Stochastic modelling of cellulose hydrolysis with Gauss and Weibull distributed transition probabilities.
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ABSTRACT:
SUBMITTER: Duru JM
PROVIDER: S-EPMC8097066 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundDuring cellulosic ethanol production, cellulose hydrolysis is achieved by synergistic action of cellulase enzyme complex consisting of multiple enzymes with different mode of actions. Enzymatic hydrolysis of cellulose is one of the bottlenecks in the commercialization of the process due to low hydrolysis rates and high cost of enzymes. A robust hydrolysis model that can predict hydrolysis profile under various scenarios can act as an important forecasting tool to improve the hydrolysis process. However, multiple factors affecting hydrolysis: cellulose structure and complex enzyme-substrate interactions during hydrolysis make it diffucult to develop mathematical kinetic models that can simulate hydrolysis in presence of multiple enzymes with high fidelity. In this study, a comprehensive hydrolysis model based on stochastic molecular modeling approch in which each hydrolysis event is translated into a discrete event is presented. The model captures the structural features of cellulose, enzyme properties (mode of actions, synergism, inhibition), and most importantly dynamic morphological changes in the substrate that directly affect the enzyme-substrate interactions during hydrolysis.ResultsCellulose was modeled as a group of microfibrils consisting of elementary fibrils bundles, where each elementary fibril was represented as a three dimensional matrix of glucose molecules. Hydrolysis of cellulose was simulated based on Monte Carlo simulation technique. Cellulose hydrolysis results predicted by model simulations agree well with the experimental data from literature. Coefficients of determination for model predictions and experimental values were in the range of 0.75 to 0.96 for Avicel hydrolysis by CBH I action. Model was able to simulate the synergistic action of multiple enzymes during hydrolysis. The model simulations captured the important experimental observations: effect of structural properties, enzyme inhibition and enzyme loadings on the hydrolysis and degree of synergism among enzymes.ConclusionsThe model was effective in capturing the dynamic behavior of cellulose hydrolysis during action of individual as well as multiple cellulases. Simulations were in qualitative and quantitative agreement with experimental data. Several experimentally observed phenomena were simulated without the need for any additional assumptions or parameter changes and confirmed the validity of using the stochastic molecular modeling approach to quantitatively and qualitatively describe the cellulose hydrolysis.
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Project description:Complex systems exhibiting critical transitions when one of their governing parameters varies are ubiquitous in nature and in engineering applications. Despite a vast literature focusing on this topic, there are few studies dealing with the effect of the rate of change of the bifurcation parameter on the tipping points. In this work, we consider a subcritical stochastic Hopf bifurcation under two scenarios: the bifurcation parameter is first changed in a quasi-steady manner and then, with a finite ramping rate. In the latter case, a rate-dependent bifurcation delay is observed and exemplified experimentally using a thermoacoustic instability in a combustion chamber. This delay increases with the rate of change. This leads to a state transition of larger amplitude compared with the one that would be experienced by the system with a quasi-steady change of the parameter. We also bring experimental evidence of a dynamic hysteresis caused by the bifurcation delay when the parameter is ramped back. A surrogate model is derived in order to predict the statistic of these delays and to scrutinize the underlying stochastic dynamics. Our study highlights the dramatic influence of a finite rate of change of bifurcation parameters upon tipping points, and it pinpoints the crucial need of considering this effect when investigating critical transitions.
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Project description:The cohesin-dockerin interaction in Clostridium thermocellum cellulosome mediates the tight binding of cellulolytic enzymes to the cellulosome-integrating protein CipA. Here, this interaction was used to study the effect of different cellulose-binding domains (CBDs) on the enzymatic activity of C. thermocellum endoglucanase CelD (1,4-beta-d endoglucanase, EC) toward various cellulosic substrates. The seventh cohesin domain of CipA was fused to CBDs originating from the Trichoderma reesei cellobiohydrolases I and II (CBD(CBH1) and CBD(CBH2)) (1,4-beta-d glucan-cellobiohydrolase, EC), from the Cellulomonas fimi xylanase/exoglucanase Cex (CBD(Cex)) (beta-1,4-d glucanase, EC), and from C. thermocellum CipA (CBD(CipA)). The CBD-cohesin hybrids interacted with the dockerin domain of CelD, leading to the formation of CelD-CBD complexes. Each of the CBDs increased the fraction of cellulose accessible to hydrolysis by CelD in the order CBD(CBH1) < CBD(CBH2) approximately CBD(Cex) < CBD(CipA). In all cases, the extent of hydrolysis was limited by the disappearance of sites accessible to CelD. Addition of a batch of fresh cellulose after completion of the reaction resulted in a new burst of activity, proving the reversible binding of the intact complexes despite the apparent binding irreversibility of some CBDs. Furthermore, burst of activity also was observed upon adding new batches of CelD-CBD complexes that contained a CBD differing from the first one. This complementation between different CBDs suggests that the sites made available for hydrolysis by each of the CBDs are at least partially nonoverlapping. The only exception was CBD(CipA), whose sites appeared to overlap all of the other sites.
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Project description:Synergistic cooperation of different enzymes is a prerequisite for efficient degradation of cellulose. The conventional mechanistic interpretation of the synergism between randomly acting endoglucanases (EGs) and chain end-specific processive cellobiohydrolases (CBHs) is that EG-generated new chain ends on cellulose surface serve as starting points for CBHs. Here we studied the hydrolysis of bacterial cellulose (BC) by CBH TrCel7A and EG TrCel5A from Trichoderma reesei under both single-turnover and "steady state" conditions. Unaccountable by conventional interpretation, the presence of EG increased the rate constant of TrCel7A-catalyzed hydrolysis of BC in steady state. At optimal enzyme/substrate ratios, the "steady state" rate of synergistic hydrolysis became limited by the velocity of processive movement of TrCel7A on BC. A processivity value of 66 ± 7 cellobiose units measured for TrCel7A on (14)C-labeled BC was close to the leveling off degree of polymerization of BC, suggesting that TrCel7A cannot pass through the amorphous regions on BC and stalls. We propose a mechanism of endo-exo synergism whereby the degradation of amorphous regions by EG avoids the stalling of TrCel7A and leads to its accelerated recruitment. Hydrolysis of pretreated wheat straw suggested that this mechanism of synergism is operative also in the degradation of lignocellulose. Although both mechanisms of synergism are used in parallel, the contribution of conventional mechanism is significant only at high enzyme/substrate ratios.
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Project description:Membrane fouling during particle filtration occurs through a variety of mechanisms, including internal pore clogging by contaminants, coverage of pore entrances and deposition on the membrane surface. In this paper, we present an efficient method for modelling the behaviour of a filter, which accounts for different retention mechanisms, particle sizes and membrane geometries. The membrane is assumed to be composed of a series of, possibly interconnected, pores. The central feature is a conductivity function, which describes the blockage of each individual pore as particles arrive, which is coupled with a mechanism to account for the stochastic nature of the arrival times of particles at the pore. The result is a system of ordinary differential equations based on the pore-level interactions. We demonstrate how our model can accurately describe a wide range of filtration scenarios. Specifically, we consider a case where blocking via multiple mechanisms can occur simultaneously, which have previously required the study through individual models; the filtration of a combination of small and large particles by a track-etched membrane and particle separation using interconnected pore networks. The model is significantly faster than comparable stochastic simulations for small networks, enabling its use as a tool for efficient future simulations.
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Project description:MotivationThe time evolution of molecular species involved in biochemical reaction networks often arises from complex stochastic processes involving many species and reaction events. Inference for such systems is profoundly challenged by the relative sparseness of experimental data, as measurements are often limited to a small subset of the participating species measured at discrete time points. The need for model reduction can be realistically achieved for oscillatory dynamics resulting from negative translational and transcriptional feedback loops by the introduction of probabilistic time-delays. Although this approach yields a simplified model, inference is challenging and subject to ongoing research. The linear noise approximation (LNA) has recently been proposed to address such systems in stochastic form and will be exploited here.ResultsWe develop a novel filtering approach for the LNA in stochastic systems with distributed delays, which allows the parameter values and unobserved states of a stochastic negative feedback model to be inferred from univariate time-series data. The performance of the methods is tested for simulated data. Results are obtained for real data when the model is fitted to imaging data on Cry1, a key gene involved in the mammalian central circadian clock, observed via a luciferase reporter construct in a mouse suprachiasmatic nucleus.Availability and implementationProgrammes are written in MATLAB and Statistics Toolbox Release 2016 b, The MathWorks, Inc., Natick, Massachusetts, USA. Sample code and Cry1 data are available on GitHub https://github.com/scalderazzo/FLNADD.Supplementary informationSupplementary data are available at Bioinformatics online.
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Project description:Cellulose hydrolysis is an extensively studied process due to its relevance in the fields of biofuels, chemicals production, and renewable nanomaterials. However, the direct visualization of the process accompanied with detailed scaling has not been reported because of the vast morphological alterations occurring in cellulosic fibers in typical heterogeneous (solid/liquid) hydrolytic systems. Here, we overcome this distraction by exposing hardwood cellulose nanofibers (CNFs) deposited on silica substrates to pressurized HCl gas in a solid/gas system and examine the changes in individual CNFs by atomic force microscopy (AFM). The results revealed that hydrolysis proceeds via an intermediate semi-fibrous stage before objects reminiscent of cellulose nanocrystals were formed. The length of the nanocrystal-like objects correlated well with molar mass, as analyzed by gel permeation chromatography, performed on CNF aerogels hydrolyzed under identical conditions. Meanwhile, X-ray diffraction showed a slight increase in crystallinity index as the hydrolysis proceeded. The results provide a modern visual complement to >100 years of research in cellulose degradation.
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