Comprehensive review of models and methods for inferences in bio-chemical reaction networks

The key processes in biological and chemical systems are described by networks of chemical reactions. From molecular biology to biotechnology applications, computational models of reaction networks are used extensively to elucidate their non-linear dynamics. The model dynamics are crucially dependent on the parameter values which are often estimated from observations. Over the past decade, the interest in parameter and state estimation in models of (bio-) chemical reaction networks (BRNs) grew considerably. The related inference problems are also encountered in many other tasks including model calibration, discrimination, identifiability, and checking, and optimum experiment design, sensitivity analysis, and bifurcation analysis. The aim of this review paper is to examine the developments in literature to understand what BRN models are commonly used, and for what inference tasks and inference methods. The initial collection of about 700 documents concerning estimation problems in BRNs excluding books and textbooks in computational biology and chemistry were screened to select over 270 research papers and 20 graduate research theses. The paper selection was facilitated by text mining scripts to automate the search for relevant keywords and terms. The outcomes are presented in tables revealing the levels of interest in different inference tasks and methods for given models in the literature as well as the research trends are uncovered. Our findings indicate that many combinations of models, tasks and methods are still relatively unexplored, and there are many new research opportunities to explore combinations that have not been considered—perhaps for good reasons. The most common models of BRNs in literature involve differential equations, Markov processes, mass action kinetics, and state space representations whereas the most common tasks are the parameter inference and model identification. The most common methods in literature are Bayesian analysis, Monte Carlo sampling strategies, and model fitting to data using evolutionary algorithms. The new research problems which cannot be directly deduced from the text mining data are also discussed

Dimension-reduction and discrimination of neuronal multi-channel signals

Dimensionsreduktion und Trennung neuronaler Multikanal-Signale

Morphoelastic rods Part 1: A single growing elastic rod

A theory for the dynamics and statics of growing elastic rods is presented. First, a single growing rod is considered and the formalism of three-dimensional multiplicative decomposition of morphoelasticity is used to describe the bulk growth of Kirchhoff elastic rods. Possible constitutive laws for growth are discussed and analysed. Second, a rod constrained or glued to a rigid substrate is considered, with the mismatch between the attachment site and the growing rod inducing stress. This stress can eventually lead to instability, bifurcation, and buckling

Parameter estimation methods for biological systems

The inverse problem of modeling biochemical processes mathematically from measured time course data falls into the category of system identification and parameter estimation. Analyzing the time course data would provide valuable insights into the model structure and dynamics of the biochemical system. Based on the types of biochemical reactions, such as metabolic networks and genetic networks, several modeling frameworks have been proposed, developed and proved effective, including the Michaelis-Menten equation, the Biochemical System Theory (BST), etc. One bottleneck in analyzing the obtained data is the estimation of parameter values within the system model. As most models for molecular biological systems are nonlinear with respect to both parameters and system state variables, estimation of parameters in these models from experimental measurement data is thus a nonlinear estimation problem. In principle, all algorithms for nonlinear optimization can be used to deal with this problem, for example, the Gauss-Newton iteration method and its variants. However, these methods do not take the special structures of biological system models into account. When the number of parameters to be determined increases, it will be challenging and computationally expensive to apply these conventional methods. In this research, several methods are proposed for estimating parameters in two classes of widely used biological system models: the S-system model and the linear fractional model (LFM), by utilizing their structure specialties. For the S-system, two estimation methods are designed. 1) Based on the two-term structure (production and degradation) of the model, an alternating iterative least squares method is proposed. 2) A separation nonlinear least squares method is proposed to deal with the partially linear structure of the model. For the LFM, two estimation methods are provided. 1) The separation nonlinear least squares method can also be adopted to treat the partially linear structure of the LFM, and moreover a modified iterative version is included. 2) A special strategy using the separation principle and the weighted least squares method is implemented to turn the cost function into a quadratic form and thus the estimates for parameters can be analytically solved. Simulation results have demonstrated the effectiveness of the proposed methods, which have shown better performance in terms of estimation accuracy and computation time, compared with those conventional nonlinear estimation methods

Discretization approach

학위논문(박사)--서울대학교 대학원 :공과대학 화학생물공학부(에너지환경 화학융합기술전공),2019. 8. 이원보.In recent years, many researchers in chemical engineering have made great efforts to develop mathematical models on the theoretical side that are consistent with experimental results. Despite these efforts, however, establishing models for a system with complex phenomena such as multiphase flow or stirred reactors is still considered to be a challenge. In the meantime, an increase in computational efficiency and stability in various numerical methods has allowed us to correctly solve and analyze the system based on the fundamental equations. This leads to the need for a mathematical model to accurately predict the behavior of systems in which there is interdependence among the internal elements. A methodology for building a model based on equations that represent fundamental phenomena can lower technical barriers in system analysis. In this thesis, we propose three mathematical models validated from laboratory or pilot-scale experiments. First, an apparatus for vaporizing liquid natural gas is surrounded with a frost layer formed on the surface during operation, and performance of the apparatus is gradually deteriorated due to the adiabatic effect. Because the system uses ambient air as a heat sink, it is necessary to consider the phase transition and mass transfer of water vapor, and natural gas in the air in order to understand the fluctuation of system characteristics. The model predicts the experimental data of a pilot-scale vaporizer within a mean absolute error of 5.5 %. In addition, we suggest the robust design methodology and optimal design which is able to maintain the efficiency under the weather conditions for a year. Two or more data analysis techniques including discrete waveform transformation and k-means clustering are used to extract features that can represent time series data. Under the settings, the performance in the optimized desgin is improved by 22.92 percentage points compared to that in the conventional system. In the second system, the continuous tubular crystallization reactor has advantages in terms of production capacity and scale-up compared with the conventional batch reactor. However, the tubular system requires a well-designed control system to maintain its stability and durability, and thus; there is a great deal of demand for the mathematical model of this system. We were able to estimate crystal size distribution by considering the population balance model simultaneously with several heat exchanger models. The model constructed based on the first principle reaction scheme successfully predicted the results from the full-factorial experiment. The experiments were conducted with LAM (L-asparagine monohydrate) solution. In the prediction, the average crystal length and standard deviation were within 20% of the results of an experiment where the crystals were not iteratively dissolved in the liquid but maintained a low-level supersaturation. Furthermore, to confirm the controllability of the crystal size distribution in the system, we replaced the LAM solution with HEWL (Hen-egg white lysozyme) solution. Finally, we propose a multi-compartment model to predict the behavior of a high-pressure autoclave reactor for polymer production. In order to simulate a complex polymer synthesis mechanism, the rotation effect of impellers in the reactor on polymerization and the influence caused by polymerization heat were sequentially evaluated. As a result, This model turned out to be able to predict the physical properties of the polymers produced in an industrial-scale reactor within 7% accuracy. In this thesis, all three systems are distributed parameter systems which can be expressed as partial differential equations for time and space. To construct a high order model, the system was interpreted based on discretization approach under minimal assumptions. This methodology can be applied not only to the systems suggested in this thesis but also to those consisting of interpdependent variables. I hope that this thesis provides guidance for further researches of chemical engineering in nearby future.최근에 몇 년에 걸쳐서 많은 연구자들이 이론을 기반으로 실험 결과와 일치하는 수학 모델을 개발하고자 많은 노력을 기울여 왔다. 하지만 이런 노력에도 불구하고 다상 흐름 혹은 교반 반응기와 같은 복잡한 현상을 내포한 시스템을 위한 모델을 수립하는 것은 여전히 화학 공학 분야에서 쉽지 않은 일로 여겨진다. 이 와중에 다양한 수치적 방법에서의 계산 효율의 증가와 안정성의 향상은 기본방정식에 기초한 시스템을 정확하게 해결하고 분석할 수 있게 해주었다. 이로 인하여 내부 요소들 간의 상호 의존성이 존재하는 시스템의 거동을 정확하게 예측하기 위한 수학적 모델의 필요성이 부각되었다. 기본 현상들을 표현할 수 있는 방정식들을 기반으로 모델을 구축하기 위한 방법론은 시스템 해석에 있어서 기술적 장벽을 낮출 수 있다. 이 학위 논문에서 우리는 실험실 또는 파일럿 규모의 실험으로부터 입증된 세 가지 수학적 모델을 제안한다. 첫 번째로, 공기를 사용하여 액상의 천연가스를 기화시키는 장치는 운전 도중에 기화기 표면에 서리 층이 형성되고 그로 인한 단열 효과로 장비의 성능이 서서히 저하된다. 시스템은 주변 공기를 열 흡수원으로 사용하기 때문에 시스템 특성의 변동을 파악하기 위해서는 공기 중 수증기 및 천연 가스의 상전이 및 전달 현상을 동시에 고려하여야 한다. 제시된 수학적 모델에 의해 예측한 결과는 파일럿 규모 기화기로부터 얻은 실험 데이터와 5.5% 평균 절대 오차를 보였다. 이에 더하여, 앞에서 제시한 기화기 모델을 이용하여 1년 동안의 기상 조건에서 운전 효율을 유지하면서 지속 운전이 가능한 기화기의 설계 방법과 결과를 제안하였다. 이산 파형 변환과 k-평균 군집화를 포함하는 두 가지 이상의 데이터 분석 기법을 사용하여 시계열 데이터를 대표할 수 있는 특징을 추출한다. 추출된 특징 아래에서 최적화된 디자인은 기존 제시된 안에 비해 22.92% 만큼 향상된 성능을 보여주었다. 두 번째 시스템은 신 제약 기술 공정인 연속 관형 결정화 반응기는 기존에 널리 쓰이던 회분식 반응기에 비하여 생산 속도 및 스케일 업 측면에서 장점이 많다. 하지만 제어기술이 기반이 되어야한다는 점에 있어서 그 도입이 늦어졌고 이에 따라 자연스럽게 개발된 모델 또한 전무하다. 우리는 이 장치에서 결정 크기 분포를 추산하기 위한 인구 균형 모델을 열 교환 모델과 동시에 고려하여 결정 크기 분포를 추산할 수 있었다. 제 1원리 결정 반응식을 기반으로 구축된 모델은 완전 요인 배치법을 기반으로 실험된 데이터를 성공적으로 예측하였다. 결정이 액상에 용해되지 않으면서 낮은 수준의 과포화 상태를 유지한 실험에 대해서는 평균 결정 길이와 표준편차가 실험 결과와 20% 이내의 오차를 보였다. 앞에서 모델의 검증에 사용된 데이터가 LAM (L-아스파라긴 일 수화물)용액으로부터 얻어진 것이었다면 이후에는 HEWL (달걀 흰자 리소자임)를 사용하여 제품의 결정 크기 분포의 조절 가능성을 보였다. 마지막으로 폴리머 생산을 위한 고압 오토클레이브 반응기의 거동을 예측하기 위한 다중 구획 모델을 제안하였다. 복잡한 고분자 합성 메커니즘을 모사하기 위하여 반응기 내 임펠러의 회전이 중합에 미치는 효과와 중합 열로 인한 영향력을 순차적으로 평가하였다. 제안된 모델은 3D 구조를 가진 산업화된 반응기에서 생산된 두 가지 고분자의 물성을 7%이내 정확도로 예측할 수 있다. 본 학위논문에서는 다루는 시스템은 모두 분포 정수계 시스템으로 시간과 공간에 대하여 편미분방정식으로 표현할 수 있다. 고차 모델을 구축하기 위해 이산화 접근법을 기반으로 최소한의 가정 하에 시스템을 해석하였다. 이는 논문에 제시한 시스템 뿐만 아니라 시공간에서 예측 어려운 분포를 가지는 변수를 가진 모든 시스템에 대하여 적용이 가능하다. 이 논문이 앞으로 화학 공학 분야의 시스템을 해석하는 데 있어서 더 발전된 연구를 위한 지침서가 되기를 희망한다.Abstract i Contents iv List of Figures viii List of Tables xii Chapter 1 1 Introduction 1 1.1 Research motivation 1 1.2 Research objective 3 1.3 Outline of the thesis 4 1.4 Associated publications 9 Chapter 2 10 Distributed parameter system 10 2.1 Introduction 10 2.2 Modeling methods 11 2.3 Conclusion 16 Chapter 3 17 Modeling and design of pilot-scale ambient air vaporizer 17 3.1 Introduction 17 3.2 Modeling and analysis of frost growth in pilot-scale ambient air vaporizer 24 3.2.1 Ambient air vaporizer 24 3.2.2 Experimental measurement 27 3.2.3 Numerical model of the vaporizer 31 3.2.4 Result and discussion 43 3.3 Robust design of ambient air vaporizer based on time-series clustering 58 3.3.1 Background 58 3.3.2 Trend of time-series weather conditions 61 3.3.3 Optimization of AAV structures under time-series weather conditions 63 3.3.4 Results and discussion 76 3.4 Conclusion 93 3.4.1 Modeling and analysis of AAV system 93 3.4.2 Robust design of AAV system 95 Chapter 4 97 Tunable protein crystal size distribution via continuous crystallization 97 4.1 Introduction 97 4.2 Mathematical modeling and experimental verification of fully automated continuous slug-flow crystallizer 101 4.2.1 Experimental methods and equipment setup 101 4.2.2 Mathematical model of crystallizer 109 4.2.3 Results and discussion 118 4.3 Continuous crystallization of a protein: hen egg white lysozyme (HEWL) 132 4.3.1 Introduction 132 4.3.2 Experimental method 135 4.3.3 Results and discussion 145 4.4 Conclusion 164 4.4.1 Mathematical model of continuous crystallizer 164 4.4.2 Tunable continuous protein crystallization process 165 Chapter 5 167 Multi-compartment model of high-pressure autoclave reactor for polymer production: combined CFD mixing model and kinetics of polymerization 167 5.1 Introduction 167 5.2 Method 170 5.2.1 EVA autoclave reactor 170 5.2.2 Multi-compartment model of the autoclave reactor 173 5.2.3 CFD simulation of mixing effects in the autoclave reactor 175 5.2.4 Region-based dividing algorithm 178 5.2.5 Polymerization kinetic model 182 5.3 Results and discussion 191 5.4 Conclusion 203 5.5 Appendix 205 Chapter 6 210 Concluding Remarks 210 6.1 Summary of contributions 210 6.2 Future work 211 Appendix 214 Acknowledgment and collaboration declaration 214 Supplementary materials 217 Reference 227 Abstract in Korean (국문초록) 249Docto

Computational fluid dynamics techniques for fixed-bed biofilm systems modeling : numerical simulations and experimental characterization

This thesis is focused on the development of one-phase and multiphase models using computational fluid dynamics (CFD) techniques to analyze biosystems behavior at mesoscale. In the first part, the operation of a fixed-bed biofilm reactor was simulated using Eulerian one-phase models, coupling fluid flow dynamics with biokinetics. The results reproduced accurately bioreactor performance, being experimentally verified hydrodynamics and species transport. However, the models had to be adapted to reproduce real scenarios where the biofilm motion can play a key role. On further consideration, this thesis suggested the development of Eulerian two-phase models using volume of fluid (VOF) method, defining the biofilm as an independent fluid phase by means of a comprehensive analysis of its rheological properties. This characterization became essential for accurately reproducing the fluid-biofilm interaction, describing the biofilm as a non-Newtonian fluid, which parameters were strongly dependent on its density. Thus, in the second part of this thesis, this novel continuum approach for biosystems modeling was tested, considering the required implementations to reproduce the species transfer at the interface (liquid-biofilm), and the possible growth of the biofilm phase. This new approach coupled fluid dynamics under laminar conditions with biochemical phenomena and/or biofilm mechanical behavior, so being able to reproduce the fluid stress over the biofilm, and its motion. The simulated results were experimentally verified evaluating transport mechanisms under different hydrodynamic conditions, and the model capability to reproduce shear-induced deformation and detachment, and recoil in biofilms was stated. In the third part of this thesis, the capacities of the new approach of continuum model were further tested, in order to reproduce wide range of hydrodynamic conditions to which biosystems can be exposed. Particularly, Eulerian multiphase models were developed and solved to characterize turbulent gas flows behavior over biofilms attached to walls. A coupled method of VOF and level-set, and shear stress transport (SST) k-omega model were used, reproducing accurately gas-biofilm interactions, turbulence and near-wall treatment. The simulated results were experimentally verified to confer identity to developed CFD approach, correctly describing the interfacial instabilities on the fixed-bed biofilm, such as ripples formation, and biofilm displacement and removal from its original position. The results also revealed that biofilm fluidization was the mechanisms behind the impact of turbulent air flows. Finally, in the last part of this thesis, the work was focused on the accurate analysis of fluid-biofilm interface, and on the necessity of acquiring local experimental data to verify models. The applicability of needle-probes as an innovative technique for in-situ biofilm layer and fluid interfaces detection was examined. The sensor probe performance was calibrated and verified in multiphase systems, revealing its practicability for interface detection, depth measuring, and surface reconstruction. So, a feasible tool for the experimental characterization of biosystems and models verification at mesoscale was provided. Therefore, the Eulerian multiphase approach proposed in this thesis, together with the experimental analyses, revealed the potential of CFD techniques as an alternative tool for fixed-bed biofilm systems modeling, allowing to reproduce simultaneous spatial and temporal, physical and biochemical phenomena under different operating conditions and biosystems configurations. The proposed approach helped to address key aspects of biofilm modeling such as its deformation and detachment under laminar and turbulent conditions.El estudio y modelización de los sistemas biológicos o biosistemas sigue siendo un reto que requiere explorar los fenómenos físicos y bioquímicos desde diferentes niveles de resolución espacial y temporal. Incluso para el régimen de flujo laminar más simple, las interacciones fluido- biopelícula deben ser investigadas en detalle. La dinámica de fluidos computacional (CFD, del inglés computational fluid dynamics) es una herramienta prometedora y extendida para modelar rigurosamente la hidrodinámica en reactores, la cual recientemente ha surgido como un enfoque alternativo para el modelo de biorreactores. Sin embargo, las complicadas interacciones entre la biopelícula y las fases fluidas (gas y líquido), aún no han sido descritas utilizando este tipo de técnicas. En esta tesis, se diseñaron y desarrollaron modelos monofásicos y multifásicos utilizando códigos comerciales CFD para analizar el comportamiento de los biosistemas a nivel de mesoescala. En la primera parte, se simuló la operación de un reactor de biopelícula de lecho fijo utilizando modelos monofásicos Eulerianos, acoplando la dinámica del flujo de fluido con la biocinética, e implementando un modelo de pérdidas de presión hidráulica para considerar las características físicas de la biopelícula. Esta técnica permitió obtener resultados precisos relacionados con el rendimiento del bioreactor, verificando experimentalmente la hidrodinámica y el transporte de las especies. Sin embargo, estos modelos necesitaron ser mejorados para poder reproducir escenarios reales donde el movimiento de la biopelícula puede jugar un papel importante. Por ello, se sugirió el desarrollo de modelos Eulerianos de dos fases utilizando el método de volumen de fluido (VOF, del inglés volume of fluid), donde la biopelícula se definió como una fase líquida independiente. Para desarrollar estos modelos, la caracterización experimental de las propiedades de la biopelícula fue imprescindible para adquirir un conocimiento profundo de los fenómenos implicados, especialmente para reproducir con precisión la interacción fluida sobre la biopelícula, ya que tiene un efecto directo sobre la estructura de la biopelícula. Como resultado, se desarrolló un análisis reológico integral bajo flujos de cizallamiento estables, oscilatorios y transitorios, para obtener las propiedades mecánicas macroscópicas y analizar los mecanismos de unión entre los componentes estructurales a microescala. Los resultados experimentales señalaron que las biopelículas mostraban un carácter gelatinoso, y teniendo un comportamiento de adelgazamiento del cizallamiento con una tensión de fluencia. Así, la biopelícula se caracterizó como un fluido no Newtoniano, cuyos parámetros dependían en gran medida de la densidad de la biopelícula estudiada. En la segunda parte de esta tesis, se propuso, implementó y probó un nuevo enfoque continuo para el modelado de biosistemas. Esto incluyó la definición de biopelícula como una fase fluida no Newtoniana, y otras implementaciones para reproducir la transferencia de especies en la interfaz (líquido-biopelícula), y para vincular el posible crecimiento de la fase de biopelícula con las especies transportadas y transferidas, entre otras consideraciones. Este nuevo enfoque combinó la dinámica de fluidos en condiciones laminares con fenómenos bioquímicos y/o comportamiento mecánico de la biopelícula, calculando con precisión la fracción volumétrica de las fases a lo largo del dominio, pudiendo así reproducir la interacción fluido-biopelícula en caso de movimiento de la biopelícula. Los resultados simulados fueron verificados experimentalmente evaluando los mecanismos de transporte bajo diferentes condiciones hidrodinámicas. Adicionalmente, se mostró la capacidad del modelo desarrollado para reproducir deformaciones y desprendimientos inducidos por cizallamiento y el retroceso (o recuperación) en las biopelículas, estando los resultados simulados en concordancia cualitativa con las observaciones experimentales. Con el fin de reproducir una amplia gama de condiciones hidrodinámicas a las que pueden estar expuestos los biosistemas, las capacidades del nuevo enfoque del modelo continuo se probaron más a fondo. En particular, se desarrollaron y resolvieron modelos Eulerianos multifásicos para caracterizar el comportamiento de los flujos de gas turbulento sobre biopelículas adheridas a la pared, utilizando un método acoplado de VOF y de conjunto de nivel (en inglés level-set) y el modelo SST k-ω, con el fin de reproducir con precisión las interacciones gas-biopelícula, la turbulencia y el tratamiento cercano a la pared. Los resultados simulados fueron verificados experimentalmente para conferir identidad al enfoque de CFD desarrollado, describiendo correctamente las inestabilidades interfaciales en la biopelícula de lecho fijo, tales como la formación de ondulaciones, y el desplazamiento y desprendimiento de la biopelícula de su posición original. Los resultados también revelaron que la fluidización del biopelícula era el mecanismo que se encontraba detrás del impacto de flujos de aire turbulentos. Finalmente, en la última parte de esta tesis, el trabajo se centró en el análisis preciso de la interfase fluido-biopelícula, y en la necesidad de adquirir datos experimentales locales para verificar modelos, como se había comentado en los capítulos anteriores. Se examinó la aplicabilidad de las sondas de aguja como técnica innovadora para la detección in-situ de la capa de biopelícula y de las interfases de los fluidos. El comportamiento de las sondas fue calibrado y verificado en sistemas multifásicos, mostrando su practicidad para la detección de interfases, medición de profundidad y reconstrucción de superficies. Así pues, se proporcionó una herramienta viable para la caracterización experimental de biosistemas y la verificación de modelos a mesoescala. Por lo tanto, el enfoque multifase Euleriano propuesto en esta tesis, junto con los análisis experimentales, reveló el potencial de las técnicas CFD como una herramienta alternativa al modelo de sistemas de biopelícula de lecho fijo, permitiendo reproducir simultáneamente fenómenos físicos y bioquímicos en espacio y tiempo, y bajo diferentes condiciones de operación y configuraciones de los biosistemas. El enfoque propuesto ayudó a abordar aspectos clave del modelado de biopelículas como su deformación y desprendimiento bajo condiciones laminares y turbulenta

Performance and Safety Enhancement Strategies in Vehicle Dynamics and Ground Contact

Recent trends in vehicle engineering are testament to the great efforts that scientists and industries have made to seek solutions to enhance both the performance and safety of vehicular systems. This Special Issue aims to contribute to the study of modern vehicle dynamics, attracting recent experimental and in-simulation advances that are the basis for current technological growth and future mobility. The area involves research, studies, and projects derived from vehicle dynamics that aim to enhance vehicle performance in terms of handling, comfort, and adherence, and to examine safety optimization in the emerging contexts of smart, connected, and autonomous driving.This Special Issue focuses on new findings in the following topics:(1) Experimental and modelling activities that aim to investigate interaction phenomena from the macroscale, analyzing vehicle data, to the microscale, accounting for local contact mechanics; (2) Control strategies focused on vehicle performance enhancement, in terms of handling/grip, comfort and safety for passengers, motorsports, and future mobility scenarios; (3) Innovative technologies to improve the safety and performance of the vehicle and its subsystems; (4) Identification of vehicle and tire/wheel model parameters and status with innovative methodologies and algorithms; (5) Implementation of real-time software, logics, and models in onboard architectures and driving simulators; (6) Studies and analyses oriented toward the correlation among the factors affecting vehicle performance and safety; (7) Application use cases in road and off-road vehicles, e-bikes, motorcycles, buses, trucks, etc