dc.contributor.author
Ali Salem Eshtewy, Neveen
dc.date.accessioned
2020-08-06T11:26:05Z
dc.date.available
2020-08-06T11:26:05Z
dc.identifier.uri
https://refubium.fu-berlin.de/handle/fub188/27898
dc.identifier.uri
http://dx.doi.org/10.17169/refubium-27651
dc.description.abstract
Systems biology deals with the computational and mathematical modeling of complex
biological systems. The aim is to understand the big picture of the system’s dynamics
rather than the individual parts by integrating different sciences, e.g., mathematics, physics,
biology, computer science, and engineering. In biological systems, mathematical models
of biochemical networks are necessary for predicting and optimizing the behavior of cells
in culture. Different mathematical models have been discussed, such as discrete models,
continuous models, and hybrid models. In a discrete model, the biological system assumes
discrete values. A continuous model uses a system of differential equations to describe the
change of concentrations of substances in a cell over time. A hybrid model combines both
discrete and continuous models. The main challenge in continuous models is to find the
kinetic parameter values. In this thesis, we build a kinetic model of a metabolic-genetic
network introduced in Covert et al., 2001 that mimics a discrete model of regulatory flux
balance analysis (rFBA) which is based on steady-state assumptions. The kinetic model
we introduce has unknown parameters, so it is necessary to perform parameter estimation
techniques. We perform a parameter estimation technique using data sets generated from a
simulation of the rFBA model. In nature, many phenomena of interest are high-dimensional
and complex. Thus, model reduction is considered a vital topic in systems biology. Model
reduction methods are mathematical techniques that aim to represent a high-dimensional,
dynamical system by a low-dimensional system that roughly preserves the main features
and characteristics of the original system. The idea of model order reduction is to use the
reduced-order model instead of the full-order model in the simulation or optimization of the
system to reduce the computational effort and the runtime of the simulations. In this thesis,
we discuss two different model reduction methods. The first method assumes a time scale
separation, i.e., it assumes two time scales, a fast time scale and a slow time scale, where
the fast time scale dynamics converge to a quasi-steady state. The second approach, proper
orthogonal decomposition, aims at obtaining low-dimensional approximate descriptions of
high-dimensional processes while retaining the most important features of the dynamics. We
apply these approaches to different biological system models from the BioModels database.
en
dc.format.extent
vi, 126 Seiten
dc.rights.uri
http://www.fu-berlin.de/sites/refubium/rechtliches/Nutzungsbedingungen
dc.subject
Mathematical modeling
en
dc.subject
Model Reduction
en
dc.subject
Continuous Model
en
dc.subject
Hybrid Model
en
dc.subject
Regulatory Flux
en
dc.subject
Balance Analysis
en
dc.subject.ddc
500 Natural sciences and mathematics::500 Natural sciences::500 Natural sciences and mathematics
dc.title
Mathematical Modeling of Metabolic-Genetic Networks
dc.contributor.gender
female
dc.contributor.firstReferee
Weber, Marcus
dc.contributor.furtherReferee
Röblitz, Susanna
dc.date.accepted
2020-07-21
dc.identifier.urn
urn:nbn:de:kobv:188-refubium-27898-4
refubium.affiliation
Mathematik und Informatik
dcterms.accessRights.dnb
free
dcterms.accessRights.openaire
open access
dcterms.accessRights.proquest
accept