Non-mechanistic Models Research Articles

Analysis of Membrane Process Model from Black Box to Machine Learning

The membrane processes include the complex frameworks, typically integrating various physio-chemical aspects, and the biological activities, based on the systems researched. In that regard, the process modeling is essential to predict and simulate the process and the performance of membranes, to infer concerning the optimum process aspects, meant to analyze fouling developments, and principally, the controls and monitoring of processes. Irrespective of the real terminological dissemination such as Machine Learning (ML), the application of computing instruments to the processes of model membrane was considered in the past are insignificant from the scholarly perspective, not contributing to our knowledge of the aspects included. Irrespective of the controversies, in the past two decades, non-mechanistic and data-driven modeling is applicable to illustrate various membrane process, and in the establishment of novel tracking and modeling approaches. In that regard, this paper concentrates on the provision of a custom aspect regarding the use of Non-Mechanistic Modeling (NMM) in membrane processing, assessing the transformations endorsed by our experience, accomplished as a research segment operational in the membrane process segment. Furthermore, the guidelines are the problems for the application of the state-of-the-art computational instruments Membrane Computing (MC).

Mechanistic gene networks inferred from single-cell data with an outlier-insensitive method

With advances in single-cell techniques, measuring gene dynamics at cellular resolution has become practicable. In contrast, the increased complexity of data has made it more challenging computationally to unravel underlying biological mechanisms. Thus, it is critical to develop novel computational methods capable of dealing with such complexity and of providing predictive deductions from such data. Many methods have been developed to address such challenges, each with its own advantages and limitations. We present an iterative regression algorithm for inferring a mechanistic gene network from single-cell data, especially suited to overcoming problems posed by measurement outliers. Using this regression, we infer a developmental model for the gene dynamics in Drosophila melanogaster blastoderm embryo. Our results show that the predictive power of the inferred model is higher than that of other models inferred with least squares and ridge regressions. As a baseline for how well a mechanistic model should be expected to perform, we find that model predictions of the gene dynamics are more accurate than predictions made with neural networks of varying architectures and complexity. This holds true even in the limit of small sample sizes. We compare predictions for various gene knockouts with published experimental results, finding substantial qualitative agreement. We also make predictions for gene dynamics under various gene network perturbations, impossible in non-mechanistic models.

Various reduced-order surrogate models for fluid flow and mass transfer in human bronchial tree.

The bronchial tree plays a main role in the human respiratory system because the air distribution throughout the lungs and gas exchange with blood occur in the airways whose dimensions vary from several centimeters to micrometers. Organization of about 60,000 conducting airways and 33 million respiratory airways in a limited space results in a complex structure. Due to this inherent complexity and a high number of airways, using target-oriented dimensional reduction is inevitable. In addition, there is no general reduced-order model for various types of problems. This necessitates coming up with an appropriate model from a variety of different reduced-order models to solve the desired problem. Lumped formulation, trumpet, or typical path model of whole or parts of bronchial tree are frequently used reduced-order models. On the other hand, using any of these models results in underestimation of flow heterogeneity leading to inaccurate prediction of the systems whose mechanisms depend on the fluid heterogeneity. In this study, a simple robust model combining mechanistic and non-mechanistic modeling approaches of the bronchial tree is proposed which overcomes the limitations of the previous reduced-order models and gives the same results of a detailed mechanistic model for the first time. This model starts from an accurate multi-branching model of conducting and respiratory airways (i.e., the base model) and suggests a proxy model of conducting airway and reduced-order model of respiratory airways based on the base model to significantly reduce computational cost while retaining the accuracy. The combination of these models suggests various reduced-order surrogate models of the human bronchial tree for different problems. The applications and limitations of each reduced-order model are also discussed. The accuracy of the proposed model in the prediction of fluid heterogeneity has been examined by the simulation of multi-breath inert gas washout because the alveolar slope is the reflection of fluid heterogeneity where the computational time decreases from 121h (using the base model) to 4.8s (using the reduced-order model). A parallel strategy for solving the equations is also proposed which decreases run time by 0.18s making the model suitable for real-time applications. Furthermore, the ability of the model has been evaluated in the modeling of asthmatic lung as an instance of abnormal lungs, and in the modeling of O2-CO2 exchange as an instance of nonlinear reacting systems. The results indicate that the proposed model outperforms previous models based on accuracy, robustness, and run time.

Predictive Model for Delivery Efficiency: Erythrocyte Membrane-Camouflaged Magnetofluorescent Nanocarriers Study.

Delivery efficiencies of theranostic nanoparticles (NPs) based on passive tumor targeting strongly depend either on their blood circulation time or on appropriate modulations of the tumor microenvironment. Therefore, predicting the NP delivery efficiency before and after a tumor microenvironment modulation is highly desirable. Here, we present a new erythrocyte membrane-camouflaged magnetofluorescent nanocarrier (MMFn) with long blood circulation time (92 h) and high delivery efficiency (10% ID for Ehrlich murine tumor model). MMFns owe their magnetic and fluorescent properties to the incorporation of manganese ferrite nanoparticles (MnFe2O4 NPs) and IR-780 (a lipophilic indocyanine fluorescent dye), respectively, to their erythrocyte membrane-derived camouflage. MMFn composition, morphology, and size, as well as optical absorption, zeta potential, and fluorescent, magnetic, and magnetothermal properties, are thoroughly examined in vitro. We then present an analytical pharmacokinetic (PK) model capable of predicting the delivery efficiency (DE) and the time of peak tumor uptake (tmax), as well as changes in DE and tmax due to modulations of the tumor microenvironment, for potentially any nanocarrier. Experimental PK data sets (blood and tumor amounts of MMFns) are simultaneously fit to the model equations using the PK modeling software Monolix. We then validate our model analytical solutions with the numerical solutions provided by Monolix. We also demonstrate how our a priori nonmechanistic model for passive targeting relates to a previously reported mechanistic model for active targeting. All in vivo PK studies, as well as in vivo and ex vivo biodistribution studies, were conducted using two noninvasive techniques, namely, fluorescence molecular tomography (FMT) and alternating current biosusceptometry (ACB). Finally, histopathology corroborates our PK and biodistribution results.

Forecasting and Uncertainty Quantification Using a Hybrid of Mechanistic and Non-mechanistic Models for an Age-Structured Population Model.

In this paper, we present a new method for the prediction and uncertainty quantification of data-driven multivariate systems. Traditionally, either mechanistic or non-mechanistic modeling methodologies have been used for prediction; however, it is uncommon for the two to be incorporated together. We compare the forecast accuracy of mechanistic modeling, using Bayesian inference, a non-mechanistic modeling approach based on state space reconstruction, and a novel hybrid methodology composed of the two for an age-structured population data set. The data come from cannibalistic flour beetles, in which it is observed that the adults preying on the eggs and pupae result in non-equilibrium population dynamics. Uncertainty quantification methods for the hybrid models are outlined and illustrated for these data. We perform an analysis of the results from Bayesian inference for the mechanistic model and hybrid models to suggest reasons why hybrid modeling methodology may enable more accurate forecasts of multivariate systems than traditional approaches.

Inferring node dates from tip dates in fossil Canidae: the importance of tree priors.

Tip-dating methods are becoming popular alternatives to traditional node calibration approaches for building time-scaled phylogenetic trees, but questions remain about their application to empirical datasets. We compared the performance of the most popular methods against a dated tree of fossil Canidae derived from previously published monographs. Using a canid morphology dataset, we performed tip-dating using BEAST v. 2.1.3 and MrBayes v. 3.2.5. We find that for key nodes (Canis, approx. 3.2 Ma, Caninae approx. 11.7 Ma) a non-mechanistic model using a uniform tree prior produces estimates that are unrealistically old (27.5, 38.9 Ma). Mechanistic models (incorporating lineage birth, death and sampling rates) estimate ages that are closely in line with prior research. We provide a discussion of these two families of models (mechanistic versus non-mechanistic) and their applicability to fossil datasets.

Indirect Effects of Environmental Change in Resource Competition Models.

Anthropogenic environmental change can affect species directly by altering physiological rates or indirectly by changing competitive outcomes. The unknown strength of competition-mediated indirect effects makes it difficult to predict species abundances in the face of ongoing environmental change. Theory developed with phenomenological competition models shows that indirect effects are weak when coexistence is strongly stabilized, but these models lack a mechanistic link between environmental change and species performance. To extend existing theory, we examined the relationship between coexistence and indirect effects in mechanistic resource competition models. We defined environmental change as a change in resource supply points and quantified the resulting competition-mediated indirect effects on species abundances. We found that the magnitude of indirect effects increases in proportion to niche overlap. However, indirect effects also depend on differences in how competitors respond to the change in resource supply, an insight hidden in nonmechanistic models. Our analysis demonstrates the value of using niche overlap to predict the strength of indirect effects and clarifies the types of indirect effects that global change can have on competing species.

Role of electrical resistance of electrodes in modeling of discharging and charging of flooded lead-acid batteries

Electrical resistance of both the electrodes of a lead-acid battery increases during discharge due to formation of lead sulfate, an insulator. Work of Metzendorf [1] shows that resistance increases sharply at about 65% conversion of active materials, and battery stops discharging once this critical conversion is reached. However, these aspects are not incorporated into existing mathematical models. Present work uses the results of Metzendorf [1], and develops a model that includes the effect of variable resistance. Further, it uses a reasonable expression to account for the decrease in active area during discharge instead of the empirical equations of previous work. The model's predictions are compared with observations of Cugnet et al. [2]. The model is as successful as the non-mechanistic models existing in literature. Inclusion of variation in resistance of electrodes in the model is important if one of the electrodes is a limiting reactant. If active materials are stoichiometrically balanced, resistance of electrodes can be very large at the end of discharge but has only a minor effect on charging of batteries. The model points to the significance of electrical conductivity of electrodes in the charging of deep discharged batteries.

Middle-Eastern plant communities tolerate 9 years of drought in a multi-site climate manipulation experiment.

For evaluating climate change impacts on biodiversity, extensive experiments are urgently needed to complement popular non-mechanistic models which map future ecosystem properties onto their current climatic niche. Here, we experimentally test the main prediction of these models by means of a novel multi-site approach. We implement rainfall manipulations—irrigation and drought—to dryland plant communities situated along a steep climatic gradient in a global biodiversity hotspot containing many wild progenitors of crops. Despite the large extent of our study, spanning nine plant generations and many species, very few differences between treatments were observed in the vegetation response variables: biomass, species composition, species richness and density. The lack of a clear drought effect challenges studies classifying dryland ecosystems as most vulnerable to global change. We attribute this resistance to the tremendous temporal and spatial heterogeneity under which the plants have evolved, concluding that this should be accounted for when predicting future biodiversity change.

A robust framework to predict mercury speciation in combustion flue gases

Mercury emissions from coal combustion have become a global concern as growing energy demands have increased the consumption of coal. The effective implementation of treatment technologies requires knowledge of mercury speciation in the flue gas, namely concentrations of elemental, oxidized and particulate mercury at the exit of the boiler. A model that can accurately predict mercury species in flue gas would be very useful in that context. Here, a Bayesian regularized artificial neural network (BRANN) that uses five coal properties and combustion temperature was developed to predict mercury speciation in flue gases before treatment technology implementation. The results of the model show that up to 97 percent of the variation in mercury species concentration is captured through the use of BRANNs. The BRANN model was used to conduct a parametric sensitivity which revealed that the coal chlorine content and coal calorific value were the most sensitive parameters, followed by the combustion temperature. The coal sulfur content was the least important parameter. The results demonstrate the applicability of BRANNs for predicting mercury concentration and speciation in combustion flue gas and provide a more efficient and effective technique when compared to other advanced non-mechanistic modeling strategies.

Phenological overlap of interacting species in a changing climate: an assessment of available approaches

Concern regarding the biological effects of climate change has led to a recent surge in research to understand the consequences of phenological change for species interactions. This rapidly expanding research program is centered on three lines of inquiry: (1) how the phenological overlap of interacting species is changing, (2) why the phenological overlap of interacting species is changing, and (3) how the phenological overlap of interacting species will change under future climate scenarios. We synthesize the widely disparate approaches currently being used to investigate these questions: (1) interpretation of long-term phenological data, (2) field observations, (3) experimental manipulations, (4) simulations and nonmechanistic models, and (5) mechanistic models. We present a conceptual framework for selecting approaches that are best matched to the question of interest. We weigh the merits and limitations of each approach, survey the recent literature from diverse systems to quantify their use, and characterize the types of interactions being studied by each of them. We highlight the value of combining approaches and the importance of long-term data for establishing a baseline of phenological synchrony. Future work that scales up from pairwise species interactions to communities and ecosystems, emphasizing the use of predictive approaches, will be particularly valuable for reaching a broader understanding of the complex effects of climate change on the phenological overlap of interacting species. It will also be important to study a broader range of interactions: to date, most of the research on climate-induced phenological shifts has focused on terrestrial pairwise resource–consumer interactions, especially those between plants and insects.

Particle cluster dynamics during fluidization

Many equations and correlations used in the design and optimization of fluidized unit operations are based on the assumptions particles that interact only with each other are collisional. However, high-speed video and image analysis suggest that in many cases particle–particle interactions go beyond Newtonian physics. Particles were found to have a strong propensity to cluster, especially fines. Result shows that particle clustering is prolific in many fluidizing environments from bubbling fluidized beds to riser to jets. For many empirical correlations, this is not an issue and cohesive behavior is often captured in the fitting of the experimental data. This is not the case for fundamental models where first principles rarely include cohesive forces. If they are included it is typically with non-mechanistic models as the nature of these cohesive forces are not completely understood. This work provides an insight to the nature of particle clustering in bubbling fluidized beds, risers and liquid jets. Each case appears to be due to a set of different cohesive forces or a sequence of cohesive forces. The nature of these forces is discussed.

Allosteric transition: a comparison of two models

IntroductionTwo recent models are in use for analysis of allosteric drug action at receptor sites remote from orthosteric binding sites. One is an allosteric two-state mechanical model derived in 2000 by David Hall. The other is an extended operational model developed in 2007 by Arthur Christopoulos’s group. The models are valid in pharmacology, enzymology, transportology as well as several other fields of biology involving allosteric concentration effects.ResultsI show here that Hall’s model for interactions between an orthoster, an alloster, and a receptive unit is the best choice of model both for simulation and analysis of allosteric concentration-responses at equilibrium or steady-state.ConclusionsAs detailed knowledge of receptors systems becomes available, systems with several pathways and states and/ or more than two binding sites should be analysed by extended forms of the Hall model rather than for instance a Hill type exponentiation of terms as introduced in non-mechanistic (operational) model approaches; yielding semi-quantitative estimates of actual system parameters based on Hill’s unlikely simultaneity model for G protein-coupled receptors.

Recent trends in the modeling of cellulose hydrolysis

This work reviews recent trends in the modeling of cellulose hydrolysis, within the perspective of application of kinetic models in a bioreactor engineering framework, including scale-up, design and process optimization. From this point of view, despite the phenomenological insight that mechanistic models can provide, the expectation that more detailed approaches could be a basis for extrapolations to different substrates and/or enzymatic pools is still not fulfilled. The complexity of the lignocellulosic matrix, the different mechanisms of catalytic action, the role of mass transfer limitations and the deviations from ideal mixing are important difficulties for the modeler, which will continue to impose more conservative approaches for scale-up. Nevertheless, the search for more robust models is a very important task, provided that the engineer is aware of their limitations. Data-driven, non-mechanistic models such as artificial neural networks, perhaps in combination with other approaches in the so-called hybrid models, is also a promising alternative.

Climate and recruitment limitation of hosts: the dynamics of American cutaneous leishmaniasis seen through semi-mechanistic seasonal models

Diseases cycle as a response to endogenous and exogenous factors. For infectious diseases caused by vector-transmitted pathogens, the exogenous factors are commonly equated to climatic forces and the endogenous factors to the recruitment of new susceptible individuals. Mathematical models that explicitly (parametrically) consider both types of factor are, however, very rare. An approach is presented to model the effects of endogenous and exogenous factors parametrically, using a time series for American cutaneous leishmaniasis (ACL) from Costa Rica. The seasonality of the disease is modelled using a seasonal autoregressive approach. The latter has the advantage of allowing the use of semi-mechanistic frameworks that consider infection clearance, while explicitly introducing the feedbacks produced by the transition between immune classes, as well as climatic forcing. It also uses a relatively small number of degrees of freedom (compared with the numbers involved in semi-parametric approaches), making it useful for relatively short time series and series with abrupt changes. Compared with non-mechanistic models built for prediction purposes, this way of modelling seems to increase the likelihood of the data being explained by a plausible mechanism. The approach used in this study of ACL could be useful in investigating the changes that occur in other diseases that show non-stationary seasonal dynamics, and can be easily adapted to model the dynamics of other infectious diseases that show trends or breakpoints. The present results support the view that humans affected by ACL are mostly incidental hosts, and indicate that, at the population level, there is a delay of about 5 months between human infection with the causative parasites and the onset of clinical symptoms. They encourage the development of surveillance systems, for monitoring the prevalence of infection in the sandflies that act as vectors, and the use of sentinel hosts, so that control measures can be rapidly applied or strengthened before a serious outbreak occurs. The development of more accurate mathematical models of ACL will depend largely on advances in the ecology of the disease and of all the hosts of the causative parasites.

Role of nickel in a semi-mechanistic analytical model for radiation embrittlement of model alloys

A model based on key embrittlement mechanisms is proposed for the analysis. The advantages of such semi-mechanistic model, when compared to non-mechanistic models, is that it allows improved fitting of data and permits the visualization of the relative contribution to embrittlement of the various damage components. Data from a set of model alloys with parametric variation of the Cu, P and Ni contents, irradiated in the High Flux Reactor Petten (The Netherlands) and Kola Nuclear Power Plant (Russia), have been thoroughly analysed. The low Ni model alloys results are studied in detail showing the semi-mechanistic model capabilities and their potential for application to commercial steels and welds; in particular for those used in Russian pressure water reactor (VVER). The additional effect of Ni and its influence in the model are also presented in this article.

An algorithmic approach for system-specific modelling of activated sludge bulking in an SBR

Using a step-by-step approach, a system-specific non-mechanistic model based on statistical analyses of real-world response of process parameters to bulking, was formulated for an aerobic Sequencing Batch Reactor (SBR). The approach involved 2 phases — “Diagnosis” and “Analysis”. In “Diagnosis” phase, model parameters were identified via statistical rules and existing knowledge base on bulking, while in the “Analysis” phase, the parameters were modelled with the Sludge Volume Index (SVI) to formulate the non-mechanistic model. Validation results yielded satisfactory results for the modelling analysis. The statistical approach was executed using a multivariate data analysis package and resulted in a non-mechanistic model that was practically appealing to practitioners for its ease of use as a predictive tool for bulking. This approach, being dependent only on data analysis and statistical modelling, required no bench-scale experiments to be conducted for preliminary identification of the bulking problem. Furthermore, such an approach being algorithmic in nature, could potentially form the concept behind design of expert systems for very rapid, and economical diagnosis of bulking problems before a more in-depth and slow analytical approach involving costly laboratory procedures is adopted.

MODELLING ENTREPRENEURIAL INCLINATION WITH AN ARTIFICIAL NEURAL NETWORK

ABSTRACT Artificial neural networks are parallel processing systems with the ability to learn by example and to generalize from inferred patterns. In this application, a neural network model of the feedforward, backpropagation type was designed to predict entrepreneurial inclination from knowledge of psychological, demographic and family characteristics. Training and testing data were drawn from results of a previous study, in which a questionnaire was administered to 200 business undergraduates in Hong Kong. The trained network had a holdout test accuracy of 80%. The results suggest a potential usefulness of neural network technology for non-mechanistic modeling in entrepreneurship research and application.

Predicting grassland community changes with an artificial neural network model

Artificial neural networks are parallel processing systems with the ability to learn by example and generalize from inferred patterns. In this application, a neural network model of the feedforward, backpropagation type is designed to predict future community composition from knowledge of present climatic factors and species cover. Training and testing data are drawn from a 30-year record of the environmental and vegetative variables of a grassland community. The resulting trained network is capable of forecasting accurately up to 4 years into the future. The results indicate a potential usefulness of neural network technology for non-mechanistic modeling in ecological research and management.

Retention model for high-performance ion-exchange chromatography

Protein retention on an ionic surface is the result of protein charge, surface charge and the charge characteristics of the surrounding medium. To date, a “net charge” model has been used to explain this phenomenon; however, retention mapping studies on strong ion-exchange columns showed it to be inadequate. Deviations may result from charge asymmetry, since it appears that only a fraction of the protein surface interacts with the stationary phase. Retention is also altered by the type of displacing salt. A non-mechanistic model has been developed which shows a positive correlation between protein retention and the number of charges associated with the adsorption-desorption process. Integration of these observations and concepts provides a deeper understanding of protein retention on ion-exchange surfaces.