In this white paper, we describe the founding of a new ELIXIR Community - the Systems Biology Community - and its proposed future contributions to both ELIXIR and the broader community of systems ...biologists in Europe and worldwide. The Community believes that the infrastructure aspects of systems biology - databases, (modelling) tools and standards development, as well as training and access to cloud infrastructure - are not only appropriate components of the ELIXIR infrastructure, but will prove key components of ELIXIR's future support of advanced biological applications and personalised medicine.
By way of a series of meetings, the Community identified seven key areas for its future activities, reflecting both future needs and previous and current activities within ELIXIR Platforms and Communities. These are: overcoming barriers to the wider uptake of systems biology; linking new and existing data to systems biology models; interoperability of systems biology resources; further development and embedding of systems medicine; provisioning of modelling as a service; building and coordinating capacity building and training resources; and supporting industrial embedding of systems biology.
A set of objectives for the Community has been identified under four main headline areas: Standardisation and Interoperability, Technology, Capacity Building and Training, and Industrial Embedding. These are grouped into short-term (3-year), mid-term (6-year) and long-term (10-year) objectives.
In this white paper, we describe the founding of a new ELIXIR Community - the Systems Biology Community - and its proposed future contributions to both ELIXIR and the broader community of systems ...biologists in Europe and worldwide. The Community believes that the infrastructure aspects of systems biology - databases, (modelling) tools and standards development, as well as training and access to cloud infrastructure - are not only appropriate components of the ELIXIR infrastructure, but will prove key components of ELIXIR's future support of advanced biological applications and personalised medicine. By way of a series of meetings, the Community identified seven key areas for its future activities, reflecting both future needs and previous and current activities within ELIXIR Platforms and Communities. These are: overcoming barriers to the wider uptake of systems biology; linking new and existing data to systems biology models; interoperability of systems biology resources; further development and embedding of systems medicine; provisioning of modelling as a service; building and coordinating capacity building and training resources; and supporting industrial embedding of systems biology. A set of objectives for the Community has been identified under four main headline areas: Standardisation and Interoperability, Technology, Capacity Building and Training, and Industrial Embedding. These are grouped into short-term (3-year), mid-term (6-year) and long-term (10-year) objectives.
Gene expression noise is known to promote stochastic drug resistance through the elevated expression of individual genes in rare cancer cells. However, we now demonstrate that chemoresistant ...neuroblastoma cells emerge at a much higher frequency when the influence of noise is integrated across multiple components of an apoptotic signaling network. Using a JNK activity biosensor with longitudinal high-content and in vivo intravital imaging, we identify a population of stochastic, JNK-impaired, chemoresistant cells that exist because of noise within this signaling network. Furthermore, we reveal that the memory of this initially random state is retained following chemotherapy treatment across a series of in vitro, in vivo, and patient models. Using matched PDX models established at diagnosis and relapse from individual patients, we show that HDAC inhibitor priming cannot erase the memory of this resistant state within relapsed neuroblastomas but improves response in the first-line setting by restoring drug-induced JNK activity within the chemoresistant population of treatment-naïve tumors.
A network of the Rho family GTPases, which cycle between inactive GDP-bound and active GTP-bound states, controls key cellular processes, including proliferation and migration. Activating and ...deactivating GTPase transitions are controlled by guanine nucleotide exchange factors (GEFs), GTPase activating proteins (GAPs) and GDP dissociation inhibitors (GDIs) that sequester GTPases from the membrane to the cytoplasm. Here we show that a cascade of two Rho family GTPases, RhoA and Rac1, regulated by RhoGDI1, exhibits distinct modes of the dynamic behavior, including abrupt, bistable switches, excitable overshoot transitions and oscillations. The RhoGDI1 abundance and signal-induced changes in the RhoGDI1 affinity for GTPases control these different dynamics, enabling transitions from a single stable steady state to bistability, to excitable pulses and to sustained oscillations of GTPase activities. These RhoGDI1-controlled dynamic modes of RhoA and Rac1 activities form the basis of cell migration behaviors, including protrusion-retraction cycles at the leading edge of migrating cells.
RhoGDI1 controls the dynamics of the RhoA and Rac1 GTPase cascade by enabling system transitions between monostable, bistable, excitable and oscillatory behaviors.
In this study, we focus on the multi‐scale dynamics involved in neuronal adaptation at two levels: signaling dynamics elicited by neuropeptide receptors and the consequences on the electrophysiology. ...The particular system considered is the angiotensin II receptor type 1 (AT1R) signaling and electrical activity in the cardiorespiratory neurons in the Nucleus Tractus Solitarius (NTS) in the brainstem. We have developed a multi‐scale mathematical model that integrates a Hodgkin‐Huxley like model of the electrophysiology and a detailed kinetic reaction model of the AT1R mediated intracellular signaling pathway. The key aspect of the integrated model is the change in conductance of different ion channels upon phosphorylation by the signaling kinases. Analysis of the model dynamics revealed distinct regulatory properties corresponding to different ion channels, a novel role for the delayed rectifier potassium channel as a dual regulator, and counteracting effects of non‐voltage‐activated transport and ion channel phosphorylation resulting in a net increase in neuronal firing rate, in concordance with experimental data.
Research Support: NIH/HLB R33 HL087361 and NIH/NIAAA R01 AA13204 to JSS.
Dynamic modelling has long been used to understand fundamental principles of cell signalling and its dysregulation in cancer. More recently these models have also been used to understand the ...individual risks of cancer patients, and predict their survival probabilities. However, the current methodologies for integrating tumour data and generating patient-specific simulations suffer from the lack of general applicability; they only work for cell signalling models in which only posttranslational protein modifications are considered, so that the total protein concentrations are conserved. Here, we present novel, generally applicable method. The method is based on a simple theoretical framework for modelling gene-regulation, and the indirect estimation of patient-specific parameters from tumour data. Because our method does not require time-invariance of the total-protein concentrations, it can be applied to models of any nature, including the many cancer signalling models involving gene-regulation.
Die Basis für viele Untersuchungen im Bereich der Systembiologie bilden mathematische Modelle. Diese hängen jedoch oftmals von einer Vielzahl unbekannter oder nur ungenau bekannter Reaktionsparameter ...ab. Die direkte Messung dieser Parameter basierend auf Einzelreaktionen ist aufgrund der damit verbundenen hohen Kosten, des notwendigen Zeitaufwands zur Durchführung der Experimente oder der sich ergebenden Komplexität durch die Vielzahl an Einzelreaktionen nicht möglich. Deshalb müssen diese Parameter aus indirekten Messungen an der realen Zelle, zum Beispiel aus Zeitreiheninformationen, gewonnen werden. Existierende Parameteridentifikationsverfahren berücksichtigen meistens nicht die speziellen Strukturen biochemischer Reaktionsnetzwerke. Oftmals liefern sie keine zuverlässigen Parameterschätzungen oder Garantien über die Eindeutigkeit der gefundenen Parameter. Im Rahmen dieser Arbeit beschreiben wir ein Parameterschätzverfahren, das einen Zustandsbeobachter verwendet und es erlaubt, die auftretenden Strukturen direkt zu berücksichtigen. Unter der Annahme, dass die auftretenden Reaktionsmechanismen durch polynomiale oder rationale Funktionen beschrieben werden können, stellen wir ein dreistufiges Schätzverfahren vor. In einem ersten Schritt wird ein erweitertes Modell hergeleitet, mit dem die Parameterschätzung von der notwendigen Schätzung der unbekannten Zustände getrennt werden kann. In einem zweiten Schritt wird der erweiterterte Zustand mittels eines geeigneten Beobachters rekonstruktiert. Basierend auf den hieraus erhaltenen Zuständen werden die Reaktionsparameter im letzten Schritt geschätzt. Die Vor- und Nachteile des vorgeschlagenen Verfahrens werden an einem vereinfachten Modell für den zirkadischen Rythmus verdeutlicht.
Dynamic models present a fundamental tool in systems biology, but rely on kinetic parameters, such as association and dissociation constants. Their direct estimation from studies on isolated reactions is usually expensive, time-consuming or even infeasible for large models. As a consequence, they must be estimated from indirect measurements, usually in the form of time-series data. We describe an observer-based parameter estimation approach taking the specific structure of biochemical reaction networks into account. Considering reaction kinetics described by polynomial or rational functions, we propose a three step approach. In a first step, the estimation of not directly measured states is decoupled from the estimation of the parameters using a suitable model extension. In a second step, a specially suited nonlinear observer estimates the extended state. Based on the obtained state estimates, the parameter estimates are calculated in a straightforward way in the final step. The applicability of the approach is exemplified considering a simplified model of the circadian rhythm.
Bimodal distributions of protein activities in signaling systems are often interpreted as indicators of underlying switch-like responses and bistable dynamics. We investigate the emergence of bimodal ...protein distributions by analyzing a less appreciated mechanism: oscillating signaling systems with varying amplitude, phase and frequency due to cell-to-cell variability. We support our analysis by analytical derivations for basic oscillators and numerical simulations of a signaling cascade, which displays sustained oscillations in protein activities. Importantly, we show that the time to reach the bimodal distribution depends on the magnitude of cell-to-cell variability. We quantify this time using the Kullback-Leibler divergence. The implications of our findings for single-cell experiments are discussed.
Issues of regulation and control are central to the study of biological and biochemical systems. Thus it is not surprising that the tools of feedback control theory, engineering techniques developed ...to design and analyze self-regulating systems, have proven useful in the study of these biological mechanisms. Such interdisciplinary work requires knowledge of the results, tools and techniques of another discipline, as well as an understanding of the culture of an unfamiliar research community. This volume attempts to bridge the gap between disciplines by presenting applications of systems and control theory to cell biology that range from surveys of established material to descriptions of new developments in the field.
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