![Cyclic attractor Stable attractor
Célia Biane, Anne Siegel
Univ. Rennes 1, Inria, CNRS, IRISA
celia.biane-fourati@inria.fr
[1] Biane, C. and Delaplace, F. (2018) Causal reasoning on Boolean control networks based on abduction: theory and application to Cancer drug discovery.
IEEE/ACM Transactions on Computational Biology and Bioinformatics. doi: 10.1109/TCBB.2018.2889102
[2] Kaminski, R., Schaub, T., Siegel, A., & Videla, S. (2013). Minimal intervention strategies in logical signaling networks with ASP. Theory and Practice of Logic
Programming, 13(4-5), 675-690.
[3] Paulevé L. (2017) Pint: A Static Analyzer for Transient Dynamics of Qualitative Networks with IPython Interface. Computational Methods in Systems Biology.
CMSB 2017. Lecture Notes in Computer Science, vol 10545. Springer, Cham.
[4] Zañudo, J. G., & Albert, R. (2015). Cell fate reprogramming by control of intracellular network dynamics. PLoS Computational Biology, 11(4), e1004193.
doi:10.1371/journal.pcbi.1004193
References
Different approaches for controlling Boolean networks
Boolean networks are discrete dynamical systems involving interacting elements whose Boolean state is influenced by the state of
other elements of the network. Boolean networks are used in Biology to model the intracellular dynamics of molecular networks. One
important task in systems biology is to propose algorithms for the identification of control strategies of such networks in order to
propose targets that reprogram cells towards a desired behavior. Moreover, such algorithms face the highly combinatorial dynamics of
these systems. Here we present some features of different approaches (Stable Motifs, Pint and ActoNet) on a toy example and
summarize their main differences.
Pint [3]
Identification of bifurcation transitions (after which a goal is no longer
reachable).
a : 10 when b=0Initial state 11XX : Goal state
𝑏
𝑎
𝑏 ∧ 𝑑
¬𝑐
Stable Motifs [4]
Identification of «locks» variables that pushes the system towards one
of its attractor.
(𝑎 = 0 or 𝑏 = 0) stable state
(𝑎 = 1 or 𝑏 = 1) cyclic attractor
ActoNet [1]
Identification of interaction perturbations for stabilizing a goal
property.
11XX Goal stable property Set 𝑑 to 1 in the function of c
𝑎 𝑏 𝑐 𝑑
Control strategies on Boolean networks
Motivations
A Boolean network Its interaction graph
Future directions
Future works concern the test of the
scalability of the different methods and
benchmarks on published biological models
of different sizes together with the
incorporation of other tools in the state of
the art (CASPO [2]).
Moreover, we plan on extending the ActoNet
approach to :
1. provide information about the set of
initial states from which the goal state is
reachable
2. the specification of goal cyclic attractors
3. the sequential control
Asynchronous state transition graph
Pint
ActoNetStable Motifs
Specification of goal states
Asymptotic dynamics
Abstract Interpretation
Transient dynamics
Guarantees reachability
Control of variables of
Boolean networks
Control of interactions
Control of transitions
Stable statesCyclic attractors
Static Analysis
Mixed static and dynamic](https://image.slidesharecdn.com/postergdrimb-190711164113/85/different-approaches-for-controlling-boolean-networks-1-320.jpg)
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- 1. Cyclic attractor Stable attractor Célia Biane, Anne Siegel Univ. Rennes 1, Inria, CNRS, IRISA celia.biane-fourati@inria.fr [1] Biane, C. and Delaplace, F. (2018) Causal reasoning on Boolean control networks based on abduction: theory and application to Cancer drug discovery. IEEE/ACM Transactions on Computational Biology and Bioinformatics. doi: 10.1109/TCBB.2018.2889102 [2] Kaminski, R., Schaub, T., Siegel, A., & Videla, S. (2013). Minimal intervention strategies in logical signaling networks with ASP. Theory and Practice of Logic Programming, 13(4-5), 675-690. [3] Paulevé L. (2017) Pint: A Static Analyzer for Transient Dynamics of Qualitative Networks with IPython Interface. Computational Methods in Systems Biology. CMSB 2017. Lecture Notes in Computer Science, vol 10545. Springer, Cham. [4] Zañudo, J. G., & Albert, R. (2015). Cell fate reprogramming by control of intracellular network dynamics. PLoS Computational Biology, 11(4), e1004193. doi:10.1371/journal.pcbi.1004193 References Different approaches for controlling Boolean networks Boolean networks are discrete dynamical systems involving interacting elements whose Boolean state is influenced by the state of other elements of the network. Boolean networks are used in Biology to model the intracellular dynamics of molecular networks. One important task in systems biology is to propose algorithms for the identification of control strategies of such networks in order to propose targets that reprogram cells towards a desired behavior. Moreover, such algorithms face the highly combinatorial dynamics of these systems. Here we present some features of different approaches (Stable Motifs, Pint and ActoNet) on a toy example and summarize their main differences. Pint [3] Identification of bifurcation transitions (after which a goal is no longer reachable). a : 10 when b=0Initial state 11XX : Goal state 𝑏 𝑎 𝑏 ∧ 𝑑 ¬𝑐 Stable Motifs [4] Identification of «locks» variables that pushes the system towards one of its attractor. (𝑎 = 0 or 𝑏 = 0) stable state (𝑎 = 1 or 𝑏 = 1) cyclic attractor ActoNet [1] Identification of interaction perturbations for stabilizing a goal property. 11XX Goal stable property Set 𝑑 to 1 in the function of c 𝑎 𝑏 𝑐 𝑑 Control strategies on Boolean networks Motivations A Boolean network Its interaction graph Future directions Future works concern the test of the scalability of the different methods and benchmarks on published biological models of different sizes together with the incorporation of other tools in the state of the art (CASPO [2]). Moreover, we plan on extending the ActoNet approach to : 1. provide information about the set of initial states from which the goal state is reachable 2. the specification of goal cyclic attractors 3. the sequential control Asynchronous state transition graph Pint ActoNetStable Motifs Specification of goal states Asymptotic dynamics Abstract Interpretation Transient dynamics Guarantees reachability Control of variables of Boolean networks Control of interactions Control of transitions Stable statesCyclic attractors Static Analysis Mixed static and dynamic