Biological control is ever present in our world, from the complex nanoscale interactions of microbial consortia to macro-scale social dynamics of ecosystems. Populations of millions of cells within a palm-sized test-tube implement stochastic and massively parallel control strategies, reprogramming their own DNA to manifest diverse control architectures over time. These control systems achieve power efficiency and robustness, at the nano to micro scale.   To imbue such properties into novel control systems is the challenge of our time.  In this talk I will overview recent experimental and computational advances that accelerate design of novel control systems in the areas of synthetic biology and biomimetic computing.  I will highlight two advances in particular: 1) the discovery of a novel biophysical mechanism for implementing feedback control in synthetic gene networks and 2) the integration of deep learning and data-driven methods to generate a computational framework for predicting the operational envelopes of synthetic biocircuits.   In the former, I will discuss how overlooked design properties of synthetic gene networks can be used to enforce local and fast feedback control, resulting in improved persistence (over 72 cell divisions) of memory in the genetic toggle switch.   In the latter, I address the challenge of predicting biological system behavior over a range of conditions, given finite and noisy experimental samples.    Specifically, we examine the problem of discovering models for moment dynamics of a biological network, as a function of small molecule control signals and temperature-based perturbations.  We introduce deep dynamic mode decomposition and show that the input-Koopman models learned from flow cytometry data predict biocircuit functionality with over 90% accuracy over 1,000 experimental conditions.  I conclude with an overview of the landscape and opportunities for data-driven design and controller synthesis for biological systems.

Discovery and characterization of novel feedback control mechanisms in synthetic gene networks: From principled models to deep learning

256th National Meeting (2018)

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