Upcoming Events
Theory Lunch: Pulin Li - How do cells message each other and how can we check their history of received messages?
MIT | Whitehead Institute
Abstract
Cells within a tissue frequently exchange information with one another through secreted signaling molecules that travel over tens to hundreds of mm. Being able to decipher the communicative relationship amongst cells is crucial for understanding how tissues are formed in embryos and how they perform physiological functions in adults. However, which cell can communicate with which other cell within a tissue is an actively regulated process. The physical distance between cells is one of the crucial factors. I will discuss our insights in how the distance by which signaling molecules travel can be regulated, based on our single-particle tracking measurements and multiscale diffusion model. I will also discuss our recent effort in computationally inferring what signals a cell has received using cell-autonomous information.
Theory Lunch: Domitilla Del Vecchio - Analog epigenetic cell memory by graded DNA methylation
MIT | Department of Mechanical Engineering
Abstract
DNA methylation and histone modifications mediate the long-term maintenance of gene expression states. Current belief, supported by data from knock-in reporter systems, is that these modifications are established in an all-or-none fashion, thereby making long-term memory an exclusive attribute of silenced and active gene states. We tracked the temporal dynamics of a chromosomally integrated reporter system with clonal resolution to investigate whether these modifications can also enable memory of any gene expression level, and thus equip cells with analog memory. Surprisingly in the context of existing data, we found that cells can maintain intermediate gene expression levels by grades of DNA methylation that remain stable over time. Our nucleosome modification model recapitulates this result when H3K9me3 does not catalyze the recruitment of DNA methylation and the remaining DNA methylation kinetics are slow. The observation that long-term memory is possible for any gene expression level deepens our understanding of epigenetic cell memory and broadens our view of what constitutes a cell type.
Pizza Talks: Mark Verheijen
On Zoom and in WAB 563
Pizza Talks: Mark Verheijen, Lu Lab
Talk Title: The Fidelity of Transcription
Theory Lunch: John Tsang - Immune variation and sensing across scales
Yale School of Medicine | Center for Systems & Engineering Immunology
Abstract
TBA
Theory Lunch: Daniele Canzio - The role of genome architecture in shaping brain architecture
University Park | Dept. of Biochemistry & Molecular Biology
Abstract
TBA
Theory Lunch: Sujit Datta - The wisdom of the crowd: how bacterial collectives (re)shape themselves
Princeton University | Dept. of Chemical and Biological Engineering
Abstract
TBA
Theory Lunch: Kate Bentley - TBA
The Francis Crick Institute | Dept. of Cellular Adaptive Behavior
Abstract
TBA
Theory Lunch: Kirill Korolev - Evolution in growing populations via pulled and pushed waves
Boston University | Department of Physics
Abstract
Why do we care about certain species or populations? Typically, because they are either going extinct or growing out of control. Tumors, pathogens, and pests are the more worrisome examples of the latter. Such growing populations are difficult to forecast and eradicate in part because they are capable of rapid evolution. My group has been studying how spatial growth affects evolutionary dynamics in minimal models inspired by range expansions of plants and animals, but applicable all the way down to microbial colonies growing on agar plates. We found that small and seemingly unimportant details of the growth and dispersal can result in dramatic changes to both genetic diversity and selective forces. For example, cooperative growth can alter the very topology of genealogical trees, and competition for empty space can nevertheless favor a slower colonizer. I will show how these and other surprising results can be obtained by using reaction-diffusion equations to reason about eco-evolutionary dynamics in space.
Theory Lunch: Denise Okafor - Investigating interdomain allosteric communication in nuclear receptors
Pennsylvania State Univ. | Department of Biochemistry and Molecular Biology
Abstract
TBA
Department Seminar: Uri Alon
WAB 563 and Zoom
200 Longwood AvenueBoston, MA, 02115United States
Theory Lunch: John Albeck - Linking cellular and organismal metabolic homeostasis: what do mTOR and AMPK really do?
UC Davis | Department of Molecular and Cell Biology
Abstract
Building models for biological, chemical, and physical systems has traditionally relied on domain-specific intuition about which interactions and features most strongly influence a system. Alternatively, machine-learning methods are adept at finding novel patterns in large data sets and building predictive models but can be challenging to interpret in terms of or integrate with existing knowledge. Our group balances traditional modeling with data-driven methods and optimization to get the best of both worlds. Sparse optimization strategies, recently developed for, and applied to, dynamical systems, can scan and select a subset of terms from a library that best describes data, automatically interfering potential model structures from a broad but well-defined class. I will discuss my group's application and development of data-driven methods for model selection to 1) recover chaotic systems models from data with hidden variables and 2) discover models for metabolic and temperature regulation in hibernating mammals. I'll briefly discuss current preliminary work and roadblocks in developing new methods for model selection of biological metabolic and regulatory networks.
Theory Lunch: Niall Mangan - Data-driven model discovery meets mechanistic modeling for biological systems
Northwestern University | Department of Engineering Science and Applied Mathematics
Abstract
Building models for biological, chemical, and physical systems has traditionally relied on domain-specific intuition about which interactions and features most strongly influence a system. Alternatively, machine-learning methods are adept at finding novel patterns in large data sets and building predictive models but can be challenging to interpret in terms of or integrate with existing knowledge. Our group balances traditional modeling with data-driven methods and optimization to get the best of both worlds. Sparse optimization strategies, recently developed for, and applied to, dynamical systems, can scan and select a subset of terms from a library that best describes data, automatically interfering potential model structures from a broad but well-defined class. I will discuss my group's application and development of data-driven methods for model selection to 1) recover chaotic systems models from data with hidden variables and 2) discover models for metabolic and temperature regulation in hibernating mammals. I'll briefly discuss current preliminary work and roadblocks in developing new methods for model selection of biological metabolic and regulatory networks.
Theory Lunch: Dani S. Bassett - How humans build models of the world
University of Pennsylvania | Department of Bioengineering
Abstract
Human learners acquire not only disconnected bits of information, but complex interconnected networks of relational knowledge. The capacity for such learning naturally depends on the architecture of the knowledge network itself. I will describe recent work assessing network constraints on the learnability of relational knowledge, and a free energy model that offers an explanation for such constraints. I will then broaden the discussion to the generic manner in which humans communicate using systems of interconnected stimuli or concepts, from language and music, to literature and science. I will describe an analytical framework to study the information generated by a system as perceived by a biased human observer, and provide experimental evidence that this perceived information depends critically on a system's network topology. Applying the framework to several real networks, we find that they communicate a large amount of information (having high entropy) and do so efficiently (maintaining low divergence from human expectations). Finally, I will use these intuitions to ask the question: Given a target network that one wishes for a human to learn, what network should one present to the human? Should one simply present the target network as-is, or should one emphasize certain parts of the network to proactively mitigate biases in learning? I will show that the accuracy of human network learning can be systematically enhanced by targeted emphasis and de-emphasis of prescribed sectors of information. Taken together, our results provide a unique network-based lens through which to understand how humans build models of their networked world.
Pizza Talks: Danylo Lavrentovich
On Zoom and in WAB 563
Pizza Talks: Danylo Lavrentovich, de bivort Lab
Talk Title: Loci of individuality in the Drosophila olfactoty circuit
Theory Lunch: Samuel Gershman - Reimagining the biology of memory
Harvard University | Department of Psychology
Abstract
Over the last half century, there has been a remarkable convergence on the idea that memories are stored at synapses. I will argue that this is only part of the story. A more complete story commands us to recognize the radical ubiquity of memory in living systems, including free-living unicellular organisms and many kinds of non-neural cells. Memory existed from the moment life began; in a sense it is built into the logic of life. Its molecular mechanisms are therefore likely to be ancient in origin, and a number of clues are already available. Computational considerations help us organize these clues into a theory of the division of labor and interaction between cell-intrinsic and synaptic storage mechanisms. From this new starting point, I will explore how we can make sense of many strange and puzzling phenomena: the transfer of memory between organisms, the survival of memory after radical synaptic remodeling (even decapitation!), the transience of amnesia following protein synthesis inhibition, the ability of unicellular organisms to learn, among others.
Pizza Talks: Sharifa Sahai
On Zoom and in WAB 563
Pizza Talks: Sharifa Sahai, Mahmood Lab
Talk Title: Machine Learning for Text-Guided Electron Microscopy in Ultrastructural Renal Abnormality Detection
Theory Lunch: Herbert Levine - Epigenetic effects can reshape cell-fate landscapes
Northeastern University | Departments of Physics and Bioengineering
Abstract
Most studies of genetic networks ignore the role played by local reorganization of chromatin structure in determining the dynamics of transcription. However, recent experiments in E coli (related to supercoiling) and cancer cells (related to epigenetic modification of histones) have revealed cases where this is not sufficient. This talk will focus on creating theoretical models which couple small-scale chromatin degrees of freedom to transcriptional dynamics and discuss the consequences for transcriptional noise and for cell fate transitions.
Pizza Talks: Sneha Rath
On Zoom and in WAB 563
Pizza Talks: Sneha Rath, Mootha Lab
Talk Title: Euploidy for the mitochondrial genome - How is it sensed and maintained?