3,018 research outputs found

    Cellular signaling networks function as generalized Wiener-Kolmogorov filters to suppress noise

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    Cellular signaling involves the transmission of environmental information through cascades of stochastic biochemical reactions, inevitably introducing noise that compromises signal fidelity. Each stage of the cascade often takes the form of a kinase-phosphatase push-pull network, a basic unit of signaling pathways whose malfunction is linked with a host of cancers. We show this ubiquitous enzymatic network motif effectively behaves as a Wiener-Kolmogorov (WK) optimal noise filter. Using concepts from umbral calculus, we generalize the linear WK theory, originally introduced in the context of communication and control engineering, to take nonlinear signal transduction and discrete molecule populations into account. This allows us to derive rigorous constraints for efficient noise reduction in this biochemical system. Our mathematical formalism yields bounds on filter performance in cases important to cellular function---like ultrasensitive response to stimuli. We highlight features of the system relevant for optimizing filter efficiency, encoded in a single, measurable, dimensionless parameter. Our theory, which describes noise control in a large class of signal transduction networks, is also useful both for the design of synthetic biochemical signaling pathways, and the manipulation of pathways through experimental probes like oscillatory input.Comment: 15 pages, 5 figures; to appear in Phys. Rev.

    On robustness in biology: from sensing to functioning

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    Living systems are subject to various types of spatial and temporal noise at all scales and stages. Nevertheless, evolving under the pressure of natural selection, biology has mastered the ability of dealing with stochasticity. This is particularly crucial because these systems encounter numerous situations which require taking robust and proper actions in the presence of noise. Due to the complexity and variability of these situations, it is impossible to have a prescribed plan for an organism that keeps it alive and fully functional. Therefore, they have to be active, rather than passive, by following three essential steps: I) gathering information about their fluctuating environment, II) processing the information and making decisions via circuits that are inevitably noisy, and finally, III) taking the appropriate action robustly with organizations crossing multiple scales. Although various aspects of this general scheme have been subject of many studies, there are still many questions that remain unanswered: How can a dynamic environmental signal be sensed collectively by cell populations? and how does the topology of interactions affect the quality of this sensing? When processing information via the regulatory network, what are the drawbacks of multifunctional circuits? and how does the reliability of the decisions decrease as the multifunctionality increases? Finally, when the right decision is made and a tissue is growing with feedbacks crossing different scales, what are the crucial features that remain preserved from one subject to another? How can one use these features to understand the mechanisms behind these processes? This thesis addresses the main challenges for answering these questions and many more using methods from dynamical systems, network science, and stochastic processes. Using stochastic models, we investigate the fundamental limits arising from temporal noise on collective signal sensing and context-dependent information processing. Furthermore, by combining stochastic models and cross-scale data analyses, we study pattern formation during complex tissue growth.Lebende Systeme sind in allen Größenordnungen und Stadien verschiedenen Arten von räumlichem und zeitlichem Rauschen ausgesetzt. Dennoch hat die Biologie, die sich unter dem Druck der natürlichen Selektion entwickelt hat, die Fähigkeit gemeistert, mit stochastischen Fluktuationen umzugehen. Dies ist besonders wichtig, da Organismen auf zahlreiche Situationen stoßen, die es erfordern, in Gegenwart von Rauschen robuste und angemessene Maßnahmen zu ergreifen. Aufgrund der Komplexität und Variabilität dieser Situationen ist es unmöglich, einen vorgeschriebenen Plan für einen Organismus zu haben, der ihn überlebens- und funktionsfähig hält. Daher können Organismen sich nicht passiv verhalten, sondern befolgen aktiv drei wesentliche Schritte: I) Das Sammeln von Informationen über ihre dynamische Umgebung, II) Das Verarbeiten von Informationen und das Treffen von Entscheidungen über Regelnetzwerke, die unvermeidlich mit Rauschen behaftet sind, und schließlich, III) das robuste Funktionieren durch organisierte Maßnahmen, welche mehrere Größenordnungen überbrücken. Obwohl verschiedene Aspekte dieses allgemeinen Schemas Gegenstand vieler Studien waren, bleiben noch viele Fragen unbeantwortet: Wie kann ein dynamisches externes Signal kollektiv von Zellpopulationen wahrgenommen werden? Wie beeinflusst die Topologie der Interaktionen die Qualität dieser Wahrnehmung? Was sind die Nachteile multifunktionaler Schaltkreise bei der Verarbeitung von Informationen über das Regelnetzwerk? Wie nimmt die Zuverlässigkeit der Entscheidungen mit zunehmender Multifunktionalität ab? Und abschließend, wenn die richtige Entscheidung getroffen wurde und ein Gewebe wächst und dabei Rückkopplungen auf verschiedenen Größenordnungen erfährt, was sind die entscheidenden Merkmale, die von einem Versuchsobjekt zum anderen erhalten bleiben? Wie kann man diese Merkmale nutzen, um die Prozesse zu verstehen? Diese Arbeit befasst sich mit den wichtigsten Herausforderungen zur Beantwortung dieser und vieler weiterer Fragen mit Methoden aus dynamischen Systemen, Netzwerkforschung und stochastischen Prozessen

    Engineering biological networks using cooperative transcriptional assembly

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    Eukaryotic genes are often regulated by multivalent transcription factor (TF) complexes. Through the process of cooperative self-assembly, these complexes carry out non-linear regulatory operations involved in cellular decision-making and signal processing. In this thesis, we apply this natural design principle to artificial networks, testing whether engineered cooperative TF assemblies can be used to program non-linear synthetic circuit behavior in yeast. Using a model-guided approach, we show that specifying strength and number of interactions in an assembly enables predictive tuning between regimes of linear and non-linear regulatory response for single- and multi-input circuits. We demonstrate that synthetic assemblies can be adjusted to control circuit dynamics, shaping the timing of activation. We harness this capability to engineer circuits that perform dynamic filtering, enabling frequency-dependent decoding in cell populations. Thru this work, we find that cooperative assembly provides a versatile way to tune nonlinearity of network connections, dramatically expanding the range engineerable behaviors available to synthetic circuits. We then extend our modeling-framework to predict genome-wide binding of our TF assemblies and find that cooperative complexes made of weakly-interacting proteins can reduce unintended activation of endogenous genes. Thus, we are able to introduce synthetic regulatory components with low fitness costs on the cell, ensuring long-term stability of our integrated circuits over time. Taken together, this dissertation outlines a synthetic framework for building cooperative transcriptional complexes in vivo in order to engineer complex regulatory behaviors that are functionally orthogonal to the host cell.2019-10-22T00:00:00

    Nonlinear brain dynamics as macroscopic manifestation of underlying many-body field dynamics

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    Neural activity patterns related to behavior occur at many scales in time and space from the atomic and molecular to the whole brain. Here we explore the feasibility of interpreting neurophysiological data in the context of many-body physics by using tools that physicists have devised to analyze comparable hierarchies in other fields of science. We focus on a mesoscopic level that offers a multi-step pathway between the microscopic functions of neurons and the macroscopic functions of brain systems revealed by hemodynamic imaging. We use electroencephalographic (EEG) records collected from high-density electrode arrays fixed on the epidural surfaces of primary sensory and limbic areas in rabbits and cats trained to discriminate conditioned stimuli (CS) in the various modalities. High temporal resolution of EEG signals with the Hilbert transform gives evidence for diverse intermittent spatial patterns of amplitude (AM) and phase modulations (PM) of carrier waves that repeatedly re-synchronize in the beta and gamma ranges at near zero time lags over long distances. The dominant mechanism for neural interactions by axodendritic synaptic transmission should impose distance-dependent delays on the EEG oscillations owing to finite propagation velocities. It does not. EEGs instead show evidence for anomalous dispersion: the existence in neural populations of a low velocity range of information and energy transfers, and a high velocity range of the spread of phase transitions. This distinction labels the phenomenon but does not explain it. In this report we explore the analysis of these phenomena using concepts of energy dissipation, the maintenance by cortex of multiple ground states corresponding to AM patterns, and the exclusive selection by spontaneous breakdown of symmetry (SBS) of single states in sequences.Comment: 31 page

    Quantifying uncertainty, variability and likelihood for ordinary differential equation models

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    <p>Abstract</p> <p>Background</p> <p>In many applications, ordinary differential equation (ODE) models are subject to uncertainty or variability in initial conditions and parameters. Both, uncertainty and variability can be quantified in terms of a probability density function on the state and parameter space.</p> <p>Results</p> <p>The partial differential equation that describes the evolution of this probability density function has a form that is particularly amenable to application of the well-known method of characteristics. The value of the density at some point in time is directly accessible by the solution of the original ODE extended by a single extra dimension (for the value of the density). This leads to simple methods for studying uncertainty, variability and likelihood, with significant advantages over more traditional Monte Carlo and related approaches especially when studying regions with low probability.</p> <p>Conclusions</p> <p>While such approaches based on the method of characteristics are common practice in other disciplines, their advantages for the study of biological systems have so far remained unrecognized. Several examples illustrate performance and accuracy of the approach and its limitations.</p

    Engineering microcompartmentalized cell-free synthetic circuits

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    Characterizing biological systems: quantitative methods for synthetic genetic circuits in plants and intracellular mechanics

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    2018 Summer.Includes bibliographical references.To view the abstract, please see the full text of the document
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