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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

Stable motif succession diagram for the T-LGL leukemia network.The color of the nodes denotes their respective node states in the stable motifs: gray for 0 and black for 1. The colored rectangle surrounding each stable motif corresponds to the respective color of the motif in Fig 3. There are two possible attractors for the system: the normal state of self-programmed cell death (apotosis) and the diseased state (T-LGL leukemia). The attractors obtained for each possible sequence of stable motifs are shown in the rightmost part of the figure.
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pcbi.1004193.g004: Stable motif succession diagram for the T-LGL leukemia network.The color of the nodes denotes their respective node states in the stable motifs: gray for 0 and black for 1. The colored rectangle surrounding each stable motif corresponds to the respective color of the motif in Fig 3. There are two possible attractors for the system: the normal state of self-programmed cell death (apotosis) and the diseased state (T-LGL leukemia). The attractors obtained for each possible sequence of stable motifs are shown in the rightmost part of the figure.

Mentions: We first use our attractor-finding method on the T-LGL leukemia network in the presence of the external signals Stimuli and IL15 to obtain the stable motifs and the succession diagram. The result is 7 different stable motifs, each of which is shown in Fig 3 with a different node/edge color (nodes and edges with multiple colors are part of several stable motifs). The stable motif succession diagram for the T-LGL network is shown in Fig 4. For simplicity we do not include the motifs associated with the node P2 in the succession diagram, as these motifs require the other stable motifs to influence the resulting attractor in the succession diagram.


Cell fate reprogramming by control of intracellular network dynamics.

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

Stable motif succession diagram for the T-LGL leukemia network.The color of the nodes denotes their respective node states in the stable motifs: gray for 0 and black for 1. The colored rectangle surrounding each stable motif corresponds to the respective color of the motif in Fig 3. There are two possible attractors for the system: the normal state of self-programmed cell death (apotosis) and the diseased state (T-LGL leukemia). The attractors obtained for each possible sequence of stable motifs are shown in the rightmost part of the figure.
© Copyright Policy
Related In: Results  -  Collection

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

pcbi.1004193.g004: Stable motif succession diagram for the T-LGL leukemia network.The color of the nodes denotes their respective node states in the stable motifs: gray for 0 and black for 1. The colored rectangle surrounding each stable motif corresponds to the respective color of the motif in Fig 3. There are two possible attractors for the system: the normal state of self-programmed cell death (apotosis) and the diseased state (T-LGL leukemia). The attractors obtained for each possible sequence of stable motifs are shown in the rightmost part of the figure.
Mentions: We first use our attractor-finding method on the T-LGL leukemia network in the presence of the external signals Stimuli and IL15 to obtain the stable motifs and the succession diagram. The result is 7 different stable motifs, each of which is shown in Fig 3 with a different node/edge color (nodes and edges with multiple colors are part of several stable motifs). The stable motif succession diagram for the T-LGL network is shown in Fig 4. For simplicity we do not include the motifs associated with the node P2 in the succession diagram, as these motifs require the other stable motifs to influence the resulting attractor in the succession diagram.

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