Systems biology is a new research field that focuses on the understanding of biological processes based on the relation between engineering, mathematics and biology. In spite of complexity of these systems in comparison with industrial systems, systems biology facilitated the use of control theory in the study of biological phenomena. Thus, control engineering tools such as system identification, modeling, design and analysis may solve some problems in systems biology. In order to identify the dynamics of a system and predict its behavior in the absence of the experimental data, modeling can be used. The model of a biological phenomenon is actually a simplified map of its behavioralmechanisms and in addition to identifying the system, it can be used in investigation of biological hypotheses which have not still been tested experimentally.Mathematical modeling of biological systems can be justify; MARGIN: 0cm 0cm 0pt; unicode-bidi: embed; DIRECTION: ltr" In this thesis, identifying and modeling of hypoxia pathway are done by the use of stochastic Petri nets and with the comparison between modeling results and experimental data, it is observed that the model describes the biological process behavior truly. Sensitivity analysisis alsoperformedon the model and keyreactionsinbiologicalpathway arespecified. Then by analyzing the results obtained from the model, cellular control mechanisms in expression of several important proteins which are involved in this pathway and the hypothesis for existence of a possible drug for controlling the effective processes in response to hypoxic condition are introduced. Quantitative model are a modeling method in order to parameter estimation and other mathematical analysis of biological systems. Also, a quantitative model based on differential equations governing the hypoxia pathway is presented and the resultsare comparedwithexperimentaldata andqualitativemodel results. Keywords: Hypoxia pathway, Modeling, Stochastic Petri nets, Systems biology
