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Franziska Hinkelmann
Researcher at Virginia Tech
Publications - 11
Citations - 352
Franziska Hinkelmann is an academic researcher from Virginia Tech. The author has contributed to research in topics: Boolean network & Dynamical systems theory. The author has an hindex of 8, co-authored 11 publications receiving 306 citations. Previous affiliations of Franziska Hinkelmann include Virginia Bioinformatics Institute & Mathematical Biosciences Institute.
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Journal ArticleDOI
A mathematical framework for agent based models of complex biological networks.
Franziska Hinkelmann,David Murrugarra,Abdul Salam Jarrah,Abdul Salam Jarrah,Reinhard Laubenbacher +4 more
TL;DR: In this paper, the authors propose an extension to the ODD protocol which allows the description of an agent-based model as a dynamical system, which provides access to computational and theoretical tools for its analysis.
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ADAM: Analysis of Discrete Models of Biological Systems Using Computer Algebra
Franziska Hinkelmann,Franziska Hinkelmann,Madison Brandon,Bonny Guang,Rustin McNeill,Grigoriy Blekherman,Alan Veliz-Cuba,Alan Veliz-Cuba,Reinhard Laubenbacher,Reinhard Laubenbacher +9 more
TL;DR: A method to identify attractors of a discrete model that is equivalent to solving a system of polynomial equations, a long-studied problem in computer algebra is proposed and the algebraic algorithms presented are fast for systems with the structure maintained by most biological systems, namely sparseness and robustness.
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Steady state analysis of Boolean molecular network models via model reduction and computational algebra
TL;DR: The algorithm presented in this paper reliably determines all steady states of sparse Boolean networks with up to 1000 nodes and compares favorably with several existing algorithms for steady state determination.
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Parameter estimation for Boolean models of biological networks
Elena S. Dimitrova,Luis David García-Puente,Franziska Hinkelmann,Abdul Salam Jarrah,Reinhard Laubenbacher,Brandilyn Stigler,Michael Stillman,Paola Vera-Licona +7 more
TL;DR: Polynome is a software package offered as a web service that helps users construct Boolean network models based on experimental data and biological input that is a discrete analog of parameter estimation for continuous models.
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Boolean Models of Bistable Biological Systems
TL;DR: If a certain motif including a feedback loop is present in the wiring diagram of the system, the Boolean model captures the bistability of molecular switches and the method is applied to two examples from biology, the lac operon and the phage lysis/lysogeny switch.