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The Axes of Life: A Roadmap for Understanding Dynamic Multiscale Systems.

Sriram Chandrasekaran1, Nicole Danos2, Uduak Z George3

  • 1Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, USA.

Integrative and Comparative Biology
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Summary

This study introduces a new framework for understanding complex biological systems by integrating experiments and computation across multiple scales. It highlights how big data analytics and artificial intelligence can bridge data gaps and promote interdisciplinary collaboration to address global biological challenges.

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Area of Science:

  • * Integrative biology and systems biology research.
  • * Computational biology and bioinformatics.
  • * Ecology, agriculture, and health sciences.

Background:

  • * Humanity faces complex, multi-factorial biological challenges impacting health, welfare, and environmental stewardship.
  • * Addressing these challenges requires integrating vast datasets across diverse scientific areas (agriculture, ecology, health) and spatio-temporal scales.
  • * Existing methodologies often struggle to bridge different scales and data types in biological research.

Purpose of the Study:

  • * To present a novel framework and roadmap for understanding dynamic biological systems across multiple scales using experiments and computation.
  • * To discuss theories relevant to complex biological systems and identify limitations of current approaches.
  • * To recommend data generation practices and strategies for integrating diverse data types.

Main Methods:

  • * Development of a new computational and experimental framework for multi-scale biological systems analysis.
  • * Review and discussion of existing theories and methodologies in systems biology.
  • * Identification of best practices for data generation and integration.
  • * Exploration of the role of big data analytics and artificial intelligence (AI).

Main Results:

  • * A proposed framework to link vast datasets across different scales and data types for biological research.
  • * Recommendations for improving data generation and ensuring model transparency and compatibility with existing biological theories.
  • * Emphasis on the potential of big data analytics and AI to advance biological understanding.
  • * Identification of sociological barriers alongside technological ones in addressing biological challenges.

Conclusions:

  • * Integrating experimental and computational approaches, aided by big data analytics and AI, is crucial for tackling complex biological challenges.
  • * The proposed framework facilitates a more holistic understanding of biological systems across scales.
  • * Overcoming both technological and sociological barriers, including fostering interdisciplinary collaboration, is essential for scientific progress.