Target controllability in genetic networks of macrophage activation
Résumé
Macrophage cells play an important role in the Multiple Sclerosis (MS) disease. They are known to participate both to the degen-erative process, myelin destruction, and to the regenerative one, coordinating remyelination. We focused on two activation statesof the macropaghes: ‘alert’, which senses the environment, and ‘pro-inflammatory’, which is entitled of the cell defense againstexternal agents. The correct genetic activation of macrophage phenotypes permits a correct remyelinating response [1], thus thepossibility to steer it towards an healthy state while acting on a limited number of genes (drivers) would be greatly advantageous.We modeled macrophage activation as a network (Figure 1.a), whereNnodes correspond to genes involved in inflammationand directed links correspond to significant influences (inhibition or activation) as retrieved from macrophages.com [2] activationpathways. To enhance interpretation, genes were assigned to four different categories, according to their position inside a cell.We assumed a linear time invariant dynamics and modeled our problem in a target controllability framework [3]. Each gene istested as a driver and target nodes are the 19 genes for which the difference in gene expression between the two states of the macr-pohage cells is most significant(p<0.05)for patients and controls (Figure 1.b). Since computing the rank of the controllabilitymatrix is ill-conditioned for a large network, it was not possible to test all target nodes at the same time. We computed thetargetcontrol centralityas the number of target nodes that can be controlled from a driver node, when the target are chosen as follows:•Step 0: the target set contains the first target node, the target control centrality is zero.•Step 1: build the subgraph to apply the Kalman criterion, nodes accessible from the driver that can reach the target set.•Step 2: check target controllability.–If the configuration is not controllable, discard the last target added to the target set and test the successive one;–else, include the successive target in the target set and increase by one the target control centrality.•Repeat steps 1 and 2 until the last target node is tested.Results showed that the driver nodesIRF7,PRKCDandSTAT1, known to be involved in the MS disease [4] and [5], can controlup to 7 target nodes. Our work is a preliminary step towards the identification of the genes influencing the inflammatory process ofmacrophages, which is a crucial mechanism in the MS’ disease
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