Research

Modular design of biological networks

Our current ability of designing synthetic genetic circuits bottom up, that is, our ability to create larger systems from the composition of simpler functional units, is severely limited by the fact that a unit’s behavior depends on its context. Because of context-dependence, any time one adds a new unit to a circuit, the behavior of the units that were already in the circuit changes. This leads to a long, error prone, and combinatorial design effort by which any time a new component is added, the existing components need to be re-designed. Our group has been developing solutions to a few root causes that contribute to context-dependence: loading (retroactivity) and resource sharing.

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  • retroactivity
    Our system concept models load effects as retroactivity signals. Based on this concept, we tackled the load problem in synthetic biology employing disturbance attenuation techniques from control theory
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  • ribosome circuitry
    We are developing models of the control circuitry that confers differential robustness to endogenous and synthetic genes
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  • distributed feedback control
    We are developing distributed feedback controllers that make nodes of a gene networks robust to sharing of ribosomes
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  • resource sharing
    We are developing a design-oriented modeling framework to capture the effect of resource sharing on the behavior of genetic networks
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Stochastic behavior

The behavior of biomolecular systems in living cells is noisy due to the intrinsic stochasticity of biochemical reactions. Stochasticity leads to subtle tradeoffs in the design of synthetic circuits and also results in stability landscapes that differ from those of the corresponding deterministic models. Time scale separation among reactions is typical in any realistic biological network and it shapes stochastic properties in fairly subtle ways. We have been developing model reduction techniques for multi-time scale stochastic systems with the main objective of deriving reduced systems that can be analytically studied. We are using these techniques to understand the relationship among multi-stability, multi-modality, and time scale separation, with application to the control of natural multi-stable gene regulatory networks.

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  • multi-modality
    We are developing algorithms that determine when a gene regulatory network displays noise-induced multi-modality
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  • singular pertrubation
    We are developing singular perturbation-like techniques for the order reduction of stochastic models of bimolecular networks
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Control of multi-stable networks

Cell fate is decided by the (controlled) switching of the state of a multi-stable gene regulatory network among different basins of attraction. The ability of controlling these switches can therefore enable us to control cell fate. To this end, we are designing synthetic genetic feedback controller circuits that once running in the cell can steer the cell state in the desired basins of attractions. A driving application of this work is the reprogramming of somatic cells to multi-potent lineages for regenerative medicine applications.

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  • feedback control
    feedback control of endogenous multi-stable networks to trigger desired transitions
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  • multistability
    When is it possible to trigger a desired transition in a multistable gene regulatory network with experimentally realizable inputs? This question is crucial for controlling cell fate
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Intelligent Transportation

We have been working since 2006 on (cooperative) active safety systems to prevent collisions with focus on traffic intersections. Our approach is controltheoretic and based on the computation of the capture set, the set of states from which collisions are unavoidable with our control freedom. The strategy of the driver assist system is to be silent unless the capture set is hit, at what point it is warning first and then forcing specific throttle brake combinations to avoid entering the capture set.

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