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Figure 1 - A Method to Identify and Analyze Biological Programs through Automated Reasoning

Figure 1: The RE:IN (Reasoning Engine for Interaction Networks) methodology, illustrated by example. First, critical network components must be identified: genes A, B and C are critical regulators of a given cell state, while S1 and S2 are input signals (panel 1). Components can be active or inactive, to fit a Boolean formalism. Second, definite and possible interactions should be defined (panel 2): S1 activates A (solid arrow), B may activate C (dashed arrow). These define the topology of an abstract network, which describes 24=16 unique, concrete networks, in which each possible interaction is present or not (panel 3). By combining this topology with known or hypothesized regulation conditions at each node (panel 4), we characterize an Abstract Boolean Network (ABN, panel 5). Next, experimental observations are encoded as constraints on state trajectories (panel 6). A constrained Abstract Boolean Network (cABN) defines an ABN together with the constraints describing system observations, thus integrating available knowledge describing the structure, dynamics and observed behavior of the process (panel 7). We can enumerate the concrete models that satisfy these constraints (panel 8). In addition, we can use the cABN to formulate predictions (panel 9): to identify minimal networks, which have the fewest optional interactions instantiated (concrete model 2, panel 8), as well as required (or disallowed) interactions that are present in all (none) concrete models. We can also study genetic perturbations. Once predictions have been tested experimentally (panel 10), they can be added to the set of experimental constraints. If no concrete models are identified, then the process is iterated, starting by re-examining our assumptions about components, interactions, dynamics and behavior.
The RE:IN (Reasoning Engine for Interaction Networks) methodology, illustrated by example. First, critical network components must be identified: genes A, B and C are critical regulators of a given cell state, while S1 and S2 are input signals (panel 1). Components can be active or inactive, to fit a Boolean formalism. Second, definite and possible interactions should be defined (panel 2): S1 activates A (solid arrow), B may activate C (dashed arrow). These define the topology of an abstract network, which describes 24=16 unique, concrete networks, in which each possible interaction is present or not (panel 3). By combining this topology with known or hypothesized regulation conditions at each node (panel 4), we characterize an Abstract Boolean Network (ABN, panel 5). Next, experimental observations are encoded as constraints on state trajectories (panel 6). A constrained Abstract Boolean Network (cABN) defines an ABN together with the constraints describing system observations, thus integrating available knowledge describing the structure, dynamics and observed behavior of the process (panel 7). We can enumerate the concrete models that satisfy these constraints (panel 8). In addition, we can use the cABN to formulate predictions (panel 9): to identify minimal networks, which have the fewest optional interactions instantiated (concrete model 2, panel 8), as well as required (or disallowed) interactions that are present in all (none) concrete models. We can also study genetic perturbations. Once predictions have been tested experimentally (panel 10), they can be added to the set of experimental constraints. If no concrete models are identified, then the process is iterated, starting by re-examining our assumptions about components, interactions, dynamics and behavior.
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