1,720,968 research outputs found
The fidelity of dynamic signaling by noisy biomolecular networks
Get PDFCells live in changing, dynamic environments. To understand cellular decision-making, we must therefore understand how fluctuating inputs are processed by noisy biomolecular networks. Here we present a general methodology for analyzing the fidelity with which different statistics of a fluctuating input are represented, or encoded, in the output of a signaling system over time. We identify two orthogonal sources of error that corrupt perfect representation of the signal: dynamical error, which occurs when the network responds on average to other features of the input trajectory as well as to the signal of interest, and mechanistic error, which occurs because biochemical reactions comprising the signaling mechanism are stochastic. Trade-offs between these two errors can determine the system's fidelity. By developing mathematical approaches to derive dynamics conditional on input trajectories we can show, for example, that increased biochemical noise (mechanistic error) can improve fidelity and that both negative and positive feedback degrade fidelity, for standard models of genetic autoregulation. For a group of cells, the fidelity of the collective output exceeds that of an individual cell and negative feedback then typically becomes beneficial. We can also predict the dynamic signal for which a given system has highest fidelity and, conversely, how to modify the network design to maximize fidelity for a given dynamic signal. Our approach is general, has applications to both systems and synthetic biology, and will help underpin studies of cellular behavior in natural, dynamic environments
A computational study of the robustness of cellular oscillators
Get PDFWolde, P.R. ten [Promotor]Lubensky, D.K. [Copromotor
Controlling DNA replication initiation: from living to synthetic cells
Get PDFThe bacterium Escherichia coli initiates replication once per cell cycle at a precise volume per origin and adds an on average constant volume between successive initiation events, independent of the initiation size. Yet, a molecular model that can explain these observations has been lacking. Experiments indicate that E. coli controls replication initiation via titration and activation of the initiator protein DnaA. In chapters 2, 3 and 4, we study by mathematical modelling how these two mechanisms interact to generate robust replication-initiation cycles. We first show that a mechanism solely based on titration generates stable replication cycles at low growth rates, but inevitably causes premature reinitiation events at higher growth rates. In this regime, the DnaA activation switch becomes essential for stable replication initiation. Conversely, while the activation switch alone yields robust rhythms at high growth rates, titration can strongly enhance the stability of the switch at low growth rates. Our analysis thus predicts that both mechanisms together drive robust replication cycles at all growth rates. In addition, it reveals how an origin-density sensor yields adder correlations. Initiating replication synchronously at multiple origins of replication allows the bacterium E. coli to divide even faster than the time it takes to replicate the entire chromosome in nutrient rich environments. What mechanisms give rise to synchronous replication initiation remains however poorly understood. In chapter 5, we identify four distinct synchronization regimes depending on two quantities: the duration of the so-called licensing period during which the initiation potential in the cell remains high after the first origin has fired and the duration of the blocking period during which already initiated origins remain blocked. For synchronous replication initiation, the licensing period must be long enough such that all origins can be initiated, but shorter than the blocking period to prevent reinitiation of origins that have already fired. We find an analytical expression for the degree of synchrony as a function of the duration of the licensing period, which we confirm by simulations. Our model reveals that the delay between the firing of the first and the last origin scales with the coefficient of variation (CV) of the initiation volume. Matching these to the values measured experimentally shows that the firing rate must rise with the cell volume with an effective Hill coefficient that is at least 20; the probability that all origins fire before the blocking period is over is then at least 94%. Our analysis thus reveals that the low CV of the initiation volume is a consequence of synchronous replication initiation. Finally, we show that the previously presented molecular model for the regulation of replication initiation in E. coli can give rise to synchronous replication initiation for biologically realistic parameters. Recent developments in synthetic biology may bring the bottom-up generation of a synthetic cell within reach. A key feature of a living synthetic cell is a functional cell cycle, in which DNA replication and segregation as well as cell growth and division are well integrated. In chapter 6, we describe different approaches to recreate these processes in a synthetic cell, based on natural systems and/or synthetic alternatives. Although some individual machineries have recently been established, their integration and control in a synthetic cell cycle remain to be addressed. In this chapter, we discuss potential paths towards an integrated synthetic cell cycle
A computational study of robust formation of spatial protein patterns
Get PDFWolde, P.R. ten [Promotor
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Design and implementation of a bacterial signaling circuit
Get PDFWolde, P.R. ten [Promotor]Shimizu, T.S. [Copromotor
Statistical Mechanics of Cytoskeletal Filaments
Get PDFAll eukaryotic cells contain a critical structure called the cytoskeleton, which is a collection of filamentous networks that consist of microtubules, actin filaments, and intermediate filaments. In this thesis, we use tools provided by statistical mechanics to characterise the movement and function of cytoskeletal filaments. In chapter 1, we summarise and derive the concepts from statistical mechanics that we use in this thesis. Specifically, we discuss equilibrium mechanics, the Fokker-Planck equation, and how to predict the rates of rare events that are caused by the crossing of a free-energy barrier. Then, we apply these techniques to the movement of two cross- linked cytoskeletal filaments in chapter 2. It has been observed that the friction between microtubules that are connected by diffusible cross-linkers increases exponentially with the number of cross-linkers, and we investigate the mechanism behind this exponential increase. We find that the relative movement of the filaments occurs via discrete steps that are caused by free-energy barriers, leading to experimentally testable predictions on how the friction coefficient between the filament scales with the density of cross- linkers. In chapter 3, we show that Kramers theory, which calculates the rates at which the filaments jump over the free-energy barriers, breaks down in the model presented in chapter 2. We review each assumption of Kramers theory and discuss why this break- down occurs. Then, in chapter 4 we examine how entropic and condensation forces de- pend on the number and density of cross-linkers, and how the interplay between these forces and the exponentially increasing friction coefficient shapes the movement of the filaments. The predicted trajectories of the filaments can be compared to experimen- tal data to test our prediction that the friction scales exponentially with the number of cross-linkers. In this chapter we also study the effect of cooperative interactions be- tween the passive cross-linkers and of multiple protofilaments taking part in the bind- ing of these cross-linkers. In chapter 5 we show how actin filaments can be transported by growing microtubules and passive cross-linkers that connect the actin filaments with the tips of the growing microtubules. The actin filament tracks the moving microtubule tip via a condensation force, which tends to maximize the overlap between the actin fil- ament and the chemically distinct tip region of the microtubule, but this transport is opposed by friction forces caused by the cross-linkers. From this model, we predict how the average transport time of these actin filaments depends on the growth velocity of the microtubule and the length of the actin filament, and we test the predicted trends exper- imentally. Finally, inspired by the Pom1 and Cdc42 systems, we investigate in chapter 6 if a protein distribution on the membrane can be polarised by the transport of the proteins along filaments that point towards a spot on the membrane. Using a minimal model, we find that polarisation requires detailed balance to be broken either by motor proteins that carry the proteins to the membrane or by a chemical modification of the proteins driving a flux from the filament to the membrane. We compare these two mechanisms by quantifying the amount of free energy that is dissipated by each, showing that the processes are not equally efficient in creating a dense protein spot on the membrane
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