A Blueprint for Identifying Phenotypes and Drug Targets in Complex Disorders with Empirical Dynamics

Krieger, Madison S.; Moreau, Joshua M.; Zhang, Haiyu; Chien, May; Zehnder, James L.; Craig, Morgan

doi:10.1016/j.patter.2020.100138

Cited by 10 publications

(8 citation statements)

References 52 publications

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“…The complementarity of our results in our independent and orthogonal approaches are outlined in Table 3 by the associations identified between the transition genes and the scEpath TFs. Our approach using distinct clustering techniques and verifying their matching or complementary results was deployed to minimize the effects of expression heterogeneity and validate our findings ( Krieger et al., 2020 ).…”

Section: Discussionmentioning

confidence: 81%

Algorithmic reconstruction of glioblastoma network complexity

Uthamacumaran

Craig

2022

iScience

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Section: Discussionmentioning

confidence: 81%

Algorithmic reconstruction of glioblastoma network complexity

Uthamacumaran

Craig

2022

iScience

Self Cite

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“…CCM allows one to embed chaotic signals from cancer time series on state space and reconstruct their underlying attractor dynamics. The implications of CCM were recently demonstrated in a study that consisted of patients with markers for cyclic thrombocytopenia, in which multiple cells and proteins undergo abnormal oscillations 149 . An R-software implementation of CCM algorithms is available under the rEDM package (see data and code ll OPEN ACCESS availability).…”

Section: Attractor Reconstruction Methodsmentioning

confidence: 97%

“…An empirical dynamical toolbox was developed for inferring networks of biomarker interactions in complex diseases in pattern space. Krieger et al 149 combined CCM, transfer entropy, and dynamical mode decomposition (DMD), three non-parametric causal inference techniques, to study attractor dynamics in these complex disease networks (e.g., hemato-immune networks). Transfer entropy is an information theoretic network inference tool that draws on mutual information and seeks to quantify the amount of entropy that is shared between two causally linked time series.…”

Section: Attractor Reconstruction Methodsmentioning

confidence: 99%

“…DMD attempts to quantify a smaller number of behaviors (modes) of a system by decomposing the time series, which, as an ensemble, fit well to complex exponential functions. 149 Empirical dynamics serve as a blueprint with broader applicability in understanding and treating complex disorders, such as cancers, as dynamical systems. These algorithms for attractor reconstruction should be paired with the network inference and epigenetic landscape reconstruction tools for single-cell datasets discussed herein.…”

Section: Attractor Reconstruction Methodsmentioning

confidence: 99%

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A review of dynamical systems approaches for the detection of chaotic attractors in cancer networks

Uthamacumaran

2021

Patterns

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Cancers are complex dynamical systems. They remain the leading cause of disease-related pediatric mortality in North America. To overcome this burden, we must decipher the state-space attractor dynamics of gene expression patterns and protein oscillations orchestrated by cancer stemness networks. The review provides an overview of dynamical systems theory to steer cancer research in pattern science. While most of our current tools in network medicine rely on statistical correlation methods, causality inference remains primitively developed. As such, a survey of attractor reconstruction methods and machine algorithms for the detection of causal structures applicable in experimentally derived time series cancer datasets is presented. A toolbox of complex systems approaches are discussed for reconstructing the signaling state space of cancer networks, interpreting causal relationships in their time series gene expression patterns, and assisting clinical decision making in computational oncology. As a proof of concept, the applicability of some algorithms are demonstrated on pediatric brain cancer datasets and the requirement of their time series analysis is highlighted. ll

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“…rEDM is an R-package for Empirical Dynamic Modelling and Convergent Cross Mapping (CCM) as devised by Sugihara et al (104). The causal relationships in complex disease signaling networks can be identified using CCM (105). The rEDM package uses a nearest neighbor forecasting method with a Simplex Projection, to produce forecasts of the time-series as the correlation between observed and predicted values are computed (104).…”

Section: Takens' Theoremmentioning

confidence: 99%

A Review of Mathematical and Computational Methods in Cancer Dynamics

Uthamacumaran

Zenil

2022

Front. Oncol.

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Cancers are complex adaptive diseases regulated by the nonlinear feedback systems between genetic instabilities, environmental signals, cellular protein flows, and gene regulatory networks. Understanding the cybernetics of cancer requires the integration of information dynamics across multidimensional spatiotemporal scales, including genetic, transcriptional, metabolic, proteomic, epigenetic, and multi-cellular networks. However, the time-series analysis of these complex networks remains vastly absent in cancer research. With longitudinal screening and time-series analysis of cellular dynamics, universally observed causal patterns pertaining to dynamical systems, may self-organize in the signaling or gene expression state-space of cancer triggering processes. A class of these patterns, strange attractors, may be mathematical biomarkers of cancer progression. The emergence of intracellular chaos and chaotic cell population dynamics remains a new paradigm in systems medicine. As such, chaotic and complex dynamics are discussed as mathematical hallmarks of cancer cell fate dynamics herein. Given the assumption that time-resolved single-cell datasets are made available, a survey of interdisciplinary tools and algorithms from complexity theory, are hereby reviewed to investigate critical phenomena and chaotic dynamics in cancer ecosystems. To conclude, the perspective cultivates an intuition for computational systems oncology in terms of nonlinear dynamics, information theory, inverse problems, and complexity. We highlight the limitations we see in the area of statistical machine learning but the opportunity at combining it with the symbolic computational power offered by the mathematical tools explored.

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A Blueprint for Identifying Phenotypes and Drug Targets in Complex Disorders with Empirical Dynamics

Cited by 10 publications

References 52 publications

Algorithmic reconstruction of glioblastoma network complexity

Algorithmic reconstruction of glioblastoma network complexity

A review of dynamical systems approaches for the detection of chaotic attractors in cancer networks

A Review of Mathematical and Computational Methods in Cancer Dynamics

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