Research
in our group

design of Robust modules

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Design to specifications

For realizing large-scale synthetic genetic systems, modules need to be designed to specs

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Robustness of behavior

One of the major indicators we look at is robustness to disturbances

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Design and test

Based on the quantitative models, we design and test multiple versions of the same module 

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Performance assessment

For every design, we assess their performance considering the design specifications

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Ranking and validation

We rank different designs and assess their behavior with respect to model predictions

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Design principles

Considering the data and the predictions, we revise and refine our quantitative models

Competition for shared resources

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Resource Scarcity

Cellular processes share building blocks and machinery that have limited availability

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Coupling

Competition for shared resources introduces coupling among "independent" processes

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Mechanistic Models

By accounting for the limited availability of resources, we can develop  predictive models

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Predict Dynamics

Based on models explicitly accounting for coupling, we can predict emergent behaviors

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Experimental Validation

Our approach is validated experimentally in the lab using our customized Chi.Bio platform

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Redesign Components

Leveraging the predictive models guides the redesign of components for increased modularity

Data-driven control

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Data-driven approach

Biological systems are complex nonlinear systems, thus we rely on data about them

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Machine Learning

Leveraging data about their behavior, we use machine learning to obtain a model of cells

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Model Predictive Control

Model predictive control enables us to steer cellular behavior towards our goal

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Optimal Control

Relying optimization methods, we can prioritize some objectives over others

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Experimental Validation

Our approach is validated experimentally in the lab using our customized Chi.Bio platform

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Learning Models from Data

All the data we collect forms the foundation of learning the biophsycal system dynamics