Invited Speakers

Employing Immersive Virtual Reality to Reveal Common Geometric Principles of Individual and Collective Decision-Making

Understanding how social influence shapes biological processes is a central challenge in contemporary science, essential for achieving progress in a variety of fields ranging from the organization and evolution of coordinated collective action among cells, or animals, to the dynamics of information exchange in human societies. Using an integrated experimental and theoretical approach, involving automated tracking, immersive Virtual Reality (VR) and computational visual field reconstruction, I will discuss the discovery of universal geometric principles of perceptual decision-making across vast scales of biological organization, from neural collectives within the brains of individual invertebrates and vertebrates, to the collective movement decisions made by fish schools and primate societies.

Methods and Tools for the Quantitative Characterization of Engineered Biomolecular Systems

Despite many years of research, no standard approach has emerged to rationally design novel genetic circuits implementing non-trivial functions. Synthetic biology still largely relies on tinkering. This comes notably from our limited capacities to quantitatively predict the behavior of biological systems in different cellular contexts. Iterative approaches, employing design-built-test-and-learn (DBTL) strategies, have the potential to circumvent this problem. In this presentation, I will describe some of our efforts to develop an integrated framework supporting DBTL approaches. Firstly, I will present experimental platforms that we have developed to run experiments in an automated manner. Automation increases throughput, and also, more importantly in fact, improve standardization and reproducibility. Secondly, I will present recent results we obtained on the characterization and modeling of several natural and engineered microbial systems, together with applications to real-time control and treatment optimization. Finally, I will conclude with recent results on the optimal design of parallel experiments.

Keywords: Bioreactor and cytometry automation, microfluidics and microscopy automation, plate reader automation, optogenetics, deterministic and stochastic modeling, model predictive control, process and treatment optimization, optimal experimental design, cybergenetics, antimicrobial resistance, protein bioproduction in yeast

Acknowledgements: this work has been done in collaboration with all the members of the InBio group at Inria and Institut Pasteur.

Context dependence of biological circuits: Predictive models and engineering solutions

Engineering biology has tremendous potential to impact applications, from energy, to environment, to health. As the sophistication of engineered biological circuits increases, the ability to predict system behavior becomes more limited. In fact, while a systems component may be well characterized in isolation, its salient properties often change in surprising ways once it interacts with other systems in the cell. This context-dependence of biological circuits makes it difficult to perform rational design and leads to lengthy, combinatorial, design procedures where each component is re-designed ad hoc when other parts are added to a system. In this talk, I will overview some causes of context-dependence. I will then focus on problems of resource loading and describe a design-oriented mathematical model that accounts for it. I will introduce a general engineering framework, grounded on control theoretic concepts, that can serve as a basis for creating devices that are insulated from context. Example devices will be introduced for both bacterial and mammalian genetic circuits. These solutions support rational and modular design of sophisticated genetic circuits and can serve for engineering biological circuits that are more reliable and predictable.