In the wake of the uprising global energy crisis, microalgae have emerged as an alternate feedstock for biofuel production. In addition, microalgae bear immense potential as bio-cell factories in terms of producing key chemicals, recombinant proteins, enzymes, lipid, hydrogen and alcohol. Abstraction of such high-value products  facilitates to make microalgae-based renewable energy an economically viable option. Synthetic biology is an emerging field that harmoniously blends science and engineering to help design and construct novel biological systems, with an aim to achieve rationally formulated objectives. However, resources and tools used for such nuclear manipulation, construction of synthetic gene network and genome-scale reconstruction of microalgae are limited. Herein, we present recent developments in the upcoming field of microalgae employed as a model system for synthetic biology applications and highlight the importance of genome-scale reconstruction models and kinetic models, to maximize the metabolic output by understanding the intricacies of algal growth. This review also examines the role played by microalgae as biorefineries, microalgal culture conditions and various operating parameters that need to be optimized to yield biofuel that can be economically competitive with fossil fuels. An upsurge in global population coupled with rapid industrialization has tremendously increased worldwide per capita energy consumption. This rising energy demand is being fulfilled by various non-renewable energy sources, particularly fossil fuels. It is estimated that at the present usage, the worldwide demand for energy is envisaged to escalate by more than 50% by the year 2030 . With a Biomass-derived biofuels are currently being considered as the most sustainable alternative to fossil fuels. Herein, problems such as water pollution and carbon dioxide emission associated with combustion of fossil fuels can be mitigated. Biofuels have been segregated into four generations based on their feedstock material –5. VarioChlamydomonas reinhardtii [Chlorella pyrenoidosa [Neodesmus sp. UTEX 2219-4 [Scenedesmus [Phaeodactylum tricornutum [Dunaliella salina [Dunaliella parva [Nannochloropsis oceanic [Nannochloropsis salina [Amongst various biomass, microalgae are gaining much attention for use as a potential feedstock by virtue of its high carbohydrate and lipid content, rapid growth rate and resistance to fluctuating environmental conditions . Advancenhardtii –15, Chloenoidosa , NeodesmX 2219-4 , Scenedenedesmus , Phaeodacornutum , Dunalie oceanic , Nannochs salina , cyanobas salina  complimeDevelopment of microalgae-based biofuels in itself is not an economically competitive alternative to existing technologies and hence focus has now shifted towards abstraction of high-value co-products from microalgae to improve the economics of microalgae-based biorefinery. Biorefinery approach is a system where energy, fuel, chemicals and high-value products such as pigments, proteins, lipids, carbohydrates, vitamins and antioxidants are produced from biomass through various processes. Microalgae are rich in proteins, lipids and carbohydrates and the relative amounts of these biochemical components vary amongst various microalgal strains . These cC. reinhardtii, cyanobacteria Anabaena cylindrica and A. variabilis are well known for hydrogen production in the presence of sunlight. The aforementioned organisms are capable to extract and direct protons and electrons derived from water to hydrogen production, catalyzed via chloroplast hydrogenases, namely HydA1 and A2 (Hydrogenase) [C. reinhardtii was improved  by inducing modification in its respiratory metabolism by eliminating potential competition for an electron with hydrogenase. Similarly, heterologous expression of hexose uptake protein (HUP1 hexose symporter from Chlorella kessleri) in a hydrogen-producing mutant (Stm6) of C. reinhardtii revealed approximately 150% increase in H2 production capacity [Porphyridium cruentum [Tetraselmis suecica [Desmodesmus sp. [Synechocystis sp. PCC 6803 produced via double homologous recombination system was capable of photoautotrophically converting CO2 to bioethanol [2, as against 0.51 g ethanol/g glucose by S. cerevisiae. Active research needs to be directed in the field of algae-based bioethanol production, focusing on improvement in the yield to make the process economically viable.Variety of algal strains such as green alga ogenase) , 29. Kruogenase)  observedcapacity . With incruentum , Tetrase suecica  and Desmsmus sp.  have beeoethanol , with a Neochloris aquatica CL-M1 [Chlorella vulgaris JSC-6 [Synechococcus elongates, was reported to produce butanol synchronized with the production of isobutyraldehyde and isobutanol [S. elongates was able to produce isobutyraldehyde at a higher rate than those reported for ethanol, hydrogen or lipid production by either cyanobacteria or algae by the upregulation of ribulose bisphosphate carboxylase/oxygenase. The major obstacle in large-scale commercialization of microalgae-based bio-butanol is, however, the generation of several byproducts which reduces the purity and yield of pure butanol.Several recent studies indicated the production of bio-butanol from various microalgae such as carbohydrate-rich microalgae ca CL-M1 , acid-pris JSC-6  and micris JSC-6 . An algaobutanol . The genNannochloropsis, Dunaliella, Chaetoceros, Botryococcus, Scenedesmus and Pseudochlorococcum are known to accumulate high amount of neutral lipids [Nannochloropsis sp. for the production of fatty acid for biodiesel production and other co-products such as carotenoids and bio-hydrogen. This study reported that when CO2 supercritical fluid extraction was used, 45 g/100 g dry biomass of lipids and 70% of pigments were extracted. In a recent study, a high-lipid-producing microalga, namely Euglena sanguinea was investigated for biodiesel production [Biodiesel has emerged as a promising alternative for fossil fuels by virtue of having similar chemical characteristics with the latter. Most microalgae act as a resource for large-scale production of biodiesel owing to the high biomass productivity coupled with the rapid accumulation of lipid. Diverse algal genera such as l lipids –40. The l lipids  employedoduction . The satoduction . Likewisoduction , 44. NowC. reinhardtii do not naturally synthesize ketocarotenoid, and β-carotene ketolase gene from Haematococcus pluvialis was expressed in C. reinhardtii to synthesize a new ketocarotenoid [C. vulgaris was transformed with promoter and terminator of the nitrate reductase gene from P. tricornutum and the transgenic strain proved valuable for the production of value-added proteins [Synechococcus elongatus PCC 7942 to produce 3-hydroxybutyrate [In addition to biofuels, microalgae are feedstock to several other high-value products such as vitamins, pigments, proteins, carbohydrates, amino acids, antioxidants, high-value long-chain polyunsaturated fatty acids (PUFAs) and biofertilizers. Natural microalgal pigments such as carotenoids, chlorophylls and phycobiliproteins serve as precursors of vitamins in food, pharmaceutical industries, cosmetics and coloring agents . Severalrotenoid . In anotproteins . Microalbutyrate . The strbutyrate . These sScenedesmusacutus was found to be capable of assimilating nitrogen from LEA residuals and was able to replace nitrate in culturing media, thus facilitating nutrient recycling. Another recent study reports that nitrogen/phosphorus limitation in microalgae leads to variation in lipid productivity, which was validated by the expression of acetyl-CoA carboxylase gene [Spent microalgal biomass or lipid-extracted algal (LEA) biomass is an attractive feedstock for the production of various products as it contains 30–60% of residual carbon that is in the form of readily fermentable sugar . In a rease gene .N. salina), wherein maximal cell density of 50 g/L was obtained within 25 days [Auxenochlorella protothecoides was employed to maximize lipid production. An optimal feeding strategy was determined by interior point optimization [Photosynthesis-dependent accumulation of biomass takes place under nitrogen-rich environment, whereas accumulation of lipids in microalgae occurs under stress conditions such as limited nutrient conditions. This contradiction hugely offsets continuous lipid production, thus affecting the economics of the system. The fed batch process has often been reported as the most suitable method for microalgal cultivation, because it offers the flexibility of customization of nutrients provided during the process. A study conducted by Zheng et al.  demonstr 25 days . Similarmization . On the mization  reportedChlorella sp., where process variables such as dilution rate and feed stream substrate concentrations were optimized for biomass productivity and continuous production of lipid-rich algal biomass was monitored in two sequential bioreactors [Although continuous feeding is required for maintenance of growth rate, the accompanying non-limiting nutrient supply may hamper accumulation of lipid and carbohydrates. To maintain a balance between lipid production and algal biomass production, a two-stage cultivation process was applied, wherein different set of conditions were supplied for growth and high lipid content/carbohydrate . Thereafreactors .Chlorella sp. and Nannochloropsis sp. have been grown mixotrophically for consideration for biodiesel production [Tribonema sp. showed enhanced growth in heterotrophic conditions, whereas lipid production was maximum during mixotrophic growth conditions [Under heterotrophic growth conditions, microalgae utilize a source of organic carbon as substrate for growth, which is converted to lipids. Due to the dependency on the availability of a continuous source of sugar, the cost of the process increases considerably. To overcome the aforementioned hurdles, microalgae are grown mixotrophically. Microalgae such as oduction . In a conditions . Though In addition to maintaining the right balance between the biomass content and lipid production, microalgae-derived biofuel faces several other challenges such as inefficient harvesting techniques and low productivity due to ineffective photobioreactor design and limited photosynthetic efficiency . GeneticThe crux of synthetic biology is to be able to build artificial regulatory circuits that can control cellular behavior based on user-defined inputs/signals/stimuli to generate the desired output such as biofuel chemicals and proteins by making required changes in metabolism. The main focus of microalgal biotechnology for large-scale application of microalgae as a sustainable and robust energy feedstock is based on (a) improving their photosynthetic efficiency through metabolic engineering for improved oil yield and enhanced carbon sequestration rate in mass cultures, (b) driving the carbon flux into energy-rich compounds useful as biofuel source and (c) development of robust and committed algal cells that can sustain low-cost large-scale cultivation resulting in lower operational costs and lower carbon footprint of the produced chemical . SynthetIn a bid to commercialize transgenic microalgae, certain challenges need to be addressed. These are primarily poorly developed molecular tools for genetic engineering and low level of heterologous gene expression from the nucleus , 71. WitC. reinhardtii, Nannochloropsis sp., P. tricornutum, Cyanidioschyzon merolae, Ostreococcus tauri and Thalassiosira pseudonana [C. reinhardtii, where existing tools are being modified and optimized, synchronous with the development of novel tools. Therefore, in C. reinhardtii, protein production has been accomplished by engineering the chloroplast genome because reverse genetics and editing methods could be attempted in chloroplasts. With the development of expression constructs [psbA that codes D1 subunit of Photo system II) in chloroplasts as “Biobricks” under the influence of endogenous regulatory system [rbcl mRNA (large subunit genes of Arabidopsis Rubsico) maturation factor MRL1 from the nuclear genome of MRL1-deficient mutant. Rubisco content has been reported to reduce by 15% in comparison with the wild type, with no reduction observed in their growth. Interestingly, in the presence of inducible promoters, Rubisco amount could be altered under varying light intensity and CO2 concentration, as shown by Johnson [Synthetic biology could be presently applied to systems with reverse genetic tools and have developed resources such as genomic sequence, selection markers, plasmid vectors, various promoters and genetic editing tools. However, these are presently being limited to selected microalgal species. The microalgal models that can be congenial to reverse genetics and synthetic biology perturbations based on the existing genetic resources are available for eudonana . Traditinstructs , 81, pronstructs , 83. Atty system . Efforts Johnson . Key eveC. reinhardtii [P. tricornutum, the ratio of short-chain fatty acids C12 and C14 were increased by enhanced expression of thioesterases derived from different plants [C. reinhardtii wherein the fatty acid profile has been engineered for better fuel properties [Nannochloropsis oceanica via reverse genetics [Lipid metabolism is highly complex and tightly regulated with an elaborate regulatory network of nodes and internodes. Hence, the characterization of pathways with an extensive knowledge about carbon sharing is imperative for fabrication of artificial pathways. Investigations have been carried out to observe carbon partitioning between starch and lipid and attempts have been made to isolate the key players of triacylglycerol (TAG) formation under stress in nhardtii . Lipid et plants . Similaroperties . A recengenetics .cis-elements, noncoding RNAs and transcription factors [C. reinhardtii by mimicking their native cis-elements, structure and sequences of highly expressing genes. Some of these synthetic promoters performed better than the best endogenous promoter hsp70/rbs2, thus opening new potentials for exploring the C. reinhardtii system through synthetic strategies [In a typical synthetic circuit, three components are present—sensors , processors  and actuators . In biological systems, regulatory circuits are mainly governed by transcriptional, post-transcriptional and post-translational components such as regulatory molecules,  factors , 70. In  factors  developerategies . Most ofThese features ideally place newly discovered CRISPR/Cas  and CRISPR–Cpf1 system as effective means to design synthetic TFs for complex synthetic gene circuits. Versatility of CRISPR/Cas system in gene regulation such as multiplex regulation , orthogonality , bi-directionality (simultaneous activation and repression of different genes) and complex activation/regulation , 91 usinE. coli. In bladder cells, dCas9 and gRNA were expressed under promoters which are cancer cell (hTERT) and bladder cell specific (hUPII). The CRISPR/dCas9 was expressed against a lacI repressor, whose repression relieved the expression of transgene therapeutic proteins, i.e., the output of the circuit [E. coli model system, a gRNA is driven by an inducible promoter, which if expressed recruits a dCas9 to bind the constitutive promoter of a gene referred as ‘output’, thus, sterically hindering the RNA polymerase to bind and generate the output in the presence of an input signal. Output is, therefore, possible in the absence of input signal molecule, i.e., a NOT gate approach is observed [CRISPR has proven to be an effective and popular strategy for exploring complex regulatory synthetic circuits . In mamm circuit . The outobserved . The gRNThese concepts inspired from other systems could be utilized and attempted in microalgae and cyanobacteria through their inducible promoters/repressors, as discussed in Table Retro-Biosynthesis Tool) [Synthetic biology not only facilitates the researchers to alter or modify metabolic pathway/s but also reprograms the cells by inserting metabolic pathways of interest. Here, the pathway of interest could be taken from one organism and inserted into another, to follow the enzymatic potential . This reis Tool)  have beeN. salina and Nannochloropsis gaditana portray the importance of such in silico techniques to predict metabolic capabilities of various microalgae [Genome-scale metabolic reconstruction is an in silico technique, which utilizes biochemical models to predict microorganisms’ response to various genetic or environmental stresses. To save time and to make the method economically feasible, this method is slowly emerging as an alternate to wet lab experiments. Metabolic engineering has enabled custom designing and production of novel molecules, bearing desirable properties for wide-scale commercial, environmental and healthcare applications. Genome-scale reconstruction of an organism accurately predicts chemical transformations taking place within cellular components and focuses on delineating genetic networks governing biochemical pathways that are industrially desirable , 119. Clcroalgae , 121. Adcroalgae . This knC. reinhardtii. AlgaGEM is a complete literature-based GEM which reports the functions of 866 unique ORFs, 1862 metabolites, 2249 gene–enzyme reaction association entries and 1725 unique reactions. It was reconstructed using available compartmentalization data of cytoplasm, mitochondrion, plastid and microbody algae and compartmentalization data of Arabidopsis thaliana. Primarily, AlgaGEM portrays a functional metabolism for Chlamydomonas and anticipates distinct algal behavior. This model was validated by simulating the growth and metabolic functions obtained from the literature [C. reinhardtii) and devised a modeling approach which predicts growth for a given light source, resolving wavelength and photon flux. They experimentally verified the transcripts, which were investigated in the network and physiologically validated model function showing high confidence in network contents and predictive applications.For the reconstruction of a novel or a less studied species, GEMs would require the entire known biochemical data and system wide data obtained through omics analyses. Progress in metabolic engineering studies and the data generated from such studies have depicted the importance of GEMs in designing new strains. Dal’Molin et al.  constructerature . Chang eterature  reconstrChlorella variabilis by employing 526 genes, 1236 metabolites and 1455 reactions. This model was effective in apprehending growth of C. variabilis under various light conditions and facilitated analysis of metabolic pathway for the synthesis of chitosan and rhamnose. This reconstructed model will aid in providing useful information regarding target metabolites and allow improved features in the strain by metabolic engineering [C. pyrenoidosa. This model included two compartments and consisted of 61 metabolites and 67 metabolic reactions, wherein the influence of carbon and energy metabolism under various trophic modes were explained by the use of metabolic flux analysis. Metabolic reconstruction modeling combined with real-time PCR and RNA-Seq gene expression data was employed to gain an insight into mechanisms that lead to rapid TAG accumulation in Tetraselmis sp. M8, as the cells transitioned from a growth phase to stationary phase [A similar compartmentalized GEM was reconstructed for ineering . Yang etineering  proposedry phase . This stThe major constraint after getting a fully annotated sequence is that it is not possible to fully predict the metabolic capabilities of the species. This leads to the construction of metabolic models, which predict probable biochemical pathways in an organism. Species-specific networks are created based on evidences from their gene–protein associations. The information about enzymes such as EC numbers, gene, protein, pathways and substrates is linked with the help of protein databases and its resources, viz. BRENDA  and ExPAGene regulatory networks describe the complex mat of transcription factors that bind to regulatory sequences of target genes for the expression of function . Gene reThere are several practical limitations in scaling up of laboratory-based algal biomass cultivation to commercial scale, economic non-feasibility being one of the major factors, as reviewed in detail by Slade and Bauen . HoweverYBM), lipid yield (Ylipid) or CO2 uptake  and low predicted residual error sum of squares (PRESS) are selected for analysis. A gradient-based numerical search technique is employed for optimization of the selected model.Algal regression models based on statistical approach and biokinetic models based on algal growth mechanism are popularly used for optimal algal cultivation. Central composite design (CCD) and response surface methodology (RSM) are tools to optimize operating conditions such as algal biomass productivity  are often influenced by the bulk concentration of nutrients  as well as NaCl concentration, which are taken care by the Monod model at the low substrate loading and Andrews inhibition model at high substrate loading.q0,S is the minimum allowable cell quota for N or P and qS is the time variant internal quota in cell. Haldane inhibition model Iavg is the average algal medium light intensity, KL and KIL are light half-saturation and inhibition constants, respectively. Temperature-induced growth was usually taken care by incorporating Arrhenius activation μ is the specific algal growth rate, Tmin and Tmax are the allowable minimum and maximum temperatures for algal growth, and b and c are constants. Lee et al. [Algal growth is also induced by the internal nutrient cell quota , which is described by Droop model e et al.  reviewede et al. . Sinha ee et al.  carried 2 fixation capacity need to be resolved. In addition to the aforementioned, novel consolidated bioprocess wherein a single microbe can generate renewable biofuel as an alternative to depleting fossil fuels is the need of the hour. This review also examines the role played by microalgae as biorefineries, microalgal culture conditions and various operating parameters that need to be optimized to yield biofuel that can be economically competitive with fossil fuels.Microalgae bear immense potential as bio-cell factories in terms of producing key chemicals, recombinant proteins, enzymes, lipid, hydrogen, alcohol etc. Abstraction of such high-value products  facilitates to make microalgae-based renewable energy as an economically viable option. Synthetic biology is an emerging field that harmoniously blends science and engineering to help design and construct novel biological systems, with an aim to achieve rationally formulated objectives. The microbial genetic information, which is easily amenable to modification via metabolic engineering, systems biology and pathway reconstruction coupled with synthetic biology, allows researchers to produce required biomolecules. However, resources and tools used for such nuclear manipulation, construction of synthetic gene network and genome-scale reconstruction of microalgae are limited. The use of synthetic biology in algal biofuel production is still in its infancy, wherein challenges in the development of more advanced genetic tools, high biomass and improved COTo summarize, algal biorefinery in the present state strategically produces multiple products—bulk and specialized co-product to increase the total revenue from cultivation and make the bulk production economically feasible. In practice, multiproduct large-scale biorefinery still poses several problems, which need to be soon addressed. Some economically important value-added products such as astaxanthin and oil; lutein and oil; EPA and oil are being produced in lab scale. Attempts should be made to facilitate their large-scale production through proper pathway prediction and growth and metabolic modeling, wherein the domains of systems biology and process optimization will play a crucial role. Process optimization can lead us to optimized combination of growth conditions such as light, temperature, mass transfer, reactor configuration, media formulation and supplementation, which produces improved growth kinetics. Systems biology, on the other hand, can aid in optimal use of carbon and energy balance so that every possible carbon atom is put to use and every ATP is spent judiciously taking into account the metabolism, physiology and induced stress response. This can fructify only when channeling metabolic flux and partitioning are efficient, both of which are predicted by reconstructed genome-scale metabolic models. These in silico models are based on systems biology data that stem from omics and labeling analysis.


Recent advances in metabolic engineering enable the production of high-value chemicals via expressing complex biosynthetic pathways in a single microbial host. However, many engineered strains suffer from poor product yields due to redox imbalance and excess metabolic burden, and require compartmentalization of the pathway for optimal function. To address this problem, significant developments have been made towards co-cultivation of more than one engineered microbial strains to distribute metabolic burden between the co-cultivation partners and improve the product yield. In this emerging approach, metabolic pathway modules can be optimized separately in suitable hosts that will then be combined to enable optimal functionality of the complete pathway. This modular approach broadens the possibilities to fine tune sophisticated production platforms and thus achieve the biosynthesis of very complex compounds.Here, we review the different applications and the overall potential of natural and artificial co-cultivation systems in metabolic engineering in order to improve bioproduction/bioconversion. In addition to the several advantages over monocultures, major challenges and opportunities associated with co-cultivation are also discussed in this review. •Benefits of using co-cultivation system in metabolic engineering.•Existence of natural consortia and their application.•Recent advancement in co-cultivation methodology for bioproductions.•Challenges in implementing microbial consortia for microbial biosynthesis. The functionality and overall conversion efficiency of the biosynthetic pathway depends on various factors including precursors, cofactor demand and optimal expression of the pathway enzymes. Problems arise however when, due to the complexity and length of the recombinant pathway, a single strain cannot cope with the pathway demand, a phenomenon commonly referred to as metabolic burden .To overcome the limitations posed by metabolic burden, significant developments have been made towards rationally designed microbial co-cultures to distribute metabolic burden of complex and long biosynthetic pathways into different strains/species in order to improve bioproduction performance  Fig. 1)Fig. 1). The modularity in co-cultivation allows rapid optimization of the strains carrying each pathway module and latter assembly of the engineered strains into a synthetic consortium that enables optimal conversion of a substrate or precursor metabolite to the desired final product. It provides a platform to optimize each segregated pathway under optimal cellular environments for functional expression of different pathway genes. It also provides balancing of the complex pathway by optimizing the ratio of the consortia members to improve overall yield . CompartCo-cultivation methodologies are widely used in animal tissue engineering. Such approaches provide fine control of the target cells through paracrine signalling to make functional tissues . Co-cultDespite extensive work on engineering microbial consortia for chemical biosynthesis, very few co-cultivation strategies have been applied in industrial biotechnology. Such industrial applications include wastewater treatment, biogas production, and the production of traditional foods. In the case of food industry, synthetic consortia are used for making dairy products such as cheese, yoghurt and kefir; bakery products like sauerkraut and sourdough; and meat products like salami . Liquor This review describes the recent successful implementation and applications of co-cultivation methods for microbial biosynthesis using metabolic engineering approaches. It also highlights the challenges and limitations in existing co-cultivation systems and discusses how it can be improved to reach their full potential for industrial applications.2Co-cultivation methodologies reduce and even eliminate metabolic burden of the engineered strains that typically encounter metabolic stress due to the overexpression of long and complex biosynthetic pathways in single cells. This is because functional expression of extensive metabolic pathways requires significant energy expenditure and imposes an additional drain of key precursors and co-factors and thus competes with biomass generation. Such competition often leads to impaired growth and finally poor product yield . Co-cultOne of the great advantages of co-cultivation approaches is that it can involve the use of multiple species forming artificial consortia. Multiple species provide diverse environments that are best suited for optimal activity of pathway enzymes, especially enzymes that are derived from higher eukaryotes. In such a scheme, the product of one engineered strain is transported to another engineered microbe where it is further metabolized to the final product  Fig. 1CCFig. 1C.S. cerevisiae and E. coli. E. coli was engineered to overproduce taxadiene, while S. cerevisiae was used for expressing cytochrome P450s (CYPs). P450s have been notoriously challenging to express in E. coli despite extensive engineering efforts and, as a result, S. cerevisiae has traditionally been used to express these enzymes  produced during taxane oxygenation inhibit taxadiene biosynthetic pathway enzymes (ISPG and ISPH). Spatial segregation of the pathway into two different cells provides membrane barrier to ISPG and ISPH from ROS and thus prevents inactivation .E. coli during co-cultivation. The maximum titer was achieved from the consortium having initial inoculation ratio of 8:2 (upstream:downstream)  .Fig. 2OpE. coli strains engineered to grow on either glucose or xylose, was used to engineer a two-strain microbial consortium for the production of the flavonoid naringenin. By tuning the concentrations of the two carbon sources, as well as other parameters , the authors developed a process with significantly improved final naringenin titers compared to the monoculture process  and 4-hydroxybenzoic acid (4HB) via a common intermediate 3-dehydroshikimic acid (DHS) by just swapping the downstream strain. Cis,cis-muconic acid was produced, when the upstream DHS precursor provider cell was co-cultivated with downstream MA producer cell while swapping the downstream cell to 4HB producer cell resulted in the production of 4HB  and the other glucose-selective (xylose deficient), utilized xylose and glucose more quickly as compared to a mono-cultivation approach  yields over half of the total product  and stops growing, while the other (Cel+) continues to grow by utilizing cellobiose. Cel– population divide faster than Cel+ population when galactose was fed as sole carbon source, suggesting bet-hedging phenomenon that helps bacteria adapt against uncertain environmental perturbations . Next, the whole fermented medium along with other essential substrates is transferred to a second bioreactor and sterilized for the second time. The following second fermentation includes K. vulgare and B. megaterium which catalyse the conversion of L-sorbose to L-sorbosone by L-sorbose dehydrogenase  and its further conversion to 2-KLG by L-sorbone dehydrogenase . Despite the high yield of more than 97%, the long and complex fermentation process remains an engineering conundrum. To address this challenge, a synthetic consortium of G. oxydans and K. vulgare was reorganized for one-step fermentation of 2-KLG from D-sorbitol. This approach allowed a total yield of 89.7% within 36 h, which was comparable to the conventional two-step fermentation. , a vitamin C precursor, is achieved by a two-step fermentation by three strains, lus spp. . In thisntation. . The sucRalstonia eutropha  and Bacillus subtilis from sucrose without precursor feeding . The co-cultivation of R. eutropha and B. subtilis in optimized media led to the production of (P(3HB-co-3HV) with 66% w/w yield having 16 mol% HV fraction  (P(3HB-co-3HV) was reported, using a synthetic consortium of  feeding . B. subtfraction .P. pastoris using methanol as an inducer and the sole carbon source. The biosynthetic pathway was segregated and balanced by rationally designing various P. pastoris- P. pastoris co-cultivation combinations. The synthetic consortium was monitored and controlled with fluorescent reporter protein to achieve balanced growth of the strain modules in a bioreactor. The optimized co-cultivation fermentation in bioreactor yielded 593.9 mg/L monacolin J and 250.8 mg/L lovastatin as compared to 60.0 mg/L monacolin J and 14.4 mg/L lovastatin in mono-cultivation fermentation  insensitive glucose-selective strain and a xylose-selective strain, that efficiently co-utilized both sugars and produced 5.2 g/L n-butanol at 63% of the theoretical yield (Clostridium thermocellum and Thermoanaerobacterium aotearoense from pretreated sugarcane bagasse (SCB). A titer of 50.05 ± 1.51 mmol/L hydrogen was achieved with 4% pretreated SCB at 55 °C  for production of different biofuels such as ethanol, butanol and hydrogen . CBP viaacterium . (ii) Prutyricum . (iii) Cal yield . Similarat 55 °C , which wH2/g SCB .E. coli strains was established, which collectively expressed 15 pathway genes from different plants and microbes for production of anthocyanins  as the sole carbon source. C. amalonaticus Y19 produced CO2 and H2 from water-gas shift reaction which were further utilized by S. ovata. The production of acetate from CO was 1.47 mM, 0.807 mM, and negligible in the co-cultivation, mono-cultivation of S. ovata, and mono-cultivation of C. amalonaticus, respectively. This syntrophic cooperation can be further utilized for production of various biofuel molecules using CO as carbon source to help address environmental problems  and 3-phenoxybenzoic acid (3-PBA) by co-cultivation of yzae M-4 . Co-cultyzae M-4 .In order to elucidate the interaction within the microbial communities B. subtilis expressing Clostridium cellulovorans gene minicbpA was co-cultivated with a B. subtilis strain expressing either an endoglucanase or a xylanase, it resulted in minicellulosome formation with both miniCbpA and the cellulosomal activity. They called this phenomenon ‘‘intercellular complementation’’ as both miniCbpA and the cellulosomal enzymes secreted out and formed a functional minicellulosome  . In one lulosome   and higher productivity. It utilizes the metabolic power and resources of each co-cultivation partner to meet the demand of specific co-factors and precursors and thus improves the conversion yield of the modularized biosynthetic pathway. This approach allows to produce more complex compounds with improved productivity by distributing the metabolic pathway between each consortium member. Co-cultivation fermentations may lead to enhanced production performance, and allow the utilization of cheaper substrates. Moreover, artificial consortia open the door to address the issues of functional expression of complex biosynthetic pathway enzymes without compromising the yield and product quality. They can also reduce the effort of reconstitution of recombinant biosynthetic pathways.As an emerging research area in the field of metabolic engineering, co-cultivation engineering is still in its infancy. Most of the recent reports on co-cultivation engineering that are based on employment of microbial consortia have only two constituent strains/species in order to achieve their engineering goals. Co-cultivation of multiple populations is more complicated, as co-cultivation behaviour of individual strains using common cultivation methods is still unknown and potentially more challenging to control when increasing the number of the constituent strains/species. However, recent development in co-cultivation engineering has greatly expanded our understanding of microbial behaviour in communities .While the potential of synthetic microbial consortia holds great promise, there are inherent challenges that need to be addressed with the help of synthetic biology approaches. It is anticipated that co-cultivations comprising multiple specialized members, or polycultures, will be developed and utilized for meeting the demand of more complicated biosynthetic pathways in the near future.


Chlorella vulgaris (iCZ946) was applied to predict feeding of multiple nutrients, including nitrate and glucose, under both autotrophic and heterotrophic conditions. The objective function was changed from optimizing growth to instead minimizing nitrate and glucose uptake rates, enabling predictions of feed rates for these nutrients. The metabolic model control (MMC) algorithm was validated for autotrophic growth, saving 18% nitrate while sustaining algal growth. Additionally, we obtained similar growth profiles by simultaneously controlling glucose and nitrate supplies under heterotrophic conditions for both high and low levels of glucose and nitrate. Finally, the nitrate supply was controlled in order to retain protein and chlorophyll synthesis, albeit at a lower rate, under nitrogen-limiting conditions. This model-driven cultivation strategy doubled the total volumetric yield of biomass, increased fatty acid methyl ester (FAME) yield by 61%, and enhanced lutein yield nearly 3 fold compared to nitrogen starvation. This study introduces a control methodology that integrates omics data and genome-scale models in order to optimize nutrient supplies based on the metabolic state of algal cells in different nutrient environments. This approach could transform bioprocessing control into a systems biology-based paradigm suitable for a wide range of species in order to limit nutrient inputs, reduce processing costs, and optimize biomanufacturing for the next generation of desirable biotechnology products.Nutrient availability is critical for growth of algae and other microbes used for generating valuable biochemical products. Determining the optimal levels of nutrient supplies to cultures can eliminate feeding of excess nutrients, lowering production costs and reducing nutrient pollution into the environment. With the advent of omics and bioinformatics methods, it is now possible to construct genome-scale models that accurately describe the metabolism of microorganisms. In this study, a genome-scale model of the green alga  C. vulgaris genome-scale model was used to control the supplies of nitrate for algal growth under autotrophic or photosynthetic conditions, saving 18% of nitrate fed. Under heterotrophic conditions with organic carbon supplied, we optimized feeding of both glucose and nitrate while maintaining robust growth. The model was also used to enhance fatty acid volumetric product yields by over 60% and lutein levels nearly 3 fold under nitrogen-limited conditions. This study demonstrates that metabolic models can be used to predict the optimal nutrient supply as we strive to make algal bioprocessing sustainable for the future.Supplying the optimal levels of nutrients such as nitrogen and carbon is an important consideration when cultivating green algae and other biological systems. Overfeeding nutrients can increase production costs while insufficient supplies can lower product yields. Finding the balance between production costs and productivity is a major challenge in biomanufacturing. Our published  For example, one approach is to control glucose concentration within a defined range during algal culture using measurements and simple feedback control algorithms. Previous studies controlled glucose concentration in the range of 5 to 40 g/L to reach a high cell density culture, increasing cell density from 6.25 to 117.18 gDW/L in 32 h.8 Mathematical equations can be constructed to represent the bioreactor system and nutrient supplies optimized based on the simulations. This strategy has been validated in silico for penicillin fermentation process, biohydrogen production in Cyanothece sp, and biofuel production in algae.11 It was also tested in Chlorella in a CSTR system for CO2 mitigation and in an open pond system under different light intensities.13 These MPC approaches typically incorporate empirical kinetic models based on experimental data in order to predict optimal algal growth conditions. Researchers obtain model parameters from experimental measurements of cell growth rate and nutrient consumption rate and construct equations in order to simulate algal growth in the models. Those parameters are often associated with the bioreactor system including volume and dilution rate or reflect external characteristics of the cell including growth rate.In order to more effectively control nonlinear biological processes like cell culture, model predictive control (MPC), a model-based control strategy, has been designed as one approach to achieve more finely tuned bioreactor control.However, these previous efforts typically lack a detailed description of the metabolic characteristics of the algal cells themselves and therefore cannot account for nutrient requirements and accompanying cell compositions. These models, while highly descriptive of growth at the macroscopic level, typically do not include the mathematical framework to describe exactly how specific nutrients such as nitrate and glucose are incorporated within the cellular metabolic framework under different culture conditions. Incorporating cellular metabolic details can enable biotechnologies to more effectively understand and potentially optimize the utilization of nutrients within algal cells during growth and lipid production, and be able to consider changes in algal metabolism for different nutrient input scenarios.14 Genome-scale metabolic models are a system biology tool that represent the metabolism of a cell based on its genomic sequence. In order to reconstruct biological networks into a genome-scale model, metabolic reactions in the form of mass balance equations that integrate nutrient transport, intracellular metabolism, and biomass accumulation are included.15 Model constraints are obtained by considering genomics, transcriptomics, proteomics as well as cellular composition information.16 Thousands of metabolic reactions are composed as a large stoichiometric matrix (m x n); followed by flux balance analysis required that is solved in order to identify the flux rates of each reaction. Furthermore, as the model is typically underdetermined, linear programming is incorporated in order to identify an optimal flux solution by maximizing an objective function, typically the biomass production rate.17Fortunately, the advent of genome-scale models has enabled the biotechnology community to better understand the allocation and distribution of nutrients to metabolites and biomass.E. coli,18 yeast19 and mammalian cells.20 Indeed, previous research has applied genome-scale models to a variety of applications including microbial strain optimization,21 intracellular metabolite pool prediction,16 and the discovery of new metabolic reactions.22 Recently, our group constructed a genome-scale model for C. vulgaris (iCZ843), which provides the most comprehensive representation of the physiology of this organism to date, including 843 genes, 2,294 reactions, and 1,770 metabolites.23 This model contains five cellular compartments including the cytoplasm, mitochondrion, chloroplast, thylakoid and glyoxysome, in addition to the extracellular environment, allowing metabolic exchange within and between the different compartments. In addition, corresponding experiments successfully validated model predictions, including the capacity to vary the growth rate of C. vulgaris by altering nutrient inputs. Furthermore, the iCZ843 model was used to contextualize metabolomics data over the course of growth, evaluating dynamic changes in the biomass composition under different nutrient conditions, resulting in an updated model of C. vulgaris model (iCZ946).16Genome-scale models have been constructed for a wide variety of industrial relevant species including 17 However, for our metabolic model control (MMC) applications, the primary objective may be the capacity to control nutrient utilizations in order to control inputs that can then be provided based on model predictions. In the current study, growth of C. vulgaris was estimated based on experimental optical density (OD) measurements and then used to predict subsequent growth rates during the exponential phase. Since the primary objective of this study was to optimize the nutrient supply for algal culture, the objective function was instead changed to optimize nutrient flux input, including nitrate or glucose uptake into algal cells. We then applied the genome-scale model in order to improve predictions of the nutrients required for growth in response to different culture conditions. As a result, we demonstrate the capacity to utilize the metabolic model control in order to more effectively and efficiently optimize bioprocessing for the generation of high levels of algal biomass and biofuel precursors for potential applications in biotechnology. More importantly, this framework demonstrates a new avenue for applying genome-scale models as a way to optimize bioprocessing across a wide range of cell lines and potential applications in biotechnology.In genome-scale model studies, biomass production is typically set as the objective function in order to drive the optimization in the model and predict cell growth rates.iCZ946-PAT1 was used to control nitrate supply to autotrophic C. vulgaris cultures. Model iCZ946-PAT1 was previously constructed based on the biomass composition obtained under phototrophic and nitrogen sufficient conditions  taken from experimental data at a previous time point (ODt-1) and the current time point (ODt). Then, the growth rate within the model was constrained to the calculated growth value (μ) and the objective function was changed to the nitrate uptake rate (FN). Specifically, the model was optimized to calculate the minimum nitrate amount required to sustain this calculated growth rate over a particular time interval   Fig. . EventuaP ≤ 0.05), growth rates comparable to the bulk-fed group were achieved in the 100% MMC cultures   Fig. . The sloIn addition, we also performed an experiment to show that by incorporating real-time measurements, this MMC approach help to enhance the performance of algal cultures and also control nutrient supply more efficiently compared with standard approaches based on biological approaches.C. vulgaris culture was fed with nitrate using 3 different feeding strategies. The control case included an initial loading of 285 mg/L nitrate with no subsequent feeding. A second case was based on the biological knowledge of the growth rate (0.0137 h−1) from the previous experiment  was used to calculate the growth rate (μ) and to constrain biomass accumulation rate in iCZ946-HT1. Next, the glucose uptake rate (FG) was set as the objective function  required to sustain algal growth. Then, glucose uptake rate was constrained using the predicted value and nitrate uptake rate (FN) was set as a new objective function in a subsequent simulation  required to sustain algal growth.After successfully applying the genome-scale model ons Fig. . By usinion Fig. . The modion Fig. . The optThis algorithm was performed at combinations of two different levels of glucose  and two different levels of nitrate . Experimental measurements showed that the low glucose groups (LGHN and LGLN) consumed all the initial glucose within 96 h Fig. . Based oC. vulgaris consumed 25 mg/L nitrate (HGLN and LGLN) and 250 mg/L nitrate (HGHN and LGHN) by 48 and 120 h. Specific amounts of nitrate were fed to the cultures every 24 h based on cell growth rate and model predictions for nitrogen uptake rate   Fig. . This mo47) Fig. . ConsideC. vulgaris consumed all the initially fed glucose in the LG group  between 61 and 85 h . 5 h Fig. . The MMC5 h Fig. . In the ium Fig. .Fig. 3Me750 from 0.08 to the range of 8.8 and 9.7 in 133 h , 25 mg/L of nitrate was consumed after 37 h and 250 mg/L of nitrate was consumed between 85 and 109 h for the HN groups  Fig. . Nitrate3 h Fig. , f.C. vulgaris has been found to accumulate high intracellular lipid levels by altering its biomass composition from low fatty acid content (~10%) to up to 60% fatty acid.4C. vulgaris is able to accomplish this shift in biomass composition by recycling intracellular protein back into amino acids, which contributes to lipid synthesis.27 However, algae suffer two major drawbacks as a result of this adjustment which are the loss of protein and chlorophyll.28 These sacrifices of critical cellular components are likely to lead to an eventual cessation in growth. Indeed, we observed that C. vulgaris ceases growth during nitrogen starvation when protein content falls below ~10%  and 2% amino acid content for the biomass composition (iCZ946-PAT5-2%AA), as shown in Fig. iCZ946-PAT5-10%AA model should permit algae to synthesize enough protein and preserve sufficient chlorophyll in order to maintain steady algal growth while also potentially yielding a high lipid content under nitrogen starvation metabolism. In contrast, C. vulgaris was expected to cease growing when the nitrate supply was restricted to 2% as predicted by the iCZ946-PAT5-2%AA model.We previously explored the impact of different nutrient condition changes on the biomass composition and the metabolism of algal cells in a genome scale model.T5 Table , which aiCZ946-PAT5-10% AA model or iCZ946-PAT5-2%AA model. For the first 165 h of growth, all C. vulgaris cultures grew similarly and reached OD750 = 1.0 . At 261 h, C. vulgaris reached OD750 = 1.1 for the nitrogen starvation culture, while OD750 increased to 1.3 in both MMC-2%AA and MMC-10%AA cultures. At the end of the experiment, around 549 h, OD750 only increased slightly to 1.3 for the nitrogen starvation culture. In the MMC-2%AA case, algal growth progressively slowed relative to the 10%AA around 309 h (P ≤ 0.01), while still greater than the complete nitrogen starvation case. In contrast, the MMC-10%AA culture continued to expand over the complete duration of the culture period and reached a final OD750 of 2.3 at 549 h (P ≤ 0.01). These results indicated that an average of 0.06 mg/L/h nitrate could support algal growth while targeting a protein content at or near 10%.To validate this concept, we again performed an autotrophic experiment in which nitrate was supplemented to cultures every 20–24 h based on our model prediction Fig. . One set1.0 Fig. . HoweverC. vulgaris maintained steady growth under stressed conditions for the MMC-10%AA condition. The biomass yield steadily increased from 340 mg/L at 261 h, to 470 mg/L at 429 h, finally reaching 610 mg/L at 549 h. The addition of nitrate using the iCZ946-PAT5-10%AA model facilitated a biomass increase of 30% at 261 h, 61% at 429 h, and 105% at 549 h compared to the complete nitrogen-starvation case (P ≤ 0.01). Compared with MMC-2%AA cultures, the biomass increased 17% at 549 h (P ≤ 0.05) with no significant difference at 261 h and 429 h , respectively.Importantly, the overall FAME yield, which incorporates both FAME content and biomass production, exhibited a different accumulation pattern. While the nitrogen starvation group yielded 175 mg/L at 549 h, the MMC-2%AA and MMC-10%AA group provided 275 mg/L and 281 mg/L total FAME yield respectively. Compared with MMC-10%AA conditions, the slower growth rate at 549 h did not affect the FAME yield in MMC-2% AA cultures. Over the three sampling periods, the MMC-10%AA group displayed an increasingly higher total FAME yield 28% and 61% when compared to the nitrogen starvation group at 429 h and 549 h . These results suggested that the iCZ946-PAT5-10%AA model can predict the level of nitrate necessary to maintain substantial chlorophyll content in C. vulgaris for longer periods, sustaining the energy capture capability from sunlight and building up biomass with an elevated fatty acid content in a limiting nitrogen environment.After successfully controlling ase Fig. . Indeed,res Fig. . However30 but this is the first study to show that continual supplementation of nitrate can maintain the protein content at a reduced level.Since nitrate is a major source of nitrogen for protein synthesis in algae, the protein contents were also measured for all three conditions. At 261 h, protein content for the nitrogen starvation, MMC-2%AA and MMC-10%AA cases were around 11–16% Fig. . By 549 P ≤ 0.01) with 42 mg/L and 49 mg/L for the MMC-2%AA and MMC-10%AA group. Previous studies with C. zofingiensis found that starch synthesis was more active during the initial stages of nitrogen starvation, but decreased gradually as nitrogen starvation continued.31 Our results indicated that without MMC, cells suppressed starch synthesis from 261 to 529 h. For both MMC groups, our results indicated that starch synthesis driven by sunlight was faster than the degradation due to limitations in the nitrogen levels. The starch content in both MMC groups was also higher than in nitrogen starvation case at most of the time points  and the MMC-2%AA group at 549 h (P ≤ 0.05), respectively .36Interestingly, we observed that even though a theoretical surplus of nitrate was fed for the 120% MMC condition according to model predictions, the cells still took up all the supplied nitrate experimentally under autotrophic conditions. Comparing the results with the 100% MMC condition suggested that this nitrate was not used to build more biomass. The phenomena of nitrate uptake rate exceeding nitrogen build up rate in biomass has been indicated previously with our genome-scale model.iCZ946-PAT5-10%AA genome-scale model could be applied to successfully limit the nitrate uptake rate while channeling the limiting element into synthesis of chlorophyll and other proteins. The goal was to retain sufficient chlorophyll and other protein content needed to maintain energetic and physiological processes critical to fatty acid production. Indeed, previous researchers found that degradation of chlorophyll decreases light energy conversion efficiency in red algae Porphyridium cruerntum.37 Further, in Chlorella, higher amounts of NADPH are required for lipid synthesis under nitrogen deprivation, but somewhat contradictory, photosynthesis from chlorophyll is the major source for NAPDH production.40 Using MMC to control the nitrate feed supply at reduced levels allowed the cells to continue to grow and accumulate biomass, including chlorophyll, while also synthesizing lipids, resulting in higher overall lipid and starch production rates as compared to the complete nitrogen deprivation case. Consequently, algal growth continued as a result of MMC, even under nitrogen restricted conditions from 261 to 549 h, which was the major fatty acid production stage, leading to higher yields of the fatty acid products compared to complete deprivation. Thus, maintaining sufficient chlorophyll level through limited nitrate feeding in the MMC-10%AA case allows the algae to support the energy demands needed for robust lipid synthesis in C. vulgaris. In addition, previous studies also found a correlation between lutein content and chlorophyll content in microalgae.41 The increase of lutein content in MMC-10%AA cultures also indicated the importance of optimizing nitrogen supply to sustain chlorophyll and lutein yield under nitrogen-limited conditions. To our knowledge, this is the first time researchers have applied a genome model to increase yields of both bioenergy precursors in the form of fatty acids and health products such as lutein in algal cultures systems under nitrogen-limited conditions.Under nitrogen restricted conditions, biomass composition analysis indicated that the C. vulgaris during a shift from nitrogen replete to nitrogen starvation conditions.42 The presence of a predominance of C18:1 fatty acid in all three cultures of this study (nitrogen depletion and nitrogen restriction) indicates that some levels of cellular response to nitrogen starvation were evident even for the two MMC groups in which the cells were fed with limited amounts of nitrate.Similar fatty acid profiles were observed for nitrogen restricted and nitrogen depleted conditions with C18:1 fatty acid as the dominant peak, as shown in Fig. C. reinhardtii genome-scale model to optimize nitrate supply and light intensity for D. salina cultures.43 In this study, we expanded the capability of our multi-compartment C. vulgaris genome-scale models to control multiple nutrients, in this case glucose and nitrate, concurrently for C. vulgaris microalgal cultivation studies, including different biomass compositions that can vary as a result of the nutrient inputs. Previously, control has been implemented for algal fed-batch heterotrophic cultures using conventional methods to control glucose concentration within a certain range44 or kinetic models to optimize feeding rate.25 However, the previous model predictive methods used simple correlations to describe the kinetic interaction between substrate uptake rate and biomass build up rate. In this study, we have demonstrated how genomic information, metabolic networks and biomass compositions can be incorporated to build metabolic model control in order to provide a more precise understanding and control of heterotrophic algal cell cultures. In addition, previous studies observed that a high amount of glucose in the medium can negatively inhibit cell growth in two different Chlorella species.44 Our strategy successfully controlled glucose and nitrate at low concentration, which may be particularly useful for scaling up bioreactors in order to limit nutrient input costs and remediate glucose inhibition challenges.Previously, a study connected a kinetic model with a 45 and mammalian cell generation of recombinant proteins.46Overall, this study demonstrates the power of building genome-scale metabolic models control for nutrient optimization with microbial algal cultures. As data collecting processes evolve, genome-scale models can be implemented into biomanufacturing in order to predict the specific amount of nutrients needed to support algal growth for different conditions and final process objectives as illustrated in supplemental Fig. Chlorella vulgaris UTEX 395 was obtained from the Culture Collection of Algae at the University of Texas at Austin and maintained on sterile agar plates (1.5% w-v) containing Bold’s Basal Medium (BBM). Liquid cultures were inoculated with a single colony in 12.5 mL of sterile BBM. Cells were transferred to 100 mL or 250 mL glass Fernbach flasks  at 25 °C using BBM. Autotrophic cultures were grown with different nitrate concentration under fluorescence illumination (30 μE m−2 s−1). Heterotrophic cultures were grown with different glucose and nitrate concentration with 24 h dark. Nitrogen starvation cultures were grown under BBM without nitrate as nitrogen source. The growth of the cultures was monitored by measuring optical density (OD) at 750 nm. During the metabolic model control, specific amounts of 25 g/L nitrate or 20 g/L glucose were added manually into the cultures based on model prediction every 20–28 h. All the cultures were done in biological triplicate in this study.Green microalgae Liquid cultures were harvested using a high-speed centrifuge  at 4000 x g for 10 minutes. The pellets were stored at −80 °C and lyophilized for 24 h at −40 °C under freeze-dried machine.47 FAMEs were analyzed using a Agilent’s Gas Chromatography (GC) system with discharge ionization detection equipped with a capillary column . GC inlet was set at 250 °C and the injections were in a volume of 1 μL. The temperature program started at 50 °C and then increased to 170 °C at a rate of 20 °C min−1, with a plateau for 1 min. After this plateau, the temperature increased from 170 °C to 220 °C at a rate of 4 °C min−1 and then kept constant for 14 minutes. The total analysis time was 35 minutes. Helium was used as carrier gas.FAME production followed the procedure provided by.48 The dried algae pellets (5–10 mg) were homogenized using a mortar and pestle with 4 mL extraction solvent, the mixture of dichloromethane (25%) and methanol (75%), for 2 min and 2 times. The extraction solution was centrifuged at 10000 x g for 10 min and kept in dark in −20 °C. The solution was filtered before HPLC analysis. The mobile phases are eluent A  and eluent B .Lutein extraction followed the procedure provided by.Starch content in dry biomass was analyzed using an assay kit . Starch in dried algae pellets were hydrolyzed into glucose by α-amylase and amyloglucosidase. Then glucose was digested by enzyme hexokinase and glucose-6-phosphate dehydrogenase. After the reaction, the absorbance at 340 nm was measured.49 Chlorophyll in the dry algae pellets was extracted by DMSO and absorbance at 665 nm and 649 nm were measured.Chlorophyll content followed procedure provided by.4 Dry Biomass was sonicated on ice for 4 °C for 30 s × 6 cycles. Lysates were centrifuged at 16,000 x g for 2 min and the supernatant were analyzed by bicinchoninic acid assay (BCA assay).Protein content followed procedure provided by.iCZ946 model was obtained from.16 Biomass composition, RNA-seq and proteomics data were collected previously under photoautotrophic (PA) conditions and heterotrophic (H) conditions.50 Six sample points were collected to build six different photoautotrophic models (PAT1-PAT6) and five samples were used to construct five different heterotrophic models respectively (HT1-HT5) as described in our previous publication.16 Data collected for all models, except PAT1 and HT1, were undertaken during nitrogen depletion conditions  was constrained in the model and the objective function was changed to minimize nitrate uptake rate (FN) or glucose uptake rate (FG). Genome-scale model simulations were performed using the Gurobi Optimizer Version 5.6.3  solver in MATLAB  with the COBRA Toolbox.51The ons Fig. . iCZ946-reporting-summarySupplementary information


Bacterial heteroresistance  can delay the clearance of bacteria even with long antibiotic exposure. Some proposed mechanisms have been successfully described with mathematical models of drug-target binding where the mechanism’s downstream of drug-target binding are not explicitly modeled and subsumed in an empirical function, connecting target occupancy to antibiotic action. However, with current approaches it is difficult to model mechanisms that involve multi-step reactions that lead to bacterial killing. Here, we have a dual aim: first, to establish pharmacodynamic models that include multi-step reaction pathways, and second, to model heteroresistance and investigate which molecular heterogeneities can lead to delayed bacterial killing. We show that simulations based on Gillespie algorithms, which have been employed to model reaction kinetics for decades, can be useful tools to model antibiotic action via multi-step reactions. We highlight the strengths and weaknesses of current models and Gillespie simulations. Finally, we show that in our models, slight normally distributed variances in the rates of any event leading to bacterial death can (depending on parameter choices) lead to delayed bacterial killing . This means that a slowly declining residual bacterial population due to heteroresistance is most likely the default scenario and should be taken into account when planning treatment length. When bacteria are exposed to antibiotics in vitro or in vivo, the elimination rate often changes dramatically over time. Typically, an initial phase of rapid decline is followed by a phase where bacterial killing is very slow or even absent. However, the bacteria that survive the rapid decline are not genetically different from those that were killed. When the bacteria are recultured from the surviving population and exposed to antibiotics at the same concentration again, they exhibit the same bi- or multiphasic killing as in the first experiment. This sets delayed bacterial killing apart from stable resistance mutations (antibiotic resistance). Just like antibiotic resistance, multiphasic bacterial killing has clinical implications: it is thought to complicate treatments, as it allows fractions of bacterial populations to survive extended exposure to antibiotics ,2.The characteristic slowdown of bacterial elimination after antibiotic exposure can be due to various mechanisms. According to a recent consensus statement , these mIndeed, models that include both intracellular drug-target binding and its effect on a bacterial population have been successfully employed to model heteroresistance . Such dr2 to 105. Modeling an additional step in the molecular cascade leading to bacterial death would then require 104 to 1010 equations. Therefore, the current mathematical tools bar us from quickly testing and evaluating models of multi-step intracellular processes, thereby making it difficult to test theories on mechanisms that can lead to heteroresistance. As a result, while it is suspected that changes in the production of toxic byproducts or diversity in other parts in intracellular kinetics can lead to heteroresistant behavior, it is yet to be shown by modeling.Master equations have several advantages, among them the ability to incorporate bacterial growth and death with the subsequent changes in target molecule content and occupancy. As deterministic models, they do not need to be run many times to get representative averages. However, these current mathematical approaches have limitations in terms of the complexity of the molecular mechanisms that lead to bacterial death or suppress bacterial replication. When antibiotics bind to their intracellular targets, they interfere with essential functions of cells and thereby inhibit bacterial replication (bacteriostatic effect) or kill the cells . Both the bactericidal and bacteriostatic effects can be multi-step processes, for example the binding of antibiotics to their target can lead to the production of toxic byproducts that eventually kill the cells in, for example, kanamycin or norfloxacin . In mastIn this work, we use Gillespie simulations—stochastic simulations of the reaction kinetics in single bacterial cells ,19—to moWhen bacteria are exposed to antibiotics, the elimination of bacteria can slow down dramatically over time. First, we show the relationship among Gillespie simulations and the already established methods—master equations and simple reaction kinetics. This relationship is well studied in the literature modeling other processes ,20, and We aimed to illustrate that modeling based on Gillespie algorithms predicts the same time–kill curves resulting from reaction kinetics as the already established heteroresistance model based on master equations . To thisWe thereby established that both methods, as expected, are equivalent under the condition that bacterial replication can be neglected. Next, we set out to investigate how these dynamics are changed by bacterial replication: the production of new (unbound) target molecules and the inheritance of bound target molecules from the mother cell.Escherichia coli) and slow  bacterial replication rates. To illustrate under which conditions bacterial replication can be neglected, we now compare target occupancy predicted with master equation-based simulations for fast . In this case, we assume a bacterium is dead once the ratio of bound target and total target molecules exceeds a threshold. Here, we introduce heterogeneity by assuming that the number of target molecules is normally distributed around its mean.The target has an “essential” function . In this case, we assume a bacterium is dead once the number of free target falls below a threshold. Here, we introduce heterogeneity by assuming that the threshold for killing is normally distributed around its mean.The antibiotic-target complex is “primary toxic” . In this case, we assume a bacterium is dead once the number of bound targets exceeds a threshold. Here, we introduce heterogeneity by assuming that the number of target molecules is normally distributed around its mean.The antibiotic-target complex is “secondary toxic” . In this case, we assume a bacterium is dead once the number of toxic metabolites exceeds a threshold. Here, we introduce heterogeneity by assuming that the rate of toxic metabolite production is normally distributed around its mean.Finally, to show the flexibility of the approach and to investigate potential sources of heteroresistance, we modelled four different scenarios that lead to bacterial death  . In our max/Hill-models) originated in 1910 [The aim of this paper is to extend the toolbox for modeling the intracellular processes that kill bacteria and to illustrate how these tools can be used to describe bacterial heteroresistance. Traditionally, antibiotic efficacy was mainly described by a single value, the minimal inhibitory concentration . Therefore, to demonstrate the how this approximation on growth changes bacterial dynamics, we modelled it with an established master equations-based approach .E. coli.In M. tuberculosis.However, Gillespie algorithms seem to yield similar results as master equations for slow replication. When we assume that bacterial death results in immediate target degradation, the total amount of target binding is slightly underestimated. Conversely, when we assume the other extreme, that targets are stable after bacterial death, the total amount of target binding is overestimated. Therefore, if realistic target degradation rates were available to inform models with replication, they would probably result in an even better concordance between Gillespie simulations and master equations. However, the results presented here are strongly dependent on the parameter setting we chose. Before making a decision to neglect replication (and therefore making modeling complex reactions possible), it is advisable to compare one-step simulations parametrized with corresponding binding rates  with and without the replication rates of the bacterial organism to be studied. max- or Hill functions when modeling antibiotic action in patients. Moreover, they can be easily incorporated in mixed effect pharmacokinetic/pharmacodynamic (PK/PD) models where the drug concentration is supplied by a PK model, and drug-target binding models are used to describe fixed pharmacodynamic effects with additional stochastic effects [The intended uses of this approach are in vitro studies that investigate antibiotic effects on bacteria. For instance, the expression of genes involved in the bacterial response to antibiotics may vary in a bacterial population thereby leading to heteroresistance . These m effects .Using our Gillespie-based approach, we have run simulations for both single-step processes as well as multi-step processes and demonstrated  that norWe would therefore caution not to interpret the criterion of a substantially different MIC in heteroresistant subpopulations too strictly, in concordance with ,36. An eFor modelling simple reaction kinetics between antibiotic molecules and their target molecules, we used the following differential equations:n = 100 , numgyrase, ,16)).).KD=krkfWe only used the simple reaction kinetics for illustrating the connection among the different ways to model chemical reaction kinetics, therefore this model does not include bacterial replication or death.An approach based on systems of ordinary differential equations fully described in  combinesThese systems of differential equations are not necessarily classical master equations because they are not linear. However, when assuming that bacterial replication is exponential and not logistic  and when assuming that the extracellular antibiotic concentration is constant, these systems become linear and therefore classical master equations. Similarly to ,8, we alThe Gillespie stochastic simulation algorithm first published in 1977 is a general algorithm to simulate stochastic processes originally developed to capture the binding kinetics . StartinIn this work, we use this algorithm to simulate chemical reaction kinetics within cells, modeling both systems with a single or two steps .www.abel-zur-wiesch-lab.com.Codes for