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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 T-LGL leukemia survival signaling network.The shape of the nodes indicates the cellular location or the type of nodes: rectangles indicate intracellular components, ellipses indicate extracellular components, diamonds indicate receptors, and hexagons represent conceptual nodes (Stimuli, Stimuli2, P2, Cytoskeleton signaling, Proliferation, and Apoptosis). Node colors are used to denote the different stable motifs of the network in the presence of the external signals Stimuli and IL15. 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 and its caption are adapted from [46].
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pcbi.1004193.g003: The T-LGL leukemia survival signaling network.The shape of the nodes indicates the cellular location or the type of nodes: rectangles indicate intracellular components, ellipses indicate extracellular components, diamonds indicate receptors, and hexagons represent conceptual nodes (Stimuli, Stimuli2, P2, Cytoskeleton signaling, Proliferation, and Apoptosis). Node colors are used to denote the different stable motifs of the network in the presence of the external signals Stimuli and IL15. 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 and its caption are adapted from [46].

Mentions: A Boolean network model of cytotoxic T cell signaling that reproduces the known experimental results of these T cells in the context of T-LGL leukemia was previously constructed by Zhang et al. [28]. This network model consists of 60 nodes and 142 regulatory edges, with the nodes representing genes, proteins, receptors, small molecules, external signals (e.g. Stimuli), or biological functions (e.g. Apoptosis). The T-LGL network is shown in Fig 3 and its logical functions are reproduced in S4 Text. Previous work by Zhang et al. [28] and Saadatpour et al. [46] has shown that in the sustained presence of the external signals IL15, PDGF, and Stimuli (antigen presentation) the system has two attractors: one that recapitulates the survival phenotype and node deregulations seen in T-LGL leukemia, and a second one that corresponds to self-programmed cell death (apoptosis) (see S4 Text for more details about attractor specification).


Cell fate reprogramming by control of intracellular network dynamics.

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

The T-LGL leukemia survival signaling network.The shape of the nodes indicates the cellular location or the type of nodes: rectangles indicate intracellular components, ellipses indicate extracellular components, diamonds indicate receptors, and hexagons represent conceptual nodes (Stimuli, Stimuli2, P2, Cytoskeleton signaling, Proliferation, and Apoptosis). Node colors are used to denote the different stable motifs of the network in the presence of the external signals Stimuli and IL15. 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 and its caption are adapted from [46].
© Copyright Policy
Related In: Results  -  Collection

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

pcbi.1004193.g003: The T-LGL leukemia survival signaling network.The shape of the nodes indicates the cellular location or the type of nodes: rectangles indicate intracellular components, ellipses indicate extracellular components, diamonds indicate receptors, and hexagons represent conceptual nodes (Stimuli, Stimuli2, P2, Cytoskeleton signaling, Proliferation, and Apoptosis). Node colors are used to denote the different stable motifs of the network in the presence of the external signals Stimuli and IL15. 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 and its caption are adapted from [46].
Mentions: A Boolean network model of cytotoxic T cell signaling that reproduces the known experimental results of these T cells in the context of T-LGL leukemia was previously constructed by Zhang et al. [28]. This network model consists of 60 nodes and 142 regulatory edges, with the nodes representing genes, proteins, receptors, small molecules, external signals (e.g. Stimuli), or biological functions (e.g. Apoptosis). The T-LGL network is shown in Fig 3 and its logical functions are reproduced in S4 Text. Previous work by Zhang et al. [28] and Saadatpour et al. [46] has shown that in the sustained presence of the external signals IL15, PDGF, and Stimuli (antigen presentation) the system has two attractors: one that recapitulates the survival phenotype and node deregulations seen in T-LGL leukemia, and a second one that corresponds to self-programmed cell death (apoptosis) (see S4 Text for more details about attractor specification).

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