March 16 & 17, 2022 

 
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ALEXANDER
AULEHLA
EMBL

Collective signaling oscillations in embryonic patterning

 

In our group we study the origin and function of collective signaling oscillations in embryonic development, using the mouse and medaka model systems. During vertebrate embryogenesis, ultradian signaling oscillations of several pathways (Notch, Wnt, Fgf) are linked to periodic formation of pre-vertebrae, somites. Most strikingly, signaling oscillations occur highly synchronized, yet phase-shifted in mesodermal cells, leading to spatio-temporal wave patterns sweeping along the embryo axis. To start revealing the essential properties of this complex oscillatory system we employ a coarse-graining synchronization and entrainment strategy, I will present the insight we gained from this approach. I will also discuss our experiments using the medaka model, in which we address how collective oscillations are linked to temperature compensation mechanisms and the robustness of embryonic patterning.

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@ruth_baker

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Functioning organisms possess an abundant array of tissues, each composed of distinct cell types with distinct cell morphologies. How is this grand diversity of fates and forms generated? Cells in the Arabidopsis stomatal lineage distill many of the key features of development: they must be chosen from initially equivalent cells, they undergo asymmetric and self-renewing divisions, they communicate among themselves and they must respond to the environment. Our lab takes a multidisciplinary approach–from cell biology to genomics to ecophysiology–that leverages the intrinsic strengths of this biological system to create a conceptual and technical framework for the study of cell fate, stem-cell self-renewal and cell polarity.

JEFF
GORE

MIT

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The Gore lab uses experimentally tractable laboratory microcosms to explore how interactions between individuals drives the evolution and ecology of communities. 

 

Three primary areas drive research in the group:

  • How do populations behave near "tipping points" leading to collapse?  

  • How can cooperative behaviors evolve within a species or community?  

  • What determines the diversity in communities?

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@stanfordstomata

@jeffchengore

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LIOR
PACHTER
Caltech

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@lpachter

The Pachter Lab develops computational and experimental methods for genomics. We are currently focused on the development of single cell sequencing based technologies and their application to RNA biology. The computational challenges we are addressing involve the analysis of high-dimensional data. Theorems are sometimes proven, experiments are occasionally successful, and usable software is distributed on GitHub.

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SASCHA HILGENFELDT & WILLIAM BRIEHER
UIUC
Year 03 Pilot Project Investigators

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@lpachter

@lpachter

Mammalian epithelial cell sheets: mechanical stability and structural dynamics

ALEXANDER DOWLING & JEREMIAH ZARTMAN
Notre Dame
Year 03 Pilot Project Investigators

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Morphogenetic cartography: Mapping morphogens to tissue shape through surrogate models and optimization of model-based design of experiments

 
 
 
 
 
 

RUTH
BAKER

Oxford

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@ruth_baker

Efficient Bayesian inference for mechanistic modelling with high-throughput data

Bayesian methods are routinely used to combine experimental data with detailed mathematical models to obtain insights into physical phenomena. However, the computational cost of Bayesian computation with detailed models has been a notorious problem. While high-throughput data presents opportunities to calibrate sophisticated models, comparing large amounts of data with model simulations quickly becomes computationally prohibitive. Inspired by the method of Stochastic Gradient Descent (SGD), we propose a minibatch approach to Bayesian computation. Through a case study of a high-throughput scratch assay experiment, we show that reliable inference can be performed at a fraction of the computational cost of a traditional Bayesian inference scheme. By applying a detailed mathematical model of cell movement, proliferation, and death to a wide range of gene knockdowns, we characterise the relative contributions of local cell density-dependent and -independent mechanisms of cell movement and proliferation. Within a screen of 118 gene knockdowns, we characterise functional subgroups of gene knockdowns, each displaying its own typical combination of local cell density-dependent and independent movement and proliferation patterns. By comparing these patterns to experimental measurements of cell counts and wound closure, we find that density-dependent interactions play a crucial role in the outcome of the scratch assay.

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DOMINIQUE 
BERGMANN
Stanford

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@stanfordstomata

How to build a leaf: Multi-scale dynamics define tissue patterns and self-renewing capacity of Arabidopsis epidermal cell lineages

During development, different precursor lineages give rise to the full complement of cell types in a multicellular organism. While some lineages are invariant, others are more flexible, producing variable numbers and types of progeny. Flexibility is a hallmark of plant development, and new organs (leaves, roots, flowers) produced thorough adult life incorporate responses to environmental conditions. The stomatal lineage in the Arabidopsis leaf epidermis offers a tractable system in which to investigate the emergence of stereotyped, but flexible cellular patterns. Through quantitative analyses of cell behaviors captured from whole-tissue time-lapses imagine, we have been able to define some of the patterning rules. For example, certain cell fates appear triggered by crossing cell size thresholds. On the other hand, behaviors of SLGCs—enigmatic cells with “latent” potential to self-renew and to contribute to the production of multiple final cell types, appear to be a multi-scale readout of the SLGC’s neighborhood, geometry, and transcriptome.

[work from Yan Gong, Hannah Fung, Renee Dale, Gabriel Amador and Dominique Bergmann]

JEFF
GORE

MIT

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@jeffchengore

Emergent phases of diversity and dynamics in ecological communities 

Natural ecological communities display striking features, such as high biodiversity and a wide range of dynamics, that have been difficult to explain in a unified framework. Using experimental bacterial microcosms, we have performed the first direct test of recent theory predicting that simple aggregate parameters dictate emergent behaviors of the community. As either the number of species or the strength of species interactions is increased, we show that microbial ecosystems transition between distinct qualitative dynamical phases in a predicted order, from a stable equilibrium where all species coexist, to partial coexistence, to emergence of persistent fluctuations in species abundance. Under the same conditions, high biodiversity and fluctuations allow and require each other. Our results demonstrate predictable emergent diversity and dynamics in ecological communties.

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LIOR
PACHTER
Caltech

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@lpachter

Cell dynamics from snapshot diagrams

The term "RNA velocity" refers to a collection of methods for inferring cell dynamics from high-throughput single-cell RNA-seq measurements. We will review the single-cell biophysics principles that inspired the method, with a view towards understanding the suitability of the various assumptions underlying popular implementations. Simulations and controlled experiments provide insight into the nuances of parameter estimation for several currently used models, and lead to a framework for Markovian analysis that points to directions for improvement and mitigation of current problems. 

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SASCHA HILGENFELDT & WILLIAM BRIEHER
UIUC
Year 03 Pilot Project Investigators

Hilgenfeldt

Brieher

Apical-basal mechanical coupling is important for inferring tissue mechanics from structural cues

An extensive body of recent work relates the geometry and structure of biological tissues to their mechanical properties, offering vital cues valuable for diagnostic and therapeutic applications. In single-layer tissues, the predominant modeling approach describes the confluent cell layer as a tessellation of two-dimensional polygons whose shape (anisotropy) correlates with mechanical information. Our pilot project shows that in the well-studied case of single-layer mature MDCK epithelial tissues, experimentally inferred mechanical behavior is not consistent with the experimental shape information when assuming this 2D model: actual cell anisotropy is so large that the tissue is inferred to be floppy, without shear modulus, while it is an elastic solid in reality. 


We find that stress and strain in the basal and apical cross sections of tissue cells are coupled: actin fiber bundles on the basal side induce stress dipoles that lead to additional anisotropy, persisting throughout the cell body to the apical side. We quantify this effect experimentally and verify that it is dependent on basal cell adhesion. We develop both a continuum mechanics theory and a simulational approach to modeling this cell-intrinsic anisotropy, crucial for restoring the predictive power of visual shape information in practical applications.

[work from Sascha Hilgenfeldt, Bill Brieher, Mayisha Zeb Nakib, Jairo Rojas]

ALEXANDER DOWLING & JEREMIAH ZARTMAN
Notre Dame
Year 03 Pilot Project Investigators

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@DowlingLab

@JeremiahZartman

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Morphogenetic cartography: Mapping morphogens to tissue shapes through surrogate models and optimization of model-based design of experiments

Morphogenetic programs spatially and temporally direct the cell signaling and nonlinear mechanical interactions between multiple cell types and tissue layers to define organ shape and size. Mechanical-biochemical signaling crosstalk directs multiple cellular processes such as the regulation of cell-cell and cell-ECM adhesion, cytoskeletal organization, metabolic growth and proliferation. A key challenge for the fields of systems and synthetic biology is to infer the optimal   combinations of intra- and inter- cellular interactions to engineer an organ’s shape, size and function. Toward this end, we are developing a fully automated Bayesian optimization framework that takes an organ shape as a desired output and proposes the optimal cellular parameters as desired set points to define the evolution of organ shape. We are calibrating and testing the method first on Drosophila wing imaginal discs, a simple, yet powerful model organ system. To do so, we developed a coarse-grained molecular simulation approach to simulate a Drosophila wing imaginal disc. Physical interactions mimicking aspects of cytoskeletal regulation during wing imaginal disc development was first defined within the model following which Surface Evolver was used to obtain a minimum energy configuration. Model complexity was chosen to incorporate key cell processes driving morphogenesis while not being computationally prohibitive.  As such,  computational parameter screens are feasible. We found that Frechet  distance was the most effective error metric for comparing model output to experimental data and that multiple sets of energy parameters are consistent with experimental data. This  demonstrates a many-to-one mapping between the model parameter space and tissue shape. This platform enables the use of  Gaussian Process Regression (GPR), a non-parametric kernel-based probabilistic modeling paradigm, to learn the mapping functions relating the morphogenetic modules that generate and maintain final organ shape. 

This pilot grant has proven instrumental towards the recent acquisition of long term external support that builds a collaboration with applied mathematicians at the University of California, Riverside. This overall effort seeks to develop multi-scale, mechano-chemical mechanistic models of morphogenesis to infer general principles of morphogenesis that operate across multiple length and time scales.

[work from Nilay Kumar,  Alex Dowling, Jeremiah J.  Zartman.]