Mathematical and Computational Biosciences Collective Colloquium - Spring 2026

For questions about the seminar schedule, please contact James MacLaurin or Kristina Wicke.

Zoom link for seminars: https://njit-edu.zoom.us/j/99264610662?pwd=zFOFhY2fJ0HMdedKu5aPROv3zaKJrg.1 

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February 04

Jorge Golowasch and Kristina Wicke, NJIT

Faculty Research Overview

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February 11

Danielle Bassett, University of Pennsylvania

How Costly is Your Brain's Activity Pattern

Neural systems in general—and the human brain in particular—are organized as networks of interconnected components. Across a range of spatial scales from single cells to macroscopic areas, biological neural networks are neither perfectly ordered nor perfectly random. Their heterogeneous organization supports---and simultaneously constrains---complex patterns of activity. How does the network constraint affect the cost of a specific brain's pattern? In this talk, I will use the formalism of network control theory to define a notion of network economy: the idea that a biological neural network’s organization partially determines the energetic cost of reaching a neural state, maintaining a neural state, and transitioning between neural states. Then, I will demonstrate how the principle of network economy can inform our study of neural system function in health and disease.

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February 18

James MacLaurin and Victor Matveev, NJIT

Faculty Research Overview

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March 4

Horacio Rotstein, NJIT

Discussion Group (Linking models and data: parameter estimation and other tools)

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March 11

Iris Yoon, Swarthmore College

How Topology Reveals Structure in Biology

In this talk, I will discuss recent developments in applied topology that study the structure of data. In particular, I will show how constructions in topology, such as Dowker complexes and path liftings to covering spaces, reveal interesting structures in data arising from cancer biology and neuroscience. I will also discuss recent efforts to generalize the application of Dowker complexes to more complex datasets. 

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March 25

Guillermo Cecchi, Icahn School of Medicine at Mount Sinai

Quantifying the Experience of Pain

Validated clinical instruments in the form of point-scale questionnaires are the backbone of psychiatric monitoring, yet their structured format imposes real costs: patient burden, clinician time, and an inherent ceiling on what can be captured. We investigated whether large language models could bypass these limitations by extracting clinical metrics directly from naturalistic patient speech. We applied LLM-based analysis to semi-structured interviews from two clinical populations, chronic low back pain (CLBP) and major depressive disorder, probing three hypotheses: (1) that automated metrics would discriminate meaningfully between diagnostic groups; (2) that LLM-derived scores would correlate with validated questionnaire measures; and (3) that the approach could surface novel constructs such as Narrative Fragmentation and Agency Deficit, theoretically relevant to pain and depression but not easily accessible to standard self-report. To test generalizability beyond the clinical setting, we applied the same framework to condition-specific Reddit communities, where individuals share experiences in free form. Results support the viability of LLM-based narrative analysis as a scalable, low-burden complement to traditional instruments, one that preserves the richness of patient experience rather than compressing it onto predetermined ordinal scales and can be extended to high-frequency (e.g., daily) interactions with patients across many mental health conditions.

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April 1

Noah Cowan, Johns Hopkins Whiting School of Engineering

Control and recalibration of path integration in the hippocampus

The hippocampus is thought to serve as a “cognitive map” wherein the events of an animal’s experience are encoded within a spatiotemporal framework. To continuously update the animal’s position and orientation on this internal map, the hippocampal system integrates self-motion signals over time. External landmarks provide feedback to correct the errors in the position estimate that would otherwise inevitably accumulate. Using a novel virtual reality apparatus, we discovered that if path integration is biased, such that the animal consistently under- or overestimates its movement through space, the landmarks (Jayakumar et al, Nature, 2019) or optic flow cues (Madhav et al, Nature Neuroscience 2024) in the environment can serve as a teaching signal for recalibration of the path integrator. Using a biophysically plausible attractor neural network model of path integration, we show that for landmark- based recalibration the path integration error, or its integral, must be encoded at the level of individual neurons in order to enable path integration recalibration (Secer et al, 2025, Nat Comm). Using this prediction, we turned back to the physiological data and discovered a rate code for error at the level of individual neurons. Finally, I’ll describe our team’s recent discovery that Area 29e, an understudied parahippocampal field, serves as specialized visuospatial hub that may carry landmark signals for anchoring hippocampal representations of space to the external world (Secer et al., bioRxiv, 2025).

This body work is the result of an equal collaboration with the lab of Prof. James Knierim, generously funded by the NIH and ARO.

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April 8

Evan Schaffer, Icahn School of Medicine at Mount Sinai

Stable and predictable geometry of neural representations is inevitable

In many brain regions, the stimulus tuning of neurons is both spatially disorganized and temporally unstable. For example, in mouse piriform cortex, neurons receive input from a random collection of glomeruli, resulting in odor representations that lack spatial organization. The piriform neurons responsive to a given odor are completely uncorrelated with those activated by the same odor two weeks later, a phenomenon often called ‘representational drift’. I will describe two general problems that emerge as properties of random connectivity and representational drift, respectively, and show how solutions to these two problems are related. First, random connectivity implies that odor representations are not only disorganized but different across individuals. How can individuals nevertheless agree on the qualities of an odor, such as how citrusy it is? We find that random representations can support consistent agreement about odor quality across a range of odors after only a single shared experience. Second, representational drift would seem to imply that piriform cortex, like other brain regions whose activity appears to drift, is useless for the retrieval of associative memories learned several weeks prior. However, we and others have found that stable decoding of drifting representations is possible. We offer a very general mathematical understanding of when and why stable decoding from drifting representations is possible and draw connections to both empirical results and experimentally testable predictions.

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April 15

Casey Diekman and Amitabha Bose, NJIT

Faculty Research Overview

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April 22

Jeff Sachs, Merck

ODEs Save Lives ~and~ How to Calculate Career Trajectories

Mathematical and Data Sciences are being applied across a broad range of industries, informing decisions by distilling into information data from across disparate domains, scales of time and space, and sources.  The introduction will give brief overview of applications (potential careers) from a broad array of healthcare and healthcare-adjacent sectors (pharmaceutical, biomedical equipment, digital health, insurance, etc.).  The remainder of the presentation will show applications of math in the pharmaceutical industry, where a very broad range of mathematical and computational sciences show up: classical applied math such as differential and partial differential equations, discrete mathematics, visualization, network inference, (stochastic) optimization, control theory, probability, dynamical systems, inverse problems, image processing, pattern recognition/AI, natural language processing, and, of course, numerical analysis and simulation.

Applications highlighted will use of ODE- and generalized-linear-model-based non-linear mixed effect models, stochastic simulation, machine-learning network inference, and other pharmacometric methods in oncology, Alzheimer’s Disease, and vaccines.  Each example will start with the decision that needs to be made and the question that modeling could help answer.  The modeling and results will be explained together with their impact on drug/vaccine discovery and development.

The seminar will include but will not focus on mathematical details, and will not have proofs, code, or detailed exposition of numerical methods.

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April 29

Yuan-Nan Young, NJIT

Dynamic deformation and positioning of nucleus

We investigate the coupled dynamics of centrosomes and the cell nucleus under microtubule-mediated pulling within a viscous cytoplasmic environment. A coarse-grained, stoichiometric framework is developed to capture the interactions between microtubules and force generators (FGs) distributed on the nuclear envelope and cortex. The model integrates hydrodynamic coupling, nuclear envelope elasticity, FG transport along the nuclear envelope, and FG binding kinetics, enabling quantitative predictions of force balance and shape evolution. Using a boundary-integral formulation benchmarked against analytical limits, we examine how envelope stiffness, permeability, and FG mobility control the stability and positioning of the centrosome–nucleus complex. Simulations in spherical and spheroidal geometries reveal that enhanced FG mobility or weakened envelope stiffness amplifies nuclear deformation and destabilizes centrosomal organization—conditions reminiscent of pathological nuclear softening. Parameter sweeps across FG number and mechanical moduli delineate the transition from stable to misaligned configurations. This framework establishes a unified mechanical description linking molecular-scale force transduction to mesoscale nuclear morphology, providing mechanistic insight into how cytoskeletal dysregulation and envelope integrity jointly govern centrosome–nucleus coupling.

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May 6

Henry Shum, University of Waterloo

Modelling the hydrodynamics of biological and artificial microswimmers

Microorganisms have developed a variety of mechanisms for propulsion and strategies for controlling their motion through fluids. Many swim by actuating slender appendages called flagella that push or pull the cell body forward. We model such microswimmers as objects immersed in a fluid governed by the equations of incompressible Stokes flow with boundary conditions on the surface of the swimmer reflecting the actuation of the flagella. In our model for a flagellated bacterium, we consider a rigid spheroidal cell body connected to a helical flagellum that rotates about an axis fixed with respect to the cell. A boundary element method is used to numerically solve the governing equations and analyze the behavior of the model swimmer in various environments, showing that the details of the swimmer shape qualitatively influence trajectories near solid boundaries.

Next, we present a modification of this model that can be used to simulate the motion of euglenids, which are larger organisms with more complex flagella beat patterns. However, given the difficulty in determining the kinematics of the flagellum and the organism's responses to encountering obstacles, we construct a simplified hydrodynamic model in which the euglenid is represented as a rigid prolate spheroid and surface slip velocities are imposed over a portion of the body to model the action of the flagellum at one pole. Such model swimmers characterized by surface slip distributions are known as squirmers. While the simplified geometry means that close-range interactions may be inaccurately treated, the squirmer model is computationally efficient and can capture the far-field hydrodynamics of arbitrary active particles.

Using the spheroidal squirmer model, we explore the motion of euglenids in straight or wavy channels. We also discuss other applications of squirmer models, such as in autophoretic particles that could be used as microrobots.

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Last updated: April 27, 2026