Construction and Modelling of an Inducible Positive Feedback Loop Stably Integrated in a Mammalian Cell-Line

Understanding the relationship between topology and dynamics of transcriptional regulatory networks in mammalian cells is essential to elucidate the biology of complex regulatory and signaling pathways. Here, we characterised, via a synthetic biology approach, a transcriptional positive feedback loop (PFL) by generating a clonal population of mammalian cells (CHO) carrying a stable integration of the construct. The PFL network consists of the Tetracycline-controlled transactivator (tTA), whose expression is regulated by a tTA responsive promoter (CMV-TET), thus giving rise to a positive feedback. The same CMV-TET promoter drives also the expression of a destabilised yellow fluorescent protein (d2EYFP), thus the dynamic behaviour can be followed by time-lapse microscopy. The PFL network was compared to an engineered version of the network lacking the positive feedback loop (NOPFL), by expressing the tTA mRNA from a constitutive promoter. Doxycycline was used to repress tTA activation (switch off), and the resulting changes in fluorescence intensity for both the PFL and NOPFL networks were followed for up to 43 h. We observed a striking difference in the dynamics of the PFL and NOPFL networks. Using non-linear dynamical models, able to recapitulate experimental observations, we demonstrated a link between network topology and network dynamics. Namely, transcriptional positive autoregulation can significantly slow down the “switch off” times, as comparared to the nonautoregulatated system. Doxycycline concentration can modulate the response times of the PFL, whereas the NOPFL always switches off with the same dynamics. Moreover, the PFL can exhibit bistability for a range of Doxycycline concentrations. Since the PFL motif is often found in naturally occurring transcriptional and signaling pathways, we believe our work can be instrumental to characterise their behaviour

Mathematical Modeling and Non-linear Analysis of Synthetic and Endogeneous Gene Regulatory Networks

Gene regulatory networks are characterised by positive or negative regulatory interactions between transcription factors and their target genes: their composition allows the network to perform diff�erent functions in di�fferent contexts, thus conferring speci�fic dynamical properties to the expressed genes. Considering the single components it is possible to use mathematical models to evaluate the behaviour of the whole system, thus revealing its function and predicting its behaviour as a result of endogeneous and exogeneous stimuli. One way to characterize the single components in order to predict the function of the complete system is to describe the single parts using di�fferential equations and looking for a solution able to reproduce the measured response of the system. Here, a mathematical study on some gene regulatory network topologies is proposed, thus explaining the function of common regulatory motifs observed at molecular level, highligthing the appeareance of unexpected behaviours due to the intrinsic nonlinearities and also revealing the versatility of some of these networks in performing di�fferent actions, simply varying the behaviour of a single component. Ad-hoc constructed synthetic networks and endogeneous ones are analysed

miRNAs confer phenotypic robustness to gene networks by suppressing biological noise

miRNAs are small non-coding RNAs able to modulate target gene expression. It has been postulated that miRNAs confer robustness to biological processes, but clear experimental evidence is still missing. Here, using a synthetic biological approach, we demonstrate that microRNAs provide phenotypic robustness to transcriptional regulatory networks by buffering fluctuations in protein levels. We construct a network motif in mammalian cells exhibiting a 'toggle-switch' phenotype in which two alternative protein expression levels define its ON and OFF states. The motif consists of an inducible transcription factor that self-regulates its own transcription and that of a miRNA against the transcription factor itself. We confirm, using mathematical modelling and experimental approaches, that the microRNA confers robustness to the toggle-switch by enabling the cell to maintain and transmit its state. When absent, a dramatic increase in protein noise level occurs, causing the cell to randomly switch between the two states

Experimental and simulated switch off time-course across the PFL and NPFL cell population.

<p>Experimental data (thin lines) and model simulations (thick lines) were reported for the PFL (left) and NOPFL (right) cells. Shaded areas represent standard deviations from replicate experiments.</p

Replicates of the experimental time-courses across the PFL and NPFL cell population.

<p>Replicates of the experimental time-courses for the PFL (left) and NOPFL (right) cells. Each line in each panel represent the average fluorescence intensity across the cell population in one switch-off experiment.</p

Design of the expression system.

<p>(A) PFL: the promoter <i>CMV-TET</i> consists of seven direct repeats of a 42-bp sequence containing the tet operator sequences (tetO), located just upstream of the minimal <i>CMV</i> promoter (PminCMV). The Tetracycline-controlled transactivator tTA derives from the addition of the VP16 activation domain to the transcriptional repressor TetR. The d2EYFP is the destabilised yellow-green variant of enhanced green fluorescent protein. (B) NOPFL: the <i>CMV</i> promoter drives the expression of the tTA, which in turns drives the transcription of the d2EYFP from the <i>CMV-TET</i> promoter. (Inset) RealTime PCR performed on DNA extracted from PFL and NOPLF cells shows that the DNA levels of tTA and d2EYFP are comparable among the two clonal cell populations.</p

Parameters identified after the fitting procedures: parameters values as well as standard deviation are reported for each parameter.

<p>Parameters identified after the fitting procedures: parameters values as well as standard deviation are reported for each parameter.</p

Phase portrait of the PFL model.

<p>The tTA-d2EYFP mRNA concentration (y axis) has been plotted against tTA protein concentration (x axis). Varying Doxycycline concentrations ( through ) were used to investigate the dependence of the two stable equilibria (“ON” and “OFF” in the graph) on the amplitude of the input. The shape and dimensions of the two basins of attraction (the set of initial conditions ending up in one of the two stable steady states) can be studied with the same technique: in this figure the grey shaded area represents the basin of attraction of the “OFF” equilibrium for Doxycycline =  nM.</p

Switch off time for varying Doxycycline concentrations from experimental data and model predictions.

<p>The model predictions for the switch off times are shown for PFL (dashed thick line) and NOPFL (solid line). Experimental quantification of the for PFL and NOPFL models have been reported for comparison with + and respectively. Observe that the experimental for the PFL at and could not be estimated since the PFL is not switching off in the experimental observation time (43 h).</p