ICTS

We will discuss models to probe and control how multicomponent, multiphasic condensates function.

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In the first part of the talk I will review a list of results on the macroecological structure of microbial communities. In the second part, I will discuss how microbial population can encode the statistical structure of the environment to anticipate changes of conditions.

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Pseudo-bulking' over hundreds of single cells in scRNA-seq datasets is often essential to overcome biological and technical noise while extracting meaningful biological information. However, this process naturally leads to loss of resolution, and is especially problematic while analysing rare cell types within tissues. Here, taking the example of phase estimation of the mammalian circadian clock, I will show how a principled approach to dimensionality reduction via probabilistic latent space modeling can significantly reduce the number of cells over which coarse-graining has to be performed. Combining a deep-embedded clustering approach with spherical latent spaces, our method significantly improves over existing techniques, as I will demonstrate with initial results from various scRNA-seq and smFISH datasets.

Biological systems explore vast spaces of possibility, yet their function is often robust to certain variations while remaining exquisitely sensitive to others. A striking example is the hypervariable repertoire of T cell receptors, which underlies the specificity of the cellular immune response. Can we construct principled coarse-grained descriptions of this diversity that retain functionally relevant variation? In this talk, I will summarize some of our recent progress on this question. First, I will show how we can quantify, in bits, the information that different receptor regions provide about antigen specificity. Second, I will describe how contrastive learning can be used to infer which receptors recognise common targets. Finally, I will discuss how these statistical insights can advance the study of human adaptive immunity to infectious disease.

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Cells often rely on signalling molecules to make cell-fate decisions during development. We can see these fate-regulating decisions as computational tasks and measure the performance of these tasks with information-theoretic quantifiers. Yet, it is often unclear under what constraints a particular task is performed. Here, I will argue that we can nevertheless learn about the computational performance of a gene regulatory system. First, I will discuss an example in early fly development, where the computational task involves interpreting signals so that cell fates can correctly be distinguished. I will discuss how clustering of transcription factors can provide a way for how binding sites can achieve optimal information transfer, and achieve optimal bounds predicted from an information bottleneck approach. Second, I will focus on an example from the canonical Wnt pathway, where we explore synthetic signals and infer which signals would need to be present naturally for the computation to be close to optimality. We find that for appropriately chosen signals, the cellular response can be precise enough to allow reliable differentiation into two distinct states. As the precision in the pathway improves, more distinct states can be reliably distinguished.

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In this talk, I will present two vignettes: (1) how manifold learning allows for olfactory habituation in turbulent environments, and (2) a contrastive framework for identifying irreversible degrees of freedom in high-dimensional stochastic systems. Common themes of finding low-dimensional representations of high-dimensional time series data will be highlighted.

I review the roughly 10-year-old literature on application of deep learning techniques to DNA sequence. Much recent attention has been paid to single basepair resolution prediction of variant effect. In contrast, I focus on a simple classification problem, where DNA is classified into two categories (for example, binding or not binding a particular protein). We recently developed a neural network architecture that outperforms published methods significantly on this basic task. We show that in silico mutation analysis indicates that the classification is not strongly biased by single nucleotides but, instead, there are sequence patterns in an extended region that lead the classifier to the correct answer, even when the central signal (binding motif) is mutated out. This builds on earlier work where we showed the importance of flanking sequence in regulatory sites. Much as how we can recognize a photo of a tiger even when its face is removed, we suggest that there is significant surrounding signal in DNA even when the core motif is removed, though the in vivo function may require the core motif. Finally, I discuss the converse, harder problem of generating synthetic DNA sequence that has a desired biological function.

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Proteins are partially disordered, creating entropy that encodes information for ligand selectivity and allosteric signaling. However, inferring entropy maps from protein dynamics has been difficult due to complex interactions between conformational degrees of freedom. I will discuss our recent progress in mapping shared entropy within protein structures to reveal how proteins compute using apparent disorder. Our approach, ciMIST, infers discrete whole-residue states from molecular dynamics simulations and converts joint residue probabilities into maximum likelihood tree networks of shared entropy. On the PDZ3 and ERalpha ligand-binding domains in multiple ligand and mutational states, ciMIST networks matched total entropy measured by calorimetry and residue-level entropy probed by NMR and hydrogen-deuterium exchange. ciMIST accurately predicts allosteric mutational hotspots in PDZ3 from saturation mutagenesis. ciMIST maps of the entire human steroid receptor family reconstructs family phylogeny, revealing entropic reprogramming of function within fixed protein folds. These results show that sparse entropy networks effectively map protein ensembles to function when static structure is insufficient.

The physical principles governing biological heteropolymer interactions are fundamental to cellular organization and function. In this talk, we first present a physics-based theory derived from extensive molecular dynamics simulations that quantitatively captures the interactions driving protein phase separation and allows for the design of sequences with predictable behavior. We then extend this theme to chromatin, presenting experimental work on synthetic nucleosome arrays that isolates the direct physical impact of histone acetylation on chromatin architecture. Using single-molecule experiments and in vitro Hi-C, we demonstrate how this epigenetic mark modulates intra-chain interactions to control the polymer's conformational ensemble. Together, these studies provide a bottom-up, quantitative framework for understanding how sequence and chemical modifications tune heteropolymer interactions to shape biological structure and function.

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Proteins can fold spontaneously into well-defined three-dimensional structures and can carry out complex biochemical reactions such as binding, catalysis, and long-range information transfer. The precision required for these properties is achieved while also preserving evolvability – the capacity to adapt in response to fluctuating selection pressures in the environment. What is the basic design of proteins that supports all of these properties? Going beyond direct physical analysis, statistical analysis of genome sequences have, in recent years, provided a powerful and general approach to this problem. Using different methodologies, this approach has exposed direct structural contacts and collective functional modes within proteins, and has yielded generative models for protein design. In this talk, I will discuss these advances and will present approaches for probing the physical mechanisms implied by the evolution-based models. An important aspect is to understand how such mechanisms may be constrained by and originate from the dynamics of the evolutionary process. This work represents a step towards a theory for the physics of proteins that is consistent with evolution.

The large scale network of genes regulating E. coli dynamics will be discussed together with a method of breaking it down into tractable dynamical subsystems that have functional saliency.

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Mammalian development is characterized with transitions from homogeneous populations of precursor to heterogeneous population of specified cells. The cells in the population continuously sense and respond to time-varying signals that are secreted, and an accurate interpretation of these signals is necessary to determine cell phenotype in a robust and reproducible manner. We demonstrate that the cell phenotype is implicitly encoded in the temporal evolution of the system’s trajectory in signaling space, and it can be directly read-out and decoded in real-time by the immediate-early genes, much earlier than the network sets to a steady-state. We propose that signaling homeorhesis determines cell phenotype in dynamic environments, and demonstrate that organization at criticality is a necessary pre-requisite to guide the system’s trajectories through the changing landscape.

Mammalian development is characterized with transitions from homogeneous populations of precursor to heterogeneous population of specified cells. The cells in the population continuously sense and respond to time-varying signals that are secreted, and an accurate interpretation of these signals is necessary to determine cell phenotype in a robust and reproducible manner. We demonstrate that the cell phenotype is implicitly encoded in the temporal evolution of the system’s trajectory in signaling space, and it can be directly read-out and decoded in real-time by the immediate-early genes, much earlier than the network sets to a steady-state. We propose that signaling homeorhesis determines cell phenotype in dynamic environments, and demonstrate that organization at criticality is a necessary pre-requisite to guide the system’s trajectories through the changing landscape.

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