Fundamental limitations of network reconstruction from temporal data

Angulo, Marco Tulio; Moreno, Jaime A.; Lippner, Gábor; Barabási, Albert‐László; Liu, Yang‐Yu

doi:10.1098/rsif.2016.0966

Cited by 61 publications

(57 citation statements)

References 51 publications

(124 reference statements)

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Section: Pitfalls In Current Dynamic Inferencementioning

confidence: 99%

“…This is suitable for simple systems but not for complex microbial systems. Indeed, accurate temporal predictions are possible even if the identified interactions look totally different from the actual ones [42]. To demonstrate the above point, we set up a synthetic microbial system with eight species, following the GLV dynamics with three binary perturbations.…”

Section: Pitfalls In Current Dynamic Inferencementioning

confidence: 99%

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Inferring human microbial dynamics from temporal metagenomics data: Pitfalls and lessons

et al. 2016

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The human gut microbiota is a very complex and dynamic ecosystem that plays a crucial role in health and well-being. Inferring microbial community structure and dynamics directly from time-resolved metagenomics data is key to understanding the community ecology and predicting its temporal behavior. Many methods have been proposed to perform the inference. Yet, as we point out in this review, there are several pitfalls along the way. Indeed, the uninformative temporal measurements and the compositional nature of the relative abundance data raise serious challenges in inference. Moreover, the inference results can be largely distorted when only focusing on highly abundant species by ignoring or grouping low-abundance species. Finally, the implicit assumptions in various regularization methods may not reflect reality. Those issues have to be seriously considered in ecological modeling of human gut microbiota.

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Section: Pitfalls In Current Dynamic Inferencementioning

confidence: 99%

Section: Pitfalls In Current Dynamic Inferencementioning

confidence: 99%

Inferring human microbial dynamics from temporal metagenomics data: Pitfalls and lessons

et al. 2016

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“…Recently, two classes of fundamental limitations of NR were characterized by deriving necessary (and in some cases sufficient) conditions to reconstruct any desired property of the interaction matrix (Angulo et al, 2015). The first class of fundamental limitations is due to our uncertainty about the coupling functions f ij (x i , x j ), leading to a natural trade-off: the more information we want to reconstruct about the interaction matrix the more certain we need to be about the coupling functions.…”

Section: Network Reconstructionmentioning

confidence: 99%

“…performing more experiments, which are sometime either infeasible to too expensive), prior knowledge of the interaction matrix, e.g. the bounds of the edge weights, is extremely useful (Angulo et al, 2015).…”

Section: Network Reconstructionmentioning

confidence: 99%

Control principles of complex systems

Liu¹,

Barabási²

2016

Rev. Mod. Phys.

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533

349

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A reflection of our ultimate understanding of a complex system is our ability to control its behavior. Typically, control has multiple prerequisites: it requires an accurate map of the network that governs the interactions between the system's components, a quantitative description of the dynamical laws that govern the temporal behavior of each component, and an ability to influence the state and temporal behavior of a selected subset of the components. With deep roots in nonlinear dynamics and control theory, notions of control and controllability have taken a new life recently in the study of complex networks, inspiring several fundamental questions: What are the control principles of complex systems? How do networks organize themselves to balance control with functionality? To address these here we review recent advances on the controllability and the control of complex networks, exploring the intricate interplay between a system's structure, captured by its network topology, and the dynamical laws that govern the interactions between the components. We match the pertinent mathematical results with empirical findings and applications. We show that uncovering the control principles of complex systems can help us explore and ultimately understand the fundamental laws that govern their behavior.

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“…Extracting coupling topology from a network of dynamical units is a challenging task that requires advanced signal processing techniques2930. Here we apply a method that uses phase models to characterize the interaction topology3132.…”

Section: Resultsmentioning

confidence: 99%

Decoding Network Structure in On-Chip Integrated Flow Cells with Synchronization of Electrochemical Oscillators

Jia

Kiss

2017

Sci Rep

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The analysis of network interactions among dynamical units and the impact of the coupling on self-organized structures is a challenging task with implications in many biological and engineered systems. We explore the coupling topology that arises through the potential drops in a flow channel in a lab-on-chip device that accommodates chemical reactions on electrode arrays. The networks are revealed by analysis of the synchronization patterns with the use of an oscillatory chemical reaction (nickel electrodissolution) and are further confirmed by direct decoding using phase model analysis. In dual electrode configuration, a variety coupling schemes, (uni- or bidirectional positive or negative) were identified depending on the relative placement of the reference and counter electrodes (e.g., placed at the same or the opposite ends of the flow channel). With three electrodes, the network consists of a superposition of a localized (upstream) and global (all-to-all) coupling. With six electrodes, the unique, position dependent coupling topology resulted spatially organized partial synchronization such that there was a synchrony gradient along the quasi-one-dimensional spatial coordinate. The networked, electrode potential (current) spike generating electrochemical reactions hold potential for construction of an in-situ information processing unit to be used in electrochemical devices in sensors and batteries.

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Fundamental limitations of network reconstruction from temporal data

Cited by 61 publications

References 51 publications

Inferring human microbial dynamics from temporal metagenomics data: Pitfalls and lessons

Inferring human microbial dynamics from temporal metagenomics data: Pitfalls and lessons

Control principles of complex systems

Decoding Network Structure in On-Chip Integrated Flow Cells with Synchronization of Electrochemical Oscillators

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