Stochastic modelling of biochemical networks and inference of modelparameters

There are many approaches to model the biochemical systems deterministically or stochastically. In deterministic approaches, we aim to describe the steady-state behaviours of the system, whereas, under stochastic models, we present the random nature of the system, for instance, during transcription or translation processes. Here, we represent the stochastic modelling approaches of biological networks and explain in details the inference of the model parameters within the Bayesian framework.


Semi-Bayesian Inference of Time Series Chain Graphical Models in Biological Networks
Farnoudkia, Hajar; Purutçuoğlu Gazi, Vilda (null; 2018-09-20)
The construction of biological networks via time-course datasets can be performed both deterministic models such as ordinary differential equations and stochastic models such as diffusion approximation. Between these two branches, the former has wider application since more data can be available. In this study, we particularly deal with the probabilistic approaches for the steady-state or deterministic description of the biological systems when the systems are observed though time. Hence, we consider time s...
Stochastic modeling of biochemical systems with filtering and smoothing
Haksever, Merve; Uğur, Ömür; Department of Scientific Computing (2019)
Deterministic modeling approach is the traditional way of analyzing the dynamical behavior of a reaction network. However, this approach ignores the discrete and stochastic nature of biochemical processes. In this study, modeling approaches, stochastic simulation algorithms and their relationships to each other are investigated. Then, stochastic and deterministic modeling approaches are applied to biological systems, Lotka-Volterra prey-predator model, Michaelis-Menten enzyme kinetics and JACK-STAT signalin...
Numerical calculation of backfilling of scour holes
Sumer, B Mutlu; Baykal, Cüneyt; Fuhrman, David R; Jacobsen, Niels G; Fredsoe, Jorgen (2014-12-04)
A fully-coupled hydrodynamic and morphologic CFD model is presented for simulating backfilling processes around structures. The hydrodynamic model is based on Reynolds-averaged Navier-Stokes equations, coupled with two-equation k-ω turbulence closure. The sediment transport model consists of separate bed and suspended load descriptions, the latter based on a turbulent diffusion equation coupled with a reference concentration function near the sea bed boundary. Bed morphology is based on the sediment continu...
Stochastic Momentum Methods For Optimal Control Problems Governed By Convection-diffusion Equations With Uncertain Coefficients
Toraman, Sıtkı Can; Yücel, Hamdullah; Department of Scientific Computing (2022-1-6)
Many physical phenomena such as the flow of an aircraft, heating process, or wave propagation are modeled mathematically by differential equations, in particular partial differential equations (PDEs). Analytical solutions to PDEs are often unknown or very hard to obtain. Because of that, we simulate such systems by numerical methods such as finite difference, finite volume, finite element, etc. When we want to control the behavior of certain system components, such as the shape of a wing of an aircraft or a...
Uncertainty quantification of parameters in local volatility model via frequentist, bayesian and stochastic galerkin methods
Animoku, Abdulwahab; Uğur, Ömür; Department of Financial Mathematics (2018)
In this thesis, we investigate and implement advanced methods to quantify uncertain parameter(s) in Dupire local volatility equation. The advanced methods investigated are Bayesian and stochastic Galerkin methods. These advanced techniques implore different ideas in estimating the unknown parameters in PDEs. The Bayesian approach assumes the parameter is a random variable to be sampled from its posterior distribution. The posterior distribution of the parameter is constructed via “Bayes theorem of inverse p...
Citation Formats
V. Purutçuoğlu Gazi, Stochastic modelling of biochemical networks and inference of modelparameters. 2018, p. 385.