Software Development for Simulating and Engineering Gene Circuits

<p>Mathematical modeling has become an increasingly important aspect of biological research. Computer simulations help to improve our understanding of complex systems by testing the validity of proposed mechanisms and generating experimentally testable hypotheses. However, significant overhead is generated by the creation, debugging, and perturbation of these computational models and their parameters, especially for researchers who are unfamiliar with programming or numerical methods. Dynetica 2.0 is a user-friendly dynamic network simulator designed to expedite this process. Models are created and visualized in an easy-to-use graphical interface, which displays all of the species and reactions involved in a graph layout. System inputs and outputs, indicators, and intermediate expressions may be incorporated into the model via the versatile "expression variable" entity. Models can also be modular, allowing for the quick construction of complex systems from simpler components. Dynetica 2.0 supports a number of deterministic and stochastic algorithms for performing time-course simulations. Additionally, Dynetica 2.0 provides built-in tools for performing sensitivity or dose response analysis for a number of different metrics. Its parameter searching tools can optimize specific objectives of the time course or dose response of the system. Systems can be translated from Dynetica 2.0 into MATLAB code or the SBML format for further analysis or publication. Finally, since it is written in Java, Dynetica 2.0 is platform independent, allowing for easy sharing and collaboration between researchers.</p>Thesi

Reconstruction and systems analysis of metabolism in apicomplexan parasites Toxoplasma gondii and Plasmodium falciparum

Understanding of metabolism in disease-causing microorganisms promotes drug design through the identification of the enzymes whose activity is indispensable for important cellular functions of the pathogens. Nowadays such understanding arises from experimental as well as computational studies. These two approaches, long considered as rather orthogonal, in recent years began to converge and form a new field, where they are utilized as complementary. In this thesis I present my endeavors in bringing closer the fields of infection and systems biology with a particular focus on large-scale metabolic models and their analysis. Integrative, interdisciplinary nature of my project also included multiple experimental inputs as well as original experimental efforts on investigating model-derived hypotheses. In the scope of this thesis I explored metabolism of two of the most experimentally amenable apicomplexan species â human parasites Plasmodium falciparum and Toxoplasma gondii. As a foundation for the studies included in this thesis I used standard as well as recently developed computational algorithms, existing experimental datasets and innovative context- specific assumptions. I produced an extensive survey of the modeling efforts previously applied for studying metabolism of P. falciparum and available large-scale experimental datasets in comparison with the similar efforts made in other species. Further, I curated an existing model of metabolism in Plasmodium falciparum with respect to an up-to-date primary literature on metabolism of the parasite and addressed several important assumptions implicitly made in this model. Using a state-of-the-art approach, I reconstructed de novo a comprehensive metabolic model of T. gondii, and performed an extensive computational analysis to explore its metabolic needs and capabilities. I identified and classified the minimal set of substrates the parasite utilizes for growth, along with the genes and pairs of genes that are essential for cellular functions such as growth and energy metabolism. Subsequently, several of the model-driven hypotheses were confirmed experimentally, while for validation of the majority of the computational predictions forthcoming high-throughput approaches shall be used. Every confirmed hypothesis expands the scope of our knowledge on peculiarities of metabolism in apicomplexan parasites and hence can serve as an input for the pipeline of developing novel medicines