Predictive biology is elusive because rigorous, data-constrained, mechanistic models of complex biological systems are difficult to derive and validate. Current approaches tend to construct and examine static interaction network models, which are descriptively rich, but often lack explanatory and predictive power, or dynamic models that can be simulated to reproduce known behavior. However, in such approaches implicit assumptions are introduced as typically only one mechanism is considered, and exhaustively investigating all scenarios is impractical using simulation. To address these limitations, we present a methodology based on automated formal reasoning, which permits the synthesis and analysis of the complete set of logical models consistent with experimental observations. We test hypotheses against all candidate models, and remove the need for simulation by characterizing and simultaneously analyzing all mechanistic explanations of observed behavior. Our methodology transforms knowledge of complex biological processes from sets of possible interactions and experimental observations to precise, predictive biological programs governing cell function. Interaction network diagrams, now used widely to represent biological systems by mapping components  and the possible molecular interactions between them, are a prime example of this challenge. In the absence of an accompanying hypothesis of dynamics and information flow, these maps provide a rich description of the complexity of biological systems, but usually do not confer any explanatory or predictive power.3 where the connectivity of a set of components represents excitatory or inhibitory molecular interactions, and logical update functions abstract the involved regulation mechanisms. This allows the dynamical behavior of the system to be studied without the need for detailed biochemical descriptions, which require hard-to-measure kinetic parameters , making the logical modeling formalism an attractive alternative to continuous models.In an effort to address such shortcomings, both continuous and discrete mathematical approaches have been applied to capture and investigate the dynamics of interaction networks , while a complete state-space exploration becomes infeasible as models increase in size.Logical models are typically constructed through a combination of manual effort and computational techniques,To address the limitations of such existing approaches, we have developed a methodology that uses automated reasoning  to transform a description of the critical components, possible interactions and hypothesized regulation rules of a biological process into a dynamic, mechanistic explanation of experimentally observed behavior. Our computational approach allows a large number of possible mechanistic hypotheses and experimental results to be considered simultaneously. Furthermore, it permits experimentally testable predictions of biological behavior to be made that have yet to be experimentally observed, based on all mechanisms consistent with experimental evidence, limiting the bias and implicit assumptions introduced when considering only a single model.7 The results from applying our approach indicated that the most parsimonious explanation of complex biological behavior can be understood not in terms of prevailing descriptions of a static network, but in terms of a precise, molecular program governing cellular decision making: a minimal set of functional components, interconnected with and regulating each other according to rules that confer to the system the capacity to process input stimuli to compute and output a biological function reliably and robustly.We applied this methodology to the analysis of mouse embryonic stem cell (mESC) self-renewal to derive a highly predictive explanation of known behavior based on simple regulation rules and an unexpectedly small number of key components and interactions, compared with vast interactome diagrams.8–14 a complete framework for the definition, synthesis and analysis of biological programs is missing. Our methodology is designed to identify and analyze such programs, thus advancing the field not only beyond existing techniques, but also beyond prevailing paradigms of thinking in biological science.We propose that a rigorous, formal definition and representation (model) of a biological program, which captures dynamic information-processing steps over time while recapitulating observed biological behavior, is better suited for explaining and predicting cellular (or bio-molecular) processes compared with vast but static interaction network diagrams. Despite the recent progress in studying dynamic interaction networks,11 our analysis procedures allow us to examine network robustness while avoiding exhaustive simulation sweeps, as well as to establish the requirement for certain interactions, and to predict how the cell cycle is disrupted by genetic perturbations; for myeloid progenitor differentiation,12 we predict the requirement for interactions and input signals not previously considered; and for cardiac development,15 we predict critical interactions omitted from current models and validate these predictions using results from the literature.Here for the first time, we present our methodology and its theoretical basis, to allow domain experts to apply the technique to their systems of study. We consider three distinct biological systems, and through comparison with studies that utilize existing analysis methodologies, we show how our approach forces us to draw new conclusions to those of the original investigators. For the cell cycle in budding yeast,We use a simple, demonstrative example, summarized in 7 which cell type to differentiate into,15 or whether to undergo division.11 This can be captured by the state of the network components.First, the input and critical components of the biological process must be defined, together with its output, which represents the biological decision to be explained , panel 1When selecting the initial set of critical components to include, they should be functionally relevant: only those that have a substantial effect on the process under study when inactivated or overactivated. Various combinations of genes, proteins, protein complexes, non-coding RNAs, metabolites and signaling molecules can be considered, identified by literature search or genetic screens. This set can be revised if model refinement is required (see below).17Within logical modeling, variables take a discrete number of states. Here we abstract the activity of each component to two possible states: ON, representing a gene that is actively expressed at endogenous levels, a transcription factor (TF) present in high enough concentrations to be functional, or a protein in its active conformational form, and OFF otherwise. While gene regulation and signaling pathways are not always digital, they have been successfully treated as Boolean values in several instances, e.g. as markers of cellular states or genes active during specific phases of the cell cycle.Second, potential interactions between components should be identified, which must have both sign  and direction (panel 2). An interaction could represent the direct binding of a TF (source) to the promoter of a downstream gene (target) or a post-transcriptional modification of the gene’s product, and can be inferred from a range of data types . Interacin vitro assays are generally accepted as strong evidence for a given interaction. As the absence of an interaction is as strong an assumption as defining one to be definite, possible interactions should be used if there is uncertainty. In the absence of sufficient experimental evidence, it is possible to consider interactions between all components as possible.Interactions are classed either as definite, if supported by multiple sets of reliable experimental evidence, or possible, to indicate the option of a putative interaction. For example, for transcriptional regulation, it is generally accepted that measuring gene expression shortly after a genetic or chemical perturbation allows secondary effects to be ruled out, but chromatin immunoprecipitation or promoter assays should also be used to further support a direct interaction before labeling it as definite. For post-translation modification, mutagenesis of individual residues and 4=16 unique, concrete topologies, in which each possible interaction is present or not.Altogether, the set of interactions define an abstract network topology (panel 3), so called because an abstract network with 4 possible interactions generates 210 and, importantly, require knowledge of the exact network topology. Instead, we generated a set of 20 biologically meaningful regulation conditions that are compatible with all topologies defined by the abstract network.18 We achieve this by defining rules according to whether none, some or all of a components activators/repressors are present. The complete set of update functions, which are consistent with several assumptions, are defined together with a threshold rule .11 If prior experimental evidence can eliminate one or more regulation mechanism for a given component, for example that a component requires at least one activator in order to switch on, then a subset of these regulation conditions can be assigned in accordance with model assumptions. The overall network can be updated synchronously  or asynchronously (one component per step). As the update functions we consider are deterministic, synchronous updates lead to deterministic behavior, while asynchronous updates lead to non-determinism due to the sequence of component updates.The next step is to augment the static network topology with information that determines transitions between system states, using logical rules that describe how each component updates in response to the state of its regulators (panel 4). We remove the need to specify individual update functions in the network, which are often difficult to elucidate19 as the state of each component is represented by a Boolean value, but not all interactions and regulation conditions are instantiated. In contrast, a concrete network includes only definite interactions, and a single regulation condition per component, and can be viewed as a BN.By defining the set of critical components, possible and definite interactions (the abstract network topology) together with the allowable regulation conditions, we construct an Abstract Boolean Network (ABN): a formal representation that defines the possible structure and dynamics of unique, concrete networks (panel 5). The ABN thus encodes all possible mechanisms that could potentially explain experimental observations. ABNs generalize the concept of Boolean Networks (BNs)We seek the set of concrete networks from the ABN that are consistent with experimental observations, which are derived both from new data and the literature, and are encoded by specifying the states of some or all of the components along unique trajectories of the system (panel 6). This introduces restrictions on the choice of possible interactions and regulation conditions assigned to each component, to ensure all observations are satisfied. When a network satisfies all observations, as part of the solution, a complete trajectory  is identified for each constraint as a demonstration and potential explanation of how the expected behavior can be realized.Observations can describe the change in system behavior under different inputs, or under genetic manipulations, by defining initial and subsequent cellular states. In the simple example in A constrained Abstract Boolean Network (cABN) is the formal representation of the ABN together with the constraints describing the observed behaviors of the system (panel 7). It thus represents all possible mechanisms, i.e., concrete topologies and regulation conditions, consistent with observed system behavior.21 implemented within the SMT solver Z322 . RE:IN is made freely available as a cloud-based application (rein.cloudapp.net), with examples and tutorials provided (research.microsoft.com/rein).The cABN description is grounded in logic and permits the application of automated reasoning. This is a powerful analysis strategy, where valid conclusions are drawn directly from the cABN definition through logical inference and efficient model finding algorithms. We encode this representation as a Satisfiability Modulo Theories (SMT) problem, in which logical expressions are constructed that define the possible combinations of interactions and regulation conditions, and the resulting network behaviors over time. This approach reflects how experimental observations might be interpreted manually given an interaction network diagram . We solve the SMT problem within a bespoke tool: the Reasoning Engine for Interaction Networks (RE:IN), which uses the bit-vector theory reasoning strategiesThe set of consistent networks can be enumerated and examined individually (panel 8) using RE:IN, which also identifies when no such networks exist, prompting us to re-examine our initial assumptions . Similarly, interactions that must be disallowed are those that if enforced as definite, would prevent the constraints from being satisfied. Note that if all outgoing interactions from a component are found to be disallowed, this reveals that the component is not required to behave as a regulator, and could be removed from the analysis if there is no additional biological evidence for its importance.Second, we can formulate predictions by determining whether a new hypothesis, encoded as an additional constraint, is satisfied by the cABN. We guarantee that the prediction is implied by all consistent mechanisms by showing that the converse of this constraint (the null hypothesis) is unsatisfiable. For example, we predict that inactivation of B in the presence of S2 and absence of S1 causes A and C to become inactive (panel 9). Indeed, useful insights are identified even when no prediction can be generated for a given query, as this signifies that some mechanisms support the hypothesis, and other mechanisms support the null hypothesis, suggesting a discriminating biological experiment to refine the set of models further.Note that, in general, the size (number of concrete models) of the cABN relates to its predictive capacity: increasing the number of possible interactions increases the number of concrete networks that can potentially produce different dynamic behavior, which in general, reduces the number of predictions that can be formulated. Interactions with less experimental support can be included as part of a model refinement process if no consistent models exist.Following experimental testing of predictions, novel biological knowledge can be incorporated as new experimental constraints (panel 10). Even if a prediction holds true it is recommended to add constraints explicitly capturing these new data before further expanding the cABN.To illustrate further the application and implementation of our methodology, we consider three separate biological systems, using models from the literature as a concise representation of the domain knowledge of critical components, interactions and behaviors. When starting from experimental data alone, domain experts can apply the workflow from et al.11 constructed a synchronous BN of 12 regulators, applying a threshold update function  to each component. The network is shown to recapitulate a trajectory through the temporally ordered phases of the cell cycle (without prescribing the exact step at which each phase is reached) upon perturbation of the stationary G1 phase, before returning to this stable state.To study the cell-cycle in budding yeast, Li 29=536, 870,912. By enumeration with RE:IN, we identified 4,480 consistent mechanisms, demonstrating that it is possible to remove interactions from the concrete network without compromising expected behavior. To infer this by simulation alone would require exhaustive, time-consuming trajectory sweeps.Encoding this concrete model in RE:IN confirms that it satisfies the cyclic constraint . HoweverFurthermore, we investigated which interactions are required to satisfy this constraint; a question that cannot easily be asked of a single, defined network. We identified that 11 of the possible interactions are required , which wWe also investigated the consequence of gene inactivation on cell cycle progression, testing whether the set of consistent models can complete the transitions between the cell cycle phases under perturbation. This allowed us to predict genes essential for cell cycle progression, and where the cycle might arrest. We predict at least one gene inactivation that will arrest each phase transition . All butHere we have demonstrated that alternative, simpler mechanisms are capable of producing the expected behavior of the cell cycle in budding yeast, and by encoding the model as a cABN, that it is robust to adaptations . This deet al.12 constructed an asynchronous BN of 11 regulators and 28 interactions based on the literature  were shown to be reachable from a common progenitor state. The gene expression pattern characterizing each attractor was shown to correlate with messenger RNA expression data obtained from erythrocyte, megakaryocyte, monocyte and granulocyte cells, with the exception of GATA-2 in megakaryocytes, which was defined as inactive in the model but observed experimentally as highly expressed.To model myeloid progenitor differentiation , Krumsieterature . By direWe first studied this proposed network topology . The spe12 no consistent models exist. This is not the case if every interaction is marked as possible, and under this scenario we identified that to reproduce the observed behavior, 15 interactions are required and 2 are disallowed  as possible. Identifying the minimal networks in this case reveals that the observations can be reproduced with only one additional interaction  that regulate haematopoiesis.25 To illustrate this, we considered two hypothetical signals (X and Y) that deterministically specify cell fate , which suggests that our approach led to the identification of plausible missing connections in the program governing cardiac development.To identify new potential interactions to resolve this inconsistency, we included all positive and negative interactions between the eleven components that were not included in the original BN as possible, while keeping the original interactions as definite. This assumes sufficient experimental evidence for the interactions identified by Herrmann ractions . There iractions ,26–32, w33 based on Answer Set Programming (ASP). The ASP approach focuses on optimization, and attempts to find the set of minimal networks that best reproduce observed behavior, with a tolerance parameter controlling network size that can be adjusted to generate sub-optimal solutions. Further details of this comparison are presented in We compared our methodology against two alternative approaches: a naive brute-force simulation strategy, and the Cell ASP Optimized (caspo) tool,For the simple cABN shown in Next, we considered deterministic myeloid differentiation with signals X and Y . AnalysiEven on this reduced model, brute-force simulation failed to identify a single valid model in over 5 days of computation, while RE:IN identified 2 minimal models in ~7 s . In contThe comparison of a brute-force, simulation-based search, an ASP-based tool and our SMT-based method highlights several important differences between approaches. First, while the brute-force approach can enumerate the entire set of concrete networks for small ABNs, this strategy quickly becomes unfeasible as non-deterministic choices  are introduced. In contrast to the ASP approach, which focuses on optimization, our approach focuses predominantly on checking whether consistent models exist. Further, we can use this technique to formulate predictions and test properties of cABNs, with enumeration of concrete models and minimal networks also supported. Thus, the identification of the entire set of minimal networks could be more expensive using RE:IN than caspo. However, our method provides direct strategies for incorporating prior knowledge, such as definite interactions or restrictions on regulation conditions, and supports richer observations, such as cyclic behavior (yeast cell cycle example). When certain constraints not easily incorporated in caspo are relaxed, the two approaches generate similar results, where small differences can be attributed to the richer Boolean update functions considered in caspo.We present a methodology for the synthesis and analysis of logical models as biological programs, in order to explain and predict cellular decision making. We employ interaction networks as the framework for explaining how computation is performed by a cell, where the critical components are variables of the biological program, which implicitly define the cell state. Interactions indicate the flow of information between components, dynamically constrained by logical regulation conditions. The framework enables us to provide a mechanistic explanation of how a cell translates input signals into a defined output, i.e., a decision. Crucially, we only consider models that fully recapitulate experimental observations, which are thus an integral and explicit part of the program definition that clearly define the biological behavior we seek to explain. As part of this methodology we define a cABN to be the formal representation of a biological program, and capture all mechanisms consistent with available knowledge.Our method is applicable to the study of a broad range of biological processes, and helps address a variety of biological questions. It enables a modeler or experimentalist starting from the experimental data alone to construct and analyze a cABN by representing the biological knowledge within our framework . By defi2 we focus on Boolean models, which provide sufficient expressive power to capture important system properties, while allowing scalable analysis. The Boolean formalism has already proved useful for the study of various systems,16 and offers an attractive starting point as the most parsimonious (Occam’s Razor) explanation of complex system behavior. To a degree, it also abstracts away from experimental noise, for example when sufficient expression is observed regardless of the precise measurement. However, our approach requires all qualitative observations to be reproduced exactly, and noise of sufficient magnitude (causing a component to be observed in the incorrect state) could impact our results. Similar robustness issues have been considered as part of other approaches.34 On the other hand, noise that is inherent to a biological mechanism could be incorporated and studied in our framework as non-determinism, using asynchronous updates or by introducing additional components with unspecified initial states. When a Boolean discretization is too coarse, a multilevel description of component states could be considered,36 and such extensions are compatible with our SMT-based approach.Among several modeling approaches for biological networks,37–39 often require separate analysis tools. Simulation provides one such analysis strategy.40–43 However, as only concrete models can be simulated, the ABNs we consider would have to be exhaustively sampled to instantiate possible interactions, regulation conditions and initial states, which becomes impractical due to the combinatorial explosion of concrete models as increasingly complex ABNs are considered.Our approach incorporates automated network construction and analysis within the same reasoning framework, whereas alternative reconstruction or training approaches36 and BIOCHAM44 for the analysis of biological networks. Despite this, expensive enumeration would still be required to deal with abstract models.Deciding that a concrete model is consistent with experimental observations is also challenging, especially for asynchronous models. Formal methods or state space analysis (as in ref. 45 and Constraint Logic Programming (CLP).47In contrast, our approach permits the exhaustive characterization of the complete set of consistent networks from an ABN, by encoding and solving satisfiability problems. This enables the integration of partial knowledge of network topology and regulation mechanisms, as well as experimental data where only a subset of the components are observed. The scalability of SMT solvers allows us to analyze models of a realistic size, and together with the generality of the SMT problem enables potential future extensions of our method for multilevel logical models, extended regulation condition definitions or analysis questions. Alternative technologies used to address similar constraint problems include Answer Set Programming (ASP)48 Where different models within the set support the hypothesis and the null hypothesis (preventing a prediction from being made) a potential experiment could be performed to eliminate one of these subsets, and refine the cABN further. Identifying such discriminating experiments automatically is a potential future extension to RE:IN and this methodology. Furthermore, we have begun to investigate biological programs that reconfigure throughout development, which requires us to adapt RE:IN for the synthesis of switching networks.49Another feature of our method is that we formulate predictions only when all consistent networks are in agreement, which limits the bias of using only one arbitrary concrete model. As a result, generally, fewer predictions are generated, but it is already understood that an ensemble of models provides more robust predictions than a single representation.51 we anticipate that automated reasoning, and in particular the approach we present here, could be integrated into the process of combining knowledge, formulating hypotheses, and designing new experiments to elucidate further the nature of biological programs. In our analysis, the entire set of consistent models, rather than a single mechanism, describes the biological program. We speculate that this may not simply be an artefact of our approach but rather relate to how a biological program is realized in individual cells. Given that there are multiple genes and interactions with redundant function, different cells could run consistent variants of the program to obtain equivalent behavior. This could explain the inherent heterogeneity and robustness of biological processes.In light of the fact that the use of formal methods to further understanding of biological systems is gaining momentum,S we use |S| to denote the cardinality (the number of elements) of S.Our methodology deals with a class of logical models where each component exists in one of two possible states: active or inactive. Such models can be seen as Boolean abstractions of more detailed descriptions, where the component states vary over multiple discrete values, or are represented by a continuous quantity. In the following, we use A Boolean network is a tupleC and a set of update functions cf is the update function for component c∈C.A transition system is a tupleq, q′∈Q, a transition from q to q′ is allowed if and only if T holds and, therefore, the relation T describes the transitions that are valid in the system. We consider deadlock free systems, i.e., where ∀q∃q′. T. A transition system is deterministic if and only if at most a single valid transition exists for each state, and otherwise it is nondeterministic. A sequence of states q0, q1, …, qK represents an execution (a trajectory) of i=0…K−1T).Given states B. The set of states of C. Given a system state c∈C, we use c. Each Boolean update function fc defines how the state of component c is updated, given the current states of all components. For example, given components c, c′, c″, the update function fc(q)=q(c′)∧q(c″) describes a rule where component c will be active in the next state if and only if both components c′ and c″ are active in the current state. A choice between two different assumptions is commonly applied to define the dynamics of Boolean networks in terms of the application of the update functions for each component. Under synchronous updates, it is assumed that the state of each network component is updated at each step. The transition relation for a synchronous Boolean network is defined asq and q′ represent the current and next system state. In contrast, under asynchronous updates, it is assumed that only the state of a single network component is updated per step, while all other components remain unchanged, and the choice of which component to update is nondeterministic. This leads to the following transition relation definition for asynchronous Boolean networks:B is a synchronous Boolean network, assuming deterministic update functions. In general, B is asynchronous.Given a Boolean network B together with the transition system B captures information about the components, interactions and update functions in the system. In contrast, the graphs representing C|=15 components contains 2C||=32,678 states and this number grows exponentially as additional components are included.An example of a Boolean network C denote the finite set of critical components. Then c∈C in state q∈Q. Let definite interactions between the components C, labeled with a regulation sign (⊤ for positive and ⊥ for negative). Similarly, let possible interactions and assume that an interaction can be either definite or possible but not both ∈I denotes the presence of a definite, positive interaction from c to c′, while ∈I? denotes the possibility of a negative interaction from c to c′.Let C, together with the definite and possible interactions I and I? between elements from C defines the abstract network topology we consider  indicates that a positive interaction between c and c′ was selected for the particular concrete network. Similarly, neg indicates that a negative interaction was selected. Clearly, ∈I→pos and ∈I→neg, i.e., definite interactions are always selected. For all other possible interactions, the functions pos and neg represent choice variables.To examine concrete networks, we introduce the functions 18 Therefore, in the following, we consider a subset of templates called regulation conditions, which describe qualitatively different regulation mechanisms. Each regulation condition must be applicable to network topologies that contain both definite and possible interactions. This is not straightforward if named regulators are used in the update functions, as a given regulatory interaction might not be included in some of the concrete models. Therefore, we define these rules only in terms of whether none, some, or all activators (repressors) of a given target are present in a given state, assigning equal significance to each regulator. We thereby guarantee that the dynamical rules we consider are consistent with the abstract network topology.The network topology alone does not capture information about system behavior over time. Rather, we must construct a dynamical model by representing explicitly the rules that govern how the system transitions between different states during executions. Arbitrary Boolean update rules might be ‘too rich’ and describe behaviors not observed in biological systems.We consider all regulation conditions consistent with the following assumptions.1. A target is activated when all of its activators are present  and none of its repressors are present . Similarly, a target is deactivated when all repressors are present and no activators are present.This guarantees that the state of the target depends on the state of its regulators and is not permanently active or inactive. We introduce a similar assumption for targets that have only activators (they are not repressible) and only repressors (they are not inducible).2. We assume that each regulation condition is monotonic. For example, if a target requires only one activator to be activated, then any greater number of activators will activate that target, assuming no change in the state of the repressors.I, I?, C), component c∈C and a state q∈Q, we define the termsAllActivators indicates that no activators, or some but not all activators, of c are present in state q. Using these expressions, we define the following regulation condition templates:We formally define the set of 18 regulation conditions consistent with these assumptions in the following. Given as network topology , as well as NoActivators for any state q∈Q, and the same holds for non-repressible components. To ensure that non-inducible and non-repressible components are not constantly activated or repressed regardless of the state of their activators, we introduce the following:11 in which the balance of activators and repressors determines whether the component is activated or repressed. The delayed threshold rule specifies that if a target node is active at time t, and the total input to the target is zero, then it will be degraded at time t=t+td. Here we consider a simplified version of the delayed threshold rule applicable for modeling a self-degradation for components that have no negative regulators when td=1. Formally, we define these rules asA ) denotes the number of activators (repressors) of component c that are active at state q.Two additional rules that are consistent with the requirements of monotonicity and the fact that no named regulators are used are the instant and delayed ‘threshold rule’,The set of 18 regulation conditions we define, together with the two threshold rules, completes the set of 20 regulation conditions.An abstract Boolean network (ABN) is a tuple● Cis the finite set of components,●is the set of definite (positive and negative) interactions between the components from C,●is the set of possible (positive and negative) interactions, and●c⊆R assigns a subset of the regulation conditions (denoted here by R={R0 … R19}) to each component from Cwhere r.C together with the definite and possible interactions I and I? define the abstract network topology. A choice from our set of 20 regulation conditions is allowed for each component. When the precise regulation mechanism for a given component is unknown, all regulation conditions can be assigned as possible rules. If prior experimental evidence can eliminate certain regulation mechanisms, then a subset can be assigned. For example, regulation conditions R0 to R8 require that an activator is present in order for a component to be activated in the next state. Combined with a requirement on the topology that each component must include one activator (to prevent the case of non-inducible components), this restriction (r(c)={R0 … R8} for some c∈C) can be used to model the specific regulation mechanisms considered to hold in higher order organisms.The set of components We define an ABN to describe the uncertainty in the precise network topology and regulation rules for each component. An ABN is transformed into a concrete model by selecting a subset of the possible interactions to be included  and assigning a specific regulation condition for each gene.pos and neg used for the definition of all regulation conditions. Let c∈C and Formally, let c in state q∈Q). As for Boolean networks, we consider both synchronous and asynchronous semantics. For synchronous systems, the transition relation i is nondeterministic.We define the semantics of such a concrete model in terms of a transition system Input signals to an ABN will not have any defined regulators and will be constantly active or inactive depending on the choice of regulation conditions see . This prSome of the unique concrete models represented by an ABN might produce behavior that is consistent with experimental observations of the modeled biological system. However, other concrete models might not be consistent, and thus we seek to impose constraints that eliminate these mechanisms. In the following, we present our approach for defining and incorporating constraints over the behavior (the possible trajectories) of an ABN.E, where each experiment e∈E represents a different execution of the system. We construct observations using terms , whereWe consider reachability properties over the states of various components at different steps during executions of the system. We seek to formalize observations obtained from different experiments, denoted by the set ● e∈E is the experiment label,● n∈0 … K denotes a specific time step,● c∈C denotes a component of the ABN, and●c.t=q0, …, qK denote a trajectory of the transition system t satisfies the term  if and only if qn(c)=v and we require that, for all experiments e∈E, there exists a trajectory te that satisfies all the terms labeled by e.Let c, was initially observed to be inactive but then observed to be active at a later time point (after 20 steps). Both of these observations describe the same execution of the system and are therefore denoted by the same experiment label, e. To represent this, we construct the expression ∧ requiring the existence of a trajectory that recapitulates these observations. This trajectory corresponds to an explanation of how the system is capable of reproducing the observed behavior. Multiple labels allow us to represent different experiments, for example when the system is initialized in different states. Note that the expressions related to different labels, for example e and e′, are not necessarily mutually exclusive and, therefore, the same trajectory might satisfy both.As an example, consider an experiment where component, KO and FE to define knockout and forced expression perturbations, which are assigned to a given experiment and component but do not depend on time. These perturbations modify the dynamics of the system along trajectory te, where component c is always active (forced expression) or inactive (knockout) when v=⊤, regardless of the regulation conditions for c or the state of each of its regulators (the update rules are applied as before when v=⊥).In addition, we use the terms Fixpoint to indicate that the trajectory te, satisfying all constraints labeled by e, must reach a fixed point at step n. In other words, the only possible transition from the state qn of te (reached at time step n) is a self-loop.Finally, we introduce the constraint e, n, c, v), KO, FE and Fixpoint are combined into logical expressions using the operators {∧, ∨, ⇒, ⇔, ¬}, which allows us to formalize various experimental observations.Different terms  as a representation of the set of concrete models that are consistent with experimental observations.The ABN can be represented by transition system A constrained abstract Boolean network (cABN) is a tuple, wherethe components C, definite and possible interactions I and I?and regulation condition assignment r define an ABN,●●E is a set of experiment labels●defines whether there is a knockout perturbation of component c∈C in experiment e∈E,●defines whether there is a forced expression perturbation of component c∈C in experiment e∈E, and●O is an expression representing experimental observations constructed using the terms defined in the previous section.c. In addition, the perturbations e∈E and component c∈C. Initially, these perturbations might be unknown or only partially constrained through the experimental observations O using the terms KO and FE. The dynamics of such a concrete model are defined through a transition system e∈E depending on the perturbations K and F. For synchronous systems, we define T asK and F. Note that K and F could also be introduced for ABNs and BNs to model perturbations in these systems.As for ABNs, a concrete model of a cABN is defined by selecting a subset of the possible interactions O  problem and apply the SMT solver Z322 to obtain the solution.The solution to Problem 1 amounts to finding 21 we encode Problem 1 as a logical expression using bit vectors.An SMT problem amounts to deciding whether a logical formula is satisfiable . SMT extends the classical Boolean satisfiability problem (SAT), which deals only with Boolean formulas, and allows the use of additional theories such as bit vectors. To apply the SMT solver Z3 and the bit vector decision procedures it implements,I? as the bit vector interactions encodes the Boolean choice variable representing whether the particular interaction is selected or not. Similarly, we encode the regulation condition for each component c∈C as the bit vector ids(rc) represents the indexes of the regulation conditions allowed for component c. A valuation of interactions and each regulationc represents one concrete model from interactions and regulationc for each c∈C. However, at this point interactions and regulationc are symbolic representations without a concrete valuation.Given e∈E we can encode the perturbations K and F as bit vectors of size |C|. This would allow perturbations for each component as part of each experiment to be specified using the observations O, but the choice of perturbation remains unconstrained (non-deterministic) unless such observations are provided. However, perturbations are often considered only for a small number of components, which requires many additional, trivial observations ∧KO∧ … when no knockout perturbations are considered for component c as part of any experiment). To simplify our encoding, we designate that only a subset of the components to be considered for knockout (CK) or forced expression (CF) perturbations and define the K and F bit vectors to be of sizes |CK| and |CF|, respectively. It should be noted that once a component is included in the subsets CK and/or CF, a knockout or forced expression can be assigned by the solver in such a way that all observations from O are satisfied. Thus, additional observations must be introduced for such components to indicate the lack of perturbation in certain experiments.For each experiment C|. Given such a representation for two states q, q′∈Q and experiment e∈E, the bit vectors interactions and regulationc allow us to encode the transition relation for synchronous systems defined in 52 where we consider a finite number of steps and ‘unroll’ the transition relation T. Given an experiment e∈E, we encode each state qi of the trajectory te=q0 … qK as a bit vector and assert the constraintte for each experiment e∈E, we assert the constraints from O over the states of these trajectories. For terms  this amounts to a constraint qn(c)=v for trajectory te. Terms KO and FE correspond to constraints over the bit vectors representing the perturbations of each experiment.The set of states of an ABN (or cABN) is Fixpoint. For synchronous systems, this corresponds to a constraint that state qn of trajectory te (corresponding to experiment e∈E) includes a self-transition, i.e., T. As the transition system is deterministic, requiring such a self-transition guarantees that this is the only outgoing transition from qn and therefore the state is stable. For asynchronous systems, which can generate non-deterministic behavior, in general it is possible that multiple outgoing transitions exist at a state. In this case, we assert a constraint for a self-transition using the transition relation for an equivalent synchronous system, instead of using the asynchronous transition relation, which is necessary and sufficient to guarantee that state qn is stable.Finally, we describe the encoding of fixed point constraints Our methodology supports a number of analysis procedures, described in the following.In many cases, we are interested in expressing end-point behavior, where an initial and final configuration of (a subset of) the system components are given. A parameter that must be specified for such experimental observations is the trajectory length that is considered. Specifying a trajectory length that is too short might exclude valid mechanisms from the cABN, which require a larger number of steps to reach the required configuration.t=q0, …, qK with the constraint that qi≠qj for i=0 … K−1, j=i+1 … K. We then use the SMT solver to check if such a loop-free trajectory t exists and increase K until this is no longer the case, at which point K represents the recurrence diameter.While an appropriate trajectory length is often hard to specify, the recurrence diameter (the longest loop-free trajectory of the system) can be used as an overapproximation that guarantees that no valid mechanisms are excluded. To find the recurrence diameter, we consider a trajectory O, this procedure allows us to compute the longest loop-free trajectory for any of the mechanisms described by the cABN. Furthermore, if no perturbation constraints are asserted for t, then the longest possible diameter under any possible perturbations is computed. However, additional constraints on perturbations or the initial configurations of components could be asserted for t, for example, to consider only initialized trajectories as part of the diameter computation.By asserting all experimental observations from In general, the computation of a recurrence diameter is a challenging problem and the procedure we apply requires prohibitively long time for certain systems  but allows us to obtain this parameter for other systems . Besides providing a convenient strategy for specifying experimental observations such that no valid mechanism are excluded, the computation of a system’s diameter also reveals an important property of the model that is being investigated .Thus far, we have considered the problem of finding a single concrete mechanism from the set of consistent mechanisms described by a cABN (Problem 1), but in many cases we are interested in enumerating a number of unique consistent mechanisms. Depending on the application, mechanisms can be considered unique if only the network topology is different, either the topology or the regulation conditions differ, or an alternative explanation for how the experimental observations are achieved is obtained. Formally, we encode each of these cases as follows.interactions is the bit-vector representing possible interactions, regulationc is the bit-vector representing the regulation condition for each component c∈C, and trajectory te is an explanation of how the experimental observations e∈E are satisfied. A concrete mechanism corresponds to a valuation of interactions and all regulationc and te.Suppose we have a cABN Let ● Only the network topology is different:● Either the network topology is different or the regulation condition of at least one component is different:● Either the network topology, the regulation condition of at least one component, or the trajectory for at least one experiment are different:A number of qualitatively different mechanisms can be enumerated by obtaining a solution using the SMT solver, asserting the uniqueness constraints defined above and applying this procedure for a number of iterations or until no additional solutions are possible. When no additional solutions can be generated (a result that is obtained by applying the SMT solver), we guarantee that all possible consistent mechanisms have been enumerated. However, this may not be feasible in practice for a large number of components and possible interactions, which permit a large possible number of solutions. Thus, in the following sections we propose analysis strategies that do not require the explicit enumeration of all consistent mechanisms.In certain studies, we are interested in identifying minimal models—the most parsimonious mechanisms from a cABN that explain all experimental observations. More specifically, we focus on mechanisms that involve the smallest number of possible interactions to achieve the specified behavior. We identify such minimal models using the following procedure.cardinality , which represents the number of selected possible interactions. To identify mechanisms with the smallest number of interactions, we first use the SMT solver to identify one consistent mechanism and then enforce the additional constraintLet Given a cABN, a required interaction is a possible interaction that appears in all consistent mechanisms, i.e., this interaction must be present to reproduce all experimental observations. Similarly, a disallowed interaction is one that does not appear in any of the consistent mechanisms, i.e., including such an interaction makes it impossible to reproduce the experimental observations. One possible approach for identifying required and disallowed interactions would involve the enumeration of all consistent mechanisms, but this strategy is not feasible in practice when the number of mechanisms from the cABN is large. Therefore, we use the following alternative approach.I′ and I?′ such that i is removed completely from the system). Applying the SMT solver to the modified cABN allows us to guarantee that i is required when no solutions can be generated. Similarly, we identify disallowed interactions, by constructing a new cABN with interaction sets I′ and I?′ such that i∈I′ and i and, therefore, this interaction is disallowed.Let More generally, we are interested in formulating predictions about the behavior of the cABN that hold for all consistent mechanisms—to remove the need to select, and therefore bias toward, one concrete model for analysis of the system. We achieve this using the following approach.First, we formulate an hypothesis of the behavior of the system that will be investigated, which can be expressed as an experimental observation. For example, such an hypothesis might specify that under a given perturbation the system must eventually stabilize in a state where certain components are inactive. If consistent mechanisms exist that satisfy this new constraint, we then instead encode the null hypothesis (the negation of the hypothesis we are testing), and use the SMT solver to check if any of the consistent mechanisms from the cABN can reproduce this alternative behavior. If this is the case, no prediction can be made, since at least one mechanism supports the null hypothesis. However, in cases where no solutions are found on testing the null hypothesis, we guarantee that all consistent mechanisms support the hypothesis, which leads to a prediction. This approach allows us to consider the entire set of consistent mechanisms from the cABN when generating predictions, avoiding both the implicit bias of a single selected mechanism, as well as the need for solution enumeration.


Near the end of life, hospice care reduces symptom-related distress and hospitalizations while improving caregiving outcomes. However, it takes time for a person to gain a sufficient understanding of hospice and decide to enroll. This decision is influenced by knowledge of hospice and its services, emotion and fear, cultural and religious beliefs, and an individual’s acceptance of diagnosis. Hospice admission interactions, a key influence in shaping decisions regarding hospice care, happen particularly late in the illness trajectory and are often complex, unpredictable, and highly variable. One goal of these interactions is ensuring patients and families have accurate and clear information about hospice care to facilitate informed decisions. So inconsistent are practices across hospices in consenting patients that a 2016 report from the Office of Inspector General (OIG) entitled “Hospices should improve their election statements and certifications of terminal illness” called for complete and accurate election statements to ensure that hospice patients and their caregivers can make informed decisions and understand the costs and benefits of choosing hospice care. Whether complete and accurate information at initial admission visits improves interactions and outcomes is unknown. Our recent qualitative work investigating interactions between patients, caregivers, and hospice nurses has uncovered diverse and often diverging stakeholder-specific expectations and perceptions which if not addressed can create discordance and inhibit decision-making. This paper focuses on better understanding the communication dynamics and practices involved in hospice admission interactions in order to design more effective interactions and support the mandate from the OIG to provide hospice patients and their caregivers with accurate and complete information. This clarity is particularly important when discussing the non-curative nature of hospice care, and the choice patients make to forego aggressive treatment measures when they enroll in hospice. In a literal sense, to enroll in hospice means to bring in support for end-of-life care. It means to identify the need for expertise around symptom management at end-of-life, and agree to having a care team come and manage someone’s physical, psychosocial, and/or spiritual needs. As with all care, hospice can be stopped if it is no longer considered appropriate. To uncover the communication tensions undergirding a hospice admission interaction, we use Street’s ecological theory of patient-centered communication to analyze a case exemplar of a hospice admission interaction. This analysis reveals diverse points of struggle within hospice decision-making processes around hospice care and the need for communication techniques that promote trust and acceptance of end-of-life care. Lessons learned from talking about hospice care can inform other quality initiatives around communication and informed decision-making in the context of advance care planning, palliative care, and end-of-life care.  Near the end of life, receiving hospice care is associated with less distress, fewer hospitalizations, and improved caregiver outcomes ,2,3,4. HThe OIG 2016 Report, “Hospices should improve their election statements and certifications of terminal illness,” states that resolving disparities and vulnerabilities in election forms is crucial to ensuring patients and caregivers make informed hospice care decisions. It calls for hospices to provide complete and accurate information at the initial consult . HoweverNot only do parties in the interaction carry with them diverse and often diverging interests and motivations, the interaction itself occurs in a stress-laden context. The hospice admissions nurse or social worker must balance the intersecting needs of patients often too ill and fatigued to participate, overburdened caregivers who fear that choosing hospice means giving up on loved ones, and referral sources or other healthcare providers anxious for a quick hospice transition. These tensions and competing needs necessitate clear, tailored communication during the consult . ReachinHospice admission nurses in particular are at the forefront of facilitating quality interactions geared towards concordance around hospice care. To illustrate the nature of this interaction and address the research gap surrounding it, we present a detailed narrative case of a hospice admission interaction and critically analyze the communicative practices used by the hospice nurse and caregiver using the social ecological model. Of note, in the initial hospice consult, we talk generally about palliative care as a program of care that is appropriate when patients are facing advanced illness and have symptom management needs , but are still continuing treatment of their disease. On the other hand, we talk generally about hospice as meaning the patient is receiving symptom management support, but is not treating their disease. Moreover, palliative care programs are not standardized (or paid for) like hospice is, so palliative programs can look different depending on where individuals are receiving care. The primary goals of the hospice admission interaction are (1) to exchange information between provider, patient, and caregiver about health-related concerns; (2) make decisions about medical care; and (3) in the best of cases, establish or maintain a relationship characterized by rapport, trust, and respect . As suchStreet’s ecological theory of patient-centered communication focuses on the complex interplay between individual, relational, community, and societal influences on interactions around health . This thInterpersonal context: includes predisposing influences of the provider, patient, and family , as well as cognitive-affective influences of participants . Political-legal context: includes Medicaid/Medicare coverage and conversations around medication discontinuation and payment.Cultural context: includes influences of ethnicity, socioeconomic status, philosophy of care, and religion.Organizational context: includes standards of care, services offered, facility restrictions on where interaction takes place, hospice admission training, and hospice organization goals. Although the hospice admission encounter may be contextualized, influenced, and understood in a number of ways, the one within which the hospice admission encounter is most fundamentally embedded is the interpersonal context.The project grew out of a long-standing community-academic partnership between a local hospice and university. Monthly agendas for this partnership meeting included an item titled, “what bugs you.” During a meeting in 2015, in response to this agenda item, the president of the hospice talked about the significant variability and non-standardization of hospice admission visits and the desire to have evidence, or a core set of best practices, surrounding admission visits so that patients and families who would like to benefit from hospice are able to do so while fully understanding what their decision to enroll means. The local hospice is a large, urban, non-profit organization that has served more than 70,000 patients and their families since its founding in 1978. It is the fifth longest-established hospice in the United States and remains a well-respected leader at local and national levels. Patient census reported 506 hospice patients and 214 palliative care patients under their care each day in 2014. In 2015, the numbers climbed to 532 and 227, respectively. Services are provided by teams of professionals and volunteers focused on individualized, integrative care in patient homes, skilled nursing facilities, or in their own Inpatient Care Center. No patient is ever turned away regardless of ability to pay. In 2015, the president of the hospice put the first author in contact with the admissions manager (A.T.) to talk about the project and the opportunity to “shadow” hospice admission nurses on their initial visits with patients and caregivers. The admissions manager developed a schedule that assigned specific days and times with different admission nurses and settings over a three-month period. Because admission visits were often scheduled the day before, the admissions manager would email the first author (C.C.) in the morning with the schedule for visits and text during the day as others were scheduled or cancelled. The schedule included home, nursing home facilities, and hospital room visits across an area in the Rocky Mountain region of the United States.When visits were scheduled, A.T. would email C.C. and the assigned nurse with a brief overview of the visit including the patient’s age, diagnosis, and anything important to know before the visit ; C.C. did not access medical charts. C.C. would then confirm a meeting place with the nurse before the interaction started so they could walk in together and as needed, be briefed about the patient, family situation, and project beforehand. The admission visits lasted between 30 min to 2 h. There were visits that had as little as three people present, and as many as nine, including the hospice nurse and C.C. To ensure a diverse sample, participants included: African Americans, Latino, undocumented, Russian, Caucasian, tribal, homeless, religious, non-religious, gay, straight, married, and widowed. Additionally, nurses that were shadowed were also diverse in terms of years at hospice and experience before coming to hospice. On site, C.C. was introduced to the patient and family as a researcher from the local university studying hospice communication to define best practices. In all cases, patient and/or caregivers verbally consented to researcher presence before starting. During these conversations, C.C. sat next to the nurse, or in some cases, wherever the family encouraged her to sit based on the arrangement of the room/setting. Ethnographic field notes were minimally taken to capture key words or phrases used during the interaction, and sometimes not at all depending on the situation. In these cases, C.C. would write them directly after the visit. Note taking was not considered obtrusive, as the admissions nurse also had a notebook and took notes during the conversation. C.C.’s role in these interactions was solely to observe, and patient and family interaction was limited to polite greetings and good-byes. At the end of each admission visit, and always dependent on the situation and comfort of participants, C.C. asked the patient and/or caregiver if they would be willing to answer five questions related to the visit. There were five instances where C.C. did not ask the participants given the perceived distress or known circumstances that would have impinged participation. For the other 20 visits, all who were asked to voluntarily participate agreed to so do. n = 30). All interviews were audio-recorded. Because of the health of participants, circumstances around each visit and ethical considerations guiding every stage of this project, interviews lasted no longer than 15 min to respect the comfort of participants and the time with their loved ones, if present. Of the observed admission visits, 20 enrolled in hospice, 3 were undecided, and 2 declined hospice. C.C. conducted nonparticipant observations of hospice admission visits between hospice admission nurses and patients and their caregiver(s) over a three-month period during 2016–2017. The setting of these interactions varied from hospice home visits, skilled nursing facility visits, and hospital room visits. The resulting data set included 60 h of observation, including interviews with 15 caregivers, 6 patients, and 9 hospice admission nurses (n = 3). Before observations began, C.C. provided a short presentation at the staff meeting about the project: what was required of admission nurses, the goals of the observation and interviews, how results would be shared back with the organization, and answered any questions they had. None of the admission nurses chose to opt-out of participating. Ethnographic field notes of these meetings were written and typed single-spaced for a total of 250 pages (8–12 pages per visit). C.C. also attended hospice admission monthly staff meetings where “defining best practices,” was a standing agenda item .This project would not be possible without a strong ethical foundation. Ethics have been incorporated from initial study design to the sharing of results. The skill of introspection and the ability to accept and process feedback regarding very personal aspects of this work are important attributes of any researcher doing end-of-life research . The insThis project has specifically been guided by post-colonial ethics of accountability, context, truthfulness, and community. More specifically, through a felt need of endorsing other models of reflexivity, we have looked to others who have done similar work, like Ellingson, Broadfoot, Foster, Hirschmann, de la Garza, and Eisenberg ,14,15,16For these reasons, we have chosen a case study exemplar for this paper, as a powerful example of the many issues associated with hospice admissions . AlthougEstablishing concordance is necessary to coordinate communication and decision-making, as it sets the tone for all future interactions around hospice . Pilot dIt is cold but sunny and glass doors open onto a dark lit room in the Alzheimer’s Care Center. The facility walls are decked out in holiday decorations, and Christmas music can be heard playing in the background. James, the hospice admission nurse and I (C.C.) have an appointment to meet with the patient’s daughter, but first we need to look through her record and talk with her nurse. There is no one in the halls as we enter, save a patient trying to exit. It smells of feces, urine, and artificial cleaning products. It is hard to breathe. Several residents sit in their wheelchairs at the entryway of their rooms, lined up as though they are waiting for a delivery. Some look up as we walk by, while others are sleeping with their heads cocked to one side. Other residents meander through the hall, scuffing their feet across the carpet to move their wheelchairs. The smell gets stronger the further we go. We pass the “living room” filled with about 15 residents gathered in a semi-circle around the TV in their wheelchairs. Every head is bowed with eyes closed.We arrive at the nurse’s station and, upon request, receive a large blue binder containing the patient’s record. As we open it, another resident approaches the desk. James greets her and asks if he can help, and she says there is a lot he can help with before walking away. A patient yells at a nurse as they walk out of the dining room because she is holding his arm . I hear a conversation between another nurse and patient involving a nail gun, as the patient complains that it sounds like someone has been kicking the wall. “Lunch is in an hour”, another nurse says to a patient sitting in the hall as she walks by a nursing aide wheeling a woman backwards into the shower room.Patient and family are undecided on whether to enroll patient into hospice services or not. Dr. Hanks has expressed to them that he does not think patient is hospice appropriate at this time, but is open to reconsidering this based on hospice’s evaluation. Family is also wanting more information and an evaluation to determine whether patient is appropriate or not. Daughter has many concerns about admitting patient to hospice, and several questions on why or why not her mother would qualify. Daughter would like hospice nurse to lay eyes on patient, then meet with her in the lobby after. The patient’s nurse tells us that Judy is a “very pleasant 93-year-old. She has congestive heart failure (CHF) and had a change of condition a few days ago: fluid in her lungs. Her appetite is good and she is ambulatory. She has a little pain, does not talk much, just general facial expressions. She had antibiotics to help treat the fluid and has gotten a little better”. James gets her medications and confirms that there is only one medication with the nurse. He asks about a note in the record that outlines the family’s request to not call the house before 8 am for non-urgent matters and asks how this affects the nurse’s interactions with them. There does not seem to be any concern, but the nurse shares that the family used to come every week but that lately no one has been around. James closes the binder, puts it back on the shelf, and signals me to follow. We make our way back up to the lobby to meet Emily, Judy’s daughter. Four patients in wheelchairs are lined up looking at the front door. It seems they are going out to Applebee’s. They wait for a staff member to pull their transport around, only to find that it is not operational. They are devastated by this news.We find Emily in the lobby after she dropped a stack of clothes at the receptionist’s desk. We introduce ourselves and head somewhere to talk.J:“Before I give you a bunch of information, can you tell me your understanding of hospice?” E:“I understand it as two levels of care. Palliative being less hands-on, and hospice being more involved.” J:“I saw your mom very briefly earlier today and she is doing well. She got up by herself and walked about the room. She’s continent.”E:“She’s continent? She has been in a diaper every time I have seen her.”J:“Sometimes around here they put diapers on almost like a security blanket so the patients can be relaxed if they have an accident or can’t get up quickly enough to the restroom. We can go see her after we finish our conversation.”E:“I don’t want to go back.”J:“What are your goals today?”E:“We want an honest assessment of where she is to determine if we should enroll in palliative or hospice care. You are the experts. We are leaning more towards palliative care, but we just don’t know. Is she eligible for hospice? Would her doctor support that decision?”J:“Yes, he said he would support that decision.”E:“I’m confused—I thought he didn’t feel she was appropriate?”’J:“Initially, Dr. Hanks said he didn’t think she was appropriate for hospice, but in the chart from November it states he would support the decision to enroll in hospice if we felt she met criteria, which I feel she does. After seeing your mother, I think she can benefit from palliative or hospice care, and I have information and paperwork on both options today. But this decision is ultimately for you and your family to make. Do you know what she wants?”E:“No, I mean she can’t communicate.”J:“Did you talk with her before about what she would want?” E:“Not really. But I think she’s ready for palliative care and until the doctors feel absolutely certain she is ready for hospice, we will go from there. Is that reasonable?”J:“Yes, absolutely.”E:“Can you re-evaluate her in 90 days? It’s risky, I know, since she could go downhill quickly and maybe need more help.”J:“It’s a very difficult decision, and we want to help you in whatever way we can. We can fill out both paperwork, and you can think more about it and let us know within a few days.”E:“Do you think she is ready for hospice?”J:“I think she is, but the decision to enroll is really up to you. She is eligible and would certainly have more eyes on her with a larger care team.”E:“Would she have more interaction and would your team interact with the team here okay?”J:“Yes, she would have more frequent visits on our hospice program, as our palliative team serves more in a consultative capacity. But know that both programs provide an extra layer of support to our patients. Hospice also works hard to provide support to you as her family member…we know this is a challenging time.”E:“I don’t know what to do. This is really hard and you’re not giving me any guidance!” (Emily gives her first brief smile.)J:“We just want you to be prepared as possible should things get worse, and we want to be able to best support you all. So often patients and families wait until they are close to dying before enrolling in these services, whereas signing up earlier could really benefit not only your mom, but you and your siblings as well. If I could make a recommendation, I would encourage you to sign your mom up for palliative care today and then we can move her onto hospice services when you feel more comfortable with that transition. If you change your mind, just give us a call; these decisions are not set in stone. Your palliative team will also help you understand when the time is right for a transition to hospice.” E:“You know, my father died 10 years ago. My mom insisted he get cancer treatment until the day he died while living at home. It was a Saturday and my mother wanted us to haul him to the hospital for another treatment. He was so sick and so weak. And my family and I finally intervened and told my mother no. He died that following Monday.” (pauses) “I want hospice. Let’s go directly to hospice. I don’t want to have her be on hospice for 4–5 years, but I also don’t want to ignore something she might really need and benefit from. This is so hard.” E:“I want her as comfortable as possible and free of pain, with an extra set of eyes.” (Emily’s eyes fill with tears. James pulls out the paperwork.)J:“Just remember that no matter what you decide, you can always change your mind at any point.” E:“You can re-evaluate her at 90 days, right? This is so hard.”J:“Yes, we can, and I will put that in the notes.”(James starts to go through the enrollment paperwork Emily needs to sign to get her mom enrolled in hospice. Emily has no questions.) Turning point #6: Organizationally, this is an effective interaction because there is a plan in place: to sign up for hospice care with the understanding that if it is not the right fit, Emily can discontinue services at any time. However, psychosocially, did Emily get the help she needed? This is the more challenging question to answer. Is it James’s fault if she did not? Many times, families cannot reach a decision for a number of reasons—societal norms around the meaning they have assigned to hospice, deep family dynamics that still need to be addressed and dealt with, acceptance of the illness, acceptance of the reality of caregiving needs, uncertainty what their loved one wants, fear of financial burden keeping their loved one home, even if it is what they want, and so on. The interaction comes to a close in the next lines when James is filling out paperwork and having Emily sign the consent forms, when he notices Emily’s birthday on the power of attorney form.][J:“Your birthday is coming up!” E:“And so is my mom’s.”J:“Are you having a party for her?”E:“No, actually. I will be in Chicago. (Judy’s nurse Katie comes in to have Emily sign a paper.) E:“Hi, Katie, how is she doing?” E:“Is she still on oxygen?”K:“No, just as needed.”E:“I left some clothes for her at the front. Can you take them back to her?”K:“Sure.”E:“Thank you, and I will talk with you soon, ok? I would like to know more about that bereavement program for children you spoke of because my mother’s granddaughter has a lot of emotional issues and would benefit from that support. Can you put that in the notes?”J:“You got it. Thanks for coming, Emily.” The hospice admission encounter, like the one outlined above, reveals a variety of reoccurring patterns of communication and struggle. Looking at the narrative, there are clear communication challenges, as well as opportunities to assess each unique situation, connect, clarify, and create new meanings. Identified turning points illustrate how the outcomes of these interactions ultimately depends on the communicative actions of participants and their goals, linguistic skills, perceptions, emotions, and knowledge, as well as the constraints and opportunities created by the responses of others . Examining this interaction through an ecological lens, the following points become apparent: Interpersonally: In not wanting Emily to feel forced into a decision, James initially struggles to provide a recommendation for one service over the other. Emily struggles to understand why James is not playing the role of expert and telling Emily what she should do. On a personal level, James struggles to understand why Emily does not want to go back and see her mother, and wonders what dynamics are at play. Emily is struggling to accept the new normal of her mother, including not wanting the same thing that happened to her father to happen to her mother. Emily is overwhelmed being her mother’s primary caregiver. Organizationally: Entering skilled nursing facilities to talk about hospice adds an extra layer of complexity to the encounter, as hospice nurses navigate the organization’s controls and rules, gaining access, finding the chart, and finding the patient’s nurse prior to starting the interaction. James’s experience working in skilled nursing facilities  as well as the training he has received at his particular organization regarding admissions conversations and communication strategies also shapes how he thinks and feels during admissions interactions and decisions. Emily’s presence inside the skilled nursing facility (and her wishes to not go past the front door) also influences the interaction. Furthermore, this is James’s second of four visits for the day. Visits can last between 30 min and 2 h, and often James does not have a lot of time between cases to debrief from the last one and prepare for the next.Culturally: Emily and James navigate several cultural subtleties in their conversation. Broadly, both dealt with the culture of the nursing facility where the conversation took place. The cultural tensions of nursing home care are often dictated by insurance reimbursements and staffing shortfalls. These challenges frequently create a culture of apathy or indifference, which is difficult to manage. Further, the culture of the nursing home directly influences the ability of hospice to enter the facility and provide care. Emily, a retired US Parks Worker, entered this interaction looking for guidance and for someone to tell her what choice to make. She viewed James as the medical expert with an exclusive knowledge base who could quickly make the decision for her. The term “expert,” is used by groups and individuals to legitimize claims of expertise or competence. As a term and ‘position’ in society, the ‘expert’ is also able to influence how people understand and act in social realms and their world. This is a common paradigm in the culture of medicine in which paternalism and physician choice in medical decisions is the longstanding cultural norm . As a hoFirst, participants play multiple roles: Hospice nurses are asked to play many different roles at any given moment . It is important to have these roles defined and understood from the outset and re-confirmed throughout the interaction.Second, participant goals vary: Patient and caregiver goals can vary, change, and evolve visit-to-visit. Clarifying the goal of the interaction enhances the likelihood of establishing trust at the beginning of the encounter and reaching concordance.Third, understanding intersection(s) is key to adaptation: Understanding that roles and goals may come into opposition, but finding intersections where concordance, or points of commonality between people’s purposes, concerns, and circumstances exists and remaining focused and adaptive leads to better shared decision-making. Finding intersections quickly and returning to them frequently helps discern between what patients/caregivers want, what they do not want, and what role the nurse must play to help the patient and family achieve those goals in light of their unique circumstances.What we learned from analyzing this interaction ecologically:Although we agree with the assertion in the OIG report that patients require complete and accurate information about the hospice benefit to make an informed decision, we have shown the impracticality of one standardized method of hospice enrollment. The competing demands and expectations of the patient, family, and provider all need to be tempered to maximize the effectiveness of these interactions. Analyzing hospice admission interactions through an ecological lens demonstrates how the OIG’s mandate for clear election statements for informed decision-making is tempered and mediated by the complexity and heterogeneity of each individual hospice admission interaction. Moreover, this case study demonstrates that achieving concordance and informed consent is far more nuanced than verbiage on election forms. It demands real-time interpretation of a patient’s goals of care, patience, adaptability, and awareness of the interpersonal, organizational, and culture tensions that impact effective communication and interaction design. Patients and families enter into hospice admission interactions with varying degrees of comfort and knowledge, having to make a significant decision at a time when their loved one is extremely ill and the family is often very fatigued. This is deep and profoundly difficult caring work for admission nurses, who often conduct multiple consults a day and are responsible for explaining the hospice benefit, completing consents and advance directives, holding a goals of care conversation, and coordinating care of each patient. Analyzing this interaction reveals the different spaces patients, families, and nurses can inhabit, and why finding concordance from the outset is integral to facilitating a meaningful interaction.The critical function of communication studies is to improve dialogue and decision-making processes. In palliative and hospice care interactions, understanding the impact of particular communicative practices on patients and families in the extremely vulnerable terrain of advanced illness requires detailed, scientific, and theory-driven explanations and constructions of alternative meaning and discourses ,8,10,20.


The search for the underlying neural activation that occurs during subjective aesthetic experiences of artwork has been enhanced through neuroimaging techniques. Recently, the dorsolateral prefrontal cortex, alongside the orbitofrontal cortex, have been implicated in aesthetic appreciation, and this is the focus of the present paper. Here, the validity of this conclusion is examined through the discussion of its neuroanatomical connections and functional properties. It is proposed that the experimental evidence challenges the view that this area could hold a privileged position in a brain network involved in aesthetic preference. In recent years, neuroimaging studies have investigated the underlying neural foundations of aesthetic experience of art . A siThat said, identifying the contribution of each PFC area is recognised to be an arduous challenge . As a mWhat is the known role of the OFC and of the DLPFC beyond the aesthetic domain? Neuroimaging  and lespassively observed on a computer monitor in the scanner. In other two fMRI experiments highlighting OFC’s role in aesthetic preference, participants were asked to rate visual or auditory stimuli  or to jr “ugly”  by pressr “ugly”  asked par “ugly” ,11, partr “ugly” , the parDid the experimental design affect the outcome of these experiments? Possibly, but this is not wholly clear yet, as the paradigms that have been used vary considerably. Importantly, a meta-analysis  revealedWallis and Miller  also demAnother issue worth commenting is the left lateralisation of the DLPFC’s activity in aesthetic tasks . The left DLPFC is considered to be part of a neural network of mood modulation . For insThus, is the behavioural effects measured by Cattaneo and colleagues  a resultThe last issue discussed here concerns whether or not tDCS, the technique employed by Cattaneo and colleagues ,11, can It is nonetheless likely that tDCS effects reached the OFC as well as other areas related to the aesthetic experience (see Table S1 in ). IndeedRecent meta-analyses of published neuroimaging studies revealed that multiple and distributed brain regions linked to reward, pleasure, emotion, judgement, decision-making, and perception are involved in aesthetic reactions to artwork ,88. Alth