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Generalized thermodynamic description of complex biological systems

This thesis is divided into four chapters.
In the opening chapter the model construction process will be discussed. After presenting the fundamental aspects of the modeling process, the analysis will be focused on the issue of models classification. Lastly, a systematic approach for the model construction process will be proposed, in order to face the following problems through a rational procedure.
In the second chapter a theoretical preface will be done; it deals with the issues which will be afterwards practically treated. Particularly, there are three main disciplines which will be touched during the essay: Physics, Mathematics and Biology.
Physics is the theoretical base of the quasi-equilibrium models. Concepts as thermodynamic approach, slow invariant manifold and quasi equilibrium manifold will be widely discussed, for a complete understanding of the dynamic models based on quasi equilibrium approximation. Quasi equilibrium dynamic models are based on the knowledge of the steady states of the analyzed system; therefore a detailed analysis of the characteristics of the equilibrium states, according to the conserved quantities of the system, will be done. Moreover, it will be explained the principal component analysis, which will help to calculate the amount of conserved quantities in a system, and the direct or Montecarlo-like analysis, which will be used for the exact identification of the conservation laws.
Mathematics provides the tools in order to optimize and “tune” models. In detail, the mathematical tools explained will be: two optimization algorithms (genetic algorithm and constrained nonlinear optimization), which will be used for fitting the models to the studied system, and the constrained Jacobian matrix, which will have a fundamental role for the network construction process.
Finally, a close examination concerning the Biology of the studied systems will make it possible to identify the utility and the potential field of application of the introduced models. In the third chapter, the quasi-equilibrium model will be tested through several well-known biological systems (already modeled thanks to a detailed kinetic approach), following an increasing order of complexity.
In particular, the model will be used for predicting dynamics of a Michaelis-Menten enzymatic network, a simplified gene regulation system, the MAPK cascades process and the Calvin cycle. The best algorithms and Matlab functions will be identified for our tasks, in order to obtain an optimized QE model before its application on an experimental case. Moreover, a comparison between one-dimensional manifold models and two-dimensional ones will be attempted. Lastly, constrained Jacobian matrices will be utilized for the reaction network construction, and the space of equilibrium states of the above systems (and additionally IкB metabolism and Purine metabolism) will be explored, in order to obtain the number of conserved quantities for the analyzed systems.
In the last chapter the QE model will be applied to some experimental data obtained thanks to a collaboration with the MBC (Molecular Biotechnology Center) located in Turin: the transcriptional regulatory networks in embryonic stem cells will be studied. More precisely, thanks to the principal components analysis of a cloud of equilibrium states, it will be possible to deduce the amount of conservation laws; while, thanks to a direct or a Montecarlo-like analysis, the conservation laws will be precisely identified. Then, species dynamics will be fitted using QE models, and the Jacobian way of network construction process will be attempted.
Finally, a comparison between the results obtained by the QE models and a model proposed in literature [Schmidt, Lipson, Distilling Free-Form Natural Laws from Experimental Data, Science, Vol.324, 2009] will be conducted in appendix C.

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Matteo Fasano Generalized thermodynamic description of complex biological systems 3 INTRODUCTION Computational Systems Biology is evolving rapidly, and not a single group of investigators has already developed a complete system that integrates both data generation and data analysis, in a way to allow full and accurate modeling of selected biological agents. Each new method or database implemented represents one or more steps on the path to a complete description of biological systems. How these tools will evolve and how they will be ultimately integrated is an area of intense research and interest. In this thesis in particular, a thermodynamic approach to complex biological systems description is attempted. The theme of non-equilibrium thermodynamics and its application to biotechnology can be considered an innovative way of modeling for a quite recent science field: Systems Biology. Systems Biology can be defined as the quantitative study of biological systems, supported by technological progress: in other words, the data-centric quantitative modeling of biological processes and systems. Systems Biology is related to three main aspects: it is experimentally driven, computationally driven, and knowledge driven. It is experimentally driven because the complexity of biological systems is difficult to penetrate without large-scale coverage of the molecular underpinnings. It is computationally driven because the data obtained from experimental investigations of complex systems need extensive quantitative analysis to be informative. Finally it is knowledge driven because it is not computationally feasible to analyze the data without incorporating all that is already known about the Biology in question. Furthermore, the use of data, computation and knowledge must be concurrent. Researchers have traditionally considered the study of biological systems rather resistant to quantitative approaches. Two events have occurred to bring the field of computational Systems Biology to the forefront. One is the advent of high-throughput methods that have generated large amounts of information about particular systems in the form of genetic studies, gene and protein expression analyses and metabolomics. The other event is the growth of computational processing power and tools. Methods used to analyze this kind of large data sets are often computationally demanding and, as it happens for other areas, the field has benefited from continuing improvements in computational hardware and methods. For the purposes of this thesis, Systems Biology is the promise to analyze Biology on a larger and quantitatively rigorous scale, thanks to a cross-fertilization of knowledge. In fact, this research is centered on the advantage that biological systems modeling can take from decades of systematic model reduction research in non-equilibrium thermodynamics In this thesis, the mathematical notion of slow invariant manifold (SIM) and its convenient approximation (the Quasi Equilibrium Manifold, QEM) have been exploited in order to study a series of complex biological systems. During the last decades several promising methods, for reducing the description of systems with a large number of degrees of freedom, have been developed in the context of physical and chemical kinetics. For instance, an intensive effort has been spent in devising such techniques for combustion mechanisms, where agents are represented by chemical species linked through highly nonlinear interactions and similar issues to biological systems have been encountered (i.e. a tremendously

Laurea liv.II (specialistica)

Facoltà: Ingegneria

Autore: Matteo Fasano Contatta »

Composta da 211 pagine.

 

Questa tesi ha raggiunto 397 click dal 17/01/2013.

Disponibile in PDF, la consultazione è esclusivamente in formato digitale.