Statistical control of structural networks with limited interventions to minimize cellular phenotypic diversity
Jongwan Kim1, Corbin Hopper1, Kwang-Hyun Cho2
1Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, Republic of Korea.
Abstract:
The underlying genetic networks of cells give rise to diverse behaviors known as phenotypes. Control of this cellular phenotypic diversity (CPD) may reveal key targets that govern differentiation during development or drug resistance in cancer. This work establishes an approach to control CPD that encompasses practical constraints, including model limitations, the number of simultaneous control targets, which targets are viable for control, and the granularity of control. Cellular networks are often limited to the structure of interactions, due to the practical difficulty of modeling interaction dynamics. However, these dynamics are essential to CPD. In response, our statistical control approach infers the CPD directly from the structure of a network, by considering an ensemble average function over all possible Boolean dynamics for each node in the network. These ensemble average functions are combined with an acyclic form of the network to infer the number of point attractors. Our approach is applied to several known biological models and shown to outperform existing approaches. Statistical control of CPD offers a new avenue to contend with systemic processes such as differentiation and cancer, despite practical limitations in the field.
Insights
This study introduces a novel statistical control method to manage cellular phenotypic diversity (CPD) by inferring it from network structure. This approach addresses practical limitations and offers new strategies for controlling cell differentiation and cancer drug resistance.
Area of Science:
- Systems Biology
- Computational Biology
- Genetics
Background:
- Cellular phenotypic diversity (CPD) arises from complex genetic networks.
- Controlling CPD is crucial for understanding development and cancer progression, including differentiation and drug resistance.
- Existing methods face limitations in modeling dynamics and identifying control targets.
Purpose of the Study:
- To develop a practical approach for controlling cellular phenotypic diversity (CPD).
- To address constraints such as model limitations, control target number, viability, and granularity.
- To infer CPD directly from network structure, accounting for network dynamics.
Main Methods:
- A statistical control approach infers CPD from network structure.
- Utilizes an ensemble average function over all possible Boolean dynamics for each network node.
- Combines ensemble average functions with an acyclic network form to determine point attractors.
Main Results:
- The proposed method successfully infers CPD from network structure.
- The approach outperforms existing methods on several biological models.
- Demonstrates the feasibility of statistical control for CPD despite practical limitations.
Conclusions:
- Statistical control of CPD provides a new strategy for managing complex biological processes.
- This method offers a pathway to target key mechanisms in cell differentiation and cancer.
- The approach is robust and applicable even with incomplete knowledge of network dynamics.
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