1,720,976 research outputs found
Modeling and analysis of a retroviral gene circuit
No full textIn this thesis a novel model of gene and protein kinetics of retrovirus Human T-cell Leukemia Virus type 1 (HTLV-1) is proposed. This model is characterized by positive and negative feedback phenomena, similarly to synthetic relaxation oscillators delivered into prokaryotes and able to exhibit limit cycles. To investigate the potential use of the HTLV-1 circuit as a novel oscillator for eukaryotes, the periodic behavior of gene and protein kinetics is analyzed. Techniques to mutate the retroviral genome in order to obtain, practically, oscillations in viral gene and protein expression are discussed.
Since, under certain conditions, discreteness and stochasticity may play important roles so that the predictions coming from deterministic differential equations do not accurately describe the system’s true behavior, this latter was tested by Gillespie’s exact stochastic simulations. These showed that: a) stochastic phenomena induce loss of synchronicity in viral expression among clones and b) the expression of the retrovirus transactivator protein Tax can substantially deviate from its deterministic steady state value; in other words, stochastic phenomena can sporadically induce relevant fluctuations of Tax expression, which is the main signal related to retrovirus activation, over the expected level corresponding to the steady state solution. These results suggest mechanisms of viral activation similar to those proposed for HIV by Weinberger et al.1: the virus tends to latency, but stochastic phenomena can induce the persistence of the transactivator protein at expression levels higher than the steady state value, which favors retrovirus activation. However, the steady state level of Tax expression should reasonably be as important, in determining retrovirus HTLV-1 activation, as the noise affecting it and causing stochastic transient expression pulses.
To gain a better insight, the characteristics of Tax expression at steady state are investigated in terms of duration of its transient pulses, due to stochastic phenomena, and its Signal-to-Noise ratio (SNR), which combines the protein expression level and the variance of the noise affecting it. As a second step, the system parameters most affecting either Tax SNR or the duration of its transient expression pulses are identified, in order to select candidates for future experiments of selective system perturbation. The rational is that a better understanding of retrovirus regulatory mechanisms, and the identification of system parameters (and corresponding biological processes) affecting them, can open the way for the selection of drug targets to hit in order to avoid retrovirus activation and protract latency.In questa tesi è proposto un nuovo modello matematico della cinetica di geni e proteine del retrovirus T-cell Leukemia Virus type 1 (HTLV-1). Il modello è dotato di fenomeni di feedback, sia positivi che negativi, al pari dei circuiti genici sintetici noti come relaxation oscillator, introdotti in cellule procariotiche in recenti esperimenti, i quali hanno mostrato cinetiche caratterizzate da cicli limite. Per investigare il potenziale uso del circuito genico di HTLV-1 quale nuovo oscillatore per cellule eucariotiche, i moti periodici caratterizzanti il modello sono stati analizzati. Tecniche biotecnologiche per mutare il genoma retrovirale allo scopo di ottenere oscillazioni nell’espressione di geni e proteine sono poi discusse.
Siccome, in certe condizioni, la stocasticità può giocare un ruolo importante cosicché le predizioni provenienti da equazioni differenziali deterministiche non riescono a descrivere accuratamente l’effettivo comportamento del sistema, quest’ultimo è stato testato tramite simulazioni stocastiche esatte di Gillespie. Queste mostrarono che: a) fenomeni stocastici inducono la perdita di sincronicità nell’espressione virale tra cloni e b) l’espressione della protein transattivatrice del retrovirus Tax può deviare in modo sostanziale dal valore deterministico di steady state; in altre parole, fenomeni stocastici possono sporadicamente indurre rilevanti fluttuazioni nell’espressione di Tax, che è il principale segnale legato all’attivazione virale, sopra ilvalore atteso corrispondente alla soluzione di steady state. Queste simulazioni suggeriscono meccanismi di attivazione retrovirale simili a quelli proposti per HIV da Weinberger et al.1: il virus tende alla latenza, ma fenomeni stocastici possono indurre la persistenza della proteina transattivatrice a livelli di espressione superiori al valore di steady state, che favoriscono l’attivazione del retrovirus. Tuttavia, il livello di steady state dell’espressione di Tax dovrebbe ragionevolmente essere altrettanto importante, nel determinare l’attivazione del retrovirus HTLV-1, del rumore che affligge tale espressione e ne causa implulsi transienti di natura stocastica.
Al fine di ottentere una migliore comprensione, le caratteristiche dell’espressione di Tax a steady state sono investigate in termini di durata degli i8mpulsi transienti, dovuti a fenomeni stocastici, e del Rapporto Segnale-Rumore (SNR) del circuito genico, che combina il livello di espressione della proteina con la varianza del rumore associato. In secondo luogo, sono stati identificati i parametri di sistema che influenzano di più e in modo esclusivo l’SNR e la durata degli impulsi transienti di espressione, allo scopo di selezionare candidati per futuri esperimenti di perturbazione selettiva del sistema. La ragione è che una migliore comprensione dei meccanismi regolatori del retrovirus, e l’identificazione dei parametri di sistema che li influenzano (assieme ai corrispondenti processi biologici), possono aprire la strada per lo sviluppo di farmaci che mirino ad evitare l’attivazione del retrovirus e a protrarne la latenza
In silico assessment of four reverse engineering methods: role of system complexity and multi-experiment design on network reconstruction and hub detection
No full textIn silico assessment of four reverse engineering algorithms: role of network complexity and multi-experiment design in network reconstruction and hub detection
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A Boolean approach to linear prediction for signaling network modeling
Full text linkThe task of the DREAM4 (Dialogue for Reverse Engineering Assessments and Methods) "Predictive signaling network modeling" challenge was to develop a method that, from single-stimulus/inhibitor data, reconstructs a cause-effect network to be used to predict the protein activity level in multi-stimulus/inhibitor experimental conditions. The method presented in this paper, one of the best performing in this challenge, consists of 3 steps: 1. Boolean tables are inferred from single-stimulus/inhibitor data to classify whether a particular combination of stimulus and inhibitor is affecting the protein. 2. A cause-effect network is reconstructed starting from these tables. 3. Training data are linearly combined according to rules inferred from the reconstructed network. This method, although simple, permits one to achieve a good performance providing reasonable predictions based on a reconstructed network compatible with knowledge from the literature. It can be potentially used to predict how signaling pathways are affected by different ligands and how this response is altered by diseases
A subcellular model of pancreatic insulin secretion
No full textWhen glucose is raised from a basal to stimulating level, the pancreatic islets respond with a typical biphasic insulin secretion pattern. Moreover, the pancreas is able to recognize the rate of change of the glucose concentration. We present a relatively simple model of insulin secretion from pancreatic beta-cells, yet founded on solid physiological grounds and capable of reproducing a series of secretion patterns from perfused pancreases as well as from stimulated islets. The model includes the notion of distinct pools of granules as well as mechanisms such as mobilization, priming, exocytosis and kiss-and-run. Based on experimental data, we suggest that the individual beta-cells activate at different glucose concentrations. The model reproduces most of the data it was tested against very well, and can therefore serve as a general model of glucose-stimulated insulin secretion. Simulations predict that the effect of an increased frequency of kiss-and-run exocytotic events is a reduction in insulin secretion without modification of the qualitative pattern. Our model also appears to be the first physiology-based one to reproduce the staircase experiment, which underlies 'derivative control', i.e. the pancreatic capacity of measuring the rate of change of the glucose concentration
Retrovirus HTLV-1 gene circuit: a potential oscillator for eukaryotes.
Full text linkRetrovirus HTLV-1 gene circuit is characterized by positive and negative feedback phenomena, thus candidating it as a potential relaxation oscillator deliverable into eukaryotes. Here we describe a model of HTLV-1 which, by providing predictions of genes and proteins kinetics, can be helpful for designing gene circuits for eukaryotes, or for optimizing gene therapy approaches which are currently carried out by means of lentiviral vectors or re-engineered adenoviruses. Oscillatory patterns of HTLV-1 gene circuit are predicted when positive feedback is faster than negative feedback. Techniques to mutate the retroviral genome in order to implement practically the above conditions are discussed. Finally, the effect of stochasticity on the system behavior is tested by means of Gillespie algorithm. Simulations show the difficulties to preserve synchronization in viral expression for a multiplicity of cells, while the long tail of the density probability function of the master regulator gene tax/rex, due to its steady state fluctuations, suggests an activation mechanism of HTLV-1 similar to that recently proposed for HIV(1): the virus tends to latency but under certain circumstances, the master regulator gene reaches high values of expression, whose persistence induces the viral replication
Adaptive changes in pancreatic beta cell fractional area and beta cell turnover in human pregnancy.
Full text linkAIMS/HYPOTHESIS:We sought to establish the extent and basis for adaptive changes in beta cell numbers in human pregnancy.
METHODS:
Pancreas was obtained at autopsy from women who had died while pregnant (n = 18), post-partum (n = 6) or were not pregnant at or shortly before death (controls; n = 20). Pancreases were evaluated for fractional pancreatic beta cell area, islet size and islet fraction of beta cells, beta cell replication (Ki67) and apoptosis (TUNEL), and indirect markers of beta cell neogenesis (insulin-positive cells in ducts and scattered beta cells in pancreas).
RESULTS:
The pancreatic fractional beta cell area was increased by approximately 1.4-fold in human pregnancy, with no change in mean beta cell size. In pregnancy there were more small islets rather than an increase in islet size or beta cells per islet. No increase in beta cell replication or change in beta cell apoptosis was detected, but duct cells positive for insulin and scattered beta cells were increased with pregnancy.
CONCLUSIONS/INTERPRETATION:
The adaptive increase in beta cell numbers in human pregnancy is not as great as in most reports in rodents. This increase in humans is achieved by increased numbers of beta cells in apparently new small islets, rather than duplication of beta cells in existing islets, which is characteristic of pregnancy in rodents
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