Reveal, a general reverse engineering algorithm for inference of genetic network architectures. Given the immanent gene expression mapping covering whole genomes during development, health and disease, we seek computational methods to maximize functional inference from such large data sets. Is it possible, in principle, to completely infer a complex regulatory network architecture from input/output patterns of its variables? We investigated this possibility using binary models of genetic networks. Trajectories, or state transition tables of Boolean nets, resemble time series of gene expression. By systematically analyzing the mutual information between input states and output states, one is able to infer the sets of input elements controlling each element or gene in the network. This process is unequivocal and exact for complete state transition tables. We implemented this REVerse Engineering ALgorithm (REVEAL) in a C program, and found the problem to be tractable within the conditions tested so far. For n = 50 (elements) and k = 3 (inputs per element), the analysis of incomplete state transition tables (100 state transition pairs out of a possible 10(15)) reliably produced the original rule and wiring sets. While this study is limited to synchronous Boolean networks, the algorithm is generalizable to include multi-state models, essentially allowing direct application to realistic biological data sets. The ability to adequately solve the inverse problem may enable in-depth analysis of complex dynamic systems in biology and other fields.

References in zbMATH (referenced in 28 articles )

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  1. Wynn, Michelle L.; Egbert, Megan; Consul, Nikita; Chang, Jungsoo; Wu, Zhi-Fen; Meravjer, Sofia D.; Schnell, Santiago: Inferring intracellular signal transduction circuitry from molecular perturbation experiments (2018)
  2. Videla, Santiago; Guziolowski, Carito; Eduati, Federica; Thiele, Sven; Gebser, Martin; Nicolas, Jacques; Saez-Rodriguez, Julio; Schaub, Torsten; Siegel, Anne: Learning Boolean logic models of signaling networks with ASP (2015)
  3. Chen, Hao; Sun, Jitao: Output controllability and optimal output control of state-dependent switched Boolean control networks (2014)
  4. Codecasa, Daniele; Stella, Fabio: Learning continuous time Bayesian network classifiers (2014)
  5. Inoue, Katsumi; Ribeiro, Tony; Sakama, Chiaki: Learning from interpretation transition (2014)
  6. Maucher, Markus; Kracht, David V.; Schober, Steffen; Bossert, Martin; Kestler, Hans A.: Inferring Boolean functions via higher-order correlations (2014)
  7. Wang, Y. X. Rachel; Huang, Haiyan: Review on statistical methods for gene network reconstruction using expression data (2014)
  8. Hopfensitz, Martin; Müssel, Christoph; Maucher, Markus; Kestler, Hans A.: Attractors in Boolean networks: a tutorial (2013)
  9. Čepek, Ondřej; Kronus, David; Kučera, Petr: Analysing DNA microarray data using Boolean techniques (2011)
  10. Charney, Ruth; Cohen, Jacques; Rizk, Aurélien: Efficient synthesis of a class of Boolean programs from I-O data: application to genetic networks (2011)
  11. Cheng, Daizhan; Zhao, Yin: Identification of Boolean control networks (2011)
  12. Cerulo, Luigi; Elkan, Charles; Ceccarelli, Michele: Learning gene regulatory networks from only positive and unlabeled data (2010) ioport
  13. Faisal, Saadia; Lichtenberg, Gerwald; Trump, Saskia; Attinger, Sabine: Structural properties of continuous representations of Boolean functions for gene network modelling (2010)
  14. Kabir, Mitra; Noman, Nasimul; Iba, Hitoshi: Reverse engineering gene regulatory network from microarray data using linear time-variant model (2010) ioport
  15. Sîrbu, Alina; Ruskin, Heather J.; Crane, Martin: Comparison of evolutionary algorithms in gene regulatory network model inference (2010) ioport
  16. Tournier, Laurent; Chaves, Madalena: Uncovering operational interactions in genetic networks using asynchronous Boolean dynamics (2009)
  17. Akutsu, Tatsuya; Hayashida, Morihiro; Ching, Wai-Ki; Ng, Michael K.: Control of Boolean networks: hardness results and algorithms for tree structured networks (2007)
  18. Datta, Suman; Sokhansanj, Bahrad A.: Accelerated search for biomolecular network models to interpret high-throughput experimental data (2007) ioport
  19. Hu, Xiaohua; Wu, Fang-Xiang: Mining and state-space based modeling and verification of sub-networks from large biomolecular networks (2007) ioport
  20. Xu, Rui; Venayagamoorthy, Ganesh K.; Wunsch, Donald C. II: Modeling of gene regulatory networks with hybrid differential evolution and particle swarm optimization (2007)

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