Use of Computation Ecosystems to Analyze the Kidney-Heart Crosstalk

Zhuojun Wu1,2, Johannes Lohmöller3, Christiane Kuhl2

  • 1Institute of Molecular Cardiovascular Research (Z.W., J.J.), Rheinisch-Westfälische Technische Hochschule Aachen University, Germany.

Circulation Research
|April 13, 2023
PubMed

Insights

Traditional organ-specific studies fail to capture complex diseases. Holistic computational ecosystems offer a new approach to understand cardiorenal syndrome by integrating diverse data for better disease modeling.

Area of Science:

  • Integrative biology and computational medicine.
  • Focus on systems biology and bioinformatics.
  • Multidisciplinary approach to complex diseases.

Background:

  • Historically, organ-centered research has limitations in understanding systemic diseases.
  • Single-organ approaches fail to capture inter-organ crosstalk and disease progression.
  • Multimorbidity, like cardiorenal syndrome, requires holistic, multidimensional analysis.

Purpose of the Study:

  • To review current knowledge on kidney-heart crosstalk.
  • To explore the application of computational ecosystems in understanding cardiorenal syndrome.
  • To highlight methods and opportunities for holistic data analysis in complex diseases.

Main Methods:

  • Integration and correlation of heterogeneous, multidimensional data (-omics and non-omics).
  • Utilizing mathematical, statistical, and computational tools to build disease models.
  • Developing computational ecosystems for analyzing complex biological systems.

Main Results:

  • Organ-centered approaches provide incomplete disease insights.
  • Holistic approaches are crucial for understanding high-dimensional interactions in multimorbidity.
  • Computational ecosystems enable comprehensive analysis of multiorgan crosstalk.

Conclusions:

  • Computational ecosystems offer a powerful framework for studying complex diseases like cardiorenal syndrome.
  • Integrating diverse data through computational approaches is key to advancing systems medicine.
  • Novel methods are needed to address the data-scientific challenges of multimodality and multimorbidity.

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