Low dimensionality of phenotypic space as an emergent property of coordinated teams in biological regulatory networks
Kishore Hari,
Pradyumna Harlapur,
Aashna Saxena,
Kushal Haldar,
Aishwarya Girish,
Tanisha Malpani,
Herbert Levine,
Mohit Kumar Jolly
Affiliations
Kishore Hari
Department of Bioengineering, Indian Institute of Science, Bengaluru, Karnataka 560012, India; Center for Theoretical Biological Physics, Northeastern University, Boston, MA 02115, USA; Department of Physics, Northeastern University, Boston, MA 02115, USA; Corresponding author
Pradyumna Harlapur
Department of Bioengineering, Indian Institute of Science, Bengaluru, Karnataka 560012, India
Aashna Saxena
Department of Bioengineering, Indian Institute of Science, Bengaluru, Karnataka 560012, India
Kushal Haldar
Department of Bioengineering, Indian Institute of Science, Bengaluru, Karnataka 560012, India; Indian Institute of Science Education and Research Kolkata, Kolkata, West Bengal 741246, India
Aishwarya Girish
Department of Bioengineering, Indian Institute of Science, Bengaluru, Karnataka 560012, India
Tanisha Malpani
Department of Bioengineering, Indian Institute of Science, Bengaluru, Karnataka 560012, India
Herbert Levine
Center for Theoretical Biological Physics, Northeastern University, Boston, MA 02115, USA; Department of Physics, Northeastern University, Boston, MA 02115, USA; Corresponding author
Mohit Kumar Jolly
Department of Bioengineering, Indian Institute of Science, Bengaluru, Karnataka 560012, India; Corresponding author
Summary: Cell-fate decisions involve coordinated genome-wide expression changes, typically leading to a limited number of phenotypes. Although often modeled as simple toggle switches, these rather simplistic representations often disregard the complexity of regulatory networks governing these changes. Here, we unravel design principles underlying complex cell decision-making networks in multiple contexts. We show that the emergent dynamics of these networks and corresponding transcriptomic data are consistently low-dimensional, as quantified by the variance explained by principal component 1 (PC1). This low dimensionality in phenotypic space arises from extensive feedback loops in these networks arranged to effectively enable the formation of two teams of mutually inhibiting nodes. We use team strength as a metric to quantify these feedback interactions and show its strong correlation with PC1 variance. Using artificial networks of varied topologies, we also establish the conditions for generating canalized cell-fate landscapes, offering insights into diverse binary cellular decision-making networks.
