureus strains according to the presence of virulence factors.Strain-specific GEMs enabled phenotype predictions via metabolic flux distributions of 64 Burkholderia mallei and Burkholderia pseudomallei) and three category A pathogens which are Yersinia pestis Bacillus anthracis and Francisella tularensis causing plague, anthrax, and tularemia, respectively  evaluated the potential threats from various microorganisms and classify them into three categories , the family of Enterobacteriaceae. The shared essential enzymes should be further exploited for the development of broad-spectrum antibiotics that can be used against this group of human pathogens.A GEM study (iPC815) on in silico analysis of Francisella strain demonstrated that Francisella tularensis undergoes significant changes of its metabolism upon its entry into the host cell (during intracellular growth)  gene in Francisella and the adenylyosuccinate lyase gene that catalyzes its formation, is a condition independent lethal gene, essential for survival and are proposed as a potential drug target. Thus, the predicted accumulation of AICAR in the host (macrophage) revealed a role as a potential master regulator during infection.The F. tularensis to select a group of enzymes as drug targets and ~20,000 small-molecule compounds were screened to list potential inhibitors against these targets , pantetheine-phosphate adenylyltransferase (CoaD), chorismate synthase (AroC) and sedoheptulose 7-phosphate isomerase (LpcA) enzymes, that are important in various metabolic pathways, these enzymes were suggested as potential drug targets. NadE takes part in NAD+ biosynthesis, CoaD in coenzyme A biosynthesis, AroC in phenylalanine, tyrosine and tryptophan biosynthesis and LpcA in lipopolysaccharide biosynthesis. These four enzymes are putative drug targets and the experimental validation of their enzymatic inhibition is an important step in the development of candidate antimicrobial compounds.A multidisciplinary approach was applied to Y. pestis, B. anthracis and F. tularensis were examined toward having a common drug target. Nine essential enzymes were shared between these three high threat pathogens  was reconstructed by combining iAB-AMØ-1410 and iNJ661 which represent Salmonella metabolism during infection was studied by using host-cell nutrient environment and gene expression data obtained from S. typhimurium growing inside macrophage cell lines  was performed in three different host niches including human bloodstream, urinary tract and macrophage  containing all metabolic reactions and associated genes from 16 E. coli genome, the experimentally-based nutrient compositions were used as simulation constraints to mimic these three host environments . Therefore, this control strain lost highest number of these genes predicted for each of the three host environments. As expected, most of the lost essential or important genes were computed in intestinal pathogen E. coli O157:H7 due to evolutionary outcomes. The simulations of E. coli UT189 successfully predicted least amount of essential and important genes lost in human bloodstream and urinary tract where it causes infection. However, the inconsistencies in urinary tract simulations demonstrated a necessity of additional constrains for more accurate predictions in this location.Prediction of essential genes for enterobacterial human pathogens  , the metabolism of the parasite Salmonella enterica subsp. enterica serovar Typhimurium (S. typhimurium) were higher if the infection occurred during the rest phase compared to the infection initiated in the middle of the active phase  can regulate cellular immunity against bacteria, viruses, and parasites  differences. The diagnosis and treatment related challenges can be solved by examining the metabolic fluxes in tissues and develop strategies for treatment options on the basis of few experimental data. In brief, HP models help elucidate the role of host environment on pathogen metabolism during the course of an infection.Host-pathogen (HP) models have unraveled the pathogen adaptation and carbon source utilization de novo synthesis from palmitoyl-CoA and serine, sphingomyelin hydrolysis by sphingomyelinases and reacylation of sphingosine catalyzed by ceramide synthase) and consumption reactions covering conversion to sphingosine, sphingomyelin, glucosylceramide and ceramide 1-phosphate catalyzed by the enzymes of ceramidase, sphingomyelin synthase, glucosylceramide synthase and ceramide kinase, respectively.During the microbial infection process, the sphingolipid molecules play a critical role in host-pathogen interaction mechanism  converts sphingomyelin to ceramide and has a great importance in membrane reorganization. Several bacterial pathogens activate the ASM and ceramide-enriched membrane platforms are formed in response to increase in ceramides  , H. influenzae, Chlamydia trachomatis, Legionella pneumophila, Candida albicans, Cryptococcus neoformans, Aspergillus fumigatus, etc.  would be an alternative solution. For the rare bacterial species involved in infections, an economically feasible route for pharmaceutical companies may be the search for broad spectrum antibiotics. Another option would be the development of combinatorial treatment protocols using current drugs that are already in use against infections. Here, it is strictly necessary not to forget the human orthologs, which need to be excluded from being drug targets in order to avoid side effects. The computational genome scale models presented in this review article have not only the ability to reduce the search space for novel drug targets in the pathogen metabolism, but can also give insights on putative side effects on host metabolism. These metabolic network models can be used as an additional screening tool to predict potential toxicity of the predicted target in a healthy cell or tissue model. Many side effects are due to patient-to-patient genetic variability. Using context specific constraint-based models integrated with omics data, the potential causes of the side effects such as drug off-target binding, downstream transcriptional effects (changes in gene expression that are induced with drug treatment), and the pharmacokinetics of drug clearance can be predicted in patients and serve as In case of extremely deadly pathogens, this genome scale modeling approach is indispensable since experimental analyses are associated with the need for high-security laboratory conditions, and in some cases prohibited. These models identify novel virulence genes as well as enable the evaluation of pathogen metabolism, predict disease phenotypes and hence get the infection scenario. They offer insight into why the same microbial pathogen may cause disease in some host environments but not others.Pathogen species, infection stages and host niches have great importance and need to be taken into consideration in clinical and computational studies to extend curative strategies against pathogens for global preventative healthcare. Throughout the infection course, different pathogens develop various stage-specific interaction mechanisms to host cells depending on their invasion and response features. The experimental elucidation of such complex infection process requires long and difficult work alone. An effective application of stage-specific genome-scale metabolic modeling approach guides experimental studies and facilitate the development of novel candidate drugs targeting key essential biomolecules at all stages infection.MS and KOU contributed to writing the manuscript and preparing the table and figures. Both authors contributed to the article 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.


Pathogenic microorganisms exploit host metabolism for sustained survival by rewiring its metabolic interactions. Therefore, several metabolic changes are induced in both pathogen and host cells in the course of infection. A systems-based approach to elucidate those changes includes the integrative use of genome-scale metabolic networks and molecular omics data, with the overall goal of better characterizing infection mechanisms for novel treatment strategies. This review focuses on novel aspects of metabolism-oriented systems-based investigation of pathogen-human interactions. The reviewed approaches are the generation of dual-omics data for the characterization of metabolic signatures of pathogen-host interactions, the reconstruction of pathogen-host integrated genome-scale metabolic networks, which has a high potential to be applied to pathogen-gut microbiota interactions, and the structure-based analysis of enzymes playing role in those interactions. The integrative use of those approaches will pave the way for the identification of novel biomarkers and drug targets for the prediction and prevention of infectious diseases. One major focus of systems biology in medical research is the integrated use of high-throughput omics data and molecular interaction networks, with the overall goal of elucidating molecular mechanisms of diseases to identify potential biomarkers and personalized treatment strategies. A wealth of research has been conducted to apply this approach on infectious diseases by focusing on cell metabolism. This included collecting transcriptomic, proteomic, or metabolomic data from pathogens or infected hosts on one side, and developing interaction models focusing on the enzymes and reactions of crucial metabolic pathways on the other side Bumann, .Rather than investigating host and pathogen metabolisms separately, novel systems biology based approaches consider the pathogen-host interaction (PHI) system as a whole to elucidate the mechanisms of infection. Here, omics data is collected from both organisms in a single experiment, termed dual-omics data, focusing on all the molecules within the PHI system . The bacteria CjR is now resistant to many antibiotics. Therefore, there is a need to predict new drug targets based on unique and new pathways of the resistant CjR. In the first phase, the authors used the complete protein set of the resistant CjR to prioritize the essential proteins of CjR taking role in unique metabolic pathways. These unique pathways were pathogen specific only, therefore, absent in the host organism. In the next step, the authors filtered out the human homolog proteins. Finally, using this subtractive genomics strategy, they identified 38 eligible therapeutic targets, about 10 of which were metabolic enzymes. They studied corresponding 3D structural interactions with FDA-approved drugs. Additionally, they identified peptidoglycan biosynthesis as a pathway based drug target, to be explored by experimental methods. To help future research, a comprehensive database was also made, and it has 3D structure and other related data of the listed drug targets.Hossain et al.  presenteEntamoeba histolytica, Leishmania donovani, Trichomonas vaginalis, and Salmonella typhimurium. Since the Sulfur metabolism pathway is absent in human and all other mammals, the enzymes taking role in this pathway were best considered as drug targets and subjected to the structure based methods to predict potential strong binders , which is the main source of nosocomial infection worldwide, Uddin et al. (The pathogens and host relationship is governed by the Protein-Protein Interactions (PPIs) and several of those interactions can involve enzymes. With methicillin resistant n et al.  developen et al.  the pathWith the birth of dual-omics technologies, we have entered a new era where interactions between pathogens and their host can be directly predicted to unravel molecular mechanisms of infectious diseases. Although integrated pathogen-host GMNs are currently scarce, we believe that the increase in the number of dual omics datasets, especially dual RNA-seq studies, will pave the way for the reconstruction of more and more integrated GMNs . MappingTÇ conceived the study. TÇ, GP, RU, and SD wrote the manuscript.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.


Pseudomonas putida KT2440, an emerging industrial microbe. From the 63 solution-sets, our omics guided process identifies one experimentally feasible solution requiring 14 simultaneous reaction interventions. We implement a total of 14 genes knockdowns using multiplex-CRISPRi. MCS-based solution shifts production from stationary to exponential phase. We achieve 25.6 g/L, 0.22 g/l/h, and ~50% maximum theoretical yield (0.33 g indigoidine/g glucose). These phenotypes are maintained from batch to fed-batch mode, and across scales .High titer, rate, yield (TRY), and scalability are challenging metrics to achieve due to trade-offs between carbon use for growth and production. To achieve these metrics, we take the minimal cut set (MCS) approach that predicts metabolic reactions for elimination to couple metabolite production strongly with growth. We compute MCS solution-sets for a non-native product indigoidine, a sustainable pigment, in  Pseudomonas putida.The trade-off between growth and production affects the application of engineered microbes. Here, the authors take the minimal cut set approach to predict metabolic reactions for elimination to couple metabolite production strongly with growth and achieve high production of indigoidine in  Yet, even the most well-designed heterologous pathway requires considerable additional work to reach the titers, rate and yield (TRY) necessary for the adoption of these systems by industry4. In addition, the production parameters of a strain at lab-scale are often not predictive of its performance and robustness when cultivated in different modes or at larger scales. Consequently, only a small fraction of such bioproduction strains have been successfully scaled and deployed2. In contrast, many native microbial processes show high productivity and reliability at scale and represent the most prominent examples of successful high-volume bioproduction. Examples include the generation of ethanol5 and organic acids7 during fermentation where production of these metabolites are required for carbon utilization during fermentative growth. Recently, a minimal cut set (MCS)-based approach8 showed that theoretically, production of a majority of metabolites can be strongly coupled to growth via elimination of a minimal set of metabolic reactions. Strong growth coupling9 is defined as metabolic rewiring, which demands production of the target metabolite  even when cells do not grow. Metabolic flux towards unwanted side-products is minimized, and metabolite production is feasible even when cell growth is sub-optimal or negligible. In this study we examine if coupling production of a heterologous product to microbial growth is possible, and if such dependence could lead to high TRY and the ability to maintain production parameters across different growth modes and scales.Synthetic biology approaches for heterologous, non-native bioproducts, can provide alternative sustainable routes to a vast number of chemicals ranging from fuels and commodities to fine chemicals. Heterologous production has been demonstrated for many desirable compounds and in a wide variety of microbial hosts10, a non-ribosomal peptide, as the heterologous product to prototype our approach. Indigoidine is a viable alternative for colorants in the dye, ink, and pigment industry11. Development of a robust production system for this compound stands to have an immediate benefit as a sustainable dye in the garment industry where the use of petrochemical derived dyes contribute to its negative impact on the environment14. We implemented this system in Pseudomonas putida KT2440 , leveraging the availability of the iJN1462 genome scale metabolic model (GSMM) for P. putida KT244016.We use indigoidine8 to compute intervention strategies that enforce strong growth coupled product formation. We combine these analyses with publicly available -omics data18 to exclude essential genes from editing. The corresponding set of gene loci are repressed using multiplex CRISPR interference (CRISPRi), which we optimize for use in P. putida KT2440. Our implementation results in a highly edited strain that, in a single iteration of strain engineering, achieves close to 50% max theoretical yield of indigoidine in P. putida KT2440 and TRY characteristics that maintain fidelity from laboratory to industrially relevant scales.Here, we use the minimal cut set (MCS)-based approachP. putida iJN146216 model that can be made essential for growth. For this analysis we used the MCS algorithm8 that identified minimal sets of reactions (cut sets), the elimination of which would enforce strong growth coupled production of a given metabolite (see “Methods”). Our initial analysis revealed that for P. putida around 99% of the producible metabolites accounted for in the genome scale model had the potential for strong growth coupling. This potential growth coupling for all metabolites is consistent with reported calculations demonstrated in other hosts8  as cofactors. The maximum theoretical yield (MTY) for glutamine and indigoidine was calculated to be 1.141 mol/mol and 0.537 mol/mol, respectively, from glucose as the carbon source , we then needed to extract the gene–protein-reaction relationships (GPRs) for each of the reactions embedded in the GSMM to translate the selected metabolic reactions into specific genes and assess their role in other functions. We eliminated potential cut sets that targeted genes coding for multi-functional proteins, as we sought to limit additional metabolic perturbations that could confound our analysis. A total of 882 reactions were excluded from consideration. These included spontaneous reactions, exchanges, transporters, some core essential reactions such as ATP maintenance, ATP synthesis reactions, reactions associated with the TOL plasmid genes and reactions that were not assigned a GPR. A total of 826 reactions out of 2928 were not associated with any genes. Of these 826 reactions, 740 reactions are either exchanges, transporters, biomass reaction, ATP maintenance, demand or sink reactions. Only 86 (10%) are true cytosolic reactions that might be associated with unidentified GPR. Their inclusion in the solution under such constraints would assist in identifying unknown GPRs and hold promise for further investigation. Of the 2030 reactions in iJN1462 that are associated with genes, only 60% have a single gene associated with them. If a metabolic reaction was catalyzed by more than one gene product (genes coding for isozymes or multi-subunit enzymes), we included both genes for inactivation. From this workflow, we analyzed 63 cMCS in total but only one feasible cMCS emerged with the predicted potential for high indigoidine titer .We then sought to confirm if the set of specific genes (rather than enzymatic reactions) for intervention was metabolically sound. For this we used flux balance analysis (FBA) and flux variability analysis (FVA) to confirm that the 16-gene cMCS strategy resulted in obligatory glutamine production. Using our constructed MCS algorithm-based platform Fig. , we set P. putida, such as para-coumarate and lysine22. These substrates are important carbon streams that could be utilized from lignocellulosic biomass hydrolysates24. FBA with these alternate carbon sources  indicated that a strain engineered using the 16-gene cMCS strategy for the glucose would fail to produce glutamine 27 from Streptomyces lavendulae that catalyzes indigoidine formation from two molecules of glutamine in an ATP-dependent manner10. Activation of BpsA requires a post-translational pantetheinylation conferred by a promiscuous Sfp from Bacillus subtilis28. The genomically integrated production strain harboring a plasmid-borne dCpf1 and non-targeting genomic RNA (gRNA) serves as the control production strain. The basal production of indigoidine in P. putida is 2.3 g/L indigoidine from 10 g/L glucose after 24 h 18 35. An endonuclease-deficient class II CRISPR-Cas enzyme, FnCpf136, was chosen over Streptococcus pasteurianus dCas9 as the Cpf1 crRNA is only 19 bp in size, compared to the corresponding crRNA (gRNA scaffold sequence) from Cas9, which is 76 bp37. Each gRNA was driven by a different P. putida tRNA ligase promoter/terminator pair, and dCpf1 was placed under the control of the lacUV5 promoter. Minimal 100-bp promoter sequences from native tRNA ligases were sufficient to express mCherry fluorescent protein, confirming that heterologous messenger RNA (mRNA) transcripts for gRNAs would be generated . Indirect changes in gene expression were detected  of the genes targeted with CRISPR interference. We used RNAseq analysis to examine the engineered strain, and compared normalized RNA expression levels to the control strain Fig. . RNA expted Fig. , consistpara-coumarate as the sole carbon source, in agreement with the modeling  of 12.5 g/L indigoidine from 60 g/L glucose. The control production strain produced 5 g/L, and production of the final molecule was realized after glucose was exhausted from the medium. When galactose was fed, the engineered strain also exhibited improved titers of 25.6 g/L of indigoidine from 60 g/L galactose as opposed to the control strain that generated only around 900 mg/L of indigoidine; a 28-fold improvement in production was observed in the engineered strain. Moving to an industrially relevant cultivation format did not impact the final product titer, allowing us to further develop cultivation methods by switching to fed-batch mode.We realized greater improvements in final product titer as well as improvements in production kinetics in the fed-batch mode using the ambr® 250 system. After administering an initial high-nutrient feed to increase biomass in the reactor, we reduced the feed rate to study indigoidine product formation during exponential phase growth Fig. . During This study implements genome scale MCS predictions via CRISPR interference that resulted in a strain where production was paired with growth. Pairing the final desired product with the carbon substrate for growth mimics native obligatory product formation such as ethanol production and results in high productivity at scale. Further, to our knowledge, there are no other reports where the production of a non-native molecule was shifted from stationary phase to exponential phase as a result of strain engineering.40 to growth decoupling41. Canonical approaches to growth coupling are FBA-based methods such as OptKnock42 and are conceptually different from MCS-based methods. Optknock and related methods  use bi-level optimization to identify gene edits, which increase flux to the target of interest around the solution space that maximizes growth. The MCS algorithm does not maximize growth and is unbiased towards any part of the solution space. Further, the MCS algorithm allows us to set boundaries for minimum demanded production and growth, thus providing strong coupling constrained MCS (cMCS) solution-sets. These optimization strategies make different assumptions on cellular metabolism and have been extensively reviewed elsewhere44. OptKnock related methods have been used in growth coupled strategies for primary alcohols  and organic acids . However, for a large subset of native metabolites, including amino acids , FBA-based approaches often result in “non unique” solutions, which are optimized for production but not necessarily for growth. In contrast, the recently delineated MCS-based approach8 provides strong coupled solutions sets for a large number of metabolites within a reasonable computation time and fit well with the downstream biological constraints for CRISPR/dCpf1 rewiring. A comparison of solution-sets generated with OptKnock  even when cells do not grow. Our approach relies on computation of cMCS to provide targets at the genome-scale levelach Fig.  also repWhile our engineered strains showed many desirable phenotypes, several aspects merit additional discussion. The predicted constrained minimal cut set (cMCS) demands zero flux through these reactions for strong growth coupling. We excluded two genes from the predicted gene set due to their essentiality. Of the remaining gene targets, our RNAseq and proteomic data indicates a partial gene knockdown, implying that a non-zero flux could occur through the predicted reactions. The resulting yield space for indigoidine production is now different from what was predicted by MCS algorithm Fig. . The yieE. coli33) would enable more efficient CRISPR mediated gene knockdown. Similar fold repression of targeted proteins by CRISPRi/dCas9 was recently reported38, suggestive of a limitation for existing CRISPR systems in P. putida. The plasmid-based CRISPRi system retained stable phenotype for 6 days, but can be further stabilized using genomic integration of the dCpf1/CRISPRi system or by developing multiplex gene deletion strategies55. Directly targeting proteins for degradation in a multiplex format56 could eventually be applied to prokaryotes57 and would sidestep the reliance on variable protein turnover kinetics. Additional reduction of competing reactions that draw on glutamate might only have a negligible impact on predicted indigoidine titer , while E. coli (0.4 mol/mol) and S. cerevisiae (0.079 mol/mol) are lower. Genome scale metabolic models are now available for over 500 bacterial and eukaryotic organisms62 and we can assess the extent to which such multi-gene engineering would be useful for a target across hosts.Selecting the best host-final product pair is crucial to developing the ideal production platform, and provides a key consideration in broadening our approach to additional studies. In earlier reports 66, may also overcome these limitations. We also do not take into consideration products or intermediates that may be toxic. Industrial processes use renewable carbon sources that may contain growth inhibitory byproducts67. To solve these issues, tolerance engineering68 or adaptive laboratory evolution (ALE)69 could be useful. Production paired growth also enables powerful strategies like ALE to be used for direct improvement of production. The indigodine production system described here is of immediate interest to the biotech industry, and our methodology provides an avenue for the rapid prototyping of other scalable microbial production systems.We propose using our workflow to calculate the % MTY for any given host-product as an early decision point to quickly filter for viable host-product pairs. It is likely that a given target will not only have different MTY limits across different hosts, but that the constraints will be different across targets. To show that our strategy is implementable for other targets, we calculated the maximum achievable yields for other product pairing regimes, and include the complete gene set required to implement two additional targets: a biodiesel precursor methyl ketonePseudomonas putida KT2440 genome scale metabolic model (GSMM) iJN146216 was used. Aerobic conditions with glucose as the sole carbon source were used to model growth parameters. The ATP maintenance demand and glucose uptake were 0.97 mmol ATP/gDW/h and 6.3 mmol glucose/gDW/h, respectively. Constrained minimal cut sets (cMCS) were calculated using the MCS algorithm8 available as part of CellNetAnalyzer (version 2018.2). Excretion of byproducts was initially set to zero, except for the reported overflow metabolites for secreted products specific to P. putida . Additional inputs including minimum demanded product yield  and minimum demanded biomass yield at 10 or 25% of maximum biomass yield were also specified in order to constrain the desired design space. The maximum size of MCS was kept at the default (50 metabolic reactions). Knockouts of export reactions and spontaneous reactions were not allowed. The algorithm computed all minimal combinations of reaction knockouts blocking all undesired flux distributions and maintaining at least one of the desired metabolic flux distributions. With the specifications used herein each calculated knockout strategy (cMCS) demands production of glutamine even when cells do not grow. All cMCS calculations were done using API functions of CellNetAnalyzer70 on MATLAB 2017b platform using CPLEX 12.8 as the MILP solver. A summary of 417 common metabolites with the respective number of cut sets and number of targeted reactions to satisfy coupling restraints is included  or Gurobi Optimizer 8.1 (http://www.gurobi.com/) as the linear optimization solver. Production envelope was obtained using the internal COBRA Toolbox function, productionEnvelope, and plotted for P. putida  was used to calculate the maximum theoretical yield (MTY) from reaction stoichiometry and redox balance and also for single-gene deletion analysis. Flux variability analysis (FVA) was used along with FBA to check for minimum and maximum glutamine or indigoidine flux under the identified cMCS strategy to confirm product obligatory growth. FVA was performed with maximization of biomass formation as the objective function and the proposed gene deletions in each cMCS strategy along with constraints that were used for cMCS calculations. A positive minimum and maximum flux through the exchange reaction for the metabolite of interest (glutamine or indigoidine) confirmed production obligatory growth. COBRA Toolbox v.3.0ida Fig.  as a fra72 as described (15 mM (NH4)2SO4, 47.9 mM Na2HPO4, 22 mM KH2PO4, 8.56 mM NaCl, 2 mM MgSO4, 100 µM CaCl2) with the following modifications. Carbon sources  were used at 56 mM unless indicated otherwise. Trace minerals were purchased from Teknova Inc  and used diluted 2000-fold.All chemicals and reagents were purchased from Sigma-Aldrich  unless mentioned otherwise. When cells were cultivated in a microtiter plate format, plates were sealed with a gas-permeable film . Tryptone and yeast extract were purchased from BD Biosciences . Engineered strains were grown on M9 Minimal MediaPseudomonas putida KT2440 was used as the host for strain engineering. All strains used in this study are described in Supplementary Table 73. Cells transformed with replicative plasmid DNA were allowed to recover at 25 °C for 2.5 h before plating on selective agar media at 23 ˚C for overnight incubation. Cells transformed with non-replicative (integrating) plasmids were allowed to recover for 4–8 h in LB media before plating on selective agar media at 23 ˚C for an additional 24 h. Kanamycin sulfate or gentamicin sulfate  was used at a concentration of 50 µg/mL or 30 µg/mL, respectively. Integration of the Escherichia coli galETKM operon or the heterologous indigoidine gene pathway was implemented using a kanamycin/sucrose-counterselection plasmid for allelic exchange74. After confirming sucrose resistance and kanamycin sensitivity by patching clones onto solid agar media, correct clones were confirmed for the genotype by colony PCR using Q5 Polymerase enzyme . The dCpf1/CRISPRi system was adapted for use in P. putida by subcloning an endonuclease dead Francisella tularensis subsp. Novicida cpf175 into a pBBR1 backbone and placed under the LacUV5 promoter. The synthetic gRNA array was constructed using gene synthesis techniques  and cloned into the dCpf1/CRISPRi backbone using isothermal DNA assembly. All plasmid constructs were verified with Sanger sequencing before transformation into P. putida.Glucose and organic acids from cell cultures were measured by an 1100 Series HPLC system equipped with a 1200 Series refractive index detector (RID)  and Aminex HPX-87H ion-exclusion column . 300 µL aliquots of cell cultures were removed at the specified time points during production and filtered through a spin-cartridge  with a 0.45-μm nylon membrane, and 10 μL of the filtrate was eluted through the column at 50 °C with 4 mM sulfuric acid at a flow rate of 600 μL/min for 30 min. Metabolites were quantified with an external standard calibration with authentic standards.P. putida with slight modifications as previously described76. Briefly, indigoidine is insoluble in most aqueous solutions and organic solvents except for dimethyl sulfoxide (DMSO) and dimethylformamide. We purified indigoidine by solubilizing all other materials with sequential resuspensions using different solvents. Cells were lysed by vortexing cells in 1% SDS and 100 mM NaCl and then centrifuged at 14,000 x g for 3 min. The supernatant was discarded and the pellet was washed with three rounds of methanol, isopropanol, water, ethanol, and hexane to remove contaminating proteins and metabolites. The pellet was allowed to dry overnight and then resuspended in DMSO at a final concentration of 1 mg/mL. Indigoidine purity was characterized by nuclear magnetic resonance. The sample was protected from direct exposure to sunlight to avoid photo-bleaching. A standard curve correlating indigoidine concentration to OD612 was generated using this reagent . Indigoidine was quantified by measuring the optical density (OD612) using a microplate reader  preheated to 25 °C and applying a standard curve generated from indigoidine. The equation used wasE. coli and P. putida biosynthetic indigoidine samples. The DMSO-solubilized cell lysate from wild-type P. putida does not contribute detectable signal when measured at OD612.To rapidly quantify indigoidine production in a high-throughput manner, a colorimetric assay was used as a proxy for indigoidine titer. Briefly, 100 µL of cells were pelleted by centrifugation at 20,000 x 600 to biomass conversion estimates77. 1.0 OD600 was converted to 0.38 g/L of dry cell weight.To correlate indigoidine yields with biomass yields, the dry cell weight was determined using OD78. RNA from trizol treated lysates were bound to a silica column  and its integrity confirmed using a Bioanalyzer RNA 6000 Nano assay . rRNA was removed from 100 ng of total RNA using Ribo-Zero(TM) rRNA Removal Kit . Stranded cDNA libraries were generated using the Illumina Truseq Stranded mRNA Library Prep kit. The rRNA depleted RNA was fragmented and reversed transcribed using random hexamers and SSII  followed by second strand synthesis. The fragmented cDNA was treated with end-pair, A-tailing, adapter ligation, and ten cycles of PCR amplification. Prepared libraries were quantified using KAPA Biosystem’s next-generation sequencing library qPCR kit  and run on a Roche LightCycler 480 real-time PCR instrument. Sequencing of the flowcell was performed on the Illumina NovaSeq sequencer using NovaSeq XP V1 reagent kits, following a 2x150nt indexed run protocol. Reported gene expression values are the total normalized transcripts per kilobase million (TPM). Volcano plots and statistical analysis was conducted using the Geneious Differential Expression package in Geneious Prime (www.geneious.com). P-values were calculated assuming a binomial distribution and a random sampling model using Fisher’s exact test was applied. All raw data is available through NCBI-SRA associated with NCBI-Bioproject or via JGI (refer to Data Availability below).Total RNA was prepared by Trizol-based RNA extractiong and flash frozen with liquid nitrogen at − 80 °C until ready for processing. Cells were lysed in 100 mM NaHCO3 using 0.1 mm glass beads in a Biospec Beadbeater  with 60 s bursts at maximum power and repeated three times. Cell lysates were cooled on ice between each round. The clarified supernatant was harvested by centrifugation at 20,000 x g. The lysate protein concentration was estimated following the manufacturer’s directions for the BCA method . Proteins were quantified for analysis using a SRM-targeted proteomic assay80. The SRM methods and data are available at Panoramaweb [https://panoramaweb.org/genome-scale-rewiring-indigoidine.url].A targeted SRM (selected reaction monitoring) method was used to quantify relative levels of pathway proteins in samples under the various tested conditions in a 60 mL cultivation format. At the time points indicated, 1 mL of each sample was pelleted by centrifugation at 20,000  x P. putida Ec.galETKM strains for growth in M9 minimal salt media with galactose had a long initial adaptation phase of around 96–120 h before cultures showed turbidity. All cultures were incubated with shaking at 200 rpm and 30 °C. To prepare cells for pathway induction, M9 adapted cultures were back-diluted to a starting OD600 of 0.1, at which point IPTG and arabinose were added as appropriate. Production cultures grown in 24-well deep-well plates  inoculated into a 200 µL culture volume and incubated InFors Multitron HT Double Stack Incubator Shaker  set to 999 rpm linear shaker, and 70% humidity. For shake flask experiments, 60 mL cultures were grown in 250 mL unbaffled Erlenmeyer shake flask and incubated at 200 rpm with orbital shaking. For all experiments, the indigoidine pathway was induced with 0.3% w/v l-arabinose, and dCpf1 mediated gene repression was induced with 500 µM IPTG. Production assays were performed in independent biological triplicate and repeated at least twice, except for the scale-up experiments (described below), which were performed in biological duplicate. Standard error or standard deviation from the mean are shown as indicated and were calculated using GraphPad Prism 8. Preparation of cellular growth curves are described in , and 50 mL overnight pre-culture in the same media. Feeding solution contained 100 g/L glucose, 300 mM NH4+ along with l-arabinose and kanamycin. The temperature was held constant at 30 °C. The bioreactor pH was monitored using the Hamilton EasyFerm Plus PHI VP 225 Pt100  and was maintained at a pH of 7 using 10 M sodium hydroxide. Dissolved oxygen concentration was monitored using Hamilton VisiFerm DO ECS 225 H0.Batch experiments were performed using a 2 L bioreactor equipped with a Sartorius BIOSTAT B® fermentation controller , fitted with two Rushton impellers fixed at an agitation speed of 800 rpm. Initial reactor volume was 1 L M9 Minimal Media . The vessels were filled with 150 mL M9 minimal salt media containing 10 g/L glucose as carbon source. Temperature was maintained at 30 ˚C throughout the fermentation process and agitation was set constant to 1300 rpm. Airflow was set constant to 0.5 VVM based on the initial working volume and pH was maintained at 7.0 using 4 N NaOH. Reactors were inoculated manually with 5 mL of pre-culture cell suspension. After an initial batch phase of 12 h, cultures were fed with a concentrated glucose feed solution  by administering feed boluses every 2 h restoring glucose concentrations to 10 g/L (feed parameters: 3.1 min @ 50 mL/h). After observing glucose accumulation, feed addition was paused and resumed at reduced feed rates when glucose levels dropped below 10 g/L (1 min @ 50 mL/h). Experiments with a continuous feeding regime were initially fed at 1.3 mL/h (0.3 mL/h after seeing glucose accumulation). Samples were taken 1–2 times every day (2 mL) and stored at −20 °C. The ambr® 250 runtime software and integrated liquid handler was used to execute all process steps unless stated otherwise.Further information on research design is available in the Supplementary InformationPeer Review FileReporting SummaryDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4