NITMB Seminar Series Recordings
Luis Bettencourt
Recorded on 03/07/2025
Title: Redefining Fitness: Evolution as a Dynamic Learning Process
Abstract: Evolution is the process of optimal adaptation of biological populations to their living environments. This is expressed via the concept of fitness, defined as relative reproductive success. However, it has been pointed out that this procedure is incomplete and logically circular. To address this issue, several authors have called for new ways to re-define fitness through the relationship between phenotypes and their environment, but a general approach has remained elusive. In this talk, I show that fitness, defined as the likelihood function that follows from mapping population dynamics to Bayesian learning, provides a principled solution to this problem. I will show how probabilistic models of fitness can easily be constructed in this way, and how their averages acquire meaning as information. I will also show how this approach leads to powerful tools to analyze problems of evolution in stochastic environments, game theory, and selection in group-structured populations.
Matthias Steinrücken
Recorded on 1/24/2025
Title: Inferring selection from time-series genetic data
Abstract: Detecting and quantifying the strength of selection is a main objective in population genetics. Since selection acts over multiple generations, many approaches have been developed to detect and quantify selection using genetic data sampled at multiple points in time. Such time series genetic data is commonly analyzed using Hidden Markov Models, but in most cases, under the assumption of additive selection. However, many examples of genetic variation exhibiting non-additive mechanisms exist, making it crucial to develop methods that can characterize selection in more general scenarios. In this talk, I present approaches for the inference of general diploid selection coefficients, where the heterozygote and homozygote fitness are parameterized independently. I furthermore introduce a framework to identify bespoke modes of diploid selection from given data, as well as a procedure for aggregating data across linked loci to increase power and robustness. Extensive simulation studies show that these methods accurately and efficiently estimates selection coefficients for different modes of diploid selection across a wide range of scenarios. Applying the approaches to ancient DNA samples from Great Britain in the last 4,450 years, shows evidence for selection in several genomic regions, including the well-characterized LCT locus.
Jasmine Nirody
Recorded on 11/15/2024
Title: A tale of two motilities: adaptive locomotion in complex, changing environments
Abstract: I will discuss two (quite different!) adaptive locomotive modes that are able to contend with the heterogeneity and uncertainty inherent to natural environments: flagellated swimming in bacteria and walking in panarthropods. (1) Bacteria have evolved mechanisms at both the molecular and whole-cell level to facilitate motility through a wide range of complex environments. At the molecular level, we characterize mechanosensitivity in the bacterial flagellar motor (BFM), the rotary nanomachine that propels bacterial swimming. At the whole cell level, we explore how bacterial motility patterns adapt to confined, disordered environments. We discuss the features of these results that generalize across bacterial species (and those that don't!), underscoring the importance of considering flagellar motility in the context of a bacterium's environment and evolutionary history. (2) Panarthropods are a diverse clade containing insects, crustaceans, myriapods, and tardigrades. We show that inter-limb coordination patterns in freely-behaving tardigrades replicate several key features of walking in insects across a range of speeds and substrates. In light of these functional similarities, we propose a simple universal locomotor circuit capable of robust multi-legged control across body sizes, skeletal structures, and habitats.
Veronica Ciocanel
Recorded on 11/08/2024
Title: Parameter identifiability for PDE models of fluorescence microscopy experiments
Abstract: The dynamics of intracellular proteins is key to many cellular functions. One of the most versatile experimental techniques for probing protein dynamics in living cells is FRAP (fluorescence recovery after photobleaching). This experiment generates time-series data that average out spatial information about diffusion, transport, and binding dynamics of proteins. Partial differential equations (PDE) models provide the appropriate framework to model the fluorescence dynamics and to infer parameters such as diffusion coefficients or reaction rates. However, it is not known whether these parameters can be identified based on the spatially-averaged data available from FRAP experiments. We recently investigated limitations of known methods in assessing parameter identifiability for PDE models and proposed methods for learning parameter combinations based on re-parametrization and profile likelihoods analysis. In this work, our motivation stems from studying dynamic RNA binding proteins in biomolecular condensates that play key roles in the development of frog oocytes.
Jorge Nocedal
Recorded on 10/25/2024
Title: How is it Possible to Train Deep Neural Networks?
Abstract: In 1961, Minsky, one of the founders of AI, perceived a fundamental flaw within the burgeoning field of artificial neural networks. He doubted that such a nonlinear system could be effectively trained using gradient methods, because unless the “structure of the search space is special, the optimization may do more harm than good.” Fast forward to today, and we observe deep neural networks — far more complex than those envisioned at the field's inception — being successfully trained with methods akin to gradient descent. It has, indeed, become evident that the objective function displays a highly benign structure that we are only starting to comprehend. In this lecture, I aim to summarize our current understanding of this enigmatic optimization process. I will discuss several themes, including intrinsic dimensionality, the optimization landscape, and implicit regularization, all within the context of deep networks and large language models. Speaker Bio: Jorge Nocedal is the Walter P. Murphy Professor of Industrial Engineering and Management Sciences and (by courtesy) Engineering Sciences and Applied Mathematics at Northwestern University. Nocedal is also the Director of the Center for Optimization and Statistical Learning. Nocedal's main area of research is optimization, with applications in machine learning, engineering design, and the physical sciences. Research activities range from the design of new algorithms, to their software implementation and mathematical analysis. Areas of emphasis include large scale problems (with millions of variables), optimization under uncertainty, and parallel computing.
Thomas P Wytock
Recorded on 10/11/2024
Title: Cell reprogramming and bacterial memory predicted by mathematical modeling and transfer learning
Abstract: Determining how molecular changes propagate to affect the whole cell is a fundamental goal in systems biology. The challenge inherent to this pursuit is the number and diversity of molecular entities that give rise to cell behavior. In this talk, I will show how nonlinear dynamics and machine learning uncover unexpected behaviors in recent studies concerning irreversibility in bacterial regulatory networks [1] and cell reprogramming [2]. In the first study, Boolean networks reveal that the same DNA sequence can counterintuitively encode multiple behaviors through information stored in the state of the gene regulatory network. In the second study, a machine learning model provides an effective representation of the regulatory interactions between genes and cell phenotypes. We use this model to identify gene perturbations that can reprogram cells to different phenotypes. Both contributions demonstrate how the abundance of biological data provides opportunities for mathematical modeling to derive unexpected insights. Speaker Bio: Thomas P. Wytock is a postdoctoral researcher in the Motter Group at Northwestern University, where he focuses on the modeling of genetic networks.