ASKE Month 9 Milestone Report

Generalizing EMMAA: a proof-of-principle model of food insecurity

Until recently, all models in EMMAA represented molecular mechanisms for a given disease or pathway. However, the EMMAA approach can be applied to models in other domains. Conceptually, the EMMAA framework is a good fit for domains where there is a constant flow of novel causal information between interacting “agents” or “concepts” appearing in a structured or unstructured form. To demonstrate the generalizability of EMMAA, we created a model of causal factors influencing food insecurity.

In principle, setting up a new EMMAA model only requires creating a new configuration file that specifies a name, a description, as well as a list of search terms, and any optional arguments used to configure the model building process. In applying EMMAA to a new domain, we extended the set of options that can be specified in the configuration file, including the following:

  • The literature catalogue to use to search for new content. Biology models use PubMed (specific to biomedicine), whereas other domain models can now use ScienceDirect (general purpose) to search for new articles.

  • The reading system to use to read new text content. The biology models in EMMAA query the INDRA Database each day to search for machine reading extractions for new publications. The Database contains outputs for two biology-specific reading systems (REACH and Sparser) for new daily literature content. Models in other domains can be configured to use the Eidos reading system (via its INDRA interface) to extract a general set of causal relationships between concepts of interest.

  • The assembly steps to perform during model extension. We added more granularity to configuration options for the model assembly process, making it possible to apply biology-specific INDRA assembly steps (e.g., protein sequence mapping) only to models where they are relevant.

  • The test corpus to use for validating the model. So far, each biology model used the same BEL Large Corpus as a source of test statements to validate against. We made it possible to configure what test corpus to use for a given model, allowing a custom set of relevant tests to be applied to the food insecurity moddel.

To set up the initial, proof-of-principle model of food insecurity, we first identified a set of core concepts of interest: food security, conflict, flooding, food production, human migration, drought, and markets. We then filtered a set of extractions by Eidos on a corpus of 500 documents to causal influences among these concepts. We also set these core concepts as search terms in the model’s configuration file. Finally, we defined a set of common sense statements as test conditions, for instance, “droughts cause a decrease in food availability” to check the model against. The model is now included on the EMMAA dashboard where it can be examined (

While this initial food insecurity model serves as a proof of principle for the generality of the EMMAA concept and the underlying technologies, there are several challenging aspects of building a good model for this domain.

  1. The identification of relevant sources of information. So far, the food insecurity model uses ScienceDirect to search for scientific publications. However, it is likely that a significant amount of timely new information is available in reports (by governments, NGOs, etc.) and news stories. In the longer term, this would require implementing ways to query and collect text content from such sources.

  2. Querying for relevant text content. We found that certain search terms (e.g., food insecurity) result in mostly relevant publications, while others, wuch as “conflict” or “markets” are too broad and ambiguous, and result in many irrelevant publications being picked up. This suggests that one has to constrain the domain, in addition to the specific concepts used as search terms when finding novel literature content.

  3. Machine reading infrastructure. The biology EMMAA models rely on a parallelized AWS infrastructure in which multiple instances of machine reading systems can process hundreds or thousands of new publications each day. In contrast, the food insecurity model currently relies on a single reader instance running as a service, and therefore has much lower throughput. Before a comparable infrastructure of readers is implemented for this domain, we had to limit the number of new publications that are processed each day to update the model.

  4. Reading with corroboration. While biology models in EMMAA rely on knowledge assembled from multiple machine reading systems as well as structured (often human curated) knowledge bases, the food insecurity model currently relies on a single reading system, Eidos. This means that any systematic errors specific to the reading system are prone to propagate into the assembled model. In the longer term, integrating more reading systems or knowledge sources could improve on this.

  5. Indirect relations. As shown by the initial test set for the food insecurity model, all test statements are satisfied by a single causal influence statement, even ones where one might reasonably expect the test to be satisfied via a chain of causal influences, e.g., “droughts cause a decrease in food availability”. We believe that this is due to the fact that authors routinely report indirect causal influences, and the reading/assembly systems currently aren’t set up to effectively differentiate between direct and indirect effects.

Extending model testing and analysis to multiple resolutions

In our Month 6 Milestone Report, we described an initial experiment to investigate the value of coarse-grained model testing using simple directed graphs. In this reporting period we have extended this concept further by developing a generalized framework for model checking using networks assembled at different levels of granularity and specificity. In particular, we are expanding the range of models assembled from a set of EMMAA Statements to include:

  • Directed networks

  • Signed directed networks

  • PyBEL networks (includes nodes with state information)

  • PySB models/Kappa influence maps

For each of these model representations, model checking can be formulated as a process consisting of three steps:

  1. Given a (source, target) statement for checking, identify the nodes associated with the source and target. Note that a source or target agent in the test statement may correspond to multiple nodes in the give network representation.

  2. Identify causal paths linking one or more source nodes to one or more target nodes. If such a path exists, the test statement is satisfied.

  3. Collect paths from the network representation and map them back to the knowledge-level (EMMAA statements) for reporting.

The second step in this process, pathfinding over the causal network, is common to all four of the network representations listed above. However, the first and third steps–identifying mappings between knowledge-level statements and the nodes and edges in the network–are specific to each network representation.

To support multi-resolution model checking we have restructured the INDRA model checker to support multiple model types, with the common code refactored out into a parent class. In addition we have created an assembler that assembles INDRA Statements into a new network representation with a metadata model that can capture the full provenance information from the source INDRA Statements. This network representation, a multi-digraph called the IndraNet, will be used to generate multiple coarse-grained “views” (digraph, signed digraph), while preserving statement metadata.

In the upcoming reporting period we will complete this refactoring procedure and extend the EMMAA web application to generate and display test results for alternative realizations of each individual knowledge model.

Implementing an object model for model analysis queries

We have previously specified a Model Analysis Query Language (MAQL) used to represent various analysis tasks that can be performed on EMMAA models, in either a user or machine-initiated way (see Model Analysis Query Language).

In this reporting period, we implemented a Python object model corresponding to MAQL. The object model provides a structure for all the attributes needed to represent a query, and methods to serialize and deserialize it into JSON. This allows linking the web front-end, the query execution engine, and the back-end query storage database in a principled way through a single standardized format. In particular, we have implemented the PathProperty query class (emmaa.queries.PathProperty), and plan to extend to the other three query types specified in MAQL in the coming months.

Detecting changes in analysis results due to model updates

One of the fundamental ideas of the EMMAA framework is to be able to detect meaningful changes to analyses of interest as model updates happen. We have implemented an initial solution to this in the QueryManager (emmaa.answer_queries.QueryManager) whereby the previous results of each registered query are compared to the new result. Any detected changes are reported in the model update logs (currently not exposed in the user-facing web front-end yet). A limitation of the current approach is that the result of a registered query is a single “top” mechanistic path that satisfies the query conditions, rather than all possible paths. This means that in some cases, when a new path is created by a new piece of knowledge, it would not be detected as a change in the query results, unless the “top” path happens to change. We are planning to improve the change detection method in this direction.

Further, we are working on adding a user registration functionality. Once user accounts and user-specific registered queries are created, the next step will be to create a notification system that exposes the detected changes in analysis results with respect to a query of interest to the user.