Gianfranco Fiore

In Vivo Real-Time Control of Gene Expression: A Comparative Analysis of Feedback Control Strategies in Yeast.

Real-time automatic regulation of gene expression is a key technology for synthetic biology enabling, for example, synthetic circuits components to operate in an optimal range. Computer-guided control of gene expression from a variety of inducible promoters has been only recently successfully demonstrated. Here we compared, in silico and in vivo, three different control algorithms: the Proportional-Integral (PI) and Model Predictive Control (MPC) controllers, which have already been used to control gene expression, and the Zero Average Dynamics (ZAD), a control technique used to regulate electrical power systems. We chose as an experimental testbed the most commonly used inducible promoter in yeast: the galactose-responsive GAL1 promoter. We set two control tasks: either force cells to express a desired constant fluorescence level of a reporter protein downstream of the GAL1 promoter (set-point) or a time-varying fluorescence (tracking). Using a microfluidics-based experimental platform, in which either glucose or galactose can be provided to the cells, we demonstrated that both the MPC and ZAD control strategies can successfully regulate gene expression from the GAL1 promoter in living cells for thousands of minutes. The MPC controller can track fast reference signals better than ZAD but with a higher actuation effort due to the large number of input switches it requires. Conversely, the PI controllers performance is comparable to that achieved by the MPC and the ZAD controllers only for the set-point regulation.

Automatic Control of Gene Expression in Mammalian Cells.

Automatic control of gene expression in living cells is paramount importance to characterize both endogenous gene regulatory networks and synthetic circuits. In addition, such a technology can be used to maintain the expression of synthetic circuit components in an optimal range in order to ensure reliable performance. Here we present a microfluidics-based method to automatically control gene expression from the tetracycline-inducible promoter in mammalian cells in real time. Our approach is based on the negative-feedback control engineering paradigm. We validated our method in a monoclonal population of cells constitutively expressing a fluorescent reporter protein (d2EYFP) downstream of a minimal CMV promoter with seven tet-responsive operator motifs (CMV-TET). These cells also constitutively express the tetracycline transactivator protein (tTA). In cells grown in standard growth medium, tTA is able to bind the CMV-TET promoter, causing d2EYFP to be maximally expressed. Upon addition of tetracycline to the culture medium, tTA detaches from the CMV-TET promoter, thus preventing d2EYFP expression. We tested two different model-independent control algorithms (relay and proportional-integral (PI)) to force a monoclonal population of cells to express an intermediate level of d2EYFP equal to 50% of its maximum expression level for up to 3500 min. The control input is either tetracycline-rich or standard growth medium. We demonstrated that both the relay and PI controllers can regulate gene expression at the desired level, despite oscillations (dampened in the case of the PI controller) around the chosen set point.

An experimental approach to identify dynamical models of transcriptional regulation in living cells

We describe an innovative experimental approach, and a proof of principle investigation, for the application of System Identification techniques to derive quantitative dynamical models of transcriptional regulation in living cells. Specifically, we constructed an experimental platform for System Identification based on a microfluidic device, a time-lapse microscope, and a set of automated syringes all controlled by a computer. The platform allows delivering a time-varying concentration of any molecule of interest to the cells trapped in the microfluidics device (input) and real-time monitoring of a fluorescent reporter protein (output) at a high sampling rate. We tested this platform on the GAL1 promoter in the yeast Saccharomyces cerevisiae driving expression of a green fluorescent protein (Gfp) fused to the GAL1 gene. We demonstrated that the System Identification platform enables accurate measurements of the input (sugars concentrations in the medium) and output (Gfp fluorescence intensity) signals, thus making it possible to apply System Identification techniques to obtain a quantitative dynamical model of the promoter. We explored and compared linear and nonlinear model structures in order to select the most appropriate to derive a quantitative model of the promoter dynamics. Our platform can be used to quickly obtain quantitative models of eukaryotic promoters, currently a complex and time-consuming process.

Interaction patterns and individual dynamics shape the way we move in synchrony

An important open problem in Human Behaviour is to understand how coordination emerges in human ensembles. This problem has been seldom studied quantitatively in the existing literature, in contrast to situations involving dual interaction. Here we study motor coordination (or synchronisation) in a group of individuals where participants are asked to visually coordinate an oscillatory hand motion. We separately tested two groups of seven participants. We observed that the coordination level of the ensemble depends on group homogeneity, as well as on the pattern of visual couplings (who looked at whom). Despite the complexity of social interactions, we show that networks of coupled heterogeneous oscillators with different structures capture well the group dynamics. Our findings are relevant to any activity requiring the coordination of several people, as in music, sport or at work, and can be extended to account for other perceptual forms of interaction such as sound or feel.

Reconstructing the structure of directed and weighted networks of nonlinear oscillators

The formalism of complex networks is extensively employed to describe the dynamics of interacting agents in several applications. The features of the connections among the nodes in a network are not always provided beforehand, hence the problem of appropriately inferring them often arises. Here, we present a method to reconstruct directed and weighted topologies (REDRAW) of networks of heterogeneous phase-locked nonlinear oscillators. We ultimately plan on using REDRAW to infer the interaction structure in human ensembles engaged in coordination tasks, and give insights into the overall behavior.The formalism of complex networks is extensively employed to describe the dynamics of interacting agents in several applications. The features of the connections among the nodes in a network are not always provided beforehand, hence the problem of appropriately inferring them often arises. Here, we present a method to reconstruct directed and weighted topologies of networks of heterogeneous nonlinear oscillators. We illustrate the theory on a set of representative examples.

A Novel Computer-Based Set-Up to Study Movement Coordination in Human Ensembles

Existing experimental works on movement coordination in human ensembles mostly investigate situations where each subject is connected to all the others through direct visual and auditory coupling, so that unavoidable social interaction affects their coordination level. Here, we present a novel computer-based set-up to study movement coordination in human groups so as to minimize the influence of social interaction among participants and implement different visual pairings between them. In so doing, players can only take into consideration the motion of a designated subset of the others. This allows the evaluation of the exclusive effects on coordination of the structure of interconnections among the players in the group and their own dynamics. In addition, our set-up enables the deployment of virtual computer players to investigate dyadic interaction between a human and a virtual agent, as well as group synchronization in mixed teams of human and virtual agents. We show how this novel set-up can be employed to study coordination both in dyads and in groups over different structures of interconnections, in the presence as well as in the absence of virtual agents acting as followers or leaders. Finally, in order to illustrate the capabilities of the architecture, we describe some preliminary results. The platform is available to any researcher who wishes to unfold the mechanisms underlying group synchronization in human ensembles and shed light on its socio-psychological aspects.

An Orthogonal Multi-input Integration System to Control Gene Expression in Escherichia coli

In many biotechnological applications, it is useful for gene expression to be regulated by multiple signals, as this allows the programming of complex behavior. Here we implement, in Escherichia coli, a system that compares the concentration of two signal molecules, and tunes GFP expression proportionally to their relative abundance. The computation is performed via molecular titration between an orthogonal σ factor and its cognate anti-σ factor. We use mathematical modeling and experiments to show that the computation system is predictable and able to adapt GFP expression dynamically to a wide range of combinations of the two signals, and our model qualitatively captures most of these behaviors. We also demonstrate in silico the practical applicability of the system as a reference-comparator, which compares an intrinsic signal (reflecting the state of the system) with an extrinsic signal (reflecting the desired reference state) in a multicellular feedback control strategy.

BSim 2.0: An Advanced Agent-Based Cell Simulator

Agent-based models (ABMs) provide a number of advantages relative to traditional continuum modeling approaches, permitting incorporation of great detail and realism into simulations, allowing in silico tracking of single-cell behaviors and correlation of these with emergent effects at the macroscopic level. In this study we present BSim 2.0, a radically new version of BSim, a computational ABM framework for modeling dynamics of bacteria in typical experimental environments including microfluidic chemostats. This is facilitated through the implementation of new methods including cells with capsular geometry that are able to physically and chemically interact with one another, a realistic model of cellular growth, a delay differential equation solver, and realistic environmental geometries.