February  2004, 4(1): 275-287. doi: 10.3934/dcdsb.2004.4.275

Evaluation of a discrete dynamic systems approach for modeling the hierarchical relationship between genes, biochemistry, and disease susceptibility


Program in Human Genetics, Department of Molecular Physiology and Biophysics, Vanderbilt University Medical School, Nashville, TN 37232, United States, United States

Received  January 2003 Revised  August 2003 Published  November 2003

A central goal of human genetics is the identification of combinations of DNA sequence variations that increase susceptibility to common, complex human diseases. Our ability to use genetic information to improve public health efforts to diagnose, prevent, and treat common human diseases will depend on our ability to understand the hierarchical relationship between complex biological systems at the genetic, cellular, biochemical, physiological, anatomical, and clinical endpoint levels. We have previously demonstrated that Petri nets are useful for building discrete dynamic systems models of biochemical networks that are consistent with nonlinear gene-gene interactions observed in epidemiological studies. Further, we have developed a machine learning approach that facilitates the automatic discovery of Petri net models thus eliminating the need for human-based trial and error approaches. In the present study, we evaluate this automated model discovery approach using four different nonlinear gene-gene interaction models. The results indicate that our model-building approach routinely identifies accurate Petri net models in a human-competitive manner. We anticipate that this general modeling strategy will be useful for generating hypotheses about the hierarchical relationship between genes, biochemistry, and measures of human health.
Citation: Jason H. Moore, Lance W. Hahn. Evaluation of a discrete dynamic systems approach for modeling the hierarchical relationship between genes, biochemistry, and disease susceptibility. Discrete & Continuous Dynamical Systems - B, 2004, 4 (1) : 275-287. doi: 10.3934/dcdsb.2004.4.275

Baltazar D. Aguda, Ricardo C.H. del Rosario, Michael W.Y. Chan. Oncogene-tumor suppressor gene feedback interactions and their control. Mathematical Biosciences & Engineering, 2015, 12 (6) : 1277-1288. doi: 10.3934/mbe.2015.12.1277


Jian Ren, Feng Jiao, Qiwen Sun, Moxun Tang, Jianshe Yu. The dynamics of gene transcription in random environments. Discrete & Continuous Dynamical Systems - B, 2018, 23 (8) : 3167-3194. doi: 10.3934/dcdsb.2018224


Ö. Uğur, G. W. Weber. Optimization and dynamics of gene-environment networks with intervals. Journal of Industrial & Management Optimization, 2007, 3 (2) : 357-379. doi: 10.3934/jimo.2007.3.357


Ying Hao, Fanwen Meng. A new method on gene selection for tissue classification. Journal of Industrial & Management Optimization, 2007, 3 (4) : 739-748. doi: 10.3934/jimo.2007.3.739


Qi Wang, Lifang Huang, Kunwen Wen, Jianshe Yu. The mean and noise of stochastic gene transcription with cell division. Mathematical Biosciences & Engineering, 2018, 15 (5) : 1255-1270. doi: 10.3934/mbe.2018058


Zizi Wang, Zhiming Guo, Huaqin Peng. Dynamical behavior of a new oncolytic virotherapy model based on gene variation. Discrete & Continuous Dynamical Systems - S, 2017, 10 (5) : 1079-1093. doi: 10.3934/dcdss.2017058


William Chad Young, Adrian E. Raftery, Ka Yee Yeung. A posterior probability approach for gene regulatory network inference in genetic perturbation data. Mathematical Biosciences & Engineering, 2016, 13 (6) : 1241-1251. doi: 10.3934/mbe.2016041


Somkid Intep, Desmond J. Higham. Zero, one and two-switch models of gene regulation. Discrete & Continuous Dynamical Systems - B, 2010, 14 (2) : 495-513. doi: 10.3934/dcdsb.2010.14.495


Marek Bodnar. Distributed delays in Hes1 gene expression model. Discrete & Continuous Dynamical Systems - B, 2019, 24 (5) : 2125-2147. doi: 10.3934/dcdsb.2019087


Feng Jiao, Qiwen Sun, Genghong Lin, Jianshe Yu. Distribution profiles in gene transcription activated by the cross-talking pathway. Discrete & Continuous Dynamical Systems - B, 2019, 24 (6) : 2799-2810. doi: 10.3934/dcdsb.2018275


Kunwen Wen, Lifang Huang, Qiuying Li, Qi Wang, Jianshe Yu. The mean and noise of FPT modulated by promoter architecture in gene networks. Discrete & Continuous Dynamical Systems - S, 2019, 12 (7) : 2177-2194. doi: 10.3934/dcdss.2019140


Reihaneh Mostolizadeh, Zahra Afsharnezhad, Anna Marciniak-Czochra. Mathematical model of Chimeric Anti-gene Receptor (CAR) T cell therapy with presence of cytokine. Numerical Algebra, Control & Optimization, 2018, 8 (1) : 63-80. doi: 10.3934/naco.2018004


Roberto Serra, Marco Villani, Alex Graudenzi, Annamaria Colacci, Stuart A. Kauffman. The simulation of gene knock-out in scale-free random Boolean models of genetic networks. Networks & Heterogeneous Media, 2008, 3 (2) : 333-343. doi: 10.3934/nhm.2008.3.333


Qiuying Li, Lifang Huang, Jianshe Yu. Modulation of first-passage time for bursty gene expression via random signals. Mathematical Biosciences & Engineering, 2017, 14 (5&6) : 1261-1277. doi: 10.3934/mbe.2017065


Pavol Bokes. Maintaining gene expression levels by positive feedback in burst size in the presence of infinitesimal delay. Discrete & Continuous Dynamical Systems - B, 2017, 22 (11) : 1-14. doi: 10.3934/dcdsb.2019070


Yun Li, Fuke Wu, George Yin. Asymptotic behavior of gene expression with complete memory and two-time scales based on the chemical Langevin equations. Discrete & Continuous Dynamical Systems - B, 2019, 24 (8) : 4417-4443. doi: 10.3934/dcdsb.2019125


Silogini Thanarajah, Hao Wang. Competition of motile and immotile bacterial strains in a petri dish. Mathematical Biosciences & Engineering, 2013, 10 (2) : 399-424. doi: 10.3934/mbe.2013.10.399


Don A. Jones, Hal L. Smith, Horst R. Thieme. Spread of phage infection of bacteria in a petri dish. Discrete & Continuous Dynamical Systems - B, 2016, 21 (2) : 471-496. doi: 10.3934/dcdsb.2016.21.471


Carlos Gutierrez, Víctor Guíñez, Alvaro Castañeda. Quartic differential forms and transversal nets with singularities. Discrete & Continuous Dynamical Systems - A, 2010, 26 (1) : 225-249. doi: 10.3934/dcds.2010.26.225


Arminda Moreno-Díaz, Gabriel de Blasio, Moreno-Díaz Jr.. Distributed, layered and reliable computing nets to represent neuronal receptive fields. Mathematical Biosciences & Engineering, 2014, 11 (2) : 343-361. doi: 10.3934/mbe.2014.11.343

2018 Impact Factor: 1.008


  • PDF downloads (5)
  • HTML views (0)
  • Cited by (0)

Other articles
by authors

[Back to Top]