Modeling, Simulating, and Parameter Fitting of Biochemical Kinetic Experiments

In many chemical and biological applications, systems of differential equations containing unknown parameters are used to explain empirical observations and experimental data. The DEs are typically nonlinear and difficult to analyze, requiring numerical methods to approximate the solutions. Compounding this difficulty are the unknown parameters in the DE system, which must be given specific numerical values in order for simulations to be run. Estrogen receptor protein dimerization is used as an example to demonstrate model construction, reduction, simulation, and parameter estimation. Mathematical, computational, and statistical methods are applied to empirical data to deduce kinetic parameter estimates and guide decisions regarding future experiments and modeling. The process demonstrated serves as a pedagogical example of quantitative methods being used to extract parameter values from biochemical data models.Comment: 23 pages, 9 figures, to be published in SIAM Revie

Thirty-third Annual Symposium of Trinity College Research

2020 annual volume of abstracts for science research projects conducted by students at Trinity College

Analytical, Numerical and Computational Multiscale Modelling Techniques for Heterogenous Materials: A Review

oai:ojs2.azojete.com.ng:article/1This paper reviews the analytical, numerical as well as the computational homogenization multiscale modelling schemes for determining the effective material properties for heterogeneous medium at the macroscopic level. It also looked at the limitations of the analytical homogenization techniques in simulating the effective non linear heterogeneous material behaviours (for example the rapid localization of damage and so on) as well as the advancements of the computational techniques in addressing these limitations. In addition, the possible future trends for the computational technique such as the development of a fully coupled micro-macro computational scheme were also discussed. It was concluded that although, the analytical technique was quite popular and straight forward, its inability to capture rapid localization of damage limited its application and that numerical and computational schemes were able to address these limitations as they relied on the establishment of constitutive relations for the macroscopic problems in a numerical form through which the macroscopic problems were constructed and solved in a nested manner

Multidimensional Active Flux Schemes

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/106467/1/AIAA2013-2940.pd

Development and application of computational aerothermodynamics flowfield computer codes

Research was performed in the area of computational modeling and application of hypersonic, high-enthalpy, thermo-chemical nonequilibrium flow (Aerothermodynamics) problems. A number of computational fluid dynamic (CFD) codes were developed and applied to simulate high altitude rocket-plume, the Aeroassist Flight Experiment (AFE), hypersonic base flow for planetary probes, the single expansion ramp model (SERN) connected with the National Aerospace Plane, hypersonic drag devices, hypersonic ramp flows, ballistic range models, shock tunnel facility nozzles, transient and steady flows in the shock tunnel facility, arc-jet flows, thermochemical nonequilibrium flows around simple and complex bodies, axisymmetric ionized flows of interest to re-entry, unsteady shock induced combustion phenomena, high enthalpy pulsed facility simulations, and unsteady shock boundary layer interactions in shock tunnels. Computational modeling involved developing appropriate numerical schemes for the flows on interest and developing, applying, and validating appropriate thermochemical processes. As part of improving the accuracy of the numerical predictions, adaptive grid algorithms were explored, and a user-friendly, self-adaptive code (SAGE) was developed. Aerothermodynamic flows of interest included energy transfer due to strong radiation, and a significant level of effort was spent in developing computational codes for calculating radiation and radiation modeling. In addition, computational tools were developed and applied to predict the radiative heat flux and spectra that reach the model surface

Magnetic nanoparticles as a versatile solid-support for fusion protein purification and antimicrobial assays

Magnetic nano-and microparticles are unique platforms for the development of bioseparation and antimicrobial devices. This work explored the application of magnetic particles for the purification of fusion proteins through the use of magnetic adsorbents coupled to novel affinity ligands towards peptidic and proteic tags. Furthermore, and in view of the novelty of these ligands, molecular modeling and simulation techniques were employed to explain the key structuralfeatures involved inthe binding of two affinity pairs: GFP/LA-A4C7 and RK-GFP/LR-A7C1.The results showed that the interaction between GFP and LA-A4C7 is mainly hydrophobicwhile the interaction between RK-GFP and LR-A7C1 is mostly driven byhydrogen bonds. Moreover, the same modeling techniques have been used to idealize a theoretical second generation library with view of maximizing the estimated free energy of binding and the correspondent affinity constant. When immobilizing the biomimetic ligands LA-A4C7 and LR-A7C1 onto magnetic nanoparticles, it was possibleto bind the protein of interest and recover pure elution fractions. The best elution condition for GFP elution was 0.1mM glycine-NaOH pH9 50% (v/v) ethylene glycoland the best elution condition for RK-GFP elution was PBS pH 7.4, 500mM arginine, which are in accordance with the theoretical results described previously. Final binding constants for the studied systems (Ka=0.83×105M-1and Qmax=4mg/g for GFP/LA-A4C7, Ka=3.21×105M-1and Qmax=2mg/g for RK-GFP/LR-A7C1) show promising results for an affinity-based protein purification system.Magnetic particleswere also functionalized with (RW)3, an peptidewith antimicrobial properties, by different routes. We were able to develop a novel antimicrobial nanodevice based on the EDC-coupling of (RW)3that has shown antimicrobial activity against Escherichiacoliand Bacillussubtilis

Methods and tools to improve performance of plant genome analysis

Multi -omics data analysis and integration facilitates hypothesis building toward an understanding of genes and pathway responses driven by environments. Methods designed to estimate and analyze gene expression, with regard to treatments or conditions, can be leveraged to understand gene-level responses in the cell. However, genes often interact and signal within larger structures such as pathways and networks. Complex studies guided toward describing dynamic genetic pathways and networks require algorithms or methods designed for inference based on gene interactions and related topologies. Classes of algorithms and methods may be integrated into generalized workflows for comparative genomics studies, as multi -omics data can be standardized between contact points in various software applications. Further, network inference or network comparison algorithmic designs may involve interchangeable operations given the structure of their implementations. Network comparison and inference methods can also guide transfer-of-knowledge between model organisms and those with less knowledge base

An Integrated Framework for Multi-paradigm Traffic Simulation

Tese de Mestrado Integrado. Engenharia Informática e Computação. Faculdade de Engenharia. Universidade do Porto. 201