Movement sans ATP and learning sans neurons in curious ciliates

Please note that Saad Bhamla will be presenting virtually via Zoom.
For those who prefer to gather in person, WAB 563 will be available to watch the talk together.
Feel free to bring your own lunch and join us! 😊

Saad Bhamla: School of Chemical and Biomolecular Engineering | Georgia Tech

Abstract

Tangled active filaments are ubiquitous in nature, from chromosomal DNA and cilia carpets to root networks and worm collectives. How activity and elasticity facilitate collective topological transformations in living tangled matter is not well understood. In this talk, I will share our discoveries on why aquatic worms braid, tangle, and knot with their neighbors to form extraordinary mechano-functional living blobs — the stuff of science fiction. I will discuss how these soft, squishy, and 3-D blobs rapidly morph their shape, crawl, float, climb, self-assemble, and disassemble topological tangles. Using both mathematical models and robotic analogs, I will discuss how these "living polymers" solve Gordian knot problems using clever biophysics mechanisms that open a path to new classes of active topologically tunable robotic swarms.

current theory lunch schedule

How flux through the cell lifecycle enables organ adaptation

Lucy O'Brien | Dept of Molecular and Cellular Physiology at Stanford University School of Medicine

Abstract

Forthcoming

current theory lunch schedule

Complex biological function as an inverse input-output problem

Andrea Liu | Dept of Physics and Astronomy at Univ. of Pennsylvania School of Arts & Sciences

Abstract

In neural networks, parameters that govern interactions between nodes are tuned to obtain desired input-output relations. I argue that it is useful to think of systems with biological function as having effective interactions that are tuned to achieve the biological function. Such systems can be viewed as members of a large class of systems that I call "tunable matter." In some cases, biological evolution has tuned the interactions, but in other cases, systems tune their effective interactions by local rules in order to maintain function on time scales much shorter than evolutionary ones. I argue that tunable matter provides a unifying conceptual framework for understanding the emergence of collective function in a wide range of living systems.

current theory lunch schedule

Amy Shyer

Abstract

Forthcoming

current theory lunch schedule

The reasonable and unreasonable effectiveness of equilibrium statistical mechanics in gene regulation

Leonor Saiz | Department of Biomedical Engineering at UC Davis

Abstract

Gene regulation exemplifies a duality wherein equilibrium statistical mechanics effectively describes quasi-equilibrium behaviors, yet systems maintain functionality under significant perturbations. Time scale separation facilitates quasi-equilibrium conditions, enabling predictive modeling of regulatory mechanisms. However, the persistence of regulatory function during substantial disturbances, such as DNA replication, challenges equilibrium assumptions. This robustness can emerge from compensatory mechanisms, such as transcription factor coordination and DNA modifications, that mitigate transient non-equilibrium effects. This talk will examine how these strategies complement quasi-equilibrium dynamics, revealing the strengths and limits of equilibrium models in explaining gene regulation resilience.

current theory lunch schedule

Science needs philosophy, but what philosophy?

Steven Orzack

Abstract

Forthcoming

current theory lunch schedule

Zev Gartner | School of Pharmacy at UCSF

Abstract

Tissues are wildly complex, with properties that emerge from the interactions of large numbers of cells comprising a dizzying number of heterogeneously expressed gene products. The tools of genomics and big data are increasingly viewed as the solution to understanding this complexity. While the utility of these approaches are undeniable, we are exploring a parallel approach. Using bioengineering tools and human mammary organoids as a model system, we provide evidence that the conceptual tools of equilibrium statistical mechanics can provide surprisingly accurate predictions of steady-state phenomena at the tissue scale from only three measurable parameters — an active surface energy, the magnitude of active mechanical fluctuations, and a configurational entropy associated with composing a tissue from different populations of cells. From these measurements, we predict the average structure of a tissue across a range of conditions as well as its microscopic variability. This conceptual formalism also provides insight into how changes to these parameters can drive corresponding changes in tissue structure, for example during development and breast cancer progression. I will discuss some assumptions and limitations of this approach, possible extensions to other systems, and the potential to understand other emergent properties of tissues such as cell plasticity and structural phase transitions.

current theory lunch schedule

Suri Vaikuntanathan | Department of Chemistry at University of Chicago

Abstract

Many biological decision-making processes can be viewed as performing a classification task over a set of inputs, using various chemical and physical processes as "biological hardware". In this context, it is important to understand the inherent limitations on the computational expressivity of classification functions instantiated in biophysical media. Here, we model biochemical networks as Markov jump processes and train them to perform classification tasks, allowing us to investigate their computational expressivity. We reveal several unanticipated limitations on the input-output functions of these systems, which we further show can be lifted using biochemical mechanisms like promiscuous binding. We analyze the flexibility and sharpness of decision boundaries as well as the classification capacity of these networks. Additionally, we identify distinctive signatures of networks trained for classification, including the emergence of correlated subsets of spanning trees and a creased "energy landscape" with multiple basins. Our findings have implications for understanding and designing physical computing systems in both biological and synthetic chemical settings.

current theory lunch schedule

Sahin Naqvi | Division of Gastroenterology at Boston Children's Hospital

Abstract:

Developmental systems must balance robustness to perturbations with sensitivity to a broad range of regulatory inputs, a tradeoff that underpins both stability and adaptability. Transcription factors (TFs) embody this tension; while development is broadly buffered against quantitative changes in TF dosage, human genetics has revealed exquisite sensitivity, as exemplified by haploinsufficiency and dosage-sensitive disease phenotypes. I will discuss how, by precisely quantifying the effects of craniofacial TF dosage on molecular and cellular phenotypes, we arrived at a model that reconciles dosage sensitivity with robustness. In this model, most SOX9-dependent regulatory elements and genes are robust to quantitative changes in TF dosage, while others act as sensitive effectors, amplifying small changes in dosage into phenotypic diversity or vulnerability. I will discuss the generalizability of this model and some evolutionary implications.

current theory lunch schedule

Director,  ICCB-Longwood Screening Facility
Talk Title: “Resources beyond screening at ICCB-Longwood Screening Facility”
Food to be Provided

Richard Watson  | Institute for Life Sciences/ Department of Computer Science (Agents, Interaction and Complexity group), University of Southampton

Title: Learning, memory and adaptation before evolution

Abstract: Forthcoming

Stephen Wolfram  | Founder & CEO of Wolfram Research

Title: Computational irreducibility and minimal models of biology

Abstract: Forthcoming

Title: Programmable Protein Therapeutics via Generative Language Models

Abstract:

CRISPR has revolutionized biotechnology by enabling the simple design of guide RNAs to target and edit almost any DNA sequence. By developing new generative protein design algorithms, my hybrid lab focuses on extending this CRISPR-like programmability to proteins and other key molecules. In this talk, we will first delve into our algorithms that design binders to undruggable proteins, such as those driving pediatric cancers (alveolar rhabdomyosarcoma and Ewing’s sarcoma) and neurodegenerative diseases (Huntington’s and Alexander Disease). Our generative language models, including SaLT&PepPr, PepPrCLIP, and PepMLM, design short binding peptides from target sequence alone, with no dependence on stable 3D structures, and by fusing these "guide" peptides to E3 ubiquitin ligases, deubiquitinases, and other modifying enzymes, we have created a CRISPR-analogous system to edit these proteins. To be even more specific, we train isoform-specific targeting models such as PTM-Mamba for PTM-specific binding, FusOn-pLM for fusion oncoprotein-specific degradation, and moPPIt for motif-specific targeting of protein-protein interactions. Inspired by the power of language models, we further show how we can extend this programmability to DNA with our PAM-free CRISPR enzymes and our recent DPAC model, as well as heavy metals through our MetaLATTE algorithm and chemical pollutants, such as PFAS. Finally, we will explore our long-term goal of generating new cell states with model-designed proteins, highlighting our recent work on transcription factor-directed stem cell differentiation to ovarian cell types, such as granulosa cells and oogonia. By combining generative design with experimental engineering, our hybrid lab aims to translate these advances into practical applications for treating intractable diseases and addressing environmental challenges.

Stacey Finley | University of Southern California Viterbi School of Engineering 

Title: Studying the tumor microenvironment as an ecosystem

Abstract: Forthcoming

Adam Palmer  | University of North Carolina

Title: How combination therapy cures some cancers

Abstract: Forthcoming

Mohit Kumar Jolly  | Biological Sciences Building Indian Institute of Science, Bengaluru, India

Title: Low dimensionality of phenotypic space as an emergent property of biological regulatory networks

Abstract: Forthcoming