A closed form for Jacobian reconstruction from time series and its application as an early warning signal in network dynamics

Barter, Edmund; Brechtel, Andreas; Drossel, Barbara; Groß, Thilo

doi:10.1098/rspa.2020.0742

Cited by 8 publications

(7 citation statements)

References 41 publications

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“…When a system is subject to some noise, it is possible to employ the theory of stochastic processes showing that correlations emerging from samples can be interpreted as a 'signature' of the underlying deterministic system [10]. This leads to a relationship between the covariance of the samples and the system's Jacobian matrix, which is derived according to [10] and [11] as follows:…”

Section: Causal Discovery By Jacobian Reconstructionmentioning

confidence: 99%

Exploring glioma heterogeneity through omics networks: from gene network discovery to causal insights and patient stratification

Kastendiek,

Coletti,

Gross

et al. 2024

Preprint

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Gliomas are primary malignant brain tumors with a typically poor prognosis, exhibiting significant heterogeneity across different cancer types. Each glioma type possesses distinct molecular characteristics determining patient prognosis and therapeutic options. This study aims to explore the molecular complexity of gliomas at the transcriptome level, employing a comprehensive approach grounded in network discovery. The graphical lasso method was used to estimate a gene co-expression network for each glioma type from a transcriptomics dataset. Causality was subsequently inferred from correlation networks by estimating the Jacobian matrix. The networks were then analyzed for gene importance using centrality measures and modularity detection, leading to the selection of genes that might play an important role in the disease. Spectral clustering based on patient similarity networks was applied to stratify patients into groups with similar molecular characteristics and to assess whether the resulting clusters align with the diagnosed glioma type. The results presented highlight the ability of the proposed methodology to uncover relevant genes associated with glioma intertumoral heterogeneity. Further investigation might encompass biological validation of the putative biomarkers disclosed.

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Section: Causal Discovery By Jacobian Reconstructionmentioning

confidence: 99%

Exploring glioma heterogeneity through omics networks: from gene network discovery to causal insights and patient stratification

Kastendiek,

Coletti,

Gross

et al. 2024

Preprint

Self Cite

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“…Microcosm and mesocosm experiments can help fill this gap by allowing experimental introductions to be performed and monitored in controlled settings (95). In addition, ongoing commercial field-scale inoculations can provide long-term data on microbial community dynamics (70,71,96,97). With monitoring, the invasions that these inoculations cause (or do not cause) can help guide future decision making to prevent additional invasions.…”

Section: Dimension 3a: Experimentsmentioning

confidence: 99%

Microbial invasions and inoculants: a call to action

Ladau¹,

Fahimipour²,

Newcomer³

et al. 2023

Preprint

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The use of non-genetically modified microbial inoculants for beneficial purposes in agriculture, bioremediation, medicine, and infrastructure is increasing. The intentional introduction of plants and animals for similar purposes has a long history, but despite successes, has resulted in thousands of plant and animal species becoming invasive, with catastrophic consequences for the environment, public health, and society. Hundreds of microbial invasions are known, and although microbial inoculants can provide benefits, they have similar potential to negatively impact ecosystems. Little action has been taken to guard against the threat of microbial invasions from non-genetically modified microbial inoculants. Now is the time to develop an effective research and management infrastructure for these microbial inoculants to avoid catastrophic outcomes similar to those caused by intentionally-introduced plants and animals. Here, we propose a unified research and management approach to spur action by regulators and practitioners. Three aims need to be addressed: developing (1) a coherent mechanistic understanding of how microbial inoculants effect invasions, (2) predictive models forecasting which microbes pose risks of invasion, and (3) effective management strategies. To guide mechanistic understanding, we develop seventeen key hypotheses. For predictive modeling, quantitative trait analysis and risk maps will be critical. Management strategies will depend on both understanding and predictions, but prevention rather than eradication or control of invasive microbes is likely to be most effective, and a precautionary regulatory approach should immediately be applied to inoculants. Multiple data types will be instrumental for understanding and predicting which microbial inoculants have invasive potential: experimental data from microcosm and mesocosm experiments, and large-scale observational data from microbial surveillance. Both phenomenological and mechanistic modeling approaches will be key in achieving these fundamental and applied research aims. Moreover, each stage of the invasion process --- transport, establishment, spread, and impacts --- often need to be investigated separately. The unified approach developed here provides a roadmap for developing a research and management infrastructure to guard against the threat of microbial invasions from non-genetically modified microbial inoculants.

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“…Hence GM is sometimes used to create Jacobians for methodological studies that need example Jacobians with certain properties. A recent example is Barter et al (2021) which proposed a method for inferring causality (in the form of the Jacobian matrix) from correlations and used GMs to create suitable examples. Other examples for this use of GMs include Höfener et al (2011), Höfener et al (2013; Do et al (2012).…”

Section: Mathematical Stability Analysis and Bifurcation Theorymentioning

confidence: 99%

Generalized Structural Kinetic Modeling: A Survey and Guide

Massing

Groß

2022

Front. Mol. Biosci.

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Many current challenges involve understanding the complex dynamical interplay between the constituents of systems. Typically, the number of such constituents is high, but only limited data sources on them are available. Conventional dynamical models of complex systems are rarely mathematically tractable and their numerical exploration suffers both from computational and data limitations. Here we review generalized modeling, an alternative approach for formulating dynamical models to gain insights into dynamics and bifurcations of uncertain systems. We argue that this approach deals elegantly with the uncertainties that exist in real world data and enables analytical insight or highly efficient numerical investigation. We provide a survey of recent successes of generalized modeling and a guide to the application of this modeling approach in future studies such as complex integrative ecological models.

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A closed form for Jacobian reconstruction from time series and its application as an early warning signal in network dynamics

Cited by 8 publications

References 41 publications

Exploring glioma heterogeneity through omics networks: from gene network discovery to causal insights and patient stratification

Exploring glioma heterogeneity through omics networks: from gene network discovery to causal insights and patient stratification

Microbial invasions and inoculants: a call to action

Generalized Structural Kinetic Modeling: A Survey and Guide

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