Wearable, Machine Washable, Breathable Polyethylenimine/Sodium Alginate Layer-by-Layer-Coated Cotton-Based Multifunctional Triboelectric Nanogenerators.

Cotton-based textiles are ubiquitous in daily life and are prime candidates for application in wearable triboelectric nanogenerators. However, pristine cotton is vulnerable to bacterial attack, lacks antioxidant and ultraviolet (UV)-protective abilities, and shows lower triboelectric charge generation against tribonegative materials because it is present in the neutral region of the triboelectric series. To overcome such drawbacks, herein, a facile layer-by-layer method is proposed, involving the deposition of alternate layers of polyethylenimine (PEI) and sodium alginate (SA) on cotton. Such modified fabric remains breathable and flexible, retains its comfort properties, and simultaneously shows multifunctionalities and improved triboelectric output, which are retained even after 50 home laundering cycles. Also, the modified fabric becomes more tribopositive than nylon, silk, and wool. A triboelectric nanogenerator consisting of modified cotton and polyester fabric is proposed that shows a maximum power density of 338 mW/m2. An open-circuit voltage of ∼97.3 V and a short-circuit current of ∼4.59 µA are obtained under 20 N force and 1 Hz tapping frequency. Further, the modified cotton exhibits excellent antibacterial, antioxidant, and UV-protective properties because of the incorporation of PEI, and its moisture management properties are retained due to the presence of sodium alginate in the layer. This study provides a simple yet effective approach to obtaining durable multifunctionalities and improved triboelectric performance in cotton substrates.

Evaluation of the potential of Delta-aminolevulinic acid for simultaneous detection of bioburden and anti-microbial photodynamic therapy of MRSA infected wounds in Swiss albino mice.

BACKGROUND: The dramatic increase of drug-resistant bacteria necessitates urgent development of platforms to simultaneously detect and inactivate bacteria causing wound infections, but are confronted with various challenges. Delta amino levulinic acid (ALA) induced protoporphyrin IX (PpIX) can be a promising modality for simultaneous bioburden diagnostics and therapeutics. Herein, we report utility of ALA induced protoporphyrin (PpIX) based simultaneous bioburden detection, photoinactivation and therapeutic outcome assessment in methicillin resistant Staphylococcus aureus (MRSA) infected wounds of mice. METHODS: MRSA infected wounds treated with 10% ALA were imaged with help of a blue LED (∼405 nm) based, USB powered, hand held device integrated with a modular graphic user interface (GUI). Effect of ALA application time, bacteria load, post bacteria application time points on wound fluorescence studied. PpIX fluorescence observed after excitation with blue LEDs was used to detect bioburden, start red light mediated antimicrobial photodynamic therapy (aPDT), determine aPDT effectiveness and assess selectivity of the approach. RESULTS: ALA-PpIX fluorescence of wound bed discriminates infected from uninfected wounds and detects clinically relevant load. While wound fluorescence pattern changes as a function of ALA incubation and post infection time, intra-wound inhomogeneity in fluorescence correlates with the Gram staining data on presence of biofilms foci. Lack of red fluorescence from wound granulation tissue treated with ALA suggests selectivity of the approach. Further, significant reduction (∼50%) in red fluorescence, quantified using the GUI, relates well with bacteria load reduction observed post topical aPDT. CONCLUSION: The potential of ALA induced PpIX for simultaneous detection of bioburden, photodynamic inactivation and "florescence-guided aPDT assessment" is demonstrated in MRSA infected wounds of mice.

In vitro photoinactivation effectiveness of a portable LED device aimed for intranasal photodisinfection and a photosensitizer formulation comprising methylene blue and potassium iodide against bacterial, fungal, and viral respiratory pathogens.

Antimicrobial photodynamic therapy (aPDT) can be a viable option for management of intranasal infections. However, there are light delivery, fluence, and photosensitizer-related challenges. We report in vitro effectiveness of an easily fabricated, low-cost, portable, LED device and a formulation comprising methylene blue (MB) and potassium iodide (KI) for photoinactivation of pathogens of the nasal cavity, namely, methicillin-resistant Staphylococcus aureus, antibiotic-resistant Klebsiella pneumoniae, multi-antibiotic-resistant Pseudomonas aeruginosa, Candida spp., and SARS-CoV-2.In a 96-well plate, microbial suspensions incubated with 0.005% MB alone or MB and KI formulation were exposed to different red light (~ 660 ± 25 nm) fluence using the LED device fitted to each well. Survival loss in bacteria and fungi was quantified using colony-forming unit assay, and SARS-CoV-2 photodamage was assessed by RT-PCR.The results suggest that KI addition to MB leads to KI concentration-dependent potentiation (up to ~ 5 log10) of photoinactivation in bacteria and fungi. aPDT in the presence of 25 or 50 mM KI shows the following photoinactivation trend; Gm + ve bacteria > Gm - ve bacteria > fungi > virus. aPDT in the presence of 100 mM KI, using 3- or 5-min red light exposure, results in complete eradication of bacteria or fungi, respectively. For SARS-CoV-2, aPDT using MB-KI leads to a ~ 6.5 increase in cycle threshold value.The results demonstrate the photoinactivation effectiveness of the device and MB-KI formulation, which may be helpful in designing of an optimized protocol for future intranasal photoinactivation studies in clinical settings.

Evaluation of antimicrobial photodynamic action of a pluronic and pectin based film loaded with methylene blue against methicillin resistantStaphylococcus aureus.

Antimicrobial wound dressings play a crucial role in treatment of wound infections. However, existing commercial options fall short due to antibiotic resistance and the limited spectrum of activity of newly emerging antimicrobials against bacteria that are frequently encountered in wound infections. Antimicrobial photodynamic therapy (aPDT) is very promising alternative therapeutic approach against antibiotic resistant microbes such as methicillin resistantStaphylococcus aureus (MRSA). However, delivery of the photosensitizer (PS) homogeneously to the wound site is a challenge. Though polymeric wound dressings based on synthetic and biopolymers are being explored for aPDT, there is paucity of data regarding theirin vivoefficacy. Moreover, there are no studies on use of PS loaded, pluoronic (PL) and pectin (PC) based films for aPDT. We report development of a polymeric film for potential use in aPDT. The film was prepared using PL and PC via solvent casting approach and impregnated with methylene blue (MB) for photodynamic inactivation of MRSAin vitroandin vivo. Atomic force microscopic imaging of the films yielded vivid pictures of surface topography, with rough surfaces, pores, and furrows. The PL:PC ratio (2:3) was optimized that would result in an intact film but exhibit rapid release of MB in time scale suitable for aPDT. The film showed good antibacterial activity against planktonic suspension, biofilm of MRSA upon exposure to red light. Investigations on MRSA infected excisional wounds of mice reveal that topical application of MB loaded film for 30 min followed by red light exposure for 5 min (fluence; ∼30 J cm-2) or 10 min (fluence; ∼60 J cm-2) reduces ∼80% or ∼92% of bioburden, respectively. Importantly, the film elicits no significant cytotoxicity against keratinocytes and human adipose derived mesenchymal stem cells. Taken together, our data demonstrate that PS-loaded PL-PC based films are a promising new tool for treatment of MRSA infected wounds.

Pd Nanoparticle-Decorated Novel Ternary Bi2O2CO3-Bi2MoO6-CuO Heterojunction for Enhanced Photo-electrocatalytic Ethanol Oxidation.

Recently, photo-electrooxidation of fuel using a noble metal-semiconductor junction has been one of the most promising approaches in fuel cell systems. Herein, we report the development of a Pd-supported Bi2MoO6-Bi2O2CO3-CuO novel ternary heterojunction for ethanol oxidation in alkali in the presence and absence of visible light. Various spectroscopic and microscopic characterization techniques confirm strong coupling between palladium nanoparticles and Bi2MoO6-Bi2O2CO3-CuO ternary heterojunction supports. The photo-electrocatalytic efficacy of the synthesized catalysts was inspected by cyclic voltammetry (CV), chronoamperometry (CA), and electrochemical impedance spectroscopy (EIS). The CV study reveals that the forward peak current density (in mA mg-1 of Pd) of the synthesized quaternary heterojunction was about 1482.5, which is 2.4, 4, and 4.6 times higher than that of Pd/CuO (608.3), Pd/Bi2MoO6-Bi2O2CO3 (368.3), and similarly synthesized Pd catalyst (321.5) under visible light radiation. The best heterojunction catalyst shows 2.21-fold higher peak current density in visible light compared to that in dark. CA study reveals that after operation for 6000 s, the current density of the quaternary electrode is 1.5 and 3.4 times greater than that of Pd/CuO and Pd/C catalysts, respectively. The greater photocurrent response, lower photoluminescence (PL) emission intensity, and smaller semicircle arc in the Nyquist plot of the quaternary catalyst demonstrate the efficient segregation and higher charge transfer conductance of photogenerated charges to facilitate the photo-electrooxidation process of ethanol. The stability test shows that the quaternary catalyst loses only 9.8 and 7.7% of its maximum current density after 500 cycles of CV operation in the dark and light, respectively, indicating that light energy is more beneficial in establishing high stability. The dramatic enhancement of the photo-electrocatalytic activity of the quaternary electrode is owing to the lower band gap, high ECSA, enhanced charge separation of photogenerated carriers (e--h+), and all cocatalytic support of Bi2MoO6, Bi2O2CO3, and CuO in Pd/ Bi2MoO6-Bi2O2CO3-CuO under visible light radiation. The morphology and structure of the used quaternary catalyst are tested using FESEM and PXRD. Finally, ex situ FTIR spectroscopy and HPLC techniques help understand the ethanol electrooxidation reaction mechanism.

Pattern-based assessment of the influence of rainfall characteristics on urban stormwater quality.

Urbanisation increases pollutant generation within catchments and their transport to receiving waters. Changes to rainfall patterns, particularly in the age of climate change, make pollution mitigation a challenging task. Understanding how rainfall characteristics could influence the changes to stormwater pollutant runoff is important for designing effective mitigation strategies. This study employed a pattern-based assessment of relationships between rainfall characteristics and stormwater quality in urban catchments to develop this understanding. The research outcomes showed that rainfall events could be distinctly clustered based on intensity and duration, and each cluster of events would produce different stormwater quality responses. The high-intensity bursts occurring in the latter part of long-duration events were found to produce uniform and low concentrations of suspended solids. One the contrary, high intensity bursts occurring in the initial part of short-duration events triggered the first-flush effect, thus producing high concentrations of suspended solids. Furthermore, the first-flush effect was likely to present when the high intensity bursts occurred in the mid portion of rainfall events and produced variable concentrations of suspended solids. It was also found that the average rainfall intensity plays a key role in mobilising and transporting pollutants accumulated on urban surfaces.

Automatic model calibration of combined hydrologic, hydraulic and stormwater quality models using approximate Bayesian computation.

A range of automatic model calibration techniques are used in water engineering practice. However, use of these techniques can be problematic due to the requirement of evaluating the likelihood function. This paper presents an innovative approach for overcoming this issue using a calibration framework developed based on Approximate Bayesian Computation (ABC) technique. Use of ABC in automatic model calibration was undertaken for a combined urban hydrologic, hydraulic and stormwater quality model. The simulated runoff hydrograph and total suspended solid (TSS) pollutograph were compared with observed data for multiple events from three different catchments, and found to be within 95% confidence intervals of the simulated results. The R programmed model was validated by comparing simulated flow with similar commercially available modeling software, MIKE URBAN output determined using mean value of parameters obtained from the calibration exercise, and performed well by satisfying statistical criteria's such as coefficient of determination (CD), root mean square error (RMSE) and maximum error (ME). The developed framework is useful for automatic calibration and uncertainty estimation using ABC approach in complex hydrologic, hydraulic and stormwater quality models with multi-input-output systems.

Pareto Optimality Explanation of the Glycolytic Alternatives in Nature.

The Entner-Doudoroff (ED) and Embden-Meyerhof-Parnas (EMP) glycolytic pathways are largely conserved across glycolytic species in nature. Is this a coincidence, convergent evolution or there exists a driving force towards either of the two pathway designs? We addressed this question by first employing a variant of the optStoic algorithm to exhaustively identify over 11,916 possible routes between glucose and pyruvate at different pre-determined stoichiometric yields of ATP. Subsequently, we analyzed the thermodynamic feasibility of all the pathways at physiological metabolite concentrations and quantified the protein cost of the feasible solutions. Pareto optimality analysis between energy efficiency and protein cost reveals that the naturally evolved ED and EMP pathways are indeed among the most protein cost-efficient pathways in their respective ATP yield categories and remain thermodynamically feasible across a wide range of ATP/ADP ratios and pathway intermediate metabolite concentration ranges. In contrast, pathways with higher ATP yield (>2) while feasible, are bound within stringent and often extreme operability ranges of cofactor and intermediate metabolite concentrations. The preponderance of EMP and ED is thus consistent with not only optimally balancing energy yield vs. enzyme cost but also with ensuring operability for wide metabolite concentration ranges and ATP/ADP ratios.

Engineering of E. coli inherent fatty acid biosynthesis capacity to increase octanoic acid production.

BACKGROUND: As a versatile platform chemical, construction of microbial catalysts for free octanoic acid production from biorenewable feedstocks is a promising alternative to existing petroleum-based methods. However, the bio-production strategy has been restricted by the low capacity of E. coli inherent fatty acid biosynthesis. In this study, a combination of integrated computational and experimental approach was performed to manipulate the E. coli existing metabolic network, with the objective of improving bio-octanoic acid production. RESULTS: First, a customized OptForce methodology was run to predict a set of four genetic interventions required for production of octanoic acid at 90% of the theoretical yield. Subsequently, all the ten candidate proteins associated with the predicted interventions were regulated individually, as well as in contrast to the combination of interventions as suggested by the OptForce strategy. Among these enzymes, increased production of 3-hydroxy-acyl-ACP dehydratase (FabZ) resulted in the highest increase (+ 45%) in octanoic acid titer. But importantly, the combinatorial application of FabZ with the other interventions as suggested by OptForce further improved octanoic acid production, resulting in a high octanoic acid-producing E. coli strain +fabZ ΔfadE ΔfumAC ΔackA (TE10) (+ 61%). Optimization of TE10 expression, medium pH, and C:N ratio resulted in the identified strain producing 500 mg/L of C8 and 805 mg/L of total FAs, an 82 and 155% increase relative to wild-type MG1655 (TE10) in shake flasks. The best engineered strain produced with high selectivity (> 70%) and extracellularly (> 90%) up to 1 g/L free octanoic acid in minimal medium fed-batch culture. CONCLUSIONS: This work demonstrates the effectiveness of integration of computational strain design and experimental characterization as a starting point in rewiring metabolism for octanoic acid production. This result in conjunction with the results of other studies using OptForce in strain design demonstrates that this strategy may be also applicable to engineering E. coli for other customized bioproducts.

Multilevel engineering of the upstream module of aromatic amino acid biosynthesis in Saccharomyces cerevisiae for high production of polymer and drug precursors.

A multilevel approach was implemented in Saccharomyces cerevisiae to optimize the precursor module of the aromatic amino acid biosynthesis pathway, which is a rich resource for synthesizing a great variety of chemicals ranging from polymer precursor, to nutraceuticals and pain-relief drugs. To facilitate the discovery of novel targets to enhance the pathway flux, we incorporated the computational tool YEASTRACT for predicting novel transcriptional repressors and OptForce strain-design for identifying non-intuitive pathway interventions. The multilevel approach consisted of (i) relieving the pathway from strong transcriptional repression, (ii) removing competing pathways to ensure high carbon capture, and (iii) rewiring precursor pathways to increase the carbon funneling to the desired target. The combination of these interventions led to the establishment of a S. cerevisiae strain with shikimic acid (SA) titer reaching as high as 2.5gL-1, 7-fold higher than the base strain. Further expansion of the platform led to the titer of 2.7gL-1 of muconic acid (MA) and its intermediate protocatechuic acid (PCA) together. Both the SA and MA production platforms demonstrated increases in titer and yield nearly 300% from the previously reported, highest-producing S. cerevisiae strains. Further examination elucidated the diverged impacts of disrupting the oxidative branch (ZWF1) of the pentose phosphate pathway on the titers of desired products belonging to different portions of the pathway. The investigation of other non-intuitive interventions like the deletion of the Pho13 enzyme also revealed the important role of the transaldolase in determining the fate of the carbon flux in the pathways of study. This integrative approach identified novel determinants at both transcriptional and metabolic levels that constrain the flux entering the aromatic amino acid pathway. In the future, this platform can be readily used for engineering the downstream modules toward the production of important plant-sourced aromatic secondary metabolites.

Designing overall stoichiometric conversions and intervening metabolic reactions.

Existing computational tools for de novo metabolic pathway assembly, either based on mixed integer linear programming techniques or graph-search applications, generally only find linear pathways connecting the source to the target metabolite. The overall stoichiometry of conversion along with alternate co-reactant (or co-product) combinations is not part of the pathway design. Therefore, global carbon and energy efficiency is in essence fixed with no opportunities to identify more efficient routes for recycling carbon flux closer to the thermodynamic limit. Here, we introduce a two-stage computational procedure that both identifies the optimum overall stoichiometry (i.e., optStoic) and selects for (non-)native reactions (i.e., minRxn/minFlux) that maximize carbon, energy or price efficiency while satisfying thermodynamic feasibility requirements. Implementation for recent pathway design studies identified non-intuitive designs with improved efficiencies. Specifically, multiple alternatives for non-oxidative glycolysis are generated and non-intuitive ways of co-utilizing carbon dioxide with methanol are revealed for the production of C2+ metabolites with higher carbon efficiency.

Using Gene Essentiality and Synthetic Lethality Information to Correct Yeast and CHO Cell Genome-Scale Models.

Essentiality (ES) and Synthetic Lethality (SL) information identify combination of genes whose deletion inhibits cell growth. This information is important for both identifying drug targets for tumor and pathogenic bacteria suppression and for flagging and avoiding gene deletions that are non-viable in biotechnology. In this study, we performed a comprehensive ES and SL analysis of two important eukaryotic models (S. cerevisiae and CHO cells) using a bilevel optimization approach introduced earlier. Information gleaned from this study is used to propose specific model changes to remedy inconsistent with data model predictions. Even for the highly curated Yeast 7.11 model we identified 50 changes (metabolic and GPR) leading to the correct prediction of an additional 28% of essential genes and 36% of synthetic lethals along with a 53% reduction in the erroneous identification of essential genes. Due to the paucity of mutant growth phenotype data only 12 changes were made for the CHO 1.2 model leading to an additional correctly predicted 11 essential and eight non-essential genes. Overall, we find that CHO 1.2 was 76% less accurate than the Yeast 7.11 metabolic model in predicting essential genes. Based on this analysis, 14 (single and double deletion) maximally informative experiments are suggested to improve the CHO cell model by using information from a mouse metabolic model. This analysis demonstrates the importance of single and multiple knockout phenotypes in assessing and improving model reconstructions. The advent of techniques such as CRISPR opens the door for the global assessment of eukaryotic models.

Improving prediction fidelity of cellular metabolism with kinetic descriptions.

Several modeling frameworks for describing and redirecting cellular metabolism have been developed keeping pace with the rapid development in high-throughput data generation and advances in metabolic engineering techniques. The incorporation of kinetic information within stoichiometry-only modeling techniques offers potential advantages for improved phenotype prediction and consequently more precise computational strain design. In addition to substrate-level kinetic regulatory information, the integration of a number of additional layers of regulation at the transcription, translation, and post-translation levels is sought after by many research groups. However, the practical integration of these complex biological processes into a unified framework amenable to design remains an ongoing challenge.

Advances in de novo strain design using integrated systems and synthetic biology tools.

Recent efforts in expanding the range of biofuel and biorenewable molecules using microbial production hosts have focused on the introduction of non-native pathways in model organisms and the bio-prospecting of non-model organisms with desirable features. Current challenges lie in the assembly and coordinated expression of the (non-)native pathways and the elimination of competing pathways and undesirable regulation. Several systems and synthetic biology approaches providing contrasting top-down and bottom-up strategies, respectively, have been developed. In this review, we discuss recent advances in both in silico and experimental approaches for metabolic pathway design and engineering, with a critical assessment of their merits and remaining challenges.

Personalized Kinetic Models for Predictive Healthcare.

Metabolomics data are used to parameterize individual-specific kinetic models of metabolism to predict medically relevant parameters underpinning disease states or outcomes.

k-OptForce: integrating kinetics with flux balance analysis for strain design.

Computational strain design protocols aim at the system-wide identification of intervention strategies for the enhanced production of biochemicals in microorganisms. Existing approaches relying solely on stoichiometry and rudimentary constraint-based regulation overlook the effects of metabolite concentrations and substrate-level enzyme regulation while identifying metabolic interventions. In this paper, we introduce k-OptForce, which integrates the available kinetic descriptions of metabolic steps with stoichiometric models to sharpen the prediction of intervention strategies for improving the bio-production of a chemical of interest. It enables identification of a minimal set of interventions comprised of both enzymatic parameter changes (for reactions with available kinetics) and reaction flux changes (for reactions with only stoichiometric information). Application of k-OptForce to the overproduction of L-serine in E. coli and triacetic acid lactone (TAL) in S. cerevisiae revealed that the identified interventions tend to cause less dramatic rearrangements of the flux distribution so as not to violate concentration bounds. In some cases the incorporation of kinetic information leads to the need for additional interventions as kinetic expressions render stoichiometry-only derived interventions infeasible by violating concentration bounds, whereas in other cases the kinetic expressions impart flux changes that favor the overproduction of the target product thereby requiring fewer direct interventions. A sensitivity analysis on metabolite concentrations shows that the required number of interventions can be significantly affected by changing the imposed bounds on metabolite concentrations. Furthermore, k-OptForce was capable of finding non-intuitive interventions aiming at alleviating the substrate-level inhibition of key enzymes in order to enhance the flux towards the product of interest, which cannot be captured by stoichiometry-alone analysis. This study paves the way for the integrated analysis of kinetic and stoichiometric models and enables elucidating system-wide metabolic interventions while capturing regulatory and kinetic effects.

Recent advances in the reconstruction of metabolic models and integration of omics data.

With the ever-accelerating pace of genome sequencing and annotation information generation, the development of computational pipelines for the rapid reconstruction of high-quality metabolic networks has received significant attention. Herein, we review the available biological databases and automated/semi-automated reconstruction tools. In addition, we describe available methodologies for the integration of high-throughput omics data to increase metabolic phenotype prediction accuracy. Data heterogeneity and lack of better integration of metabolic reconstruction pipelines with omics data generation protocols have hampered rapid progress thus far.

Systems metabolic engineering design: fatty acid production as an emerging case study.

Increasing demand for petroleum has stimulated industry to develop sustainable production of chemicals and biofuels using microbial cell factories. Fatty acids of chain lengths from C6 to C16 are propitious intermediates for the catalytic synthesis of industrial chemicals and diesel-like biofuels. The abundance of genetic information available for Escherichia coli and specifically, fatty acid metabolism in E. coli, supports this bacterium as a promising host for engineering a biocatalyst for the microbial production of fatty acids. Recent successes rooted in different features of systems metabolic engineering in the strain design of high-yielding medium chain fatty acid producing E. coli strains provide an emerging case study of design methods for effective strain design. Classical metabolic engineering and synthetic biology approaches enabled different and distinct design paths towards a high-yielding strain. Here we highlight a rational strain design process in systems biology, an integrated computational and experimental approach for carboxylic acid production, as an alternative method. Additional challenges inherent in achieving an optimal strain for commercialization of medium chain-length fatty acids will likely require a collection of strategies from systems metabolic engineering. Not only will the continued advancement in systems metabolic engineering result in these highly productive strains more quickly, this knowledge will extend more rapidly the carboxylic acid platform to the microbial production of carboxylic acids with alternate chain-lengths and functionalities.

Succinate Overproduction: A Case Study of Computational Strain Design Using a Comprehensive Escherichia coli Kinetic Model.

Computational strain-design prediction accuracy has been the focus for many recent efforts through the selective integration of kinetic information into metabolic models. In general, kinetic model prediction quality is determined by the range and scope of genetic and/or environmental perturbations used during parameterization. In this effort, we apply the k-OptForce procedure on a kinetic model of E. coli core metabolism constructed using the Ensemble Modeling (EM) method and parameterized using multiple mutant strains data under aerobic respiration with glucose as the carbon source. Minimal interventions are identified that improve succinate yield under both aerobic and anaerobic conditions to test the fidelity of model predictions under both genetic and environmental perturbations. Under aerobic condition, k-OptForce identifies interventions that match existing experimental strategies while pointing at a number of unexplored flux re-directions such as routing glyoxylate flux through the glycerate metabolism to improve succinate yield. Many of the identified interventions rely on the kinetic descriptions that would not be discoverable by a purely stoichiometric description. In contrast, under fermentative (anaerobic) condition, k-OptForce fails to identify key interventions including up-regulation of anaplerotic reactions and elimination of competitive fermentative products. This is due to the fact that the pathways activated under anaerobic condition were not properly parameterized as only aerobic flux data were used in the model construction. This study shed light on the importance of condition-specific model parameterization and provides insight on how to augment kinetic models so as to correctly respond to multiple environmental perturbations.