Char act erizing beha viour al differentiation in g ene regulat ory netw orks with r epr esentation graphs

We introduce the formal notion of representation graphs, encapsulating the state space str uct ure of gene regulatory network models in a compact and concise form that highlights the most significant features of st able st ates and differentiation processes leading to distinct st abilit y regions. The concept has been de v eloped in the context of a hybrid system-based gene network modelling framework; however, we anticipate that it can also be adapted to other approaches of modelling gene networks in discrete terms. We describe a practical algorithm for representation graph computation as well as two case studies demonstrating their real-world application and utility. The first case study presents models for three phage viruses. It shows that the process of differentiation into lytic and lysogenic behavioural states for all these models is described by the same representation graph despite the distinctive underlying mechanisms for differentiation. The second case study shows the advantages of our approach for modelling the process of myeloid cell differentiation from a common progenitor into different cell types. Both case studies also demonstrate the potential of the representation graph approach for deriving and validating hypotheses about regulatory interactions that must be satisfied for biologically viable behaviours.