Decoding early myelopoiesis from dynamics of core endogenous network

Area of Science:

• Systems Biology
• Computational Biology
• Developmental Biology

Background:

• Understanding complex biological dynamics like cancer genesis from a network perspective was previously considered challenging.
• Early myelopoiesis (blood cell formation) has a known roadmap, but its underlying dynamical mechanisms were poorly understood.
• Experimental challenges included conflicting hematopoietic roadmaps and cell-fate inter-conversion events.

Purpose of the Study:

• To investigate the connection between normal biological processes and network dynamics using early myelopoiesis as a model.
• To construct a molecular network model for early myeloid cell-fate determination.
• To explain cell phenotypes, developmental routes, and heterogeneity in myeloid progenitors through network dynamics.

Main Methods:

• Constructed a core molecular endogenous network based on gene regulation and signal transduction knowledge.
• Translated the network into a set of dynamical equations for computational analysis.
• Identified structurally robust states and the transitions between them.

Main Results:

• Identified several computationally robust states corresponding to known myeloid cell phenotypes.
• Revealed developmental routes connecting these stable states, forming a multi-stable state landscape.
• Explained existing myeloid cell phenotypes, the standard roadmap, and recent challenging observations mechanistically.
• Predicted additional cell states and developmental routes, including non-sequential and cross-branch transitions.

Conclusions:

• Endogenous network dynamics provide an integrated quantitative framework for understanding myeloid progenitor heterogeneity and lineage commitment.
• The multi-stable state landscape offers a mechanistic explanation for cell-fate diversification.
• The model's predictive power allows for testable experimental validation of novel developmental routes.