ERC FUNDED PROJECTS

With the advent of genome-scale sequencing, molecular phylogeny, which reconstructs gene trees from homologous sequences, has reached an impasse. Instead of answering open questions, new genomes have reignited old debates. The problem is clear, gene trees are not species trees, each is the unique result of series of evolutionary events. If, however, we model these differences in the context of a common species tree, we can access a wealth of information on genome evolution and the diversification of species that is not available to traditional methods. For example, as horizontal gene transfer (HGT) can only occur between coexisting species, HGTs provide information on the order of speciations. When HGT is rare, lineage sorting can generate incongruence between gene trees and the dating problem can be formulated in terms of biologically meaningful parameters (such as population size), that are informative on the rate of evolution and hence invaluable to molecular dating. My first goal is to develop methods that systematically extract information on the pattern and timing of genomic evolution by explaining differences between gene trees. This will allow us to, for the first time, reconstruct a dated tree of life from genome-scale data. We will use parallel programming to maximise the number of genomes analysed. My second goal is to apply these methods to open problems, e.g.: i) to resolve the timing of microbial evolution and its relationship to Earth history, where the extreme paucity of fossils limits the use of molecular dating methods, by using HGT events as “molecular fossils”; ii) to reconstruct rooted phylogenies from complete genomes and harness phylogenetic incongruence to answer long standing questions, such as the of diversification of animals or the position of eukaryotes among archaea; and iii) for eukaryotic groups such as Fungi, where evidence of significant amounts of HGT is emerging our methods will also allow the quantification of the extent of HGT.

1 453 542 €

Start date: 2017-07-01, End date: 2022-06-30

The evolution of multicellularity (MC) has been one of the major transitions in the history of life. Despite immense interest in its evolutionary origins, the genomic changes leading to the emergence of MC, especially that of complex MC (differentiated 3-dimensional structures) are poorly known. Previous comparative genomics projects aiming to understand the genetic bases of MC in one way or another relied on gene content-based analyses. However, a pattern emerging from these studies is that gene content provides only an incomplete explanation for the evolution of MC even at ancient timescales. We hypothesize that besides gene duplications, changes to cis-regulatory elements and gene expression patterns (including protein isoforms) have significantly contributed to the evolution of MC. To test this hypothesis, we will deploy a combination of computational methods, phylogenomics, comparative transcriptomics and genome-wide assays of regulatory elements. Our research focuses on fungi as a model system, where complex MC evolved convergently and in subsequent two steps. Fungi are ideal models to tackle this question for several reasons: a) multicellularity in fungi evolved multiple times, b) there are rich genomic resources (>500 complete genomes), c) complex multicellular structures can be routinely grown in the lab and d) genetic manipulations are feasible for several cornerstone species. We set out to examine which genes participate in the building of simple and complex multicellular structures and whether the evolution of regulome complexity and gene expression patterns can explain the evolution of MC better than can traditionally assayed sources of genetic innovations (e.g. gene duplications). Ultimately, our goal is to reach a general synthesis on the genetic bases of the evolution of MC and that of organismal complexity.

1 486 500 €

Start date: 2018-01-01, End date: 2022-12-31

Social networks affect many economic interactions, and the social capital embedded in them may help explain broad, macro level outcomes. A recent theory literature develops models of economic allocations in networks. But at this point, we have little evidence on which network mechanisms are most important in practice, how networks interact with markets, and how these micro forces translate into aggregate outcomes. In this proposal I combine micro level measurement with theory to evaluate various mechanisms through which networks affect economic allocations. I explore this theme in four domains. (1) Borrowing and informal insurance in development. I measure the value of connections for borrowing, and study how transfers propagate through the network using field experiments in Peru. (2) Peer effects and the social multiplier. In field experiments I measure financial and peer-based incentives, and how they reinforce each other. I also measure knowledge diffusion about exporting in corporate networks, and the resulting multiplier effect of reducing trade barriers. (3) Information aggregation. I measure how different pieces of information are filtered and aggregated in the social network. (4) Favouritism. I study the economic causes and consequences of favouring friends in a field experiment. I also measure economic misallocation resulting from politicians favouring connected firms in Hungarian data, and the cost to aggregate productivity. In all projects, my measurement emphasizes causality through field experiments and a unique firm level dataset with many sources of variation. Estimating models allows me to contrast theories and generalize the empirical findings. The results will help evaluate the importance of social networks for microeconomic and aggregate allocations, yield lessons on how organizations and policies leverage social mechanisms, and may open a new research area on mechanism design with network effects.

1 165 350 €

Start date: 2011-11-01, End date: 2017-02-28

There is no other field that is more controversial in psychology than that of human reasoning. This project advances a novel theoretical framework focused on the nature and the origins of rationality and could potentially resolve some of these controversies. Theories targeting the mechanisms that allow rational inferences have defined rationality as a function of how much reasoning adheres to formal rules of probability calculus and logic. Classical research with adults and older children collected a large amount of data both in favor and against human rationality, suggesting that reasoning abilities follow a slow maturation. Recent findings on infants’ probabilistic reasoning, including my own earlier research, however, do not support this view. Already preverbal infants seem to form expectations about probabilistic events in accordance with Bayesian rules of inference (Téglás et al, 2011). Here I argue for a similar paradigm change in a related domain, that of deductive reasoning. In contrast to earlier accounts, I propose that even preverbal infants may possess a core set of logical operations that empower them with sophisticated inferential abilities. First, I focus on the representational precursors of this competence. I argue that infants recruit specific abilities to exploit the conceptual structure of specific event categories that enable them to form logical representations. Thus, information could be stored in a format that can potentially serve as input for subsequent inferences. Further, I will investigate infants’ core logical operations and test how they integrate multiple steps of inferences. This system - indispensable for integrating different bits of knowledge - helps infants to discover information that was not actually present in the input. Such investigations, informed also by adequate neuropsychological evidence would thus contribute to understand the unique nature of human rationality.

1 498 137 €

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