Project acronym 4C
Project 4C technology: uncovering the multi-dimensional structure of the genome
Researcher (PI) Wouter Leonard De Laat
Host Institution (HI) KONINKLIJKE NEDERLANDSE AKADEMIE VAN WETENSCHAPPEN - KNAW
Call Details Starting Grant (StG), LS2, ERC-2007-StG
Summary The architecture of DNA in the cell nucleus is an emerging epigenetic key contributor to genome function. We recently developed 4C technology, a high-throughput technique that combines state-of-the-art 3C technology with tailored micro-arrays to uniquely allow for an unbiased genome-wide search for DNA loci that interact in the nuclear space. Based on 4C technology, we were the first to provide a comprehensive overview of long-range DNA contacts of selected loci. The data showed that active and inactive chromatin domains contact many distinct regions within and between chromosomes and genes switch long-range DNA contacts in relation to their expression status. 4C technology not only allows investigating the three-dimensional structure of DNA in the nucleus, it also accurately reconstructs at least 10 megabases of the one-dimensional chromosome sequence map around the target sequence. Changes in this physical map as a result of genomic rearrangements are therefore identified by 4C technology. We recently demonstrated that 4C detects deletions, balanced inversions and translocations in patient samples at a resolution (~7kb) that allowed immediate sequencing of the breakpoints. Excitingly, 4C technology therefore offers the first high-resolution genomic approach that can identify both balanced and unbalanced genomic rearrangements. 4C is expected to become an important tool in clinical diagnosis and prognosis. Key objectives of this proposal are: 1. Explore the functional significance of DNA folding in the nucleus by systematically applying 4C technology to differentially expressed gene loci. 2. Adapt 4C technology such that it allows for massive parallel analysis of DNA interactions between regulatory elements and gene promoters. This method would greatly facilitate the identification of functionally relevant DNA elements in the genome. 3. Develop 4C technology into a clinical diagnostic tool for the accurate detection of balanced and unbalanced rearrangements.
Summary
The architecture of DNA in the cell nucleus is an emerging epigenetic key contributor to genome function. We recently developed 4C technology, a high-throughput technique that combines state-of-the-art 3C technology with tailored micro-arrays to uniquely allow for an unbiased genome-wide search for DNA loci that interact in the nuclear space. Based on 4C technology, we were the first to provide a comprehensive overview of long-range DNA contacts of selected loci. The data showed that active and inactive chromatin domains contact many distinct regions within and between chromosomes and genes switch long-range DNA contacts in relation to their expression status. 4C technology not only allows investigating the three-dimensional structure of DNA in the nucleus, it also accurately reconstructs at least 10 megabases of the one-dimensional chromosome sequence map around the target sequence. Changes in this physical map as a result of genomic rearrangements are therefore identified by 4C technology. We recently demonstrated that 4C detects deletions, balanced inversions and translocations in patient samples at a resolution (~7kb) that allowed immediate sequencing of the breakpoints. Excitingly, 4C technology therefore offers the first high-resolution genomic approach that can identify both balanced and unbalanced genomic rearrangements. 4C is expected to become an important tool in clinical diagnosis and prognosis. Key objectives of this proposal are: 1. Explore the functional significance of DNA folding in the nucleus by systematically applying 4C technology to differentially expressed gene loci. 2. Adapt 4C technology such that it allows for massive parallel analysis of DNA interactions between regulatory elements and gene promoters. This method would greatly facilitate the identification of functionally relevant DNA elements in the genome. 3. Develop 4C technology into a clinical diagnostic tool for the accurate detection of balanced and unbalanced rearrangements.
Max ERC Funding
1 225 000 €
Duration
Start date: 2008-09-01, End date: 2013-08-31
Project acronym ABCTRANSPORT
Project Minimalist multipurpose ATP-binding cassette transporters
Researcher (PI) Dirk Jan Slotboom
Host Institution (HI) RIJKSUNIVERSITEIT GRONINGEN
Call Details Starting Grant (StG), LS1, ERC-2011-StG_20101109
Summary Many Gram-positive (pathogenic) bacteria are dependent on the uptake of vitamins from the environment or from the infected host. We have recently discovered the long-elusive family of membrane protein complexes catalyzing such transport. The vitamin transporters have an unprecedented modular architecture consisting of a single multipurpose energizing module (the Energy Coupling Factor, ECF) and multiple exchangeable membrane proteins responsible for substrate recognition (S-components). The S-components have characteristics of ion-gradient driven transporters (secondary active transporters), whereas the energizing modules are related to ATP-binding cassette (ABC) transporters (primary active transporters).
The aim of the proposal is threefold: First, we will address the question how properties of primary and secondary transporters are combined in ECF transporters to obtain a novel transport mechanism. Second, we will study the fundamental and unresolved question how protein-protein recognition takes place in the hydrophobic environment of the lipid bilayer. The modular nature of the ECF proteins offers a natural system to study the driving forces used for membrane protein interaction. Third, we will assess whether the ECF transport systems could become targets for antibacterial drugs. ECF transporters are found exclusively in prokaryotes, and their activity is often essential for viability of Gram-positive pathogens. Thus they could turn out to be an Achilles’ heel for the organisms.
Structural and mechanistic studies (X-ray crystallography, microscopy, spectroscopy and biochemistry) will reveal how the different transport modes are combined in a single protein complex, how transport is energized and catalyzed, and how protein-protein recognition takes place. Microbiological screens will be developed to search for compounds that inhibit prokaryote-specific steps of the mechanism of ECF transporters.
Summary
Many Gram-positive (pathogenic) bacteria are dependent on the uptake of vitamins from the environment or from the infected host. We have recently discovered the long-elusive family of membrane protein complexes catalyzing such transport. The vitamin transporters have an unprecedented modular architecture consisting of a single multipurpose energizing module (the Energy Coupling Factor, ECF) and multiple exchangeable membrane proteins responsible for substrate recognition (S-components). The S-components have characteristics of ion-gradient driven transporters (secondary active transporters), whereas the energizing modules are related to ATP-binding cassette (ABC) transporters (primary active transporters).
The aim of the proposal is threefold: First, we will address the question how properties of primary and secondary transporters are combined in ECF transporters to obtain a novel transport mechanism. Second, we will study the fundamental and unresolved question how protein-protein recognition takes place in the hydrophobic environment of the lipid bilayer. The modular nature of the ECF proteins offers a natural system to study the driving forces used for membrane protein interaction. Third, we will assess whether the ECF transport systems could become targets for antibacterial drugs. ECF transporters are found exclusively in prokaryotes, and their activity is often essential for viability of Gram-positive pathogens. Thus they could turn out to be an Achilles’ heel for the organisms.
Structural and mechanistic studies (X-ray crystallography, microscopy, spectroscopy and biochemistry) will reveal how the different transport modes are combined in a single protein complex, how transport is energized and catalyzed, and how protein-protein recognition takes place. Microbiological screens will be developed to search for compounds that inhibit prokaryote-specific steps of the mechanism of ECF transporters.
Max ERC Funding
1 500 000 €
Duration
Start date: 2012-01-01, End date: 2017-12-31
Project acronym ACTIVATION OF XCI
Project Molecular mechanisms controlling X chromosome inactivation
Researcher (PI) Joost Henk Gribnau
Host Institution (HI) ERASMUS UNIVERSITAIR MEDISCH CENTRUM ROTTERDAM
Call Details Starting Grant (StG), LS2, ERC-2010-StG_20091118
Summary In mammals, gene dosage of X-chromosomal genes is equalized between sexes by random inactivation of either one of the two X chromosomes in female cells. In the initial phase of X chromosome inactivation (XCI), a counting and initiation process determines the number of X chromosomes per nucleus, and elects the future inactive X chromosome (Xi). Xist is an X-encoded gene that plays a crucial role in the XCI process. At the start of XCI Xist expression is up-regulated and Xist RNA accumulates on the future Xi thereby initiating silencing in cis. Recent work performed in my laboratory indicates that the counting and initiation process is directed by a stochastic mechanism, in which each X chromosome has an independent probability to be inactivated. We also found that this probability is determined by the X:ploïdy ratio. These results indicated the presence of at least one X-linked activator of XCI. With a BAC screen we recently identified X-encoded RNF12 to be a dose-dependent activator of XCI. Expression of RNF12 correlates with Xist expression, and a heterozygous deletion of Rnf12 results in a marked loss of XCI in female cells. The presence of a small proportion of cells that still initiate XCI, in Rnf12+/- cells, also indicated that more XCI-activators are involved in XCI. Here, we propose to investigate the molecular mechanism by which RNF12 activates XCI in mouse and human, and to search for additional XCI-activators. We will also attempt to establish the role of different inhibitors of XCI, including CTCF and the pluripotency factors OCT4, SOX2 and NANOG. We anticipate that these studies will significantly advance our understanding of XCI mechanisms, which is highly relevant for a better insight in the manifestation of X-linked diseases that are affected by XCI.
Summary
In mammals, gene dosage of X-chromosomal genes is equalized between sexes by random inactivation of either one of the two X chromosomes in female cells. In the initial phase of X chromosome inactivation (XCI), a counting and initiation process determines the number of X chromosomes per nucleus, and elects the future inactive X chromosome (Xi). Xist is an X-encoded gene that plays a crucial role in the XCI process. At the start of XCI Xist expression is up-regulated and Xist RNA accumulates on the future Xi thereby initiating silencing in cis. Recent work performed in my laboratory indicates that the counting and initiation process is directed by a stochastic mechanism, in which each X chromosome has an independent probability to be inactivated. We also found that this probability is determined by the X:ploïdy ratio. These results indicated the presence of at least one X-linked activator of XCI. With a BAC screen we recently identified X-encoded RNF12 to be a dose-dependent activator of XCI. Expression of RNF12 correlates with Xist expression, and a heterozygous deletion of Rnf12 results in a marked loss of XCI in female cells. The presence of a small proportion of cells that still initiate XCI, in Rnf12+/- cells, also indicated that more XCI-activators are involved in XCI. Here, we propose to investigate the molecular mechanism by which RNF12 activates XCI in mouse and human, and to search for additional XCI-activators. We will also attempt to establish the role of different inhibitors of XCI, including CTCF and the pluripotency factors OCT4, SOX2 and NANOG. We anticipate that these studies will significantly advance our understanding of XCI mechanisms, which is highly relevant for a better insight in the manifestation of X-linked diseases that are affected by XCI.
Max ERC Funding
1 500 000 €
Duration
Start date: 2011-04-01, End date: 2016-03-31
Project acronym ALMP_ECON
Project Effective evaluation of active labour market policies in social insurance programs - improving the interaction between econometric evaluation estimators and economic theory
Researcher (PI) Bas Van Der Klaauw
Host Institution (HI) STICHTING VU
Call Details Starting Grant (StG), SH1, ERC-2007-StG
Summary In most European countries social insurance programs, like welfare, unemployment insurance and disability insurance are characterized by low reemployment rates. Therefore, governments spend huge amounts of money on active labour market programs, which should help individuals in finding work. Recent surveys indicate that programs which aim at intensifying job search behaviour are much more effective than schooling programs for improving human capital. A second conclusion from these surveys is that despite the size of the spendings on these programs, evidence on its effectiveness is limited. This research proposal aims at developing an economic framework that will be used to evaluate the effectiveness of popular programs like offering reemployment bonuses, fraud detection, workfare and job search monitoring. The main innovation is that I will combine economic theory with recently developed econometric techniques and detailed administrative data sets, which have not been explored before. While most of the literature only focuses on short-term outcomes, the available data allow me to also consider the long-term effectiveness of programs. The key advantage of an economic model is that I can compare the effectiveness of the different programs, consider modifications of programs and combinations of programs. Furthermore, using an economic model I can construct profiling measures to improve the targeting of programs to subsamples of the population. This is particularly relevant if the effectiveness of programs differs between individuals or depends on the moment in time the program is offered. Therefore, the results from this research will not only be of scientific interest, but will also be of great value to policymakers.
Summary
In most European countries social insurance programs, like welfare, unemployment insurance and disability insurance are characterized by low reemployment rates. Therefore, governments spend huge amounts of money on active labour market programs, which should help individuals in finding work. Recent surveys indicate that programs which aim at intensifying job search behaviour are much more effective than schooling programs for improving human capital. A second conclusion from these surveys is that despite the size of the spendings on these programs, evidence on its effectiveness is limited. This research proposal aims at developing an economic framework that will be used to evaluate the effectiveness of popular programs like offering reemployment bonuses, fraud detection, workfare and job search monitoring. The main innovation is that I will combine economic theory with recently developed econometric techniques and detailed administrative data sets, which have not been explored before. While most of the literature only focuses on short-term outcomes, the available data allow me to also consider the long-term effectiveness of programs. The key advantage of an economic model is that I can compare the effectiveness of the different programs, consider modifications of programs and combinations of programs. Furthermore, using an economic model I can construct profiling measures to improve the targeting of programs to subsamples of the population. This is particularly relevant if the effectiveness of programs differs between individuals or depends on the moment in time the program is offered. Therefore, the results from this research will not only be of scientific interest, but will also be of great value to policymakers.
Max ERC Funding
550 000 €
Duration
Start date: 2008-07-01, End date: 2013-06-30
Project acronym ASAP
Project Thylakoid membrane in action: acclimation strategies in algae and plants
Researcher (PI) Roberta Croce
Host Institution (HI) STICHTING VU
Call Details Starting Grant (StG), LS1, ERC-2011-StG_20101109
Summary Life on earth is sustained by the process that converts sunlight energy into chemical energy: photosynthesis. This process is operating near the boundary between life and death: if the absorbed energy exceeds the capacity of the metabolic reactions, it can result in photo-oxidation events that can cause the death of the organism. Over-excitation is happening quite often: oxygenic organisms are exposed to (drastic) changes in environmental conditions (light intensity, light quality and temperature), which influence the physical (light-harvesting) and chemical (enzymatic reactions) parts of the photosynthetic process to a different extent, leading to severe imbalances. However, daily experience tells us that plants are able to deal with most of these situations, surviving and happily growing. How do they manage? The photosynthetic membrane is highly flexible and it is able to change its supramolecular organization and composition and even the function of some of its components on a time scale as fast as a few seconds, thereby regulating the light-harvesting capacity. However, the structural/functional changes in the membrane are far from being fully characterized and the molecular mechanisms of their regulation are far from being understood. This is due to the fact that all these mechanisms require the simultaneous presence of various factors and thus the system should be analyzed at a high level of complexity; however, to obtain molecular details of a very complex system as the thylakoid membrane in action has not been possible so far. Over the last years we have developed and optimized a range of methods that now allow us to take up this challenge. This involves a high level of integration of biological and physical approaches, ranging from plant transformation and in vivo knock out of individual pigments to ultrafast-spectroscopy in a mix that is rather unique for my laboratory and will allow us to unravel the photoprotective mechanisms in algae and plants.
Summary
Life on earth is sustained by the process that converts sunlight energy into chemical energy: photosynthesis. This process is operating near the boundary between life and death: if the absorbed energy exceeds the capacity of the metabolic reactions, it can result in photo-oxidation events that can cause the death of the organism. Over-excitation is happening quite often: oxygenic organisms are exposed to (drastic) changes in environmental conditions (light intensity, light quality and temperature), which influence the physical (light-harvesting) and chemical (enzymatic reactions) parts of the photosynthetic process to a different extent, leading to severe imbalances. However, daily experience tells us that plants are able to deal with most of these situations, surviving and happily growing. How do they manage? The photosynthetic membrane is highly flexible and it is able to change its supramolecular organization and composition and even the function of some of its components on a time scale as fast as a few seconds, thereby regulating the light-harvesting capacity. However, the structural/functional changes in the membrane are far from being fully characterized and the molecular mechanisms of their regulation are far from being understood. This is due to the fact that all these mechanisms require the simultaneous presence of various factors and thus the system should be analyzed at a high level of complexity; however, to obtain molecular details of a very complex system as the thylakoid membrane in action has not been possible so far. Over the last years we have developed and optimized a range of methods that now allow us to take up this challenge. This involves a high level of integration of biological and physical approaches, ranging from plant transformation and in vivo knock out of individual pigments to ultrafast-spectroscopy in a mix that is rather unique for my laboratory and will allow us to unravel the photoprotective mechanisms in algae and plants.
Max ERC Funding
1 696 961 €
Duration
Start date: 2011-12-01, End date: 2017-11-30
Project acronym BayesianMarkets
Project Bayesian markets for unverifiable truths
Researcher (PI) Aurelien Baillon
Host Institution (HI) ERASMUS UNIVERSITEIT ROTTERDAM
Call Details Starting Grant (StG), SH1, ERC-2014-STG
Summary Subjective data play an increasing role in modern economics. For instance, new welfare measurements are based on people’s subjective assessments of their happiness or their life satisfaction. A problem of such measurements is that people have no incentives to tell the truth. To solve this problem and make those measurements incentive compatible, I will introduce a new market institution, called Bayesian markets.
Imagine we ask people whether they are happy with their life. On Bayesian markets, they will trade an asset whose value is the proportion of people answering Yes. Only those answering Yes will have the right to buy the asset and those answering No the right to sell it. Bayesian updating implies that “Yes” agents predict a higher value of the asset than “No” agents do and, consequently, “Yes” agents want to buy it while “No” agents want to sell it. I will show that truth-telling is then the optimal strategy.
Bayesian markets reward truth-telling the same way as prediction markets (betting markets) reward people for reporting their true subjective probabilities about observable events. Yet, unlike prediction markets, they do not require events to be objectively observable. Bayesian markets apply to any type of unverifiable truths, from one’s own happiness to beliefs about events that will never be observed.
The present research program will first establish the theoretical foundations of Bayesian markets. It will then develop the proper methodology to implement them. Finally, it will disseminate the use of Bayesian markets via applications.
The first application will demonstrate how degrees of expertise can be measured and will apply it to risks related to climate change and nuclear power plants. It will contribute to the political debate by shedding new light on what true experts think about these risks. The second application will provide the first incentivized measures of life satisfaction and happiness.
Summary
Subjective data play an increasing role in modern economics. For instance, new welfare measurements are based on people’s subjective assessments of their happiness or their life satisfaction. A problem of such measurements is that people have no incentives to tell the truth. To solve this problem and make those measurements incentive compatible, I will introduce a new market institution, called Bayesian markets.
Imagine we ask people whether they are happy with their life. On Bayesian markets, they will trade an asset whose value is the proportion of people answering Yes. Only those answering Yes will have the right to buy the asset and those answering No the right to sell it. Bayesian updating implies that “Yes” agents predict a higher value of the asset than “No” agents do and, consequently, “Yes” agents want to buy it while “No” agents want to sell it. I will show that truth-telling is then the optimal strategy.
Bayesian markets reward truth-telling the same way as prediction markets (betting markets) reward people for reporting their true subjective probabilities about observable events. Yet, unlike prediction markets, they do not require events to be objectively observable. Bayesian markets apply to any type of unverifiable truths, from one’s own happiness to beliefs about events that will never be observed.
The present research program will first establish the theoretical foundations of Bayesian markets. It will then develop the proper methodology to implement them. Finally, it will disseminate the use of Bayesian markets via applications.
The first application will demonstrate how degrees of expertise can be measured and will apply it to risks related to climate change and nuclear power plants. It will contribute to the political debate by shedding new light on what true experts think about these risks. The second application will provide the first incentivized measures of life satisfaction and happiness.
Max ERC Funding
1 500 000 €
Duration
Start date: 2016-01-01, End date: 2020-12-31
Project acronym BURSTREG
Project Single-molecule visualization of transcription dynamics to understand regulatory mechanisms of transcriptional bursting and its effects on cellular fitness
Researcher (PI) Tineke LENSTRA
Host Institution (HI) STICHTING HET NEDERLANDS KANKER INSTITUUT-ANTONI VAN LEEUWENHOEK ZIEKENHUIS
Call Details Starting Grant (StG), LS1, ERC-2017-STG
Summary Transcription in single cells is a stochastic process that arises from the random collision of molecules, resulting in heterogeneity in gene expression in cell populations. This heterogeneity in gene expression influences cell fate decisions and disease progression. Interestingly, gene expression variability is not the same for every gene: noise can vary by several orders of magnitude across transcriptomes. The reason for this transcript-specific behavior is that genes are not transcribed in a continuous fashion, but can show transcriptional bursting, with periods of gene activity followed by periods of inactivity. The noisiness of a gene can be tuned by changing the duration and the rate of switching between periods of activity and inactivity. Even though transcriptional bursting is conserved from bacteria to yeast to human cells, the origin and regulators of bursting remain largely unknown. Here, I will use cutting-edge single-molecule RNA imaging techniques to directly observe and measure transcriptional bursting in living yeast cells. First, bursting properties will be quantified at different endogenous and mutated genes to evaluate the contribution of cis-regulatory promoter elements on bursting. Second, the role of trans-regulatory complexes will be characterized by dynamic depletion or gene-specific targeting of transcription regulatory proteins and observing changes in RNA synthesis in real-time. Third, I will develop a new technology to visualize the binding dynamics of single transcription factor molecules at the transcription site, so that the stability of upstream regulatory factors and the RNA output can directly be compared in the same cell. Finally, I will examine the phenotypic effect of different bursting patterns on organismal fitness. Overall, these approaches will reveal how bursting is regulated at the molecular level and how different bursting patterns affect the heterogeneity and fitness of the organism.
Summary
Transcription in single cells is a stochastic process that arises from the random collision of molecules, resulting in heterogeneity in gene expression in cell populations. This heterogeneity in gene expression influences cell fate decisions and disease progression. Interestingly, gene expression variability is not the same for every gene: noise can vary by several orders of magnitude across transcriptomes. The reason for this transcript-specific behavior is that genes are not transcribed in a continuous fashion, but can show transcriptional bursting, with periods of gene activity followed by periods of inactivity. The noisiness of a gene can be tuned by changing the duration and the rate of switching between periods of activity and inactivity. Even though transcriptional bursting is conserved from bacteria to yeast to human cells, the origin and regulators of bursting remain largely unknown. Here, I will use cutting-edge single-molecule RNA imaging techniques to directly observe and measure transcriptional bursting in living yeast cells. First, bursting properties will be quantified at different endogenous and mutated genes to evaluate the contribution of cis-regulatory promoter elements on bursting. Second, the role of trans-regulatory complexes will be characterized by dynamic depletion or gene-specific targeting of transcription regulatory proteins and observing changes in RNA synthesis in real-time. Third, I will develop a new technology to visualize the binding dynamics of single transcription factor molecules at the transcription site, so that the stability of upstream regulatory factors and the RNA output can directly be compared in the same cell. Finally, I will examine the phenotypic effect of different bursting patterns on organismal fitness. Overall, these approaches will reveal how bursting is regulated at the molecular level and how different bursting patterns affect the heterogeneity and fitness of the organism.
Max ERC Funding
1 950 775 €
Duration
Start date: 2018-01-01, End date: 2022-12-31
Project acronym CAFES
Project Causal Analysis of Feedback Systems
Researcher (PI) Joris Marten Mooij
Host Institution (HI) UNIVERSITEIT VAN AMSTERDAM
Call Details Starting Grant (StG), PE6, ERC-2014-STG
Summary 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.
Summary
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.
Max ERC Funding
1 405 652 €
Duration
Start date: 2015-09-01, End date: 2020-08-31
Project acronym CRITIQUEUE
Project Critical queues and reflected stochastic processes
Researcher (PI) Johannes S.H. Van Leeuwaarden
Host Institution (HI) TECHNISCHE UNIVERSITEIT EINDHOVEN
Call Details Starting Grant (StG), PE1, ERC-2010-StG_20091028
Summary Our primary motivation stems from queueing theory, the branch of applied probability that deals with congestion phenomena. Congestion levels are typically nonnegative, which is why reflected stochastic processes arise naturally in queueing theory. Other applications of reflected stochastic processes are in the fields of branching processes and random graphs.
We are particularly interested in critically-loaded queueing systems (close to 100% utilization), also referred to as queues in heavy traffic. Heavy-traffic analysis typically reduces complicated queueing processes to much simpler (reflected) limit processes or scaling limits. This makes the analysis of complex systems tractable, and from a mathematical point of view, these results are appealing since they can be made rigorous. Within the large
body of literature on heavy-traffic theory and critical stochastic processes, we launch two new research lines:
(i) Time-dependent analysis through scaling limits.
(ii) Dimensioning stochastic systems via refined scaling limits and optimization.
Both research lines involve mathematical techniques that combine stochastic theory with asymptotic theory, complex analysis, functional analysis, and modern probabilistic methods. It will provide a platform enabling collaborations between researchers in pure and applied probability and researchers in performance analysis of queueing systems. This will particularly be the case at TU/e, the host institution, and at
the affiliated institution EURANDOM.
Summary
Our primary motivation stems from queueing theory, the branch of applied probability that deals with congestion phenomena. Congestion levels are typically nonnegative, which is why reflected stochastic processes arise naturally in queueing theory. Other applications of reflected stochastic processes are in the fields of branching processes and random graphs.
We are particularly interested in critically-loaded queueing systems (close to 100% utilization), also referred to as queues in heavy traffic. Heavy-traffic analysis typically reduces complicated queueing processes to much simpler (reflected) limit processes or scaling limits. This makes the analysis of complex systems tractable, and from a mathematical point of view, these results are appealing since they can be made rigorous. Within the large
body of literature on heavy-traffic theory and critical stochastic processes, we launch two new research lines:
(i) Time-dependent analysis through scaling limits.
(ii) Dimensioning stochastic systems via refined scaling limits and optimization.
Both research lines involve mathematical techniques that combine stochastic theory with asymptotic theory, complex analysis, functional analysis, and modern probabilistic methods. It will provide a platform enabling collaborations between researchers in pure and applied probability and researchers in performance analysis of queueing systems. This will particularly be the case at TU/e, the host institution, and at
the affiliated institution EURANDOM.
Max ERC Funding
970 800 €
Duration
Start date: 2010-08-01, End date: 2016-07-31
Project acronym DECODINGSUMO
Project Cracking the SUMO Signalling Code
Researcher (PI) Alfredus Cornelis Otto Vertegaal
Host Institution (HI) ACADEMISCH ZIEKENHUIS LEIDEN
Call Details Starting Grant (StG), LS1, ERC-2012-StG_20111109
Summary "Functional activity of proteins is tightly controlled via reversible post-translational modifications including phosphorylation, acetylation and ubiquitylation. These modifications enable the orchestration of cellular responses to a wide variety of stimuli. Due to these modifications, proteomes are overwhelmingly complex. Progress in the field has been greatly accelerated by the development of novel approaches to study these post-translational modifications at a proteome-wide scale using the sensitivity and robustness of mass spectrometry (MS). This has enabled the identification of thousands of dynamically regulated phosphorylation, acetylation and ubiquitylation sites by MS. The functional significance of these modifications is now being addressed worldwide at an unprecedented scale. In contrast, global understanding of ubiquitin-like signalling networks in a site-specific manner is very challenging.
Over the last few years, my lab has established novel methodology for the purification and identification of endogenous SUMO target proteins and SUMOylation sites of endogenous targets. The first aim of this project is to uncover small ubiquitin-like modifier (SUMO) signalling pathways in a site-specific manner at a proteome-wide level.
The second aim of this project is to reveal how SUMOylation cooperates with ubiquitylation to maintain genome integrity. SUMOylation plays a critical role during the DNA damage response, an important barrier against genome instability linked diseases including cancer and neurodegeneration. Selected target proteins will be studied at the functional and mechanistic level to obtain novel insight in cellular processes that protect against genome instability.
The developed methodology is generic and can be applied to study all ubiquitin-like proteins at a proteome-wide level in a site-specific manner, enabling global understanding of ubiquitin-like signalling networks in health and disease."
Summary
"Functional activity of proteins is tightly controlled via reversible post-translational modifications including phosphorylation, acetylation and ubiquitylation. These modifications enable the orchestration of cellular responses to a wide variety of stimuli. Due to these modifications, proteomes are overwhelmingly complex. Progress in the field has been greatly accelerated by the development of novel approaches to study these post-translational modifications at a proteome-wide scale using the sensitivity and robustness of mass spectrometry (MS). This has enabled the identification of thousands of dynamically regulated phosphorylation, acetylation and ubiquitylation sites by MS. The functional significance of these modifications is now being addressed worldwide at an unprecedented scale. In contrast, global understanding of ubiquitin-like signalling networks in a site-specific manner is very challenging.
Over the last few years, my lab has established novel methodology for the purification and identification of endogenous SUMO target proteins and SUMOylation sites of endogenous targets. The first aim of this project is to uncover small ubiquitin-like modifier (SUMO) signalling pathways in a site-specific manner at a proteome-wide level.
The second aim of this project is to reveal how SUMOylation cooperates with ubiquitylation to maintain genome integrity. SUMOylation plays a critical role during the DNA damage response, an important barrier against genome instability linked diseases including cancer and neurodegeneration. Selected target proteins will be studied at the functional and mechanistic level to obtain novel insight in cellular processes that protect against genome instability.
The developed methodology is generic and can be applied to study all ubiquitin-like proteins at a proteome-wide level in a site-specific manner, enabling global understanding of ubiquitin-like signalling networks in health and disease."
Max ERC Funding
1 517 699 €
Duration
Start date: 2013-01-01, End date: 2017-12-31
