Factored expectation propagation for input-output FHMM models in systems biology

We consider the problem of joint modelling of metabolic signals and gene expression in systems biology applications. We propose an approach based on input-output factorial hidden Markov models and propose a structured variational inference approach to infer the structure and states of the model. We start from the classical free form structured variational mean field approach and use a expectation propagation to approximate the expectations needed in the variational loop. We show that this corresponds to a factored expectation constrained approximate inference. We validate our model through extensive simulations and demonstrate its applicability on a real world bacterial data set

Learning and Designing Stochastic Processes from Logical Constraints

Stochastic processes offer a flexible mathematical formalism to model and reason about systems. Most analysis tools, however, start from the premises that models are fully specified, so that any parameters controlling the system's dynamics must be known exactly. As this is seldom the case, many methods have been devised over the last decade to infer (learn) such parameters from observations of the state of the system. In this paper, we depart from this approach by assuming that our observations are {\it qualitative} properties encoded as satisfaction of linear temporal logic formulae, as opposed to quantitative observations of the state of the system. An important feature of this approach is that it unifies naturally the system identification and the system design problems, where the properties, instead of observations, represent requirements to be satisfied. We develop a principled statistical estimation procedure based on maximising the likelihood of the system's parameters, using recent ideas from statistical machine learning. We demonstrate the efficacy and broad applicability of our method on a range of simple but non-trivial examples, including rumour spreading in social networks and hybrid models of gene regulation

De novo prediction of RNA-protein interactions with graph neural networks

RNA-binding proteins (RBPs) are key co- and post-transcriptional regulators of gene expression, playing a crucial role in many biological processes. Experimental methods like CLIP-seq have enabled the identification of transcriptome-wide RNA-protein interactions for select proteins; however, the time- and resource-intensive nature of these technologies call for the development of computational methods to complement their predictions. Here, we leverage recent, large-scale CLIP-seq experiments to construct a de novo predictor of RNA-protein interactions based on graph neural networks (GNN). We show that the GNN method allows us not only to predict missing links in an RNA-protein network, but to predict the entire complement of targets of previously unassayed proteins, and even to reconstruct the entire network of RNA-protein interactions in different conditions based on minimal information. Our results demonstrate the potential of modern machine learning methods to extract useful information on post-transcriptional regulation from large data sets

Autoregressive Point-Processes as Latent State-Space Models: a Moment-Closure Approach to Fluctuations and Autocorrelations

Modeling and interpreting spike train data is a task of central importance in computational neuroscience, with significant translational implications. Two popular classes of data-driven models for this task are autoregressive Point Process Generalized Linear models (PPGLM) and latent State-Space models (SSM) with point-process observations. In this letter, we derive a mathematical connection between these two classes of models. By introducing an auxiliary history process, we represent exactly a PPGLM in terms of a latent, infinite dimensional dynamical system, which can then be mapped onto an SSM by basis function projections and moment closure. This representation provides a new perspective on widely used methods for modeling spike data, and also suggests novel algorithmic approaches to fitting such models. We illustrate our results on a phasic bursting neuron model, showing that our proposed approach provides an accurate and efficient way to capture neural dynamics

Computational Methods in Systems Biology. 17th International Conference, CMSB 2019, Trieste, Italy, September 18\u201320, 2019, Proceedings

This volume contains the papers presented at CMSB 2019, the 17th Conference on Computational Methods in Systems Biology, held during September 18\u201320, 2019, at the University of Trieste, Italy. The CMSB annual conference series, initiated in 2003, provides a unique discussion forum for computer scientists, biologists, mathematicians, engineers, and physicists interested in a system-level understanding of biological processes. Topics covered by the CMSB proceedings include: formalisms for modeling biological processes; models and their biological applications; frameworks for model verification, validation, anal- ysis, and simulation of biological systems; high-performance computational systems biology and parallel implementations; model inference from experimental data; model integration from biological databases; multi-scale modeling and analysis methods; computational approaches for synthetic biology; and case studies in systems and synthetic biology. This year there were 53 submissions in total for the 4 conference tracks. Each regular submission and tool paper submission were reviewed by at least three Program Committee members. Additionally, tools were subjected to an additional review by members of the Tool Evaluation Committee, testing the usability of the software and the reproducibility of the results. For the proceedings, the Program Committee decided to accept 14 regular papers, 7 tool papers, and 11 short papers. This rich program of talks was complemented by a poster session, providing an opportunity for informal discussion of preliminary results and results in related fields. In view of the broad scope of the CMSB conference series, we selected the fol- lowing five high-profile invited speakers: Kobi Benenson (ETH Zurich, Switzerland), Trevor Graham (Barts Cancer Hospital, London, UK), Gaspar Tkacik (IST, Austria), Adelinde Uhrmacher (Rostock University, Germany), and Manuel Zimmer (University of Vienna, Austria). Their invited talks covered a broad area within the technical and applicative domains of the conference, and stimulated fruitful discussions among the conference attendees. Further details on CMSB 2019 are available on the following website: https://cmsb2019.units.it. Finally, as the program co-chairs, we are extremely grateful to the members of the Program Committee and the external reviewers for their peer reviews and the valuable feedback they provided to the authors. Our special thanks go to Laura Nenzi as local organization co-chair, Dimitrios Milios as chair of the Tool Evaluation Committee, and to Fran\ue7ois Fages and all the members of the CMSB Steering Committee, for their advice on organizing and running the conference. We acknowledge the support of the EasyChair conference system during the reviewing process and the production of these proceedings. We also thank Springer for publishing the CMSB proceedings in its Lecture Notes in Computer Science series. Additionally, we would like to thank the Department of Mathematics and Geo- sciences of the University of Trieste, for sponsoring and hosting this event, and Confindustria Venezia Giulia, for supporting this event and providing administrative help. Finally, we would like to thank all the participants of the conference. It was the quality of their presentations and their contribution to the discussions that made the meeting a scientific success