Cell fate reprogramming by control of intracellular network dynamics

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.Comment: 61 pages (main text, 15 pages; supporting information, 46 pages) and 12 figures (main text, 6 figures; supporting information, 6 figures). In revie

Stable motifs of a logical (Boolean) network.

<p>(a) An example of a logical network indicating the regulatory relationships and the logical update function of each node. (b) The four stable motifs of the logical network in (a) and their corresponding node states. These stable motifs are strongly connected components and partial fixed points of the logical network.</p

Experimental support for successful control targets in Tables 1 and 2.

<p>Experimental support for successful control targets in Tables <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004193#pcbi.1004193.t001" target="_blank">1</a> and <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004193#pcbi.1004193.t002" target="_blank">2</a>.</p

Intervention targets for each control strategy in the T-LGL leukemia network model.

<p>Intervention targets for each control strategy in the T-LGL leukemia network model.</p

Minimal subsets of stable motifs associated to each helper T cell subtype.

<p>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 <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004193#pcbi.1004193.g005" target="_blank">Fig 5</a>.</p

The helper T cell differentiation network.

<p>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 [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004193#pcbi.1004193.ref048" target="_blank">48</a>].</p

Intervention targets for each control strategy in the helper T cell network.

<p>Intervention targets for each control strategy in the helper T cell network.</p