ERC FUNDED PROJECTS

The phytohormone auxin has profound importance for plant development. The extracellular AUXIN BINDING PROTEIN1 (ABP1) and the nuclear AUXIN F-BOX PROTEINs (TIR1/AFBs) auxin receptors perceive fast, non-genomic and slow, genomic auxin responses, respectively. Despite the fact that ABP1 mainly localizes to the endoplasmic reticulum (ER), until now it has been proposed to be active only in the extracellular matrix (reviewed in Sauer and Kleine-Vehn, 2011). Just recently, ABP1 function was also linked to genomic responses, modulating TIR1/AFB-dependent processes (Tromas et al., 2013). Intriguingly, the genomic effect of ABP1 appears to be at least partially independent of the endogenous auxin indole 3-acetic acid (IAA) (Paque et al., 2014). In this proposal my main research objective is to unravel the importance of the ER for genomic auxin responses. The PIN-LIKES (PILS) putative carriers for auxinic compounds also localize to the ER and determine the cellular sensitivity to auxin. PILS5 gain-of-function reduces canonical auxin signaling (Barbez et al., 2012) and phenocopies abp1 knock down lines (Barbez et al., 2012, Paque et al., 2014). Accordingly, a PILS-dependent substrate could be a negative regulator of ABP1 function in the ER. Based on our unpublished data, an IAA metabolite could play a role in ABP1-dependent processes in the ER, possibly providing feedback on the canonical nuclear IAA-signaling. I hypothesize that the genomic auxin response may be an integration of auxin- and auxin-metabolite-dependent nuclear and ER localized signaling, respectively. This proposed project aims to characterize a novel auxin-signaling paradigm in plants. We will employ state of the art interdisciplinary (biochemical, biophysical, computational modeling, molecular, and genetic) methods to assess the projected research. The identification of the proposed auxin conjugate-dependent signal could have far reaching plant developmental and biotechnological importance.

1 441 125 €

Start date: 2015-06-01, End date: 2020-05-31

Many questions in science, policy making and everyday life are of a causal nature: how would changing A influence B? Causal inference, a branch of statistics and machine learning, studies how cause-effect relationships can be discovered from data and how these can be used for making predictions in situations where a system has been perturbed by an external intervention. The ability to reliably make such causal predictions is of great value for practical applications in a variety of disciplines. Over the last two decades, remarkable progress has been made in the field. However, even though state-of-the-art causal inference algorithms work well on simulated data when all their assumptions are met, there is still a considerable gap between theory and practice. The goal of CAFES is to bridge that gap by developing theory and algorithms that will enable large-scale applications of causal inference in various challenging domains in science, industry and decision making. The key challenge that will be addressed is how to deal with cyclic causal relationships ("feedback loops"). Feedback loops are very common in many domains (e.g., biology, economy and climatology), but have mostly been ignored so far in the field. Building on recently established connections between dynamical systems and causal models, CAFES will develop theory and algorithms for causal modeling, reasoning, discovery and prediction for cyclic causal systems. Extensions to stationary and non-stationary processes will be developed to advance the state-of-the-art in causal analysis of time-series data. In order to optimally use available resources, computationally efficient and statistically robust algorithms for causal inference from observational and interventional data in the context of confounders and feedback will be developed. The work will be done with a strong focus on applications in molecular biology, one of the most promising areas for automated causal inference from data.

1 405 652 €

Start date: 2015-09-01, End date: 2020-08-31

What makes music so important, what can make a performance so special and stirring? It is the things the music expresses, the emotions it induces, the associations it evokes, the drama and characters it portrays. The sources of this expressivity are manifold: the music itself, its structure, orchestration, personal associations, social settings, but also – and very importantly – the act of performance, the interpretation and expressive intentions made explicit by the musicians through nuances in timing, dynamics etc. Thanks to research in fields like Music Information Research (MIR), computers can do many useful things with music, from beat and rhythm detection to song identification and tracking. However, they are still far from grasping the essence of music: they cannot tell whether a performance expresses playfulness or ennui, solemnity or gaiety, determination or uncertainty; they cannot produce music with a desired expressive quality; they cannot interact with human musicians in a truly musical way, recognising and responding to the expressive intentions implied in their playing. The project is about developing machines that are aware of certain dimensions of expressivity, specifically in the domain of (classical) music, where expressivity is both essential and – at least as far as it relates to the act of performance – can be traced back to well-defined and measurable parametric dimensions (such as timing, dynamics, articulation). We will develop systems that can recognise, characterise, search music by expressive aspects, generate, modify, and react to expressive qualities in music. To do so, we will (1) bring together the fields of AI, Machine Learning, MIR and Music Performance Research; (2) integrate theories from Musicology to build more well-founded models of music understanding; (3) support model learning and validation with massive musical corpora of a size and quality unprecedented in computational music research.

2 318 750 €

Start date: 2016-01-01, End date: 2021-12-31

Many processes in the chemical, petrochemical and/or biological industries involve three phase gas-liquidsolid flows, where the solid material acts as a catalyst carrier, the gas phase supplies the reactants for the (bio-)chemical transformations and the liquid phase carries the product. In these processes the performance and operation of the reactor is mostly constrained by the interfacial mass transfer rate and the achievable insitu heat removal rate. A micro-structured bubble column reactor that significantly improves these crucial properties is proposed in this project. This novel type of reactor takes advantage of micro-structuring of the catalyst carrier in the form of a wire-mesh (see Figure 1). The aim of the wire-mesh is i) to cut bubbles into smaller pieces leading to a larger interfacial area, ii) to enhance the bubble interface dynamics and mass transfer due to the interaction between the bubbles and the wires, and iii) to save costs in practical operation due to the smaller required reactor volume and the fact that there is no need for an external filtration unit. Cutting edge three-phase direct numerical simulation (DNS) tools and novel non-invasive optical (highspeed camera) techniques are used to study the micro-scale interaction between bubbles and a wire-mesh to gain understanding of the splitting and merging of bubbles and associated mass transfer characteristics. Furthermore, a proof-of-principle of the micro-structured reactor will be given through lab-scale experiments and macroscopic Euler-Lagrange numerical simulations, employing bubble-wire interaction closures based on the DNS simulations. In addition to the novel reactor type, the project will generate a broad set of fundamental numerical and experimental research tools that can be used for the improvement of various gas-liquid-solid processes. Several large companies (AkzoNobel, DSM, Sabic and Shell) have indicated their interest in the proposed project and would like to be involved in a users committee.

1 500 000 €

Start date: 2010-09-01, End date: 2015-08-31