Keynote - Bragg Lecturer: Naama Barkai Weizmann Institute, Israel
How Transcription Factor Locate Their Binding Sites In Large Genomes: A Role For Intrinsically Disordered Regions Cells are constantly "making decisions" - monitoring their environment, modulating their metabolism and 'deciding' whether to divide, differentiate or die. For this, they use biochemical circuits composed of interacting genes and proteins. Advances over the past decades have mapped many of these circuits. Still, can we infer the underlying logic from the detailed circuit structure? Can we deduce the selection forces that shaped these circuits during evolution? What are the principles that govern the design and function of these circuits and how similar or different are they from principles that guide the design of man-made machines? The interplay between variability and robustness is a hallmark of biological computation: Biological systems are inherently noisy, yet control their behavior precisely. Research projects in our lab quantify biological variability and identify its genetic origins, examine how variability is buffered by molecular circuits and investigate whether variability can in fact be employed to improve cellular computation. We encourage a multi-disciplinary approach, combining wet-lab experiments, dynamic-system theory and computational data analysis. This is achieved through fruitful interactions between students with backgrounds in physics, biology, computer science, mathematics and chemistry.
Sticking together: How bacterial collectives use chemical cues to (re)shape themselves Sujit Datta is an Associate Professor and Director of Graduate Studies of Chemical and Biological Engineering at Princeton University. He earned a BA in Mathematics and Physics and an MS in Physics in 2008 from the University of Pennsylvania, and then a PhD in Physics in 2013 from Harvard, where he studied fluid dynamics and instabilities in soft and disordered media with Dave Weitz. His postdoctoral training was in Chemical Engineering at Caltech, where he studied the biophysics of the gut with Rustem Ismagilov.
Datta joined Princeton in 2017, where his lab studies the dynamics, self-organization, and applications of complex, soft (“squishy”), and living systems. Datta’s research has revealed and shed new light on the fascinating behaviors manifested by complex fluids and bacterial populations in complex environments, guiding the development of new approaches to environmental remediation, energy production, agriculture, water security, and biotechnology. He also actively leads outreach efforts in STEM to bring together diverse perspectives and provide access to researchers from traditionally under-represented groups in studies of soft and living systems.
Datta’s scholarship has been recognized by awards from a broad range of different communities, reflecting its multidisciplinary nature, including through the AIChE 35 Under 35 Award, ACS Unilever Award, Camille Dreyfus Teacher-Scholar Award, three awards from the APS (Early Career Award in Biological Physics, Andreas Acrivos Award in Fluid Dynamics, and the Apker Award), the Arthur Metzner of the Society of Rheology, Pew Biomedical Scholar Award, NSF CAREER Award, and multiple commendations for teaching.
It’s getting hot in here The impact of temperature on growth is typically considered only under heat- or cold-shock conditions that elicit specific regulation. Over intermediate temperatures, the growth rate of all cells varies according to the Arrhenius law of thermodynamics; growth rate dynamics during transitions between temperatures remain mostly unstudied. How this behavior arises and what determines temperature sensitivity are largely unknown. Using a device that enables single-cell tracking during switches across a wide range of temperatures (0 °C to 47 °C), we show that many bacteria respond to temperatures upshifts on a characteristic time scale of ~1.6 doublings at the higher temperature, regardless of initial/final temperature or nutrient source. We rule out transcriptional, translational, and membrane reconfiguration as potential mechanisms, and instead discover that an autocatalytic enzyme network incorporating temperature-sensitive Michaelis-Menten kinetics recapitulates all temperature-shift dynamics and successfully predicts the altered temperature responses observed under simple-sugar and low-nutrient growth conditions. These findings suggest that the temperature sensitivity of metabolite flux dictates responses to temperature fluctuations.
Cell growth under mechanical pressure Morgan Delarue is a biophysicist working at LAAS-CNRS in Toulouse, France. Their research lies at the interface between physics, engineering, biology and medicine, and aims at the understanding of the physiological response to compressive stress in different organisms, with a particular emphasis on cancer.
Round the clock: circadian gene expression, growth and division in cyanobacteria Our lab applies quantitative approaches to study the design principles of regulatory circuits and cellular physiology. Our present focus is on understanding how the circadian clock of single-celled cyanobacteria is embedded within the regulatory fabric of the cell, and their dynamics in different environmental contexts. We believe the best way to understand natural circuits and to build new ones is through an iteration of experiment and theory. Our approach is therefore quite interdisciplinary, combining mathematical models, single-cell microscopy, microfluidics and synthetic biology. Examples of specific questions we are interested in include: how do the clock and the cell cycle interact with one another to coordinate growth, chromosomal replication, and cell division? How is the clock modulated by intracellular energy states, as well as extracellular environmental changes? Can we use synthetic biology to control clock oscillations and design new circuits?
Collagen organisation regulates context-dependent cellular dynamics of mesenchymal collectives in vivo The emergent geometric interactions arising between cells in a collective can organise patterns of cell motion as well as differentiation, generating new structure that informs further dynamic change. How geometric information such as cell shape, organization, and construction of an extracellular matrix is integrated across scales remains poorly understood in mesenchymal tissues where both cell and tissue complexity is challenging to visualize and quantify. The Tabler lab aims to overcome these challenges and uncover biophysical mechanisms governing the cellular dynamics that regulate morphogenesis and fate choice in vivo using the skull vault as a model of mesenchymal collectives. Through interdisciplinary collaboration, we explore the relationship between the emergence of complex and extreme physical structure, cell movements, and fate regulation using high-resolution quantitative imaging, physical perturbations, bioinformatic approaches, and novel analytic methods.
70 years later: revisiting Turing patterns with synthetic biology, mathematical modelling, and machine learning My group works at the physics-biology interface on self-organised emergent phenomena in complex biological systems, ranging from the physical limits of sensing to cell migration and collective behaviour. We develop fundamental theories of living systems, but also work on more data-driven topics in close collaboration with experimentalists. More recently we delved into combining mechanistic modelling and machine learning.
Micro-patterns: understanding how flowers sculpt their surface Our group aims to understand how plants develop, evolve and adapt to change. In particular, we are exploring how decisions made at the cellular level impact on plant fitness and biodiversity. This warrants a multiscale and interdisciplinary strategy at the core of our research. We focus on petal patterns as those are readily observable and associated with a wealth of fascinating biology that until recently was largely unexplorable. Our research aims to uncover what mechanisms epidermis cells use to make decisions, how these decisions are coordinated at the petal scale to generate a robust pattern, and what is their contribution to plant fitness and the creation of biodiversity. To do so, we combine genetic approaches with imaging, modelling and behavioural experiments in a small species of Hibiscus I developed as a new model system. Our results help us understand how plants can set-up boundaries within the petal epidermis and how evolution tinkers with these processes to generate the diversity of patterns observed in nature.
Growth Control Across Scales Rita Mateus is a joint group leader at the Max Planck Institute of Molecular Cell Biology and Genetics and the Cluster of Excellence Physics of Life (TUD) in Dresden, Germany. She has always been interested in understanding how cells coordinate precise growth and form of tissues, allowing them to become fully functional. To this end, Rita did her PhD in Prof. Antonio Jacinto's laboratory in Portugal, where she investigated how, upon injury, the zebrafish caudal fin precisely regenerates its shape and size, over and over, error-free. During her postdoc in the group of Prof. Marcos Gonzalez-Gaitán in Switzerland, Rita turned to development to investigate how morphogens control organ growth, using the zebrafish pectoral fin as a model. In parallel, she became more and more interested in understanding size and shape at the subcellular level, in particular to try to understand the physics and cell biology underlying structural colors, that require the formation of specific organelles with particular morphologies. Now, in her laboratory, Rita is pursuing these two research avenues to explore the biophysical properties involved in controlling growth across these very different length scales.
Optimization and historical contingency in protein sequences In biological evolution, populations are pushed to optimality, but may also be shaped by the contingency of their evolutionary history. The recent major growth of sequence data gives us access to the outcomes of molecular evolution. In this exciting context, my group studies the importance of optimization and contingency at the molecular scale and at the scale of microorganism populations. We perform physics-inspired mathematical modeling, numerical simulations and data analysis. To analyze protein sequence data, we employ both statistical physics-based inference methods and deep learning methods.
Pattern formation by living droplets The primary interest of my laboratory is to understand how cells make decisions about their identity during development. To allow a deeper understanding of cell decision making, and the generation of more useful conceptual frameworks, we have developed and implemented a range of imaging technologies- with the view that to understand the decision of cell requires monitoring a broad range of regulatory features of single cells, as they differentiate. In particular, we use imaging to directly observe the bursts of activity of individual genes in living cells in parallel with direct measurements of different signalling inputs.
Peter Swain University of Edinburgh, UK - Cancelled -
The ups and downs of glucose transport in budding yeast My laboratory studies the systems biology of cellular decision-making using budding yeast. We are interested in how genes and proteins interact to allow cells to sense and evaluate change in their environment. Our goal is to discover the strategies cells use in their decision-making because these strategies are likely to be more deeply conserved than the biochemistry implementing them. We use microfluidics and time-lapse microscopy so that we can watch individual cells as they respond and machine learning and mathematical modelling to analyse what we observe.
Towards a single molecule perspective of nuclear dynamics in the early mammalian embryo Our research focuses on developing live-cell single-cell and single-molecule imaging approaches to improve our understanding of cell fate transitions within the early mammalian embryo. We provide a single molecule perspective of the dynamics of nuclear proteins and the 3D genome as pluripotent cells differentiate and we use these datasets to build biophysical models.
Plenty of room at the top: integrative insights from the posture-scale dynamics of animal behavior How do we quantify the emergent dynamics of entire organisms? What principles characterize living movement? Research in our group addresses these questions with a modern biophysics approach and model systems ranging from the nematode C. elegans to zebrafish and honeybee collectives. We combine theoretical ideas from statistical physics, information theory and dynamical systems, with novel quantitative experiments, to understand the natural behavior of organisms.
How the embryo gets its shape: Understanding early mouse development with light-sheet microscopy With form, comes function. How cells organize to build tissues and how those tissues are then sculpted to form complex, working organs during development is largely unknown. While there have been significant advances in the organoid and stem cell fields in differentiating specific cell types and populations, we are not yet able to replicate the physical environment required to build more elaborate structures. In the McDole lab we use the mouse embryo to study how mechanical forces shape complex three-dimensional structures out of simple cell populations. We use a combination of cutting-edge live-imaging, biochemical techniques, computational methods, genetics and biophysics to dissect the role of forces during development.
From sea cells to sea shells (sometimes on the seashore) My lab explores how cells and small organisms control and orchestrate complex behaviours, and respond to dynamic environmental stimuli. We integrate experimental and theoretical approaches to explore fundamental biological questions such as the how aneural single-celled organisms actuate their motility appendages for swimming. We are particularly interested in understanding the origins and diversification of multiciliary coordination.
