Publications

Learning in living organisms is typically associated with networks of neurons. The use of large numbers of adjustable units has also been a crucial factor in the continued success of artificial neural networks. In light of the complexity of both living and artificial neural networks, it is surprising to see that very simple organisms -- even unicellular organisms that do not possess a nervous system -- are capable of certain forms of learning. Since in these cases learning may be implemented with much simpler structures than neural networks, it is natural to ask how simple the building blocks required for basic forms of learning may be. The purpose of this study is to discuss the simplest dynamical systems that model a fundamental form of non-associative learning, habituation, and to elucidate technical implementations of such systems, which may be used to implement non-associative learning in neuromorphic computing and related applications.

Gametes in many species develop in cysts—clusters of germ cells formed by incomplete cytokinesis—that remain connected through intercellular bridges (ICBs). These connections enable sharing of cytoplasmic components between germ cells and, in the female germ line, enrich select cells in the cyst to become the oocyte(s). In mice, germline cysts of variable sizes are generated during embryonic development, thought to result from cyst fractures. Studies of fixed samples failed to capture fracture events, and thus, the mechanism remained elusive. Here, we use high-resolution live imaging of germ cells within their native tissue environment to visualize germline cyst dynamics. With this novel approach, we reveal a striking motile phenotype of gonad-resident germ cells and show that this randomly oriented cell-autonomous motile behavior during cyst formation underlies fracture events. Conversely, we show that stabilized ICBs help resist excessive fracturing. Additionally, we find that motility and thus fracture rates gradually decrease during development in a sex-dependent manner, completely ceasing by the end of cyst-forming divisions. These results lead to a model where the opposing activities of developmentally regulated cell motility and stable ICBs give rise to cysts of variable sizes. We corroborate these results by developing a model that uses experimentally measured fracture rates to simulate cyst formation and fracture and show that it can reproduce experimentally measured cyst sizes in both male and female. Understanding how variable cysts form will enable further studies of mammalian oocyte selection and establishment of the ovarian reserve.

This paper briefly reprises, with added commentary, a talk I gave on transport and flows within living cells at an APS-DFD meeting. Directed transport is especially important in large cells, such as eggs where developmental factors need to be properly localized, and early embryos whose organelles and genetic material must be properly positioned before cell division. I discuss two cases—a nematode single-cell embryo and a fruit fly egg cell—where advances in mathematical modeling and large-scale simulation of fluid-structure interactions have helped us understand fundamental mechanisms of force transduction and self-organization within the cell.

Cells modified outside of the body and then reintroduced provide an advantage over most small-molecule therapeutics in that cells can be designed to recognize target molecules in specific tissues and then act locally. Two studies now demonstrate advances in cell engineering for treating human disease (see the Perspective by Davila and Brentjens). Reddy et al. engineered human T cells to make a synthetic receptor that recognized overactive T cells such as those causing autoimmune disease and organ rejection. The most effective modified cells tested were ones in which the synthetic receptor initiated a program causing the production of both an anti-inflammatory cytokine and a receptor that acted as sink for a locally produced proinflammatory cytokine. In mouse models, such cells could be designed with logic programs that protect the desired tissues without detrimental systemic immunosuppression. Simic et al. modified T cells to produce a synthetic receptor that recognized an antigen localized to the extracellular matrix of the brain. The synthetic receptor activated a circuit stimulating the production of chimeric antigen receptors that targeted and killed cancer cells in the brain but not those implanted elsewhere in the mouse. A mouse model of neuroinflammatory brain disease could be treated with cells engineered to locally produce an anti-inflammatory cytokine.

Extracellular-signal-regulated kinase (ERK) signaling controls development and homeostasis and is genetically deregulated in human diseases, including neurocognitive disorders and cancers. Although the list of ERK functions is vast and steadily growing, the full spectrum of processes controlled by any specific ERK activation event remains unknown. Here, we show how ERK functions can be systematically identified using targeted perturbations and global readouts of ERK activation. Our experimental model is the Drosophila embryo, where ERK signaling at the embryonic poles has thus far only been associated with the transcriptional patterning of the future larva. Through a combination of live imaging and phosphoproteomics, we demonstrated that ERK activation at the poles is also critical for maintaining the speed and synchrony of embryonic cleavages. The presented approach to interrogating phosphorylation networks identifies a hidden function of a well-studied signaling event and sets the stage for similar studies in other organisms.

Studies in Drosophila were essential in delineating the highly conserved RAS signaling pathway. Indeed, some pathway components, such as Son of sevenless or Corkscrew, were named after mutant phenotypes in flies. Here, we discuss how Drosophila, with its ever-expanding arsenal of precise genetic manipulations and quantitative phenotypic assays, can be harnessed for investigating how RAS signaling is genetically deregulated in human diseases. The general approach is based on analyzing how disease mutations affect well-studied RAS-dependent developmental processes. Focusing on our work in the fly embryo and larval trachea, we illustrate this approach for missense mutations in MEK, a central kinase in the RAS cascade, which is deregulated in developmental abnormalities and cancers. The established approach provides clear insights into genotype/phenotype associations and can be extended to other signaling systems.

Ice in nature is dynamic at all scales, from glacial sheets that deform and flow to icebergs that melt down and capsize [1,2]. For the latter, much of the ice and much of the action is unseen beneath the surface [3–5]. Here we study laboratory-scale icebergs that freely float and melt, where direct visualizations show interesting and interconnected changes in the shape of the ice, its posture, and the flows of the surrounding water.

Our experiments reveal that free-floating ice persistently melts into unstable geometries, causing it to repeatedly capsize. Figure 1 shows the shape progression for a cylindrical piece of ice floating at the surface of room temperature water. It locks to an orientation, melts in place for several minutes, then abruptly rotates to a new posture and again locks. This process repeats for about 10 to 15 flips over the 30 minutes it takes to melt away. The photographs sample some of the locked orientations. Figure 2 displays the flows of the melt waters beneath the iceberg, where the two photos capture views along the axis and from the side, respectively. Below we describe the specialized techniques that enabled these images.

Electron cryomicroscopy (cryo-EM) is a technique in structural biology used to reconstruct accurate volumetric maps of molecules. One step of the cryo-EM pipeline involves solving an inverse-problem. This inverse-problem, referred to as \textit{ab-initio} single-particle reconstruction, takes as input a collection of 2d-images -- each a projection of a molecule from an unknown viewing-angle -- and attempts to reconstruct the 3d-volume representing the underlying molecular density.
Most methods for solving this inverse-problem search for a solution which optimizes a posterior likelihood of generating the observed image-data, given the reconstructed volume. Within this framework, it is natural to study the Hessian of the log-likelihood: the eigenvectors and eigenvalues of the Hessian determine how the likelihood changes with respect to perturbations in the solution, and can give insight into the sensitivity of the solution to aspects of the input.
In this paper we describe a simple strategy for estimating the smallest eigenvalues and eigenvectors (i.e., the `softest modes') of the Hessian of the log-likelihood for the \textit{ab-initio} single-particle reconstruction problem. This strategy involves rewriting the log-likelihood as a 3d-integral. This interpretation holds in the low-noise limit, as well as in many practical scenarios which allow for noise-marginalization.
Once we have estimated the softest modes, we can use them to perform many kinds of sensitivity analysis. For example, we can determine which parts of the reconstructed volume are trustworthy, and which are unreliable, and how this unreliability might depend on the data-set and the imaging parameters. We believe that this kind of analysis can be used alongside more traditional strategies for sensitivity analysis, as well as in other applications, such as free-energy estimation.

Gastrulation is a critical process during embryonic development that transforms a single-layered blastula into a multilayered embryo with distinct germ layers, which eventually give rise to all the tissues and organs of the organism. Studies across species have uncovered the mechanisms underlying the building blocks of gastrulation movements, such as localized in-plane and out-of-plane epithelial deformations. The next challenge is to understand dynamics on the scale of the embryo: this requires quantifying strain tensors, which rigorously describe the differences between the deformed configurations taken on by local clusters of cells at time instants of observation and their reference configuration at an initial time. We present a systematic strategy for computing such tensors from the local dynamics of cell clusters, which are chosen across the embryo from several regions whose morphogenetic fate is central to viable gastrulation. As an application of our approach, we demonstrate a strategy of identifying distinct Drosophila morphological domains using strain tensors.