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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

Minimal subsets of stable motifs associated to each helper T cell subtype.Each stable motif is enclosed by a colored rectangle, and motifs which are part of the same minimal subset have their enclosing rectangles touching each other. The node colors denotes their respective node states in the stable motifs: gray for 0 and black for 1. The color of the rectangle enclosing each stable motif corresponds to the respective color of that motif in Fig 5.
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pcbi.1004193.g006: Minimal subsets of stable motifs associated to each helper T cell subtype.Each stable motif is enclosed by a colored rectangle, and motifs which are part of the same minimal subset have their enclosing rectangles touching each other. The node colors denotes their respective node states in the stable motifs: gray for 0 and black for 1. The color of the rectangle enclosing each stable motif corresponds to the respective color of that motif in Fig 5.

Mentions: We obtain 17 stable motifs, each of which is shown in Fig 5 with a different node/edge color, and a stable motif succession diagram composed of 697 sequences. Despite the large size of the succession diagram, a closer look at it gives a simple interpretation: the stable motifs associated with each attractor regulate the characteristic transcription factor of each helper T cell subtype. To check this, we look at the minimal subsets of stable motifs that are sufficient for a sequence to lead to a single differentiated helper T cell subtype (see Fig 6 and S5 Text). We use the stable motif succession diagram and our stable motif control and stable motif blocking strategies to find intervention targets for each helper T cell subtype (see Table 2).


Cell fate reprogramming by control of intracellular network dynamics.

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

Minimal subsets of stable motifs associated to each helper T cell subtype.Each stable motif is enclosed by a colored rectangle, and motifs which are part of the same minimal subset have their enclosing rectangles touching each other. The node colors denotes their respective node states in the stable motifs: gray for 0 and black for 1. The color of the rectangle enclosing each stable motif corresponds to the respective color of that motif in Fig 5.
© Copyright Policy
Related In: Results  -  Collection

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

pcbi.1004193.g006: Minimal subsets of stable motifs associated to each helper T cell subtype.Each stable motif is enclosed by a colored rectangle, and motifs which are part of the same minimal subset have their enclosing rectangles touching each other. The node colors denotes their respective node states in the stable motifs: gray for 0 and black for 1. The color of the rectangle enclosing each stable motif corresponds to the respective color of that motif in Fig 5.
Mentions: We obtain 17 stable motifs, each of which is shown in Fig 5 with a different node/edge color, and a stable motif succession diagram composed of 697 sequences. Despite the large size of the succession diagram, a closer look at it gives a simple interpretation: the stable motifs associated with each attractor regulate the characteristic transcription factor of each helper T cell subtype. To check this, we look at the minimal subsets of stable motifs that are sufficient for a sequence to lead to a single differentiated helper T cell subtype (see Fig 6 and S5 Text). We use the stable motif succession diagram and our stable motif control and stable motif blocking strategies to find intervention targets for each helper T cell subtype (see Table 2).

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