Projects & Collaborations 6 foundShow per page10 10 20 50 Analysis of the stability of the neuronal differentiation states Research Project | 1 Project MembersThe understanding and control of cellular differentiation has become a major focus of molecular life sciences and biomedical therapy. While animal cells are less plastic than plant cells, an increasing number of molecules, especially transcription factors, have been identified that are capable of reprogramming cell lineages. This plasticity is, however, associated with an inherent instability of the differentiated or reprogrammed states. Therefore, it is of major interest to unravel mechanisms that can stabilize the desired cell phenotypes. Transcriptional positive feedback loops have been often suggested to stabilize phenotypes, a claim supported by well-characterized molecular mechanisms to support bistability in simpler organisms. Recent evidence suggests, however, that chromosomal regulatory landscapes can also support epigenetic inheritance and differentiation. This project aims at unveiling whether and to what extent these two mechanisms contribute to the neuronal differentiation and the stability of neuronal phenotypes. By focusing on neurogenic transcription factors, we will examine whether they orchestrate transcriptional feedback loops and assess their ability to support bistable phenotypes. Concurrently, we will explore the correlation between phenotypic diversity and the changes in the chromosomal regulatory landscape. In the second subproject, we will use tools of synthetic biology to dissect the components of the chromosomal regulatory landscape to characterize the minimal set of elements that can promote phenotypic diversification through the control of the interdependence in the stochastic expression of the genes in a chromosomal segment. Our experimental systems analysis will shed light on the control principles that can stabilize differentiated or reprogrammed cell states, which will help to engineer neurons with precise functioning for ex vivo studies and for therapeutic applications. ANALYSIS OF RNA AND PROTEIN TURNOVER TO PREDICT STOCHASTIC NETWORK BEHAVIOR Research Project | 1 Project MembersUnderstanding and prediction of the behavior of cellular networks is a major goal of systems biology. Nonlinearities and stochasticity in networks make this prediction difficult but they also promote the emergence of complex behaviors ranging from oscillations, adaptation in chemotaxis to cellular differentiation. Substantial efforts have been invested into deciphering how nonlinearity and noise are influenced by transcription and translation. On the other hand, little is known about how they are affected by decay processes. Our project proposal aims at uncovering how RNA and protein turnover rates determine fluctuations and precision in cellular functions. Our first subproject aims at studying how RNA production and degradation jointly determines fluctuation in gene expression. Recent studies have revealed that decay processes can feedback on biosynthetic processes, which compounds the problem of distinguishing the stochastic effects of RNA degradation. Special attention will be paid to histone gene expression which is very tightly regulated since alterations in the histone gene dosage can lead to changes in the stoichiometry in the histone octamer with pleiotropic effects on the expression of the entire genome. The promoter of the histone gene is regulated by a cooperatively binding transcriptional activator and the decay of histone mRNAs is time dependent, linked to the cell cycle. In the second subproject, we examine how protein decay shapes non-linear responses and how these - in conjunction with fluctuations - determine the stochastic transitions in positive feedback loops. The results will help to design measurements to explain the kinetic mechanisms of cellular memory and the stability of cellular differentiation states. To realize these subprojects, we will use cutting edge techniques involving single molecule RNA detection and quantitative proteomics in yeast cells in combination with mathematical modeling of stochastic processes. Our results will advance the interpretation of non-invasive methods and will help to define the extent to which fluctuations have to be taken into account to efficiently model and understand nonlinear network dynamics. This in turn will be helpful to measure the most relevant biochemical reaction rates in genetically heterogeneous cell populations and to engineer cells with precise functioning. We expect applications in tissue engineering in the future. Genomic analysis of information transfer along the DNA by transcriptional interference Research Project | 2 Project MembersPrior studies have indicated that expression driven by a promoter sequence can be successfully modeled by describing the equilibrium binding of transcriptional activators and repressors to the promoter sequences. The cooperative binding of these factors is also affected by transcriptional interference as revealed in our earlier studies (A. Buetti‐Dinh et al, Molecular Systems Biology (2009) 5:300). Transcriptional interference has been recognized as an important factor in the regulation of gene expression due to the pervasive production of non‐coding RNAs during transcriptional initiation, which then acts as a horizontal transmitter of information along the chromosome. The expression due to the endogenous activator (Gal4p) is not determined only by its (cooperative) binding to the DNA. Gal80 binds and blocks to the activator domain of Gal4, while Gal3 unblocks Gal4 when activated by galactose. Thus, the prediction of the effect of interference requires the measurement and modeling of the cooperative binding of these proteins to Gal4p and in addition to the modeling of the binding of Gal4p to DNA. Using proteomic approaches, we will start studying the cooperative effects in the Gal4‐Gal80‐Gal3 protein‐protein interactions on gene expression. StoNets - Controlling and exploiting stochasticity in gene regulatory networks Research Project | 30 Project MembersThe precision with which cells undergo differentiation programs or respond to external stimuli is remarkable, especially when considering the inherently "noisy" molecular interactions underlying these processes. StoNets aims to understand how stochasticity is controlled - and even exploited - to allow the development of robust behaviors of genetic networks, cells and cellular systems. Although all cells within an organism carry the same genetic information, regulatory mechanisms that operate at essentially all steps of gene expression lead to a large variety of cell types and behaviors. Progress in measurement technologies has enabled the precise and high-throughput probing of molecules and cells. This in turn has revealed that stochasticity is pervasive in all gene expression regulatory systems. The central question we will address in this StoNets project is how robust and reproducible cellular behaviors can emerge in spite of these noisy molecular interactions. A "slice" across the levels of gene expression organization We will undertake a systematic investigation into the mechanisms that have emerged to control noise at different organizational scales of gene expression regulation, from transcription of individual genes to cell fate switching. Each of the example systems that we selected is interesting in its own right and exhibits important stochastic aspects. Questions motivated by modeling With the continuous progress in understanding the basic mechanisms of gene regulation, many key molecular players have been identified and numerous direct regulatory interactions have been mapped. Theoreticians have started to model the way in which specific behaviors emerge from the underlying interactions. These efforts have led to a large number of new, inherently quantitative questions regarding the biological systems. Through a very tight integration of experimental and computational approaches StoNets aims to answer such questions ultimately contributing to an improved, quantitative understanding and thereby controllability of cellular behaviors. Exploration of mRNA degradation at the single molecule level Research Project | 2 Project MembersRegulation of mRNA degradation is an important part of regulation of gene expression. Many of the underlying molecular and kinetic mechanisms remain to be resolved. In this project, we will devise combinatorial approaches to study the stochastic aspects of mRNA degradation through synthetic biology and computational modeling in yeast and mammalian cells. We will combine a genetic approach with single molecule Fluorescence In Situ Hybridization to study the decay of single mRNA molecules and fit the data computationally to generate a model for mRNA degradation at the molecular level. Our results will reveal how stochasticity in mRNA degradation drives complex cellular processes. Analysis of signalling response functions underlying switch-like cellular decisions Research Project | 1 Project MembersDynamical systems biology aims at explaining the behaviour of cellular molecular networks with the help of mathematical models. To understand the behaviour of the whole system it is desirable to understand quantitatively how each individual network component modulates the activity of its interaction partners within the living cell, an action described by response functions. The transmission of information along the network links is bounded by basal interactions and saturation at the lower and higher ranges of signal intensities, respectively. Secondly, the strength of interaction determines the intensity the input signal has to pass to generate the desired output. Thus, the dynamic range and the interaction strength define the limits of information transmission, while the ranges of the signalling frequency and amplitude reveal how a network link is exploited. Consecutive steps in pathways can utilize different values for the above parameters. The mutual relation of these values delimits the types of behaviours a network topology can generate. This proposal focuses on how the response functions of steps in a signalling pathway are aligned to optimally operate switches in different regulatory networks, including a metabolic, a cell-fate and cell-cycle network. Cellular switches are fundamental to promote short- or long-term decisions between different functional states of a cell. The first part of the proposal explores how yeast cells integrate signals elicited by glucose and galactose to switch between high and low galactose utilization states. The second part explores whether a transcriptional switch underlies the meiotic developmental transition. The third part explores how oscillatory expressions of cell cycle regulators are aligned to optimize the G1 / S cell cycle switch in order to prevent genomic instability. The above analysis requires the quantitative measurements of the relevant pathways without the interference from pleiotropic cellular signals. This will be achieved by substituting parts of the networks with synthetic genetic elements so that specific responses can be extracted. Our results will reveal principles of pathway alignment and provide design principles and tools for cell and tissue engineering, for which efficient cell differentiation and genomic stability are essential. 1 1
Analysis of the stability of the neuronal differentiation states Research Project | 1 Project MembersThe understanding and control of cellular differentiation has become a major focus of molecular life sciences and biomedical therapy. While animal cells are less plastic than plant cells, an increasing number of molecules, especially transcription factors, have been identified that are capable of reprogramming cell lineages. This plasticity is, however, associated with an inherent instability of the differentiated or reprogrammed states. Therefore, it is of major interest to unravel mechanisms that can stabilize the desired cell phenotypes. Transcriptional positive feedback loops have been often suggested to stabilize phenotypes, a claim supported by well-characterized molecular mechanisms to support bistability in simpler organisms. Recent evidence suggests, however, that chromosomal regulatory landscapes can also support epigenetic inheritance and differentiation. This project aims at unveiling whether and to what extent these two mechanisms contribute to the neuronal differentiation and the stability of neuronal phenotypes. By focusing on neurogenic transcription factors, we will examine whether they orchestrate transcriptional feedback loops and assess their ability to support bistable phenotypes. Concurrently, we will explore the correlation between phenotypic diversity and the changes in the chromosomal regulatory landscape. In the second subproject, we will use tools of synthetic biology to dissect the components of the chromosomal regulatory landscape to characterize the minimal set of elements that can promote phenotypic diversification through the control of the interdependence in the stochastic expression of the genes in a chromosomal segment. Our experimental systems analysis will shed light on the control principles that can stabilize differentiated or reprogrammed cell states, which will help to engineer neurons with precise functioning for ex vivo studies and for therapeutic applications.
ANALYSIS OF RNA AND PROTEIN TURNOVER TO PREDICT STOCHASTIC NETWORK BEHAVIOR Research Project | 1 Project MembersUnderstanding and prediction of the behavior of cellular networks is a major goal of systems biology. Nonlinearities and stochasticity in networks make this prediction difficult but they also promote the emergence of complex behaviors ranging from oscillations, adaptation in chemotaxis to cellular differentiation. Substantial efforts have been invested into deciphering how nonlinearity and noise are influenced by transcription and translation. On the other hand, little is known about how they are affected by decay processes. Our project proposal aims at uncovering how RNA and protein turnover rates determine fluctuations and precision in cellular functions. Our first subproject aims at studying how RNA production and degradation jointly determines fluctuation in gene expression. Recent studies have revealed that decay processes can feedback on biosynthetic processes, which compounds the problem of distinguishing the stochastic effects of RNA degradation. Special attention will be paid to histone gene expression which is very tightly regulated since alterations in the histone gene dosage can lead to changes in the stoichiometry in the histone octamer with pleiotropic effects on the expression of the entire genome. The promoter of the histone gene is regulated by a cooperatively binding transcriptional activator and the decay of histone mRNAs is time dependent, linked to the cell cycle. In the second subproject, we examine how protein decay shapes non-linear responses and how these - in conjunction with fluctuations - determine the stochastic transitions in positive feedback loops. The results will help to design measurements to explain the kinetic mechanisms of cellular memory and the stability of cellular differentiation states. To realize these subprojects, we will use cutting edge techniques involving single molecule RNA detection and quantitative proteomics in yeast cells in combination with mathematical modeling of stochastic processes. Our results will advance the interpretation of non-invasive methods and will help to define the extent to which fluctuations have to be taken into account to efficiently model and understand nonlinear network dynamics. This in turn will be helpful to measure the most relevant biochemical reaction rates in genetically heterogeneous cell populations and to engineer cells with precise functioning. We expect applications in tissue engineering in the future.
Genomic analysis of information transfer along the DNA by transcriptional interference Research Project | 2 Project MembersPrior studies have indicated that expression driven by a promoter sequence can be successfully modeled by describing the equilibrium binding of transcriptional activators and repressors to the promoter sequences. The cooperative binding of these factors is also affected by transcriptional interference as revealed in our earlier studies (A. Buetti‐Dinh et al, Molecular Systems Biology (2009) 5:300). Transcriptional interference has been recognized as an important factor in the regulation of gene expression due to the pervasive production of non‐coding RNAs during transcriptional initiation, which then acts as a horizontal transmitter of information along the chromosome. The expression due to the endogenous activator (Gal4p) is not determined only by its (cooperative) binding to the DNA. Gal80 binds and blocks to the activator domain of Gal4, while Gal3 unblocks Gal4 when activated by galactose. Thus, the prediction of the effect of interference requires the measurement and modeling of the cooperative binding of these proteins to Gal4p and in addition to the modeling of the binding of Gal4p to DNA. Using proteomic approaches, we will start studying the cooperative effects in the Gal4‐Gal80‐Gal3 protein‐protein interactions on gene expression.
StoNets - Controlling and exploiting stochasticity in gene regulatory networks Research Project | 30 Project MembersThe precision with which cells undergo differentiation programs or respond to external stimuli is remarkable, especially when considering the inherently "noisy" molecular interactions underlying these processes. StoNets aims to understand how stochasticity is controlled - and even exploited - to allow the development of robust behaviors of genetic networks, cells and cellular systems. Although all cells within an organism carry the same genetic information, regulatory mechanisms that operate at essentially all steps of gene expression lead to a large variety of cell types and behaviors. Progress in measurement technologies has enabled the precise and high-throughput probing of molecules and cells. This in turn has revealed that stochasticity is pervasive in all gene expression regulatory systems. The central question we will address in this StoNets project is how robust and reproducible cellular behaviors can emerge in spite of these noisy molecular interactions. A "slice" across the levels of gene expression organization We will undertake a systematic investigation into the mechanisms that have emerged to control noise at different organizational scales of gene expression regulation, from transcription of individual genes to cell fate switching. Each of the example systems that we selected is interesting in its own right and exhibits important stochastic aspects. Questions motivated by modeling With the continuous progress in understanding the basic mechanisms of gene regulation, many key molecular players have been identified and numerous direct regulatory interactions have been mapped. Theoreticians have started to model the way in which specific behaviors emerge from the underlying interactions. These efforts have led to a large number of new, inherently quantitative questions regarding the biological systems. Through a very tight integration of experimental and computational approaches StoNets aims to answer such questions ultimately contributing to an improved, quantitative understanding and thereby controllability of cellular behaviors.
Exploration of mRNA degradation at the single molecule level Research Project | 2 Project MembersRegulation of mRNA degradation is an important part of regulation of gene expression. Many of the underlying molecular and kinetic mechanisms remain to be resolved. In this project, we will devise combinatorial approaches to study the stochastic aspects of mRNA degradation through synthetic biology and computational modeling in yeast and mammalian cells. We will combine a genetic approach with single molecule Fluorescence In Situ Hybridization to study the decay of single mRNA molecules and fit the data computationally to generate a model for mRNA degradation at the molecular level. Our results will reveal how stochasticity in mRNA degradation drives complex cellular processes.
Analysis of signalling response functions underlying switch-like cellular decisions Research Project | 1 Project MembersDynamical systems biology aims at explaining the behaviour of cellular molecular networks with the help of mathematical models. To understand the behaviour of the whole system it is desirable to understand quantitatively how each individual network component modulates the activity of its interaction partners within the living cell, an action described by response functions. The transmission of information along the network links is bounded by basal interactions and saturation at the lower and higher ranges of signal intensities, respectively. Secondly, the strength of interaction determines the intensity the input signal has to pass to generate the desired output. Thus, the dynamic range and the interaction strength define the limits of information transmission, while the ranges of the signalling frequency and amplitude reveal how a network link is exploited. Consecutive steps in pathways can utilize different values for the above parameters. The mutual relation of these values delimits the types of behaviours a network topology can generate. This proposal focuses on how the response functions of steps in a signalling pathway are aligned to optimally operate switches in different regulatory networks, including a metabolic, a cell-fate and cell-cycle network. Cellular switches are fundamental to promote short- or long-term decisions between different functional states of a cell. The first part of the proposal explores how yeast cells integrate signals elicited by glucose and galactose to switch between high and low galactose utilization states. The second part explores whether a transcriptional switch underlies the meiotic developmental transition. The third part explores how oscillatory expressions of cell cycle regulators are aligned to optimize the G1 / S cell cycle switch in order to prevent genomic instability. The above analysis requires the quantitative measurements of the relevant pathways without the interference from pleiotropic cellular signals. This will be achieved by substituting parts of the networks with synthetic genetic elements so that specific responses can be extracted. Our results will reveal principles of pathway alignment and provide design principles and tools for cell and tissue engineering, for which efficient cell differentiation and genomic stability are essential.
