Learning Delayed Influences of Biological Systems

Ribeiro, Tony; Magnin, Morgan; Inoue, Katsumi; Sakama, Chiaki

doi:10.3389/fbioe.2014.00081

Cited by 15 publications

(11 citation statements)

References 37 publications

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“…Although it is relevant and interesting from a mathematical or computational point of view to study the dynamics generated by every possible update scheme in the latter context, this exercise might turn to be rather impractical An alternative approach to allow adding asynchronicity to the dynamics of a boolean network is based in the concept of delay that is generally defined as an internal clock, that could be independent from the original dynamics of the system, and that dictates its dynamical behaviour during a fixed time interval. This latter concept was first introduced by Thomas in [35,36] and then studied in different frameworks such as in [1,4,7,31,32]. Particularly, here we are interested in specific type of delay called firing memory.…”

Section: Introductionmentioning

confidence: 99%

On the Effects of Firing Memory in the Dynamics of Conjunctive Networks

Goles

Montealegre

Ríos-Wilson

2019

Cellular Automata and Discrete Complex Systems

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Boolean networks are one of the most studied discrete models in the context of the study of gene expression. In order to define the dynamics associated to a Boolean network, there are several update schemes that range from parallel or synchronous to asynchronous. However, studying each possible dynamics defined by different update schemes might not be efficient. In this context, considering some type of temporal delay in the dynamics of Boolean networks emerges as an alternative approach. In this paper, we focus in studying the effect of a particular type of delay called firing memory in the dynamics of Boolean networks. Particularly, we focus in symmetric (non-directed) conjunctive networks and we show that there exist examples that exhibit attractors of non-polynomial period. In addition, we study the prediction problem consisting in determinate if some vertex will eventually change its state, given an initial condition. We prove that this problem is PSPACE-complete.

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Section: Introductionmentioning

confidence: 99%

On the Effects of Firing Memory in the Dynamics of Conjunctive Networks

Goles

Montealegre

Ríos-Wilson

2019

Cellular Automata and Discrete Complex Systems

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“…The LFIT framework proposes several modeling and learning algorithms to tackle those different semantics. So far the following systems are tackled: memory-less synchronous consistent systems [7], systems with memory [13], non-consistent systems [10].…”

Section: Introductionmentioning

confidence: 99%

Inductive Learning from State Transitions over Continuous Domains

Ribeiro

Tourret

Folschette

et al. 2018

Inductive Logic Programming

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Learning from interpretation transition (LFIT) automatically constructs a model of the dynamics of a system from the observation of its state transitions. So far, the systems that LFIT handles are restricted to discrete variables or suppose a discretization of continuous data. However, when working with real data, the discretization choices are critical for the quality of the model learned by LFIT. In this paper, we focus on a method that learns the dynamics of the system directly from continuous time-series data. For this purpose, we propose a modeling of continuous dynamics by logic programs composed of rules whose conditions and conclusions represent continuums of values.

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“…In [20], the authors also propose algorithms to learn the delayed dynamic of systems from time series data. However, they focus only on synchronous dynamics, and changes take only one step to occur.…”

Section: Definition 7 (Fireable Timed Local Transition)mentioning

confidence: 99%

“…The merits of other hybrid formalisms in biology have been studied, for instance timed automata [15], hybrid automata [16], the hybrid model of a neural oscillator [17] and Boolean representation [18,19]. In [20], the authors also propose algorithms to learn the delayed dynamics of systems from time series data. However, they focus only on synchronous dynamics, and the delays they consider are different from the duration of the reaction that we model here.…”

Section: Introductionmentioning

confidence: 99%

Modeling Delayed Dynamics in Biological Regulatory Networks from Time Series Data

et al. 2017

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Abstract:Background: The modeling of Biological Regulatory Networks (BRNs) relies on background knowledge, deriving either from literature and/or the analysis of biological observations. However, with the development of high-throughput data, there is a growing need for methods that automatically generate admissible models. Methods: Our research aim is to provide a logical approach to infer BRNs based on given time series data and known influences among genes. Results: We propose a new methodology for models expressed through a timed extension of the automata networks (well suited for biological systems). The main purpose is to have a resulting network as consistent as possible with the observed datasets. Conclusion: The originality of our work is three-fold: (i) identifying the sign of the interaction; (ii) the direct integration of quantitative time delays in the learning approach; and (iii) the identification of the qualitative discrete levels that lead to the systems' dynamics. We show the benefits of such an automatic approach on dynamical biological models, the DREAM4(in silico) and DREAM8 (breast cancer) datasets, popular reverse-engineering challenges, in order to discuss the precision and the computational performances of our modeling method.

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Learning Delayed Influences of Biological Systems

Cited by 15 publications

References 37 publications

On the Effects of Firing Memory in the Dynamics of Conjunctive Networks

On the Effects of Firing Memory in the Dynamics of Conjunctive Networks

Inductive Learning from State Transitions over Continuous Domains

Modeling Delayed Dynamics in Biological Regulatory Networks from Time Series Data

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