If you would like to attend the seminar, please register here.

If you would like to attend the seminar, please register here.

The availability of time- and disease-dependent single-cell gene expression data has opened new opportunities for integrating these datasets into mathematical models representing the evolution and switching between cellular states. In this talk, I will focus on Hopfield recurrent networks, a mathematical framework from statistical physics that captures the multi-stable dynamics inherent in complex cell signaling networks, interpreting gene expression patterns as associative memories. Hopfield recurrent networks can mathematically implement Waddington’s interpretation of normal and abnormal cell phenotypes as dynamical attractors within epigenetic landscapes. I will discuss applications of this framework in modeling the onset of angiogenesis, the dynamics of disease progression in Multiple Myeloma (MM), and the cell cycle. In our angiogenesis model, we use data to visualize the cellular transition from stalk-like to tip-like endothelial cells, corresponding to the formation of capillary sprouts in blood vessels. The MM model employs scRNA-seq data from bone marrow aspirates of MM patients and those diagnosed with two medical conditions that often progress to full MM.

If you would like to attend the seminar, please register here.

If you would like to attend the seminar, please register here.

Microbial communities provide countless ecological services essential for sustaining life on Earth, and they perform a wide array of functions in biotechnology—from food production to biofuel synthesis. The quantitative functions delivered by microbial communities depend on their composition, i.e. the specific genotypes present and their relative abundances. To engineer microbial consortia that optimize these functions, we must establish a predictive, quantitative link between community composition and function. Yet, developing mechanistic mathematical models to achieve this is exceptionally challenging due to the complex network of interactions involved. In this talk, I will explore how concepts from fitness landscape theory in genetics can help overcome these challenges and lead to the creation of predictive, quantitative models of community function that can guide the optimization of  synthetic microbial consortia.

If you would like to attend the seminar, please register here.

Collective cell motion is ubiquitous in biology, occurring in normal development, wound healing and disease (cancer). Over the past decade I have been collaborating with the lab of Paul Kulesa in Kansas on a study of cranial neural crest cell migration in the chick. In this talk, I will review our work and illustrate how a basic hybrid agent-based mathematical model, combined with experimental studies, has led to new insights into this phenomenon. These include understanding the role of (i) environmentally-induced phenotypic switching, (ii) extracellular matrix degrading factors, (iii) DAN-induced cell velocity control, and (iv) Colec12 and Trial as factors confining cells to move along a corridor.

If you would like to attend the seminar, please register here.

Perceptual decisions rely on accumulating sensory evidence. This computation has been studied using phenomenological models, e.g. the drift diffusion model, or neurobiological network models exhibiting attractor dynamics. It remains unclear whether the dynamics of both models are qualitatively equivalent and whether attractor models can integrate evidence optimally. Here, I will present distinctive features of attractor models that allow them to perform flexible temporal weighting of stimulus evidence. In the discrete attractor model, this is due to transitions between decision states that can reverse initially-incorrect categorizations. Moving from categorical choices to continuous perceptual judgments, I will show that a continuous bump attractor network can integrate a circular feature, such as stimulus direction, nearly optimally. As required by optimal integration, the population activity of the network unfolds on a two-dimensional manifold, in which the position of the network’s activity bump tracks the stimulus average, and, simultaneously, the bump amplitude tracks stimulus uncertainty. Moreover, the model can flexibly switch between different temporal weighting profiles by changing a single control parameter, the global excitatory drive. Predictions of the models are validated with psychophysical data. Finally, I will outline how these attractor models can provide a comprehensive and experimentally testable computational framework to study the neural mechanisms underlying stimulus integration and bias effects in combined discrimination-estimation tasks.

If you would like to attend the seminar, please register here.