Mathematical Biology Seminar - Fall 2019
Seminars are held at 11:30AM in Cullimore Hall, Room 611, unless noted otherwise.
For questions about the seminar schedule, please contact Enkeleida Lushi
| Date | Speaker, Affiliation, and Title | Host |
|---|---|---|
| September 10 |
Christopher Miles, New York University Diffussive Search for Diffusing Receptors Cells send and receive signals in the form of diffusing particles that search for target sites called receptors, which themselves diffuse along a membrane. This raises the natural question: does the target diffusing help or hurt the ability of a diffusing particle to locate it? We'll modify a classical PDE describing the so-called "narrow escape problem" into a PDE with stochastic boundary conditions, which we'll study using matched asymptotic analysis and Monte Carlo simulations. This is joint work with Sean Lawley at Utah and Alan Lindsay at Notre Dame. |
James Maclaurin |
| September 24 |
Tapomoy Bhattacharjee, Princeton University Bacteria Motility in Three-dimensional Disordered Media While bacterial motility is well-studied for motion on flat surfaces or in unconfined liquid media, most bacteria are found in heterogeneous porous media, such as biological gels and tissues, soils, and sediments. However, how pore-scale confinement alters bacterial motility is unknown due to the opacity of typical 3D media. Here, we reveal that the paradigm of run-and-tumble motility is dramatically altered in a porous medium. By directly visualizing individual E. coli, we find a new form of motility in which cells are intermittently and transiently trapped as they navigate the pore space; analysis of these dynamics enables prediction of single-cell transport over large length and time scales. Moreover, we show that concentrated populations can collectively migrate through a porous medium -- despite being strongly confined -- by chemotactically “surfing” a self-generated nutrient gradient. This behavior depends sensitively on pore-scale confinement, initial colony density, and nutrient consumption, providing a means to control collective migration in bacterial populations. Our work provides a revised picture of active matter transport in complex media, with implications for healthcare, agriculture, and bioremediation.
|
Enkeleida Lushi |
| October 1 |
Nikolai Chapochnikov, Flatiron Institute Does the Fly Know PCA? Evidence for Signal Decorrelation from Connectomics and Neural Activity in Olfaction Odor discrimination and tracking of odor concentration change are crucial features for animal navigation and survival. It is however not entirely understood how the olfactory neural circuitry implements and computes these features. Here using mathematical modeling and data analysis, we study the sensory information processing in the olfactory lobe of the Drosophila larva, which is homologous but highly simplified compared to that of the adult fly and vertebrates (Wilson, 2013). The neural circuit is composed of 21 olfactory receptor neurons (ORNs) which reciprocally connect with several classes of inhibitory local neurons (LNs); the LNs also reciprocally interconnect. To understand the circuit function and computation, we use an objective function approach (Pehlevan et al., 2018), which accounts for the circuit architecture and dynamics and incorporates Hebbian synaptic plasticity. We demonstrate that such a circuit whitens and decorrelates the ORN inputs by subtracting top principal components via the negative feedback loops from different LNs. We also show that the subspace created by the ORN → LN connectivity vectors is expected to align with the principal activity subspace. To test these predictions, we use the available knowledge of the circuit connectome, including synaptic weights quantified by the number of contacts in parallel (Berck et al., 2016), as well as the responses of ORNs to a wide range of odors (Si et al., 2019). We indeed find that the top principal subspace of ORN responses significantly aligns with the subspace of ORN→ LNs connection vectors. In summary, using a normative approach, we can explain the observed relationships between synaptic weight vectors and ORN activity as well as relate the neural circuit organization to the computational task and a Hebbian learning paradigm. Our study thus supports the idea that the olfactory circuit learns the PCA directions of the input to enable better downstream odor differentiation.
|
Victor Matveev |
| October 8 |
Emma Greenspon, Monmouth University Sensorimotor Mapping of Pitch In order to converse with your colleague or sing the song “Happy Birthday to You” at a birthday party, your brain needs to accomplish online sensory and motor processing. For instance, you not only need accurate representations of auditory information being produced by yourself or others, but you will also need to be able to convert auditory representations into motor signals of the vocal system in order to use your own voice. Although the human voice is the most accessible musical instrument available to us, people vary greatly in their ability to vocally produce pitch (i.e. singing). My work focuses on the accuracy and flexibility of sensorimotor associations of pitch across different forms of musical performance. I will discuss a general model of sensorimotor mapping as well as other recent work addressing the roles of learning and memory in music performance.
|
Amit Bose |
| October 15 |
Sophie Marback, New York University Active Mixing and Sieving at the Nanoscale Mixing particles is an everyday experience, like stirring your morning coffee with a spoon. Nanoscale spoons, though, do not exist, and small biological organisms have thus to rely on alternative strategies to mix and separate. One striking feature of living systems is that most transport tasks are completed in fluctuating environments – like transport driven by fluctuations in biological nanopores or peristalsis in fungal species or in the small intestine. These observations point to a definite impact of surface agitation on transport in confined geometries. Yet, the simple question of “do fluctuations enhance or diminish transport?” is surprisingly difficult to answer. We propose a general theory to relate diffusive transport and surface fluctuations and draw from this theory a simple understanding of competing effects at play. This allows us to identify key components for the design of active channels, with applications in filtration and desalination. |
Enkeleida Lushi |
| October 22 |
Phillip Barden, Department of Biological Sciences, NJIT One Hundred Million Years on Forty Sextillion Legs: How Ants and Fossil Amber help us to Understand Evolution |
Enkeleida Lushi |
| November 12 |
Calina Copos, New York University A Model for How Cells form a Front and a Rear
In order to initiate locomotion, cells need to establish a well-defined cell front and rear through the process of cellular polarization. Polarization is a crucial component in cell differentiation, development, and motility and it is not yet well understood. Theoretical models that have been developed to understand the onset of polarization have explored either signaling or mechanical pathways, yet few have proposed mechanochemical mechanisms. However, most intrinsically motile cells rely on both signaling modules and actin cytoskeleton to break symmetry and achieve a stable polarized state. We propose a general mechanochemical polarization framework based on the interaction between a stochastic model for the segregation of signaling molecules and a simplified mechanical model for actin cytoskeleton network competition. Three competing hypotheses are tested to understand the origin of the cell polarization: (1) mechanically, (2) biochemically, and (3) a combined mechanochemical pathway. Computational investigation of the emergent polarity in these three models and scans of the models' parameter spaces lead to insights into these different polarization mechanisms.
|
Enkeleida Lushi |
| November 19 |
Matthew Mizuhara, The College of New Jersey Synchronization and Pattern Formation in the Kuramoto Model on Random Graphs A common occurrence in nature is the emergence of spontaneous, collective behavior from a group of individual agents: a particularly striking example is the ability of certain species of reies to synchro- nize their ashes. The Kuramoto model is a non-linear dynamical system of phase oscillators which has been used to explain and explore such synchronization and other types of pattern formation. We will review several classical tools and results in this area, and introduce re- cent advances in the study of the Kuramoto model on random graphs. In particular, we will show how the Kuramoto model on small-world graphs may not only transition to synchronization, but also exhibits bifurcation to so-called twisted states. |
James Maclaurin |
Updated: November 22, 2019