Colocalization and Sequential Enzyme Activity in Aqueous Biphasic Systems: Experiments and Modeling.

Subcellular compartmentalization of biomolecules and their reactions is common in biology and provides a general strategy for improving and/or controlling kinetics in metabolic pathways that contain multiple sequential enzymes. Enzymes can be colocalized in multiprotein complexes, on scaffolds or inside subcellular organelles. Liquid organelles formed by intracellular phase coexistence could provide an additional means of sequential enzyme colocalization. Here we use experiment and computation to explore the kinetic consequences of sequential enzyme compartmentalization into model liquid organelles in a crowded polymer solution. Two proteins of the de novo purine biosynthesis pathway, ASL (adenylosuccinate lyase, Step 8) and ATIC (5-aminoimidazole-4-carboxamide ribonucleotide transformylase/inosine monophosphate cyclohydrolase, Steps 9 and 10), were studied in a polyethylene glycol/dextran aqueous two-phase system. Dextran-rich phase droplets served as model liquid compartments for enzyme colocalization. In this system, which lacks any specific binding interactions between the phase-forming polymers and the enzymes, we did not observe significant rate enhancements from colocalization for the overall reaction under our experimental conditions. The experimental results were used to adapt a mathematical model to quantitatively describe the kinetics. The mathematical model was then used to explore additional, experimentally inaccessible conditions to predict when increased local concentrations of enzymes and substrates can (or cannot) be expected to yield increased rates of product formation. Our findings indicate that colocalization within these simplified model liquid organelles can lead to enhanced metabolic rates under some conditions, but that very strong partitioning into the phase that serves as the compartment is necessary. In vivo, this could be provided by specific binding affinities between components of the liquid compartment and the molecules to be localized within it.

Direct Determination of High-Affinity Binding Constants by Continuous Injection Isothermal Titration Calorimetry.

Isothermal titration calorimetry (ITC) is a method to determine thermodynamic values (ΔG, ΔH, and ΔS) for ligand-receptor binding in biological and abiological systems. It is challenging to directly determine subnanomolar dissociation constants using a standard incremental injection approach ITC (IIA-ITC) measurement. We recently demonstrated a continuous injection approach ITC (CIA-ITC) [ J. Phys. Chem. B 2021, 125, 8075-8087]enables the estimation of thermodynamic parameters in situ. In this work, we demonstrate a label-free and surface modification-free CIA-ITC to determine the complete binding thermodynamics of a ligand with a subnanomolar dissociation constant KD. The KD for desthiobiotin (DTB)-avidin binding was determined to be 6.5 pM with respect to the ligand by CIA-ITC, a quantity unsuccessfully measured with IIA-ITC and surface plasmon resonance spectroscopy (SPR). This value compares well with literature-reported spectroscopic determination of DTB-avidin binding. Criteria with respect to the concentration of the ligand and receptor and flow rate for obtaining true equilibrium dissociation constants without displacement titration are presented.

Using dynamic data reconciliation to improve the performance of PID feedback control systems with Gaussian/non-Gaussian distributed disturbance and measurement noise.

For a stochastic PID feedback control system, the uncertainty of the working environment often leads to the unsatisfied performance of the system, which does not meet the profit requirements. The working environment generally includes external disturbance and measurement noise, etc. Gaussian distributed measurement noise and disturbances are widely considered while non-Gaussian distributed measurement noise and disturbances are rarely considered. In this paper, the performance degradation of Gaussian/non-Gaussian disturbances and measurement noise on a stochastic PID feedback system is considered and analyzed. An efficient method, dynamic data reconciliation (DDR) is developed to filter measurement noise and disturbances and improve the performance of the stochastic PID feedback control system. By utilizing model-based and measured information, DDR avoids time delays in output estimation. With the detailed theoretical analysis and simulation verification, the effectiveness of the proposed DDR technology on the stochastic PID feedback control system is verified. Compared with conventional exponential filters, DDR can achieve better control performance. The proposed DDR is also used for the control system of the DC-AC​ converter. The improved effect of DDR on the output quality is demonstrated by the results.

Designing social distancing policies for the COVID-19 pandemic: A probabilistic model predictive control approach.

The effective control of the COVID-19 pandemic is one the most challenging issues of recent years. The design of optimal control policies is challenging due to a variety of social, political, economical and epidemiological factors. Here, based on epidemiological data reported in recent studies for the Italian region of Lombardy, which experienced one of the largest and most devastating outbreaks in Europe during the first wave of the pandemic, we present a probabilistic model predictive control (PMPC) approach for the systematic study of what if scenarios of social distancing in a retrospective analysis for the first wave of the pandemic in Lombardy. The performance of the proposed PMPC was assessed based on simulations of a compartmental model that was developed to quantify the uncertainty in the level of the asymptomatic cases in the population, and the synergistic effect of social distancing during various activities, and public awareness campaign prompting people to adopt cautious behaviors to reduce the risk of disease transmission. The PMPC takes into account the social mixing effect, i.e. the effect of the various activities in the potential transmission of the disease. The proposed approach demonstrates the utility of a PMPC approach in addressing COVID-19 transmission and implementing public relaxation policies.

Establishing an inexpensive, space efficient colony of Bemisia tabaci MEAM1 utilizing modelling and feedback control principles.

A stable, synchronized colony of whitefly (Bemisia tabaci MEAM1 Gennadius) was established in a single ~30 cu.ft. reach-in incubator and supported on cabbage host plants which were grown in a 2 × 2' mesh cage without the need for a greenhouse or dedicated growth rooms. The colony maintenance, including cage cleaning and rotation of plants, was reduced to less than 10 h per week and executed by minimally experienced researchers. In our hands, this method was approximately 10-fold less expensive in personnel and materials than current typical implementations. A predator-prey model of whitefly colony maintenance that included whitefly proliferation and host plant health was developed to better understand and avoid colony collapse. This quantitative model can be applied to inform decisions such as inoculum planning and is a mathematical framework to assess insect control strategies. Extensive measurements of colony input and output (such as image analysis of leaf area and whitefly population size) were performed to define basic 'feedback control' parameters to gain reproducibility of this inherently unstable scaled-down whitefly colony. Quantitative transfer of ~100 whiteflies repeatedly produced more than 5000 adult whiteflies over a 6-week, two-generation period. Larger scale experimentation could be easily accommodated by transferring adult whiteflies from the maintenance colony with a low flow vacuum capture device. This approach to colony maintenance would be useful to programs that lack extensive plant growth room or greenhouse access and require a "clean" implementation that will not contaminate an axenic tissue culture laboratory.

Continuous Injection Isothermal Titration Calorimetry for In Situ Evaluation of Thermodynamic Binding Properties of Ligand-Receptor Binding Models.

We utilize a continuous injection approach (CIA) rather than the traditional incremental injection approach (IIA) to deliver ligand (or receptor) to the calorimeter cell to evaluate thermodynamic binding parameters for three common ligand-receptor binding models-single independent, competitive, and two independent binding sites-using isothermal titration calorimetry (ITC). A general mathematical expression for the binding isotherm for any binding stoichiometry under continuous delivery of ligand (or receptor) resulting in an analytical solution for the thermodynamic binding parameters is presented. The advantages of CIA include reduction in experimental time, estimation of thermodynamic binding parameter values, and automation of the experiment since thermodynamic parameters are estimated in situ. We demonstrate the inherent advantages of CIA over IIA for the three binding models. For the single independent site model, we utilized the binding of Ba2+ ions to ethylenediaminetetraacetic acid (EDTA), while competitive binding was captured by titration of Ca2+ ions into a buffered solution of Ba2+ and EDTA. We experimentally simulated a two independent binding site system by injecting Ca2+ into a solution of EDTA and 1,3-diaminopropane-N,N,N',N'-tetraacetic acid (DPTA). The results demonstrate estimation of thermodynamic parameters with greater confidence and simultaneous reduction in the experimental time of 83% and titrating reagent of 50%, as compared to IIA.

Dynamic data reconciliation to improve the result of controller performance assessment based on GMVC.

Due to the complexity of the industrial working environment, controllers are susceptible to various disturbance signals, resulting in unsatisfactory control performance. Therefore, it is especially important to assess the controller performance. Considering the harmful effect of measurement noise on controller performance assessment (CPA) based on generalized minimum variance control (GMVC), this paper proposes dynamic data reconciliation (DDR) to improve the accuracy of CPA based on GMVC. The paper first introduces CPA based on GMVC, and then analyzes the influence of measurement noise on GMVC based CPA index. DDR combined with GMVC based CPA is then proposed and analyzed in both SISO and MIMO systems to weaken the impact of measurement noise on CPA index. For both Gaussian distributed noise and non-Gaussian distributed noise, the formulation of DDR is derived from the Bayesian formula and maximum likelihood estimate. The effectiveness of the proposed method is verified in different case studies (involving both SISO and MIMO systems), and further verified by the control process of DC-AC converter. The simulation and experiment results demonstrate that the results of CPA based on GMVC can be obviously improved by using DDR.

Development of a stochastic model for the efficacy of NRTIs using known mechanisms of action.

We analyze the mechanisms by which nucleoside-analogue reverse transcriptase inhibitors, the most common class of drugs used in the treatment of HIV-1, exert their antiviral effects. We then seek to identify ways in which those known mechanisms can be employed to generate mathematical models for drug efficacy in terms of measurable physical values. We demonstrate that the probability a NRTI instead of a natural nucleotide is included can be expressed in terms of intracellular drug concentrations, natural nucleotide concentrations, and relevant rate constants derived from reverse transcriptase's mechanism of nucleotide addition. In order to determine the ultimate effect, the resistance of the NRTI to removal from the genome must be considered, which is achieved via stochastic modeling. We employ this model to determine the relationship between efficacy and drug concentration, as well as other drug characteristics like half life. We also investigate the effect of drug administration time on the overall efficacy. The model is employed for four different drugs and a sensitivity analysis on mutation and resistance is performed.

A computational procedure for optimal engineering interventions using kinetic models of metabolism.

The identification of optimal intervention strategies is a key step in designing microbial strains with enhanced capabilities. In this paper, we propose a general computational procedure to determine which genes/enzymes should be eliminated, repressed or overexpressed to maximize the flux through a product of interest for general kinetic models. The procedure relies on the generalized linearization of a kinetic description of the investigated metabolic system and the iterative application of mixed-integer linear programming (MILP) optimization to hierarchically identify all engineering interventions allowing for reaction eliminations and/or enzyme level modulations. The effect of the magnitude of the allowed changes in concentrations and enzyme levels is investigated, and a variant of the method to explore high-fold changes in enzyme levels is also analyzed. The proposed framework is demonstrated using a kinetic model modeling part of the central carbon metabolism of E. coli for serine overproduction. The proposed computational procedure is a general approach that can be applied to any metabolic system for which a kinetic description is provided.

Coupled enzyme reactions performed in heterogeneous reaction media: experiments and modeling for glucose oxidase and horseradish peroxidase in a PEG/citrate aqueous two-phase system.

The intracellular environment in which biological reactions occur is crowded with macromolecules and subdivided into microenvironments that differ in both physical properties and chemical composition. The work described here combines experimental and computational model systems to help understand the consequences of this heterogeneous reaction media on the outcome of coupled enzyme reactions. Our experimental model system for solution heterogeneity is a biphasic polyethylene glycol (PEG)/sodium citrate aqueous mixture that provides coexisting PEG-rich and citrate-rich phases. Reaction kinetics for the coupled enzyme reaction between glucose oxidase (GOX) and horseradish peroxidase (HRP) were measured in the PEG/citrate aqueous two-phase system (ATPS). Enzyme kinetics differed between the two phases, particularly for the HRP. Both enzymes, as well as the substrates glucose and H2O2, partitioned to the citrate-rich phase; however, the Amplex Red substrate necessary to complete the sequential reaction partitioned strongly to the PEG-rich phase. Reactions in ATPS were quantitatively described by a mathematical model that incorporated measured partitioning and kinetic parameters. The model was then extended to new reaction conditions, i.e., higher enzyme concentration. Both experimental and computational results suggest mass transfer across the interface is vital to maintain the observed rate of product formation, which may be a means of metabolic regulation in vivo. Although outcomes for a specific system will depend on the particulars of the enzyme reactions and the microenvironments, this work demonstrates how coupled enzymatic reactions in complex, heterogeneous media can be understood in terms of a mathematical model.