A mathematical model of intracellular behavior of microalgae for predicting growth and intracellular components syntheses under nutrient-replete and -deplete conditions.

Microalgae is a promising biomass source for renewable fuels and chemicals production. To describe microalgal behavior and improve their cultivation, various kinetic models have been proposed. However, previous works have focused on biomass formation and lipids production only, even though carbohydrates and proteins are also important products, not only for understanding the metabolic behavior of microalgae but also for enhancing the economic viability through value-added side products. In this study, a new mathematical model is proposed to explain core biological mechanisms of growth and macromolecules syntheses based on the central metabolism of carbon and nitrogen. In the model, microalgal growth is separated as hyperplasia and hypertrophy, to describe the cell growth more precisely under nutrient-replete and -deplete conditions. Sensitivity analysis performed using the model indicates that cell state (e.g., cell death rate) has a strong effect on the lipid production explaining the difficulty of reproducing a microalgae culture experiment.

State Estimation of the Time-Varying and Spatially Localized Concentration of Signal Molecules from the Stochastic Adsorption Dynamics on the Carbon Nanotube-Based Sensors and Its Application to Tumor Cell Detection.

This paper addresses a problem of estimating time-varying, local concentrations of signal molecules with a carbon-nanotube (CNT)-based sensor array system, which sends signals triggered by monomolecular adsorption/desorption events of proximate molecules on the surfaces of the sensors. Such sensors work on nano-scale phenomena and show inherently stochastic non-Gaussian behavior, which is best represented by the chemical master equation (CME) describing the time evolution of the probabilities for all the possible number of adsorbed molecules. In the CME, the adsorption rate on each sensor is linearly proportional to the local concentration in the bulk phase. State estimators are proposed for these types of sensors that fully address their stochastic nature. For CNT-based sensors motivated by tumor cell detection, the particle filter, which is nonparametric and can handle non-Gaussian distributions, is compared to a Kalman filter that approximates the underlying distributions by Gaussians. In addition, the second-order generalized pseudo Bayesian estimation (GPB2) algorithm and the Markov chain Monte Carlo (MCMC) algorithm are incorporated into KF and PF respectively, for detecting latent drift in the concentration affected by different states of a cell.

An effective chemical pretreatment method for lignocellulosic biomass with substituted imidazoles.

Lignocellulosic biomass is the most abundant naturally renewable organic resource for biofuel production. Because of its recalcitrance to enzymatic degradation, pretreatment is a crucial step before hydrolysis of the feedstock. A variety of pretreatment methods have been developed and intensively studied to achieve optimal yield without imposing significant adverse impact on the environment. Herein, we present a novel chemical pretreatment method using substituted heterocycles with low temperature and short residence time requirements. 1-Methylimidazole (MI) is a precursor to some imidazolium-based ionic liquids. In this study, its potential utilization as a biomass pretreatment agent is being investigated for the first time. At mild conditions, such as 25°C for 5 min at ambient pressure, a substantial increase in the hydrolysis rate throughout the entire course of conversion for cellulose substrate was obtained. Furthermore, the pretreatment effectiveness of MI on both untreated and steam-exploded lignocellulosic biomass including loblolly pine, switchgrass, and sugarcane bagasse has been studied and MI was found to be an efficient delignifier. Remarkable rate enhancement was also observed for the non-woody lignocellulosic substrates after a short period of MI pretreatment at ambient conditions. The mechanism of MI pretreatment is explored through analysis of cellulose physical properties including crystallinity index, degree of polymerization, accessibility, and lignin dissolution quantification.

Protein engineering of cellulases.

This review covers the topic of protein engineering of cellulases, mostly after 2009. Two major trends that are identified in this work are: first, the increased importance of results from computational protein engineering to drive ideas in the field, as experimental ideas and results often are still scarce, and, second, the further development of helper proteins for cellulose hydrolysis, such as lytic polysaccharide monooxygenase (LPO). The discussion in this work focuses both on improved attributes of cellulases and on the domains of cellulase that have been improved.

Progress and challenges in control of chemical processes.

This review covers key developments and trends in chemical process control during the past two decades. Control methodologies and related supporting technologies are covered, and recent trends in applications are also examined. After the widespread adoption of model-based techniques by industry, control interest has begun to move beyond the traditional realm of readily measured variables to include chemical compositions and particle features. However, the shift is being slowed by the shortage of accurate, reliable, and inexpensive sensing devices. Although the past two decades saw no new major theoretical or methodological advances, several important incremental improvements and extensions have been made to help the ripening of the technologies developed in the preceding two decades. Control is regaining its importance owing to society's renewed focus on energy and the maturation of various emerging technologies, but a community-wide consensus on what general problems should be solved is lacking.

Genome-scale metabolic model of the fission yeast Schizosaccharomyces pombe and the reconciliation of in silico/in vivo mutant growth.

BACKGROUND: Over the last decade, the genome-scale metabolic models have been playing increasingly important roles in elucidating metabolic characteristics of biological systems for a wide range of applications including, but not limited to, system-wide identification of drug targets and production of high value biochemical compounds. However, these genome-scale metabolic models must be able to first predict known in vivo phenotypes before it is applied towards these applications with high confidence. One benchmark for measuring the in silico capability in predicting in vivo phenotypes is the use of single-gene mutant libraries to measure the accuracy of knockout simulations in predicting mutant growth phenotypes. RESULTS: Here we employed a systematic and iterative process, designated as Reconciling In silico/in vivo mutaNt Growth (RING), to settle discrepancies between in silico prediction and in vivo observations to a newly reconstructed genome-scale metabolic model of the fission yeast, Schizosaccharomyces pombe, SpoMBEL1693. The predictive capabilities of the genome-scale metabolic model in predicting single-gene mutant growth phenotypes were measured against the single-gene mutant library of S. pombe. The use of RING resulted in improving the overall predictive capability of SpoMBEL1693 by 21.5%, from 61.2% to 82.7% (92.5% of the negative predictions matched the observed growth phenotype and 79.7% the positive predictions matched the observed growth phenotype). CONCLUSION: This study presents validation and refinement of a newly reconstructed metabolic model of the yeast S. pombe, through improving the metabolic model's predictive capabilities by reconciling the in silico predicted growth phenotypes of single-gene knockout mutants, with experimental in vivo growth data.

Elucidation of cellulose accessibility, hydrolysability and reactivity as the major limitations in the enzymatic hydrolysis of cellulose.

The precipitous decline in the rates of enzymatic hydrolysis of cellulose with conversion is one of the major limitations to the commercialization of second-generation biofuel. In this work, various rate-limiting factors (fractal kinetics, changes in crystallinity, accessibility, reactivity and hydrolysable fraction, enzyme clogging, and degree of polymerization) were investigated employing experimental as well as computational studies. Model-guided experiments showed cellulose accessibility and the hydrolysable fraction of accessible substrate (a previously undefined and unreported quantity) to decrease steadily until a conversion level of nearly 70%, while cellulose reactivity, defined in terms of hydrolytic activity per amount of actively adsorbed cellulase, remained constant. Substrate depletion, accessibility and hydrolysability decrease accounted for approximately 90% of rate retardation up to 70% conversion. Faster restart rates were observed on partially converted cellulose as compared to uninterrupted hydrolysis rates, supporting an enzyme clogging phenomenon that could possibly be responsible for the additional rate decrease.

Biological pretreatment of cellulose: enhancing enzymatic hydrolysis rate using cellulose-binding domains from cellulases.

In this study, cellulose-binding domains (CBDs) of cellulases from Trichoderma reesei were used in a pretreatment step and were found to effectively reduce the crystallinity of cellulose (both Avicel and fibrous cellulose). This, in turn, led to higher glucose concentrations (up to 25% increase) in subsequent hydrolysis of cellulose using a mixture of cellulases and without the need for any intermediate purification step. CBDs were shown to be active in a range of temperatures (up to 50°C), while cellulase hydrolytic activity was greatly reduced after incubation at 50°C. This was explained by retention of full binding capacity after incubation at 50°C for 15 h. Our findings suggest that CBDs may be a valuable tool in pretreating cellulose and eventually afford faster enzymatic conversion of cellulose to glucose, thus contributing to more affordable processes in the production of biofuels.

Cellulose crystallinity--a key predictor of the enzymatic hydrolysis rate.

The enzymatic hydrolysis of cellulose encounters various limitations that are both substrate- and enzyme-related. Although the crystallinity of pure cellulosic Avicel plays a major role in determining the rate of hydrolysis by cellulases from Trichoderma reesei, we show that it stays constant during enzymatic conversion. The mode of action of cellulases was investigated by studying their kinetics on cellulose samples. A convenient method for reaching intermediate degrees of crystallinity with Avicel was therefore developed and the initial rate of the cellulase-catalyzed hydrolysis of cellulose was demonstrated to be linearly proportional to the crystallinity index of Avicel. Despite correlation with the adsorption capacity of cellulases onto cellulose, at a given enzyme loading, the initial enzymatic rate continued to increase with a decreasing crystallinity index, even though the bound enzyme concentration stayed constant. This finding supports the determinant role of crystallinity rather than adsorption on the enzymatic rate. Thus, the cellulase activity and initial rate data obtained from various samples may provide valuable information about the details of the mechanistic action of cellulase and the hydrolysable/reactive fractions of cellulose chains. X-ray diffraction provides insight into the mode of action of Cel7A from T. reesei. In the conversion of cellulose, the (021) face of the cellulose crystal was shown to be preferentially attacked by Cel7A from T. reesei.

Multivariate statistical analysis of X-ray data from cellulose: a new method to determine degree of crystallinity and predict hydrolysis rates.

The enzymatic hydrolysis of cellulose by cellulases is one of the major steps in the production of ethanol from lignocellulosics. However, cellulosic biomass is not particularly susceptible to enzymatic attack and crystallinity of the substrates is one of the key properties that determine the hydrolysis rates. In this work, by quantifying the respective contributions of amorphous and crystalline cellulose to the X-ray diffraction spectra of cellulose with intermediate degrees of crystallinity, a new method to obtain consistent crystallinity index values was developed. Multivariate statistical analysis was applied to spectra obtained from phosphoric acid pretreated cellulose samples of various intermediate (but undetermined) crystallinity indices to reduce their dimensionality. The crystallinity indices obtained were found to be linearly related to the enzymatic hydrolysis rates. The method was validated by predicting the degree of crystallinity of samples containing various ratios of microcrystalline cellulose and amorphous cellulose, both of known crystallinity indices. Dimensionality reduction of the spectra was also used to predict the enzymatic hydrolysis rates of various cellulose samples from X-ray data. The method developed in this work could be generalized to accurately assess the degree of crystallinity for a wide range of varieties of cellulose.

Modeling cellulase kinetics on lignocellulosic substrates.

The enzymatic hydrolysis of cellulose to glucose by cellulases is one of the major steps involved in the conversion of lignocellulosic biomass to yield biofuel. This hydrolysis by cellulases, a heterogeneous reaction, currently suffers from some major limitations, most importantly a dramatic rate slowdown at high degrees of conversion. To render the process economically viable, increases in hydrolysis rates and yields are necessary and require improvement both in enzymes (via protein engineering) and processing, i.e. optimization of reaction conditions, reactor design, enzyme and substrate cocktail compositions, enzyme recycling and recovery strategies. Advances in both areas in turn strongly depend on the progress in the accurate quantification of substrate-enzyme interactions and causes for the rate slowdown. The past five years have seen a significant increase in the number of studies on the kinetics of the enzymatic hydrolysis of cellulose. This review provides an overview of the models published thus far, classifies and tabulates these models, and presents an analysis of their basic assumptions. While the exact mechanism of cellulases on lignocellulosic biomass is not completely understood yet, models in the literature have elucidated various factors affecting the enzymatic rates and activities. Different assumptions regarding rate-limiting factors and basic substrate-enzyme interactions were employed to develop and validate these models. However, the models need to be further tested against additional experimental data to validate or disprove any underlying hypothesis. It should also provide better insight on additional parameters required in the case that more substrate and enzyme properties are to be included in a model.

Identifying interacting residues using Boolean Learning and Support Vector Machines: case study on mRFP and DsRed proteins.

In a protein, interactions exist between amino acid residues that influence the protein's structural integrity or stability and thus affect its catalytic function. The loss of this interaction due to mutations in these amino acids usually leads to a non-functional protein. Probing the sequence space of a protein through mutations or recombinations, as performed in directed evolution to search for an improved variant, frequently results in such inactive sequences. In this work, we demonstrate the use of machine learning to identify such interacting residues and the use of template engineering strategies to increase the fraction of active variants in a library. We show that using the sequences from recombination of monomeric red fluorescent protein (mRFP) and Discosoma red fluorescent protein (DsRed), we were able to identify a pair of interacting residues using an algorithm based on Boolean Learning and Support Vector Machines. The interaction between the identified residues was verified through point mutations on the mRFP and DsRed genes. We also show that it is possible to use such results to alter the parental genes such that the probability of disrupting the important interactions is minimized. This will result in a larger fraction of active variants in the recombinant library and allow us to access more functional space. We demonstrate this effect by comparing the recombinant library of wild-type (WT) DsRed, mRFP and an altered sequence of DsRed with mRFP WT genes.

Pooling for improved screening of combinatorial libraries for directed evolution.

Following diversity generation in combinatorial protein engineering, a significant amount of effort is expended in screening the library for improved variants. Pooling, or combining multiple cells into the same assay well when screening, is a means to increase throughput and screen a larger portion of the library with less time and effort. We have developed and validated a Monte Carlo simulation model of pooling and used it to screen a library of beta-galactosidase mutants randomized in the active site to increase their activity toward fucosides. Here, we show that our model can successfully predict the number of highly improved mutants obtained via pooling and that pooling does increase the number of good mutants obtained. In unpooled conditions, we found a total of three mutants with higher activity toward p-nitrophenyl-beta-D-fucoside than that of the wild-type beta-galactosidase, whereas when pooling 10 cells per well we found a total of approximately 10 improved mutants. In addition, the number of "supermutants", those with the highest activity increase, was also higher when pooling was used. Pooling is a useful tool for increasing the efficiency of screening combinatorial protein engineering libraries.

Identifying the interacting positions of a protein using Boolean learning and support vector machines.

It is known that in the three-dimensional structure of a protein, certain amino acids can interact with each other in order to provide structural integrity or aid in its catalytic function. If these positions are mutated the loss of this interaction usually leads to a non-functional protein. Directed evolution experiments, which probe the sequence space of a protein through mutations in search for an improved variant, frequently result in such inactive sequences. In this work, we address the use of machine learning algorithms, Boolean learning and support vector machines (SVMs), to find such pairs of amino acid positions. The recombination method of imparting mutations was simulated to create in silico sequences that were used as training data for the algorithms. The two algorithms were combined together to develop an approach that weighs the structural risk as well as the empirical risk to solve the problem. This strategy was adapted to a multi-round framework of experiments where the data generated in the present round is used to design experiments for the next round to improve the generated library, as well as the estimation of the interacting positions. It is observed that this strategy can greatly improve the number of functional variants that are generated as well as the average number of mutations that can be made in the library.

Simulation modeling of pooling for combinatorial protein engineering.

Pooling in directed-evolution experiments will greatly increase the throughput of screening systems, but important parameters such as the number of good mutants created and the activity level increase of the good mutants will depend highly on the protein being engineered. The authors developed and validated a Monte Carlo simulation model of pooling that allows the testing of various scenarios in silico before starting experimentation. Using a simplified test system of 2 enzymes, betagalactosidase (supermutant, or greatly improved enzyme) and beta-glucuronidase (dud, or enzyme with ancestral level of activity), the model accurately predicted the number of supermutants detected in experiments within a factor of 2. Additional simulations using more complex activity distributions show the versatility of the model. Pooling is most suited to cases such as the directed evolution of new function in a protein, where the background level of activity is minimized, making it easier to detect small increases in activity level. Pooling is most successful when a sensitive assay is employed. Using the model will increase the throughput of screening procedures for directed-evolution experiments and thus lead to speedier engineering of proteins.

Support vector machines for learning to identify the critical positions of a protein.

A method for identifying the positions in the amino acid sequence, which are critical for the catalytic activity of a protein using support vector machines (SVMs) is introduced and analysed. SVMs are supported by an efficient learning algorithm and can utilize some prior knowledge about the structure of the problem. The amino acid sequences of the variants of a protein, created by inducing mutations, along with their fitness are required as input data by the method to predict its critical positions. To investigate the performance of this algorithm, variants of the beta-lactamase enzyme were created in silico using simulations of both mutagenesis and recombination protocols. Results from literature on beta-lactamase were used to test the accuracy of this method. It was also compared with the results from a simple search algorithm. The algorithm was also shown to be able to predict critical positions that can tolerate two different amino acids and retain function.