Personalized logical models to investigate cancer response to BRAF treatments in melanomas and colorectal cancers
Fig 2
Logical modeling principles and personalization.
(A) A logical model with three nodes: the regulatory graph, the corresponding logical rules and the transition rates as used in MaBoSS [31]. (B) Part of the state transition graph with the two possible transitions resulting from the given initial conditions and the probabilities of choosing stochastically one of them. (C) Schematic representation of a logical model simulation with MaBoSS: average trajectory obtained from the mean of many individual stochastic trajectories. (D) Personalization with discrete data (e.g., mutations) with some nodes forced to 0 based on loss of function alteration (left) or 1 based on gain of function/constitutive activation (right). (E) Personalization with continuous data used to define the initial conditions of nodes and to influence the transitions rates and the subsequent probabilities of transition in asynchronous update; the graph on the left represents the normalized values of genes A, B and C for patients 1, 2 and N; the right side represents the personalization of logical model using values from patient N (red profile), first defining the initial probabilities of node activation (middle) and then influencing the probabilities of transitions from one state to another (right): here, since gene A is highly expressed in the red patient, the probability of activation of the corresponding node is increased (resp. probability of inhibition is decreased for gene B).