skip to main content
research-article

Planning interventions in biological networks

Published: 03 December 2010 Publication History

Abstract

Modeling the dynamics of biological processes has recently become an important research topic in computational biology and systems engineering. One of the most important reasons to model a biological process is to enable high-throughput in-silico experiments that attempt to predict or intervene in the process. These experiments can help accelerate the design of therapies through their rapid and inexpensive replication and alteration. While some techniques exist for reasoning with biological processes, few take advantage of the flexible and scalable algorithms popular in AI research. In reasoning about interventions in biological processes, where scalability is crucial for feasible application, we apply AI planning-based search techniques and demonstrate their advantage over existing enumerative methods. We also present a novel formulation of intervention planning that relies on models that characterize and attempt to change the phenotype of a system. We study three biological systems: the yeast cell cycle, a model of the human aging process, and the Wnt5a network governing the metastasis of melanoma in humans. The contribution of our investigation is in demonstrating that: (i) prior approaches, based on dynamic programming, cannot scale as well as heuristic search, and (ii) the newly found scalability enables us to plan previously unknown sequences of interventions that reveal novel and biologically significant responses in the systems which are consistent with biological knowledge in the literature.

References

[1]
Albert, R. and Othmer, H. G. 2003. The topology of the regulatory interactions predicts the expression pattern of the segment polarity genes in drosophila melanogaster. J Theor. Biol. 223, 1, 1--18.
[2]
Bittner, M., Meltzer, P., Chen, Y., Jiang, Y., Seftor, E., et al. 2000. Molecular classification of cutaneous malignant melanoma by gene expression profiling. Nature 406, 536--40.
[3]
Blythe, J. 1994. Planning with external events. In Proceedings of the 10th Conference on Uncertainty in Artificial Intelligence. 94--101.
[4]
Bryant, R. 1986. Graph-Based algorithms for Boolean function manipulation. IEEE Trans. Comput. C-35, 8, 677--691.
[5]
Bryce, D., Kambhampati, S., and Smith, D. 2006. Planning graph heuristics for belief space search. J. Res. Artif. Intell. 26, 35--99.
[6]
Bryce, D. and Kim, S. 2007. Planning for gene regulatory network intervention. In Proceedings of the 20th International Joint Conference on Artificial Intelligence. 1834--1839.
[7]
Datta, A., Choudhary, A., Bittner, M., and Dougherty, E. 2004. External control in Markovian genetic regulatory networks: The imperfect information case. Bioinf. 20, 6, 924--930.
[8]
Datta, A., Choudhary, A., and Dougherty, E. 2003. External control in Markovian genetic regulatory networks. Mach. Learn. 52, 52, 169--191.
[9]
Davidich, M. I. and Bornholdt, S. 2008. Boolean network model predicts cell cycle sequence of fission yeast. PLoS ONE 3, 2, e1672.
[10]
de Jong, H. 2002. Modeling and simulation of genetic regulatory systems: A literature review. J. Comput. Biol. 9, 1, 67--103.
[11]
Dougherty, E. R., Brun, M., Trent, J. M., and Bittner, M. L. 2009. Conditioning-Based modeling of contextual genomic regulation. IEEE/ACM Trans. Comput. Biol. Bioinf. 6, 2, 310--320.
[12]
Elowitz, M., Levine, A., Siggia, E., and Swain, P. 2002. Stochastic gene expression in a single cell. Sci. 297, 1183--1186.
[13]
Ferrezuelo, F., Colomina, N., Futcher, B., and Aldea, M. 2010. The transcriptional network activated by cln3 cyclin at the g1-to-s transition of the yeast cell cycle. Genome Biol. 11, 6, R67.
[14]
Friedman, N., Linial, M., Nachman, I., and Peér, D. 2000. Using Bayesian networks to analyze expression data. J. Comput. Biol. 7, 601--620.
[15]
Furber, J. D. 2010. Systems biology of human aging: Network model of biochemical and physiological interactions in human senescence. http://www.legendarypharma.com/chartbg.html
[16]
Goutsias, J. and Kim, S. 2004. A nonlinear discrete dynamical model for transcriptional regulation: Construction and properties. Biophys. J. 864, 1922--1945.
[17]
Goutsias, J. and Kim, S. 2006. Stochastic transcriptional regulatory systems with time delays: A mean-field approximation. J. Comput. Biol. 135, 1049--1076.
[18]
Hartemink, A., Gifford, D., Jaakkola, T., and Young, R. 2001. Using graphical models and genomic expression data to statistically validate models of genetic regulatory networks. In Proceedings of the Pacific Symposium on Biocomputing. 422--433.
[19]
Kaelbling, L. P., Littman, M. L., and Cassandra, A. R. 1995. Planning and acting in partially observable stochastic domains. Artif. Intell. 101, 99--134.
[20]
Kauffman, S. 1969a. Homeostasis and differentiation in random genetic control networks. Nature 224, 215, 177--8.
[21]
Kauffman, S. 1969b. Metabolic stability and epigenesis in randomly constructed genetic nets. J. Theor. Biol. 22, 437--467.
[22]
Kauffman, S. 1993. The Origins of Order: Self-Organization and Selection in Evolution. Oxford University Press, Oxford, UK.
[23]
Khan, S., Decker, K., Gillis, W., and Schmidt, C. 2003. A multi-agent system-driven AI planning approach to biological pathway discovery. In Proceedings of 13th International Conference on Automated Planning and Scheduling.
[24]
Kim, S., Dougherty, E., Bittner, M., Chen, Y., Sivakumar, K., Meltzer, P., and Trent, J. 2000. Multivariate measurement of gene expression relationships. Genom. 67, 2, 201--209.
[25]
Kim, S., Li, H., Dougherty, E., Cao, N., Chen, Y., Bittner, M., and Suh, E. 2002. Can Markov chain models mimic biological regulation? J. Biol. Syst. 10, 4, 337--357.
[26]
Li, F., Long, T., Lu, Y., Ouyang, Q., and Tang, C. 2004. The yeast cell-cycle network is robustly designed. Proc. Nat. Acad. Sci. United States Amer. 101, 14, 4781--4786.
[27]
McDermott, D. 2003. The formal semantics of processes in PDDL. In ICAPS Workshop on PDDL.
[28]
Murphy, K. and Mian, S. 1999. Modeling gene expression data using dynamic Bayesian networks. Tech. rep., University of California, Berkeley.
[29]
Murphy, K. P. 2002. Dynamic bayesian networks: Representation, inference and learning. Ph.D. thesis, UC Berkley.
[30]
Nilsson, N. 1980. Principles of Artificial Intelligence. Morgan Kaufmann.
[31]
Pal, R., Datta, A., Bittner, M. L., and Dougherty, E. R. 2005. Intervention in context-sensitive probabilistic Boolean networks. Bioinf. 21, 7, 1211--1218.
[32]
Pineau, J., Gordon, G. J., and Thrun, S. 2006. Anytime point-based approximations for large POMDPs. J. Artif. Intell. Res. 27, 335--380.
[33]
Ramsey, S., Orrell, D., and Bolouri, H. 2005. Dizzy: Stochastic simulation of large-scale genetic regulatory networks. J. Bioinf. Comput. Biol. 3, 2, 415--436.
[34]
Shmulevich, I., Dougherty, E., Kim, S., and Zhang, W. 2002a. Probabilistic Boolean networks: A rule-based uncertainty model for gene regulatory networks. Bioinf. 18, 2, 261--274.
[35]
Shmulevich, I., Dougherty, E., and Zhang, W. 2002b. Gene perturbation and intervention in probabilistic boolean networks. Bioinf. 18, 10, 1319--1331.
[36]
Smith, T. and Simmons, R. G. 2004. Heuristic search value iteration for POMDPs. In Proceedings of the 20th Conference on Uncertainty in Artificial Intelligence. 520--527.
[37]
Sosman, J., Weeraratna, A. T., and Sondak, V. K. 2004. When will melanoma vaccines be proven effective? J. Clinic. Oncol. 22, 387--389.
[38]
Tran, N. and Baral, C. 2005. Issues in reasoning about interaction networks in cells: necessity of event ordering knowledge. In Proceedings of the 20th National Conference on Artificial Intelligence.
[39]
Verdicchio, M. and Kim, S. 2010. Reduction of Boolean network basins of attraction reveals intervention targets. Tech. rep. TR-10-004, Arizona State University, Tempe, Arizona. April.
[40]
Weeraratna, A. T., Jiang, Y., Hostetter, G., Rosenblatt, K., Duray, P., Bittner, M., and Trent, J. M. 2002. Wnt5a signaling directly affects cell motility and invasion of metastatic melanoma. Cancer Cell 1, 3, 279--88.
[41]
Wuensche, A. 1998. Genomic regulation modeled as a network with basins of attraction. In Proceedings of the Pacific Symposium on Biocomputing. 89--102.
[42]
Xiao, Y. 2009. A tutorial on analysis and simulation of boolean gene regulatory network models. Current Genom. 10, 7, 511--525.
[43]
Younes, H. L. S., Littman, M. L., Weissman, D., and Asmuth, J. 2005. The first probabilistic track of the international planning competition. J. Artif. Intell. Res. 24, 851--887.

Cited By

View all
  • (2020)A Framework to Shift Basins of Attraction of Gene Regulatory Networks through Batch Reinforcement LearningArtificial Intelligence in Medicine10.1016/j.artmed.2020.101853(101853)Online publication date: May-2020
  • (2018)Control of Gene Regulatory Networks Basin of Attractions with Batch Reinforcement Learning2018 7th Brazilian Conference on Intelligent Systems (BRACIS)10.1109/BRACIS.2018.00030(127-132)Online publication date: Oct-2018
  • (2017)Batch Mode TD$\lambda$ for Controlling Partially Observable Gene Regulatory NetworksIEEE/ACM Transactions on Computational Biology and Bioinformatics10.1109/TCBB.2016.259557714:6(1214-1227)Online publication date: 1-Nov-2017
  • Show More Cited By

Recommendations

Comments

Information & Contributors

Information

Published In

cover image ACM Transactions on Intelligent Systems and Technology
ACM Transactions on Intelligent Systems and Technology  Volume 1, Issue 2
November 2010
153 pages
ISSN:2157-6904
EISSN:2157-6912
DOI:10.1145/1869397
Issue’s Table of Contents
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 03 December 2010
Accepted: 01 July 2010
Revised: 01 June 2010
Received: 01 April 2010
Published in TIST Volume 1, Issue 2

Permissions

Request permissions for this article.

Check for updates

Author Tags

  1. Planning
  2. search
  3. systems biology

Qualifiers

  • Research-article
  • Research
  • Refereed

Funding Sources

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • Downloads (Last 12 months)1
  • Downloads (Last 6 weeks)0
Reflects downloads up to 17 Feb 2025

Other Metrics

Citations

Cited By

View all
  • (2020)A Framework to Shift Basins of Attraction of Gene Regulatory Networks through Batch Reinforcement LearningArtificial Intelligence in Medicine10.1016/j.artmed.2020.101853(101853)Online publication date: May-2020
  • (2018)Control of Gene Regulatory Networks Basin of Attractions with Batch Reinforcement Learning2018 7th Brazilian Conference on Intelligent Systems (BRACIS)10.1109/BRACIS.2018.00030(127-132)Online publication date: Oct-2018
  • (2017)Batch Mode TD$\lambda$ for Controlling Partially Observable Gene Regulatory NetworksIEEE/ACM Transactions on Computational Biology and Bioinformatics10.1109/TCBB.2016.259557714:6(1214-1227)Online publication date: 1-Nov-2017
  • (2016)Real-time dynamic programming for Markov decision processes with imprecise probabilitiesArtificial Intelligence10.1016/j.artint.2015.09.005230:C(192-223)Online publication date: 1-Jan-2016
  • (2016)Robust probabilistic planning with ilaoApplied Intelligence10.1007/s10489-016-0780-445:3(662-672)Online publication date: 1-Oct-2016
  • (2014)Template-based intervention in Boolean network models of biological systemsEURASIP Journal on Bioinformatics and Systems Biology10.1186/s13637-014-0011-42014:1Online publication date: 19-Jul-2014
  • (2013)Employing batch reinforcement learning to control gene regulation without explicitly constructing gene regulatory networksProceedings of the Twenty-Third international joint conference on Artificial Intelligence10.5555/2540128.2540421(2042-2048)Online publication date: 3-Aug-2013

View Options

Login options

Full Access

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Figures

Tables

Media

Share

Share

Share this Publication link

Share on social media