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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 example in Fig 1.The stable motif succession diagram shows the stable motifs obtained successively during the attractor finding process and the attractors they finally lead to. A more detailed representation of the first steps of the attractor finding method is shown in S1 Fig. Nodes are colored based on their respective node states in the motifs or the attractors: gray for 0 and black for 1. The four stable motifs of the original logical network and their matching node states are shown in the leftmost part of the figure. The attractors obtained for each possible sequence of stable motifs are shown in the rightmost part of the figure. The result of applying network reduction using a stable motif is represented by each dashed arrow. If network reduction due to a stable motif leads to a simplified network with at least one stable motif, then the dashed arrows point from the stable motif being considered to the stable motifs of the simplified network. Otherwise, network reduction leads directly to an attractor and the dashed arrow points towards the attractor.
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pcbi.1004193.g002: Stable motif succession diagram for the example in Fig 1.The stable motif succession diagram shows the stable motifs obtained successively during the attractor finding process and the attractors they finally lead to. A more detailed representation of the first steps of the attractor finding method is shown in S1 Fig. Nodes are colored based on their respective node states in the motifs or the attractors: gray for 0 and black for 1. The four stable motifs of the original logical network and their matching node states are shown in the leftmost part of the figure. The attractors obtained for each possible sequence of stable motifs are shown in the rightmost part of the figure. The result of applying network reduction using a stable motif is represented by each dashed arrow. If network reduction due to a stable motif leads to a simplified network with at least one stable motif, then the dashed arrows point from the stable motif being considered to the stable motifs of the simplified network. Otherwise, network reduction leads directly to an attractor and the dashed arrow points towards the attractor.

Mentions: As an illustration, consider the logical network shown in Fig 1(a). This logical network has four stable motifs (Fig 1(b)): (i) {A = 1, B = 1}, (ii) {A = 0}, (iii) {E = 1}, and (iv) {C = 1, D = 1, E = 0}. Network reduction for each of these stable motif yields four reduced networks, each of which has its own stable motifs, all of which are shown in S1 Fig. For example, the reduced logical network obtained from the first stable motif consists of two nodes (D and E) and has two stable motifs: {E = 1} and {E = 0}. The stable motifs of the remaining three reduced logical networks are, respectively: {E = 1} and {D = 1}; {A = 1, B = 1} and {A = 0}; {A = 1} and {A = 0}. Repeating the same network reduction procedure with each of the new stable motifs leads to either a new reduced network or one of four attractors (𝒜i,i = 1,…,4). The stable motifs obtained from the original network and from each reduced network, and the attractors they lead to are shown in Fig 2. This diagram is a compressed representation of the successive steps of the attractor finding process, which include the original network, the stable motifs of the original network, the reduced networks obtained for each stable motif, the stable motifs of these reduced networks, and so on (see S1 Fig). We refer to such a diagram as a stable motif succession diagram, and we note that it is closely analogous to a cell fate decision diagram. We propose to use this stable motif succession diagram to guide the system to an attractor of interest.


Cell fate reprogramming by control of intracellular network dynamics.

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

Stable motif succession diagram for the example in Fig 1.The stable motif succession diagram shows the stable motifs obtained successively during the attractor finding process and the attractors they finally lead to. A more detailed representation of the first steps of the attractor finding method is shown in S1 Fig. Nodes are colored based on their respective node states in the motifs or the attractors: gray for 0 and black for 1. The four stable motifs of the original logical network and their matching node states are shown in the leftmost part of the figure. The attractors obtained for each possible sequence of stable motifs are shown in the rightmost part of the figure. The result of applying network reduction using a stable motif is represented by each dashed arrow. If network reduction due to a stable motif leads to a simplified network with at least one stable motif, then the dashed arrows point from the stable motif being considered to the stable motifs of the simplified network. Otherwise, network reduction leads directly to an attractor and the dashed arrow points towards the attractor.
© Copyright Policy
Related In: Results  -  Collection

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

pcbi.1004193.g002: Stable motif succession diagram for the example in Fig 1.The stable motif succession diagram shows the stable motifs obtained successively during the attractor finding process and the attractors they finally lead to. A more detailed representation of the first steps of the attractor finding method is shown in S1 Fig. Nodes are colored based on their respective node states in the motifs or the attractors: gray for 0 and black for 1. The four stable motifs of the original logical network and their matching node states are shown in the leftmost part of the figure. The attractors obtained for each possible sequence of stable motifs are shown in the rightmost part of the figure. The result of applying network reduction using a stable motif is represented by each dashed arrow. If network reduction due to a stable motif leads to a simplified network with at least one stable motif, then the dashed arrows point from the stable motif being considered to the stable motifs of the simplified network. Otherwise, network reduction leads directly to an attractor and the dashed arrow points towards the attractor.
Mentions: As an illustration, consider the logical network shown in Fig 1(a). This logical network has four stable motifs (Fig 1(b)): (i) {A = 1, B = 1}, (ii) {A = 0}, (iii) {E = 1}, and (iv) {C = 1, D = 1, E = 0}. Network reduction for each of these stable motif yields four reduced networks, each of which has its own stable motifs, all of which are shown in S1 Fig. For example, the reduced logical network obtained from the first stable motif consists of two nodes (D and E) and has two stable motifs: {E = 1} and {E = 0}. The stable motifs of the remaining three reduced logical networks are, respectively: {E = 1} and {D = 1}; {A = 1, B = 1} and {A = 0}; {A = 1} and {A = 0}. Repeating the same network reduction procedure with each of the new stable motifs leads to either a new reduced network or one of four attractors (𝒜i,i = 1,…,4). The stable motifs obtained from the original network and from each reduced network, and the attractors they lead to are shown in Fig 2. This diagram is a compressed representation of the successive steps of the attractor finding process, which include the original network, the stable motifs of the original network, the reduced networks obtained for each stable motif, the stable motifs of these reduced networks, and so on (see S1 Fig). We refer to such a diagram as a stable motif succession diagram, and we note that it is closely analogous to a cell fate decision diagram. We propose to use this stable motif succession diagram to guide the system to an attractor of interest.

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