- author = "Pedro Tiago Evangelista",
- title = "Novel approaches for dynamic modelling of {E.} coli and their application in Metabolic Engineering",
- school = "University of Minho",
- year = "2016",
- address = "Portugal",
- month = "5 " # oct,
- keywords = "genetic algorithms, genetic programming",
-
URL = "
http://hdl.handle.net/1822/43446",
-
URL = "
http://repositorium.sdum.uminho.pt/bitstream/1822/43446/1/PedroEvangelistaThesis.pdf",
- size = "331 pages",
-
abstract = "One of the trends of modern societies is the
replacement of chemical processes by biochemical ones,
with new compounds being synthesized by engineered
microorganisms, while some waste products are also
being degraded by biotechnological means. Biotechnology
holds the promise of creating a more profitable and
environmental friendly industry, with a reduced number
of waste products, when contrasted with the traditional
chemical industry.
However, in an era in which genomes are sequenced at a faster pace than ever before, and with the advent omic measurements, this information is not directly translated into the targeted design of new microorganisms, or biological processes. These experimental data in isolation do not explain how the different cell constituents interact. Reductionist approaches that dominated science in the last century study cellular entities in isolation as separate chunks, without taking into consideration interactions with other molecules. This leads to an incomplete view of biological processes, which compromises the development of new knowledge.
To overcome these hurdles, a formal systems approach to Biology has been surging in the last thirty years. Systems biology can be defined as the conjugation of different fields (such as Mathematics, Computer Science, Biology),o describe formally and non-ambiguously the behaviour of the different cellular systems and their interactions, using to models and simulations. Metabolic Engineering takes advantage of these formal specifications, using mathematically based methods to derive strategies to optimize the microbial metabolism, in order to achieve a desired goal, such as the increase of the production of a relevant industrial compound. In this work, we develop a mechanistic dynamic model based on ordinary differential equations, comprised by elementary mass action descriptions of each reaction, from an existing model of Escherichia coli in the literature. We also explore different calibration processes for these reaction descriptions.
We also contribute to the field of strain design by using evolutionary algorithms with a new representation scheme that allows to search for enzyme modulations, in continuous or discrete scales, as well as reaction knockouts, in existing dynamic metabolic models, aiming at the maximization of product yields.
In the bioprocess optimization field, we extended the Dynamic Flux Balance Analysis formulation to incorporate the possibility to simulate fed-batch bioprocesses. This formulation is also enhanced with methods that possess the capacity to design feed profiles to attain a specific goal, such as maximizing the bioprocess yield or productivity.
All the developed methods involved some form of sensitivity and identifiability analysis, to identify how model outputs are affected by their parameters.
All the work was constructed under a modular software framework (developed during this thesis), that permits the interaction of distinct algorithms and languages, being a flexible tool to use in a cluster environment. The framework is available as an open-source software package, and has appeal to systems biologists describing biological processes with ordinary differential equations.",
-
notes = "Is this GP?
PhD thesis in Bioengineering
supervisor: Isabel Cristina Santos Rocha, Bruce Tidor, Miguel Rocha",
