Microbial community assembly in engineered bioreactors.

Microbial community assembly (MCA) processes that shape microbial communities in environments are being used to analyze engineered bioreactors such as activated sludge systems and anaerobic digesters. The goal of studying MCA is to be able to understand and predict the effect of design and operation procedures on bioreactor microbial composition and function. Ultimately, this can lead to bioreactors that are more efficient, resilient, or resistant to perturbations. This review summarizes the ecological theories underpinning MCA, evaluates MCA analysis methods, analyzes how these MCA-based methods are applied to engineered bioreactors, and extracts lessons from case studies. Furthermore, we suggest future directions in MCA research in engineered bioreactor systems. The review aims to provide insights and guidance to the growing number of environmental engineers who wish to design and understand bioreactors through the lens of MCA.

Microplate-Based Cell Viability Assay as a Cost-Effective Alternative to Flow Cytometry for Microalgae Analysis.

Cell viability is a critical indicator for assessing culture quality in microalgae cultivation for biorefinery and bioremediation. Fluorescent dyes that distinguish viable from nonviable cells can enable viability quantification based on the percentage of live cells. However, fluorescence analysis using the typical flow cytometry method is costly and impractical for industrial applications. To address this, we developed new microplate assays utilizing fluorescein diacetate as a live cell stain and erythrosine B as a dead cell stain. These assays provide a low-cost, simple, and reliable method of assessing cell viability. The proposed microplate assays were successfully applied to monitor the viability of the microalgae Dunaliella viridis under carbon and nitrogen limitation stresses and demonstrated good agreement with flow cytometry measurements. We conducted a systematic investigation of the effects of dye concentration, incubation time, and background fluorescence on the microplate assays' performance. Further, we provide a comprehensive review of commonly used fluorescent dyes for microalgae staining, discuss strategies to enhance assay performance, and offer recommendations for dye selection and protocol development. This study presents a comprehensive new method for microplate-based viability analysis, providing valuable insights for future microalgae viability assessments and applications.

Growth of Dunaliella viridis in multiple cycles of reclaimed media after repeated high pH-induced flocculation and harvesting.

Minimizing the use of water for growing microalgae is crucial for lowering the energy and costs of animal feed, food, and biofuel production from microalgae. Dunaliella spp., a haloterant species that can accumulate high intracellular levels of lipids, carotenoids, or glycerol can be harvested effectively using low-cost and scalable high pH-induced flocculation. However, the growth of Dunaliella spp. in reclaimed media after flocculation and the impact of recycling on the flocculation efficiency have not been explored. In this study, repeated cycles of growth of Dunaliella viridis in repeatedly reclaimed media from high pH-induced flocculation were studied by evaluating cell concentrations, cellular components, dissolved organic matter (DOM), and bacterial community shifts in the reclaimed media. In reclaimed media, D. viridis grew to the same concentrations of cells and intracellular components as fresh media-107 cells/mL with cellular composition of 3 % lipids, 40 % proteins, and 15 % carbohydrates-even though DOM accumulated and the dominant bacterial populations changed. There was a decrease in the maximum specific growth rate and flocculation efficiency from 0.72 d-1 to 0.45 d-1 and from 60 % to 48 %, respectively. This study shows the potential of repeated (at least five times) flocculation and reuse of media as a possible way of reducing the costs of water and nutrients with some tradeoffs in growth rate and flocculation efficiency.

Measurement of heat release during hydration and carbonation of ash disposed in landfills using an isothermal calorimeter.

Temperatures as high as 100 °C have been reported at a few municipal solid waste (MSW) landfills in the U.S. A recently published model describing landfill heat accumulation identified reactions that contribute significant heat to landfills including the hydration and carbonation of Ca-containing wastes such as ash from MSW and coal combustion. The objective of this study was to develop a method to measure heat release from Ca-containing ash by isothermal calorimetry. The method was confirmed by comparing measured heat release from hydration and carbonation of pure CaO and Ca(OH)2 to the theoretical heat. Theoretical heat release was determined by characterizing test materials before and after experiments using thermogravimetric analysis (TGA) and X-ray diffraction (XRD). Heat recovery efficiencies with both water and synthetic leachate ranged from 79 to 90% for CaO hydration and between 65 and 74% for Ca(OH)2 carbonation, with no effect attributable to leachate. Additionally, simultaneous hydration and carbonation of CaO/Ca(OH)2 mixtures resulted in efficiencies of 65 to 74%. The developed method was applied to eight samples that were excavated from a landfill and known to contain coal ash, and the ratio of measured to theoretical heat was 0.5 to 4. Thus, calculation of theoretical heat release from XRD data was not a good predictor of the experimentally measured heat release. The developed method can be used by landfill operators to evaluate the heat potential of a waste, thereby facilitating decisions on the quantity of a waste that can be buried in consideration of landfill temperatures.

Reducing fat, oil, and grease (FOG) deposits formation and adhesion on sewer collection system structures through the use of fly ash replaced cement-based materials.

The accumulation of fat, oil, and grease (FOG) deposits in sewer pipes reduces their conveyance and results in Sanitary Sewer Overflows (SSOs). Previous research has shown that concrete used in sewer lines is a significant source for calcium ion, which participates in the FOG deposit formation mechanism. However, no research has been conducted to understand the effect of calcium leaching from cement on FOG deposits formation and adhesion. This study quantifies the reduction in FOG deposit formation when Fly Ash (FA), a Supplementary Cementitious Material (SCM), is used to replace cement in the production of High Volume Fly Ash (HVFA) concrete materials. Results show that after 90 days of leaching test under controlled pH conditions, 75% and 86% reduction in calcium release were achieved from 50% and 75% FA replacement, respectively. After 30 days of FOG deposits formation tests on HVFA samples, 58% and 81% reduction in FOG deposit formation was found for 50% and 75% FA replacement, respectively. FTIR analyses of FOG deposits formed on concrete samples without FA replacement exhibited high calcium soap content (48%), while, FOG deposit formed on HVFA concrete materials showed low calcium soap percentage (22~29%). Furthermore, FTIR analyses report the first spatial variation found in FOG deposits that includes a surface layer of hard FOG deposits with high calcium soap absorbance and an outer layer of soft FOG deposits consisting of a low calcium absorbance. FTIR analyses revealed that the FOG deposit formation mechanism is affected by the availability of calcium and pH near the concrete surface. Finally, HVFA concrete materials were tested for compressive strength and durability against microbially induced concrete corrosion (MICC). After 180 days of sealed curing, HVFA concrete exhibited adequate compressive strength necessary for the sewer line construction and 50% FA replacement revealed satisfactory durability against MICC.

Increased loading stress leads to convergence of microbial communities and high methane yields in adapted anaerobic co-digesters.

Enhancing biogas production, while avoiding inhibition of methanogenesis during co-digestion of grease interceptor waste (GIW), can help water resource recovery facilities reduce their carbon footprint. Here we used pre-adapted and non-adapted digesters to link microbial community structure to digester function. Before disturbance, the pre-adapted and non-adapted digesters showed similar methane production and microbial community diversity but dissimilar community composition. When exposed to an identical disturbance, the pre-adapted digester achieved better performance, while the non-adapted digester was inhibited. When re-exposed to disturbance after recovery, communities and performance of both digesters converged, regardless of the temporal variations. Co-digestion of up to 75% GIW added on a volatile solids (VS) basis was achieved, increasing methane yield by 336% from 0.180 to 0.785 l-methane/g-VS-added, the highest methane yield reported to date for lipid-rich waste. Progressive perturbation substantially enriched fatty acid-degrading Syntrophomonas from less than 1% to 24.6% of total 16S rRNA gene sequences, acetoclastic Methanosaeta from 2.3% to 11.9%, and hydrogenotrophic Methanospirillum from less than 1% to 6.6% in the pre-adapted digester. Specific hydrolytic and fermentative populations also increased. These ecological insights demonstrated how progressive perturbation can be strategically used to influence methanogenic microbiomes and improve co-digestion of GIW.

Modeling cell aggregate morphology during aerobic granulation in activated sludge processes reveals the combined effect of substrate and shear.

Past research on AGS (aerobic granular sludge technology) has mainly focused on macro-environment factors, such as settling time, feeding pattern, OLR (organic loading rate), SRT (sludge retention time), among others, and their effects on the granulation process. The biomass granulation process, however, is significantly affected by the micro-environment surrounding these biomass aggregates. In this research, an in silico computational approach was adopted to study the impact of the micro-environment on the biomass granulation process. A 2-D biofilm model based on the cellular automata algorithm and computational fluid dynamics was used to simulate the development of an individual biomass aggregate under specific hydrodynamic and substrate availability conditions. The simulation results indicated that shear and bulk substrate concentration combined to create the optimal conditions for aerobic granule formation. This process can be characterized by the RT (reversed Thiele) modulus value, which is the ratio of the maximum substrate transport over the maximum substrate reaction rate and an indicator of substrate availability. For AGS formation, the RT value should be greater than 0.1. Many common strategies, such as the application of batch reactors, selection for slow-growing microorganism, F/M (food/mass) ratio adjustment, feast and famine condition, and short settling time, for biomass granulation production can be explained by the RT value. The results suggest that rethinking unit process configurations in wastewater treatment facilities will be required to achieve reliable AGS formation.

Dynamic Modeling of Microalgae Growth and Lipid Production under Transient Light and Nitrogen Conditions.

We developed a new dynamic model to characterize how light and nitrogen regulate the cellular processes of photosynthetic microalgae leading to transient changes in the production of neutral lipids, carbohydrates, and biomass. Our model recapitulated the versatile neutral lipid synthesis pathways via (i) carbon reuse from carbohydrate metabolism under nitrogen sufficiency and (ii) fixed carbon redirection under nitrogen depletion. We also characterized the effects of light adaptation, light inhibition hysteresis, and nitrogen limitation on photosynthetic carbon fixation. The formulated model was calibrated and validated with experimental data of Dunaliella viridis cultivated in a lab-scale photobioreactor (PBR) under various light (low/moderate/high) and nitrogen (sufficient/limited) conditions. We conducted the identifiability, uncertainty, and sensitivity analyses to verify the model reliability using the profile likelihood method, the Markov chain Monte Carlo (MCMC) technique, and the extended Fourier Amplitude Sensitivity Test (eFAST). Our model predictions agreed well with experimental observations and suggested potential model improvement by incorporating a lipid degradation mechanism. The insights from our model-driven analysis helped improve the mechanistic understanding of transient algae growth and bioproducts formation under environmental variations and could be applied to optimize biofuel and biomass production.

Measuring the Shape and Size of Activated Sludge Particles Immobilized in Agar with an Open Source Software Pipeline.

Experimental bioreactors, such as those treating wastewater, contain particles whose size and shape are important parameters. For example, the size and shape of activated sludge flocs can indicate the conditions at the microscale, and also directly affect how well the sludge settles in a clarifier. Particle size and shape are both misleadingly 'simple' measurements. Many subtle issues, often unaddressed in informal protocols, can arise when sampling, imaging, and analyzing particles. Sampling methods may be biased or not provide enough statistical power. The samples themselves may be poorly preserved or undergo alteration during immobilization. Images may not be of sufficient quality; overlapping particles, depth of field, magnification level, and various noise can all produce poor results. Poorly specified analysis can introduce bias, such as that produced by manual image thresholding and segmentation. Affordability and throughput are desirable alongside reproducibility. An affordable, high throughput method can enable more frequent particle measurement, producing many images containing thousands of particles. A method that uses inexpensive reagents, a common dissecting microscope, and freely-available open source analysis software allows repeatable, accessible, reproducible, and partially-automated experimental results. Further, the product of such a method can be well-formatted, well-defined, and easily understood by data analysis software, easing both within-lab analyses and data sharing between labs. We present a protocol that details the steps needed to produce such a product, including: sampling, sample preparation and immobilization in agar, digital image acquisition, digital image analysis, and examples of experiment-specific figure generation from the analysis results. We have also included an open-source data analysis pipeline to support this protocol.

Flux modeling for monolignol biosynthesis.

The pathway of monolignol biosynthesis involves many components interacting in a metabolic grid to regulate the supply and ratios of monolignols for lignification. The complexity of the pathway challenges any intuitive prediction of the output without mathematical modeling. Several models have been presented to quantify the metabolic flux for monolignol biosynthesis and the regulation of lignin content, composition, and structure in plant cell walls. Constraint-based models using data from transgenic plants were formulated to describe steady-state flux distribution in the pathway. Kinetic-based models using enzyme reaction and inhibition constants were developed to predict flux dynamics for monolignol biosynthesis in wood-forming cells. This review summarizes the recent progress in flux modeling and its application to lignin engineering for improved plant development and utilization.

Controlling aerobic biological floc size using Couette-Taylor Bioreactors.

Biological floc size is an important reactor microenvironment parameter that is often not experimentally controlled due to a lack of suitable methods. Here, we introduce the Couette-Taylor bioreactor (CTB) as an improved tool for controlling biological floc size, specifically as compared with bubble-column sequencing batch reactors (SBRs). A CTB consists of two concentric walls, either of which may be rotated to induce fluid motion. The induced flow produces hydrodynamic shear which is more uniform than that produced through aeration in SBRs. Because hydrodynamic shear is a major parameter controlling floc size, we hypothesized the ability to better control shear rates within a CTB would enable better-controlled floc sizes. To test this hypothesis, we measured the particle size distributions of activated sludge flocs from CTBs with either inner (iCTB) or outer (oCTB) rotating walls as well as SBRs with varying height to diameter ratios (0.5, 1.1, and 9.4). The rotation speed of the CTBs and aeration rate of the SBRs were varied to produce predicted mean shear rates from 25 to 250 s-1. Further, the shear rate distributions for each experiment were estimated using computational fluid dynamics (CFD). In all SBR experiments, the floc distributions did not significantly vary with shear rate or geometry, likely because shear rates (estimated by CFD) differed much less than originally predicted by theory. In the CTB experiments, the mean particle size decreased proportionally with increased hydrodynamic shear, and iCTBs produced particle size distributions with smaller coefficients of variation than oCTBs (0.3 vs. 0.5-0.7, respectively).

Development of Photochemical Microsensors for Evaluating Photosynthetic Light Dose Distributions in Microalgal Photobioreactors.

We describe the development and testing of a Lagrangian method for quantifying light dose distributions within photobioreactors (PBRs) using novel photochemical microsensors. These microsensors were developed using 3-µm microspheres coated with a fluorescent dye that responds to wavelengths of visible light that are critical for photosynthesis. The dose-response kinetics of the microsensors was established by varying known doses of collimated light and quantifying the fluorescence responses of individual particles using flow cytometry. A deconvolution scheme was used to determine the light dose distribution from the fluorescence distribution of the microsensors. As proof-of-concept, the microsensors were used to quantify the photosynthetic light dose distributions within a gently mixed, 3 L flat-plate, batch PBR with and without algae and no gas bubbling and without algae but with gas bubbling. The microsensor approach not only provided information about the photosynthetic light distributions within the PBRs but also predicted the average light attenuation due to algal cells within 1% of estimates made with an in situ light sensor. The results showed that bubbles, under the conditions tested, increased the overall light irradiance by 18%; a result not captured by static measurements. The Lagrangian microsensors provide a novel approach for quantifying light within a photobioreactor.

Improving wood properties for wood utilization through multi-omics integration in lignin biosynthesis.

A multi-omics quantitative integrative analysis of lignin biosynthesis can advance the strategic engineering of wood for timber, pulp, and biofuels. Lignin is polymerized from three monomers (monolignols) produced by a grid-like pathway. The pathway in wood formation of Populus trichocarpa has at least 21 genes, encoding enzymes that mediate 37 reactions on 24 metabolites, leading to lignin and affecting wood properties. We perturb these 21 pathway genes and integrate transcriptomic, proteomic, fluxomic and phenomic data from 221 lines selected from ~2000 transgenics (6-month-old). The integrative analysis estimates how changing expression of pathway gene or gene combination affects protein abundance, metabolic-flux, metabolite concentrations, and 25 wood traits, including lignin, tree-growth, density, strength, and saccharification. The analysis then predicts improvements in any of these 25 traits individually or in combinations, through engineering expression of specific monolignol genes. The analysis may lead to greater understanding of other pathways for improved growth and adaptation.

Assessing the impact of the 4CL enzyme complex on the robustness of monolignol biosynthesis using metabolic pathway analysis.

Lignin is a polymer present in the secondary cell walls of all vascular plants. It is a known barrier to pulping and the extraction of high-energy sugars from cellulosic biomass. The challenge faced with predicting outcomes of transgenic plants with reduced lignin is due in part to the presence of unique protein-protein interactions that influence the regulation and metabolic flux in the pathway. Yet, it is unclear why certain plants have evolved to create these protein complexes. In this study, we use mathematical models to investigate the role that the protein complex, formed specifically between Ptr4CL3 and Ptr4CL5 enzymes, have on the monolignol biosynthesis pathway. The role of this Ptr4CL3-Ptr4CL5 enzyme complex on the steady state flux distribution was quantified by performing Monte Carlo simulations. The effect of this complex on the robustness and the homeostatic properties of the pathway were identified by performing sensitivity and stability analyses, respectively. Results from these robustness and stability analyses suggest that the monolignol biosynthetic pathway is resilient to mild perturbations in the presence of the Ptr4CL3-Ptr4CL5 complex. Specifically, the presence of Ptr4CL3-Ptr4CL5 complex increased the stability of the pathway by 22%. The robustness in the pathway is maintained due to the presence of multiple enzyme isoforms as well as the presence of alternative pathways resulting from the presence of the Ptr4CL3-Ptr4CL5 complex.

Heat Generation and Accumulation in Municipal Solid Waste Landfills.

There have been reports of North American landfills that are experiencing temperatures in excess of 80-100 °C. However, the processes causing elevated temperatures are not well understood. The objectives of this study were to develop a model to describe the generation, consumption and release of heat from landfills, to predict landfill temperatures, and to understand the relative importance of factors that contribute to heat generation and accumulation. Modeled heat sources include energy from aerobic and anaerobic biodegradation, anaerobic metal corrosion, ash hydration and carbonation, and acid-base neutralization. Heat removal processes include landfill gas convection, infiltration, leachate collection, and evaporation. The landfill was treated as a perfectly mixed batch reactor. Model predictions indicate that both anaerobic metal corrosion and ash hydration/carbonation contribute to landfill temperatures above those estimated from biological reactions alone. Exothermic pyrolysis of refuse, which is hypothesized to be initiated due to a local accumulation of heat, was modeled empirically to illustrate its potential impact on heat generation.

Construction and Setup of a Bench-scale Algal Photosynthetic Bioreactor with Temperature, Light, and pH Monitoring for Kinetic Growth Tests.

The optimal design and operation of photosynthetic bioreactors (PBRs) for microalgal cultivation is essential for improving the environmental and economic performance of microalgae-based biofuel production. Models that estimate microalgal growth under different conditions can help to optimize PBR design and operation. To be effective, the growth parameters used in these models must be accurately determined. Algal growth experiments are often constrained by the dynamic nature of the culture environment, and control systems are needed to accurately determine the kinetic parameters. The first step in setting up a controlled batch experiment is live data acquisition and monitoring. This protocol outlines a process for the assembly and operation of a bench-scale photosynthetic bioreactor that can be used to conduct microalgal growth experiments. This protocol describes how to size and assemble a flat-plate, bench-scale PBR from acrylic. It also details how to configure a PBR with continuous pH, light, and temperature monitoring using a data acquisition and control unit, analog sensors, and open-source data acquisition software.

Quantifying fat, oil, and grease deposit formation kinetics.

Fat, oil, and grease (FOG) deposits formed in sanitary sewers are calcium-based saponified solids that are responsible for a significant number of nationwide sanitary sewer overflows (SSOs) across United States. In the current study, the kinetics of lab-based saponified solids were determined to understand the kinetics of FOG deposit formation in sewers for two types of fat (Canola and Beef Tallow) and two types of calcium sources (calcium chloride and calcium sulfate) under three pH (7 ± 0.5, 10 ± 0.5, and ≈14) and two temperature conditions (22 ± 0.5 and 45 ± 0.5 °C). The results of this study displayed quick reactions of a fraction of fats with calcium ions to form calcium based saponified solids. Results further showed that increased palmitic fatty acid content in source fats, the magnitude of the pH, and temperature significantly affect the FOG deposit formation and saponification rates. The experimental data of the kinetics were compared with two empirical models: a) Cotte saponification model and b) Foubert crystallization model and a mass-action based mechanistic model that included alkali driven hydrolysis of triglycerides. Results showed that the mass action based mechanistic model was able to predict changes in the rate of formation of saponified solids under the different experimental conditions compared to both empirical models. The mass-action based saponification model also revealed that the hydrolysis of Beef Tallow was slower compared to liquid Canola fat resulting in smaller quantities of saponified solids. This mechanistic saponification model, with its ability to track the saponified solids chemical precursors, may provide an initial framework to predict the spatial formation of FOG deposits in municipal sewers using system wide sewer collection modeling software.

Clustering and Differential Alignment Algorithm: Identification of Early Stage Regulators in the Arabidopsis thaliana Iron Deficiency Response.

Time course transcriptome datasets are commonly used to predict key gene regulators associated with stress responses and to explore gene functionality. Techniques developed to extract causal relationships between genes from high throughput time course expression data are limited by low signal levels coupled with noise and sparseness in time points. We deal with these limitations by proposing the Cluster and Differential Alignment Algorithm (CDAA). This algorithm was designed to process transcriptome data by first grouping genes based on stages of activity and then using similarities in gene expression to predict influential connections between individual genes. Regulatory relationships are assigned based on pairwise alignment scores generated using the expression patterns of two genes and some inferred delay between the regulator and the observed activity of the target. We applied the CDAA to an iron deficiency time course microarray dataset to identify regulators that influence 7 target transcription factors known to participate in the Arabidopsis thaliana iron deficiency response. The algorithm predicted that 7 regulators previously unlinked to iron homeostasis influence the expression of these known transcription factors. We validated over half of predicted influential relationships using qRT-PCR expression analysis in mutant backgrounds. One predicted regulator-target relationship was shown to be a direct binding interaction according to yeast one-hybrid (Y1H) analysis. These results serve as a proof of concept emphasizing the utility of the CDAA for identifying unknown or missing nodes in regulatory cascades, providing the fundamental knowledge needed for constructing predictive gene regulatory networks. We propose that this tool can be used successfully for similar time course datasets to extract additional information and infer reliable regulatory connections for individual genes.

Heuristic optimization of a continuous flow point-of-use UV-LED disinfection reactor using computational fluid dynamics.

Alternative disinfection sources such as ultraviolet light (UV) are being pursued to inactivate pathogenic microorganisms such as Cryptosporidium and Giardia, while simultaneously reducing the risk of exposure to carcinogenic disinfection by-products (DBPs) in drinking water. UV-LEDs offer a UV disinfecting source that do not contain mercury, have the potential for long lifetimes, are robust, and have a high degree of design flexibility. However, the increased flexibility in design options will add a substantial level of complexity when developing a UV-LED reactor, particularly with regards to reactor shape, size, spatial orientation of light, and germicidal emission wavelength. Anticipating that LEDs are the future of UV disinfection, new methods are needed for designing such reactors. In this research study, the evaluation of a new design paradigm using a point-of-use UV-LED disinfection reactor has been performed. ModeFrontier, a numerical optimization platform, was coupled with COMSOL Multi-physics, a computational fluid dynamics (CFD) software package, to generate an optimized UV-LED continuous flow reactor. Three optimality conditions were considered: 1) single objective analysis minimizing input supply power while achieving at least (2.0) log10 inactivation of Escherichia coli ATCC 11229; and 2) two multi-objective analyses (one of which maximized the log10 inactivation of E. coli ATCC 11229 and minimized the supply power). All tests were completed at a flow rate of 109 mL/min and 92% UVT (measured at 254 nm). The numerical solution for the first objective was validated experimentally using biodosimetry. The optimal design predictions displayed good agreement with the experimental data and contained several non-intuitive features, particularly with the UV-LED spatial arrangement, where the lights were unevenly populated throughout the reactor. The optimal designs may not have been developed from experienced designers due to the increased degrees of freedom offered by using UV-LEDs. The results of this study revealed that the coupled optimization routine with CFD was effective at significantly decreasing the engineer's design decision space and finding a potentially near-optimal UV-LED reactor solution.

Complete proteomic-based enzyme reaction and inhibition kinetics reveal how monolignol biosynthetic enzyme families affect metabolic flux and lignin in Populus trichocarpa.

We established a predictive kinetic metabolic-flux model for the 21 enzymes and 24 metabolites of the monolignol biosynthetic pathway using Populus trichocarpa secondary differentiating xylem. To establish this model, a comprehensive study was performed to obtain the reaction and inhibition kinetic parameters of all 21 enzymes based on functional recombinant proteins. A total of 104 Michaelis-Menten kinetic parameters and 85 inhibition kinetic parameters were derived from these enzymes. Through mass spectrometry, we obtained the absolute quantities of all 21 pathway enzymes in the secondary differentiating xylem. This extensive experimental data set, generated from a single tissue specialized in wood formation, was used to construct the predictive kinetic metabolic-flux model to provide a comprehensive mathematical description of the monolignol biosynthetic pathway. The model was validated using experimental data from transgenic P. trichocarpa plants. The model predicts how pathway enzymes affect lignin content and composition, explains a long-standing paradox regarding the regulation of monolignol subunit ratios in lignin, and reveals novel mechanisms involved in the regulation of lignin biosynthesis. This model provides an explanation of the effects of genetic and transgenic perturbations of the monolignol biosynthetic pathway in flowering plants.