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A Methodology For Parametrizing Discrete Model of Biological Networks

Renaudie, Nathan (2018) A Methodology For Parametrizing Discrete Model of Biological Networks. Master's Thesis, University of Pittsburgh. (Unpublished)

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Humans have always had the desire to understand the world that surrounds us. With the progress of science in the last decades, our knowledge has drastically increased, following the fast pace at which scientists obtain and publish new results. With this rapid increase in the volume of available information about the systems that scientists study, modeling has become crucial in the process of learning and understanding these systems. In biology, models can be developed to capture different system scales. Here, we are focusing on modeling cellular signaling networks, using a discrete modeling approach, where system components are represented as discrete variables, and their regulatory functions are approximated with logical, weighted sum, or min-max functions. We study cellular signaling networks through stochastic simulation, in which model switches from one state to another according to elements’ regulatory functions. To start simulations, we define scenarios of interest (e.g., cell activation to induce fate change, or a drug added to a cancer cell). These scenarios are implemented through parametrizing the model, that is, assigning initial values to all model elements, as well as defining patterns at model inputs, or perturbations inside the model. However, the information about the initial state is most often incomplete, as experts are familiar with values in some parts of the network, but not in the whole network. We have developed several methods to initialize model elements when the knowledge about the initial values for a particular scenario is sparse. Next, we applied our initialization methods on several cancer cell models. Our results show that varying the initial values can significantly influence model behavior, and therefore, emphasize the importance of choosing a suitable initialization method. We expect that our methods and the conclusions from our studies will enable more accurate setup of future modeling experiments.


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Item Type: University of Pittsburgh ETD
Status: Unpublished
CreatorsEmailPitt UsernameORCID
Renaudie, NathanNAR83@pitt.eduNAR83
ETD Committee:
TitleMemberEmail AddressPitt UsernameORCID
Thesis AdvisorMiskov-Zivanov, Natasanmzivanov@pitt.edunmzivanov
Committee MemberAkcakaya, Muratakcakaya@pitt.eduakcakaya
Committee MemberDickerson, Samueldickerson@pitt.edudickerson
Committee MemberTelmer,
Date: 11 June 2018
Date Type: Publication
Defense Date: 4 April 2018
Approval Date: 11 June 2018
Submission Date: 3 April 2018
Access Restriction: No restriction; Release the ETD for access worldwide immediately.
Number of Pages: 61
Institution: University of Pittsburgh
Schools and Programs: Swanson School of Engineering > Electrical and Computer Engineering
Degree: MS - Master of Science
Thesis Type: Master's Thesis
Refereed: Yes
Uncontrolled Keywords: Discrete Modeling, Biological Networks
Date Deposited: 11 Jun 2018 17:51
Last Modified: 11 Jun 2018 17:51

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  • A Methodology For Parametrizing Discrete Model of Biological Networks. (deposited 11 Jun 2018 17:51) [Currently Displayed]


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