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Cell fate reprogramming by control of intracellular network dynamics.

Zañudo JG, Albert R - PLoS Comput. Biol. (2015)

Bottom Line: Identifying control strategies for biological networks is paramount for practical applications that involve reprogramming a cell's fate, such as disease therapeutics and stem cell reprogramming.We illustrate our method's potential to find intervention targets for cancer treatment and cell differentiation by applying it to a leukemia signaling network and to the network controlling the differentiation of helper T cells.Moreover, several of the predicted interventions are supported by experiments.

View Article: PubMed Central - PubMed

Affiliation: Department of Physics, The Pennsylvania State University, University Park, Pennsylvania, United States of America.

ABSTRACT
Identifying control strategies for biological networks is paramount for practical applications that involve reprogramming a cell's fate, such as disease therapeutics and stem cell reprogramming. Here we develop a novel network control framework that integrates the structural and functional information available for intracellular networks to predict control targets. Formulated in a logical dynamic scheme, our approach drives any initial state to the target state with 100% effectiveness and needs to be applied only transiently for the network to reach and stay in the desired state. We illustrate our method's potential to find intervention targets for cancer treatment and cell differentiation by applying it to a leukemia signaling network and to the network controlling the differentiation of helper T cells. We find that the predicted control targets are effective in a broad dynamic framework. Moreover, several of the predicted interventions are supported by experiments.

No MeSH data available.


Related in: MedlinePlus

The helper T cell differentiation network.The nodes that encode the environmental conditions (APC = ON, TGFB_e = ON, IL2_e = ON) are located in the upper part of the network diagram. Node colors are used to denote the different stable motifs of the network in the used environmental conditions. Nodes and edges with multiple colors are part of several stable motifs. An arrowhead or a short perpendicular bar at the end of an edge indicates activation or inhibition, respectively. This figure is adapted from [48].
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pcbi.1004193.g005: The helper T cell differentiation network.The nodes that encode the environmental conditions (APC = ON, TGFB_e = ON, IL2_e = ON) are located in the upper part of the network diagram. Node colors are used to denote the different stable motifs of the network in the used environmental conditions. Nodes and edges with multiple colors are part of several stable motifs. An arrowhead or a short perpendicular bar at the end of an edge indicates activation or inhibition, respectively. This figure is adapted from [48].

Mentions: A logical network model of the regulatory and signaling pathways controlling helper T cell activation and differentiation was constructed by Naldi et al. [48]. This network model has several attractors, which correspond to the known canonical helper T cell subtypes, and also to some hybrid cell types (see [48] and S5 Text). The reachability of each attractor depends on the presence of several external environmental signals (either cytokines or antigen), which are represented as input nodes in the network. For our study we use one of the environmental conditions studied by Naldi et al. (TGFB_e = ON, IL2_e = ON, and APC = ON) [48] because it allows us to explore control targets for all T cell subtypes. The helper T cell differentiation network under the selected environmental conditions consists of 55 nodes and 121 edges and is shown in Fig 5. Its corresponding logical functions are reproduced in S5 Text.


Cell fate reprogramming by control of intracellular network dynamics.

Zañudo JG, Albert R - PLoS Comput. Biol. (2015)

The helper T cell differentiation network.The nodes that encode the environmental conditions (APC = ON, TGFB_e = ON, IL2_e = ON) are located in the upper part of the network diagram. Node colors are used to denote the different stable motifs of the network in the used environmental conditions. Nodes and edges with multiple colors are part of several stable motifs. An arrowhead or a short perpendicular bar at the end of an edge indicates activation or inhibition, respectively. This figure is adapted from [48].
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4388852&req=5

pcbi.1004193.g005: The helper T cell differentiation network.The nodes that encode the environmental conditions (APC = ON, TGFB_e = ON, IL2_e = ON) are located in the upper part of the network diagram. Node colors are used to denote the different stable motifs of the network in the used environmental conditions. Nodes and edges with multiple colors are part of several stable motifs. An arrowhead or a short perpendicular bar at the end of an edge indicates activation or inhibition, respectively. This figure is adapted from [48].
Mentions: A logical network model of the regulatory and signaling pathways controlling helper T cell activation and differentiation was constructed by Naldi et al. [48]. This network model has several attractors, which correspond to the known canonical helper T cell subtypes, and also to some hybrid cell types (see [48] and S5 Text). The reachability of each attractor depends on the presence of several external environmental signals (either cytokines or antigen), which are represented as input nodes in the network. For our study we use one of the environmental conditions studied by Naldi et al. (TGFB_e = ON, IL2_e = ON, and APC = ON) [48] because it allows us to explore control targets for all T cell subtypes. The helper T cell differentiation network under the selected environmental conditions consists of 55 nodes and 121 edges and is shown in Fig 5. Its corresponding logical functions are reproduced in S5 Text.

Bottom Line: Identifying control strategies for biological networks is paramount for practical applications that involve reprogramming a cell's fate, such as disease therapeutics and stem cell reprogramming.We illustrate our method's potential to find intervention targets for cancer treatment and cell differentiation by applying it to a leukemia signaling network and to the network controlling the differentiation of helper T cells.Moreover, several of the predicted interventions are supported by experiments.

View Article: PubMed Central - PubMed

Affiliation: Department of Physics, The Pennsylvania State University, University Park, Pennsylvania, United States of America.

ABSTRACT
Identifying control strategies for biological networks is paramount for practical applications that involve reprogramming a cell's fate, such as disease therapeutics and stem cell reprogramming. Here we develop a novel network control framework that integrates the structural and functional information available for intracellular networks to predict control targets. Formulated in a logical dynamic scheme, our approach drives any initial state to the target state with 100% effectiveness and needs to be applied only transiently for the network to reach and stay in the desired state. We illustrate our method's potential to find intervention targets for cancer treatment and cell differentiation by applying it to a leukemia signaling network and to the network controlling the differentiation of helper T cells. We find that the predicted control targets are effective in a broad dynamic framework. Moreover, several of the predicted interventions are supported by experiments.

No MeSH data available.


Related in: MedlinePlus