Constructing minimal models for complex system dynamics

Barzel, Baruch; Liu, Yang‐Yu; Barabási, Albert‐László

doi:10.1038/ncomms8186

Cited by 80 publications

(43 citation statements)

References 44 publications

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“…A differential algebraic model of ␤ 1 -adrenergic signaling in the cardiac myocyte was formulated using the singular perturbation method (18), which decomposed the network into characteristic time scales. Sub-networks with dynamics on very fast time scales (Ͻ0.1 s) were assumed to be at quasi-equilibrium (e.g.…”

Section: Methodsmentioning

confidence: 99%

“…To bypass the need of a full knowledge of kinetic parameters, other studies have investigated the interplay between generic dynamical models and topological structure in the context of biological networks (17,18). These studies have focused on retrieving global perturbation statistical properties from microscopic models (17), or retrieving the most probable underlying dynamical model from perturbation statistics (18).…”

Section: Introductionmentioning

confidence: 99%

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Predicting perturbation patterns from the topology of biological networks

Santolini

Barabási

2018

Preprint

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High-throughput technologies, offering unprecedented wealth of quantitative data underlying the makeup of living systems, are changing biology. Notably, the systematic mapping of the relationships between biochemical entities has fueled the rapid development of network biology, offering a suitable framework to describe disease phenotypes and predict potential drug targets. Yet, our ability to develop accurate dynamical models remains limited, due in part to the limited knowledge of the kinetic parameters underlying these interactions. Here, we explore the degree to which we can make reasonably accurate predictions in the absence of the kinetic parameters. We find that simple dynamically agnostic models are sufficient to recover the strength and sign of the biochemical perturbation patterns observed in 87 biological models for which the underlying kinetics is known. Surprisingly, a simple distance-based model achieves 65% accuracy. We show that this predictive power is robust to topological and kinetic parameters perturbations, and we identify key network properties that can increase up to 80% the recovery rate of the true perturbation patterns. We validate our approach using experimental data on the chemotactic pathway in bacteria, finding that a network model of perturbation spreading predicts with ~80% accuracy the directionality of gene expression and phenotype changes in knock-out and overproduction experiments. These findings show that the steady advances in mapping out the topology of biochemical interaction networks opens avenues for accurate perturbation spread modeling, with direct implications for medicine and drug development.. CC-BY-NC-ND 4.0 International license It is made available under a (which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.The copyright holder for this preprint . http://dx.doi.org/10.1101/349324 doi: bioRxiv preprint first posted online Jun. 17, 2018; Significance statementThe development of high-throughput technologies has allowed to map a significant proportion of interactions between biochemical entities in the cell. However, it is unclear how much information is lost given the lack of measurements on the kinetic parameters governing the dynamics of these interactions. Using biochemical networks with experimentally measured kinetic parameters, we show that a knowledge of the network topology offers 65% to 80% accuracy in predicting the impact of perturbation patterns. In other words, we can use the increasingly accurate topological models to approximate perturbation patterns, bypassing expensive kinetic constant measurement. These results could open new avenues in modeling drug action, and in identifying drug targets relying on the human interactome only.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Predicting perturbation patterns from the topology of biological networks

Santolini

Barabási

2018

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…Other approaches pertaining to the global fitting of kinetic parameters (13,14) by optimizing the model agreement to available data often yield large parameter uncertainties (15,16). To bypass the need of a full knowledge of kinetic parameters, other studies have investigated the interplay between generic dynamical models and topological structure in the context of biological networks (17,18). These studies have focused on retrieving global perturbation statistical properties from microscopic models (17), or retrieving the most probable underlying dynamical model from perturbation statistics (18).…”

mentioning

confidence: 99%

Predicting perturbation patterns from the topology of biological networks

Santolini

Barabási

2018

Proc. Natl. Acad. Sci. U.S.A.

Self Cite

212

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High-throughput technologies, offering an unprecedented wealth of quantitative data underlying the makeup of living systems, are changing biology. Notably, the systematic mapping of the relationships between biochemical entities has fueled the rapid development of network biology, offering a suitable framework to describe disease phenotypes and predict potential drug targets. However, our ability to develop accurate dynamical models remains limited, due in part to the limited knowledge of the kinetic parameters underlying these interactions. Here, we explore the degree to which we can make reasonably accurate predictions in the absence of the kinetic parameters. We find that simple dynamically agnostic models are sufficient to recover the strength and sign of the biochemical perturbation patterns observed in 87 biological models for which the underlying kinetics are known. Surprisingly, a simple distance-based model achieves 65% accuracy. We show that this predictive power is robust to topological and kinetic parameter perturbations, and we identify key network properties that can increase up to 80% the recovery rate of the true perturbation patterns. We validate our approach using experimental data on the chemotactic pathway in bacteria, finding that a network model of perturbation spreading predicts with ∼80% accuracy the directionality of gene expression and phenotype changes in knock-out and overproduction experiments. These findings show that the steady advances in mapping out the topology of biochemical interaction networks opens avenues for accurate perturbation spread modeling, with direct implications for medicine and drug development.

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“…The feed-forward architecture is replicative in both inputs and outputs so that the representation of hidden units is dependent on not only current inputs but also encoded historicial information. The adaptive size of hidden units and nonlinear activation function (e.g., sigmoid, tangent hyperbolic or rectifier function) make neural network capable of approximating arbitrary complex function in huge function space [2].…”

Section: Problem Formulationmentioning

confidence: 99%

Marked Temporal Dynamics Modeling Based on Recurrent Neural Network

Wang

Liu

Shen

et al. 2017

Advances in Knowledge Discovery and Data Mining

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Abstract.We are now witnessing the increasing availability of event stream data, i.e., a sequence of events with each event typically being denoted by the time it occurs and its mark information (e.g., event type). A fundamental problem is to model and predict such kind of marked temporal dynamics, i.e., when the next event will take place and what its mark will be. Existing methods either predict only the mark or the time of the next event, or predict both of them, yet separately. Indeed, in marked temporal dynamics, the time and the mark of the next event are highly dependent on each other, requiring a method that could simultaneously predict both of them. To tackle this problem, in this paper, we propose to model marked temporal dynamics by using a mark-specific intensity function to explicitly capture the dependency between the mark and the time of the next event. Extensive experiments on two datasets demonstrate that the proposed method outperforms state-of-the-art methods at predicting marked temporal dynamics.

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Constructing minimal models for complex system dynamics

Cited by 80 publications

References 44 publications

Predicting perturbation patterns from the topology of biological networks

Predicting perturbation patterns from the topology of biological networks

Predicting perturbation patterns from the topology of biological networks

Marked Temporal Dynamics Modeling Based on Recurrent Neural Network

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