This webpage is dedicated to the tool RE:IN, providing information on the latest version available, together with a tutorial, FAQ, and example files that correspond to investigations carried out in our publications.
The Reasoning Engine for Interaction Networks (RE:IN) is a tool that runs online in your web browser, which is designed for the synthesis and analysis of biological programs. Specifically, it encapsulates a methodology that uses automated reasoning to transform a set of critical components, ‘possible’ interactions and regulation functions into a dynamic explanation of experimental observations. Components and possible interactions are defined to construct an ‘abstract’ network topology, which implicitly defines a number of concrete topologies. This formalism allows us to capture some of the uncertainty in the interactions that may or may not exist between biological components due to noisy or limited experimental data. Experimental observations are encoded as constraints on network trajectories. RE:IN synthesises those concrete networks that satisfy the constraints, and permits the user to query the set of consistent models to formulate predictions of untested behaviour.
Software design patterns provide abstract, reusable solutions to frequently encountered problem. In this project, we will explore whether biology also exploits design patterns in the regulatory programs controlling cellular behaviours. We will focus on the role of small regulatory motifs that are known to be enriched in living systems, and which cluster in specific ways, and employ formal methods to understand how structure gives rise to function in these motifs. A new regulatory system based on engineered RNA interactions will be developed to allow for the reliable creation of large regulatory circuits, which will then be used to implement novel regulatory programs from motif-based design rules.
4-year PhD Scholarship, Supervisor: Dr Thomas Gorochowski (School of Biological Sciences, University of Bristol, UK) Co-supervisors: Dr Boyan Yordanov, Dr Sara-Jane Dunn (Biological Computation Group, Microsoft Research Cambridge, UK) and Lucia Marucci (Engineering Mathematics, University of Bristol, UK).
Find out more information here.
To use the tool directly, please click here.For new users of the tool, please see our tutorial. This also provides detail of the updated syntax in the latest version of RE:IN. We have compiled a set of Frequently Asked Questions for reference and common queries. It may also be useful to start with this simple toy example.
The following links navigate to examples from the indicated publications. The files used to generate these can be saved directly from the tool for examination, or further editing and exploration.
Together with Austin Smith (Wellcome-MRC Cambridge Stem Cell Institute) and Graziano Martello (University of Padova), we developed RE:IN to uncover the transcriptional program governing naive pluripotency in mouse ESCs. The following links correspond to the investigations presented in this publication.
- The network governing naive pluripotency in mouse embryonic stem cells. This corresponds to the initial set of components, interactions and constraints that we encoded.
- Identifying the minimal transcriptional network governing naive pluripotency. In this example, use the ‘Find Minimal Models’ functionality.
In this publication, we expose the methodology that underlies RE:IN, and provided three exemplars of the analysis procedures that the software provides.
In this publication, we demonstrate that a common molecular program governs both maintenance and installation of naive pluripotency. We carried out an iterative process of model prediction and refinement to shed insight into the critical dynamics of the final stage of cellular reprogramming. A ‘News and Views’ article was commissioned by the EMBO Journal to introduce and discuss this publication, written by researchers Owen Rackham (Duke-NUS Medical School) and Jose Polo (Monash University). This is an excellent review of the work, and a great starting point to understand what we achieved.
- The 0.782 cABN. This is the cABN that was used to generate the initial set of predictions concerning the dynamics of EpiSC resetting.
- The 0.717 cABN. This is the final, refined set of models that was also used to predict the dynamics of reprogramming from MEFs.