Biology subjects

Brenner, M. P.

Publications and source records attributed to Brenner, M. P..

12 recordsLinked to original sources

bioRxiv2025

Coupling differential adhesion to cell signaling avoids kinetic traps to yield robust multicellular self-organization

Differential adhesion, where cells physically reorganize based on their heterogeneous adhesion preferences, is one of the major models for self-organization in development and tissue formation. However, accumulating evidence suggests that differential adhesion is many times insufficient for robust convergence to a target minimal energy multicellular structure. Here we use computational simulations and engineered synthetic cell circuits to systematically explore alternative mechanisms for programming formation of a simple two-cell type core-shell morphology. Starting with two pre-differentiated cell types with constitutively high differential adhesion leads to kinetic trapping in variable, multi-core structures. In contrast, hybrid mechanisms that gradually induce differential adhesion upon cell-cell contact signaling consistently converge to the target single-core structure, in a manner robust to variation in cell numbers, interaction energy, and noise. This work delineates intrinsic limitations of self-organizing systems based solely on differential adhesion, and shows how inducible systems provide a way to invoke the strong adhesion required to maintain a multicellular structure, while avoiding the pitfall of kinetic traps. This study illustrates how joint computational and experimental exploration of synthetic circuits can be used to probe key developmental principles and tradeoffs and inform the design of synthetic development and self-organization.

biophysics↗

bioRxiv2024

Generalized design of sequence-ensemble-function relationships for intrinsically disordered proteins

The design of folded proteins has advanced significantly in recent years. However, many proteins and protein regions are intrinsically disordered (IDPs) and lack a stable fold i.e., the sequence of an IDP encodes a vast ensemble of spatial conformations that specify its biological function. This conformational plasticity and heterogeneity makes IDP design challenging. Here, we introduce a computational framework for de novo design of IDPs through rational and efficient inversion of molecular simulations that approximate the underlying sequence to ensemble relationship. We highlight the versatility of this approach by designing IDPs with diverse properties and arbitrary sequence constraints. These include IDPs with target ensemble dimensions, loops and linkers, highly sensitive sensors of physicochemical stimuli, and binders to target disordered substrates with distinct conformational biases. Overall, our method provides a general framework for designing sequence-ensemble-function relationships of biological macromolecules.

biophysics↗

bioRxiv2023

Measuring intramolecular connectivity in long RNA molecules using two-dimensional DNA patch-probe arrays

We describe a simple method to infer intramolecular connections in a population of long RNA molecules in vitro. First we add DNA oligonucleotide "patches" that perturb the RNA connections, then we use a microarray containing a complete set of DNA oligonucleotide "probes" to record where perturbations occur. The pattern of perturbations reveals couplings between different regions of the RNA sequence, from which we infer connections as well as their prevalences in the population. We validate this patch-probe method using the 1,058-nucleotide RNA genome of satellite tobacco mosaic virus (STMV), which has previously been shown to have multiple long-range connections. Our results not only indicate long duplexes that agree with previous structures but also reveal the prevalence of competing connections. Together, these results suggest that globally-folded and locally-folded structures coexist in solution. We show that the prevalence of connections changes when pseudouridine, an important component of natural and synthetic RNA molecules, is substituted for uridine in STMV RNA.

biophysics↗

bioRxiv2022

Multiphase coexistence capacity in complex fluids

Complex fluids like the cytoplasm comprise hundreds of species organized into many coexisting phases. How molecular interactions, reflecting sequence, design, or functional constraints, dictate multiphase coexistence is a major open question. To answer this, we consider models of multicomponent fluids with both designed and random interactions. When crosstalk is introduced, we show that coexisting phases lose specificity beyond a common threshold. In a sequence model, we demonstrate that phase capacity is limited by sequence length and grows logarithmically/linearly with sequence length/number. These results provide a general route to program multiphase coexistence from molecular features.

biophysics↗

bioRxiv2022

The assembly yield of complex, heterogeneous structures: A computational toolbox

The self-assembly of complex structures from a set of non-identical building blocks is a hallmark of soft matter and biological systems, including protein complexes, colloidal clusters, and DNA-based assemblies. Predicting the dependence of the equilibrium assembly yield on the concentrations and interaction energies of building blocks is highly challenging, owing to the difficulty of computing the entropic contributions to the free energy of the many structures that compete with the ground state configuration. While these calculations yield well known results for spherically symmetric building blocks, we discovered that they do not hold when the building blocks have internal rotational degrees of freedom. Here we present an approach for solving this problem that works with arbitrary building blocks, including proteins with known structure and complex colloidal building blocks. Our algorithm combines classical statistical mechanics with recently developed computational tools for automatic differentiation. Automatic differentiation allows efficient evaluation of equilibrium averages over configurations that would otherwise be intractable. We demonstrate the validity of our framework by comparison to molecular dynamics simulations of simple examples, and apply it to calculate the yield curves for known protein complexes and for the assembly of colloidal shells.

biophysics↗

bioRxiv2022

Uncovering the mechanism for aggregation in repeat expanded RNA reveals a reentrant transition

Repeat expanded RNA molecules aggregate under certain conditions both in vitro and in vivo. Understanding the mechanism for aggregation--including how aggregation properties change with sequence and environmental conditions--would explain and predict the behavior of RNA-based biomolecular condensates, and enable the rational design of RNA-based materials. Here, we introduce an analytical framework to predict aggregation for any repeat RNA sequence, accounting for both intra- and inter-molecular bonding. By enumerating the equilibrium landscape of multimers, we reveal the driving force for aggregation: the increased configurational entropy associated with the multiplicity of ways to form bonds in the aggregate. Our model uncovers rich phase behavior, including a sequence-dependent reentrant phase transition, and repeat parity-dependent aggregation. We validate our results by comparison to a complete computational enumeration of the landscape, and to previously published molecular dynamics simulations. Our work unifies and extends published results, and enables the design of programmable RNA condensates.

biophysics↗

bioRxiv2022

Brain embeddings with shared geometry to artificial contextual embeddings, as a code for representing language in the human brain

Contextual embeddings, derived from deep language models (DLMs), provide a continuous vectorial representation of language. This embedding space differs fundamentally from the symbolic representations posited by traditional psycholinguistics. Do language areas in the human brain, similar to DLMs, rely on a continuous embedding space to represent language? To test this hypothesis, we densely recorded the neural activity in the Inferior Frontal Gyrus (IFG, also known as Brocas area) of three participants using dense intracranial arrays while they listened to a 30-minute podcast. From these fine-grained spatiotemporal neural recordings, we derived for each patient a continuous vectorial representation for each word (i.e., a brain embedding). Using stringent, zero-shot mapping, we demonstrated that brain embeddings in the IFG and the DLM contextual embedding space have strikingly similar geometry. This shared geometry allows us to precisely triangulate the position of unseen words in both the brain embedding space (zero-shot encoding) and the DLM contextual embedding space (zero-shot decoding). The continuous brain embedding space provides an alternative computational framework for how natural language is represented in cortical language areas.

neuroscience↗

bioRxiv2021

Longitudinal analysis of invariant natural killer T cell activation reveals a cMAF-associated transcriptional state of NKT10 cells

Innate T cells, including CD1d-restricted invariant natural killer T (iNKT) cells, are characterized by their rapid activation in response to non-peptide antigens, such as lipids. While the transcriptional profiles of naive, effector and memory adaptive T cells have been well studied, less is known about transcriptional regulation of different iNKT cell activation states. Here, using single cell RNA-sequencing, we performed longitudinal profiling of activated iNKT cells, generating a transcriptomic atlas of iNKT cell activation states. We found that transcriptional signatures of activation are highly conserved among heterogeneous iNKT cell populations, including NKT1, NKT2 and NKT17 subsets, and human iNKT cells. Strikingly, we found that regulatory iNKT cells, such as adipose iNKT cells, undergo blunted activation, and display constitutive enrichment of memory-like cMAF+ and KLRG1+ populations. Moreover, we identify a conserved cMAF-associated transcriptional network among NKT10 cells, providing novel insights into the biology of regulatory and antigen experienced iNKT cells.

immunology↗

bioRxiv2021

Phase separation in fluids with many interacting components

Fluids in natural systems, like the cytoplasm of a cell, often contain thousands of molecular species that are organized into multiple coexisting phases that enable diverse and specific functions. How interactions between numerous molecular species encode for various emergent phases is not well understood. Here we leverage approaches from random matrix theory and statistical physics to describe the emergent phase behavior of fluid mixtures with many species whose interactions are drawn randomly from an underlying distribution. Through numerical simulation and stability analyses, we show that these mixtures exhibit staged phase separation kinetics and are characterized by multiple coexisting phases at equilibrium with distinct compositions. Random-matrix theory predicts the number of existing phases at equilibrium, validated by simulations with diverse component numbers and interaction parameters. Surprisingly, this model predicts an upper bound on the number of phases, derived from dynamical considerations, that is much lower than the limit from the Gibbs phase rule, which is obtained from equilibrium thermodynamic constraints. Using a biophysically motivated model of pairwise interactions between components, we design ensembles that encode either linear or non-monotonic scaling relationships between number of components and co-existing phases, which we validate through simulation and theory. Finally, inspired by parallels in biological systems, we show that including nonequilibrium turnover of components through chemical reactions can tunably modulate the number of coexisting phases at steady-state without changing overall fluid composition. Together, our study provides a model framework that describes the emergent dynamical and steady-state phase behavior of liquid-like mixtures with many interacting constituents.

biophysics↗

bioRxiv2020

Programming cell growth into different cluster shapes using diffusible signals

Recent advances in genetic engineering technologies has made it possible to construct artificial genetic circuits and use them to control how cells respond to their surroundings. This has been used to generate spatial patterns of differential gene expression. In addition to the spatial arrangement of different cell types, another important aspect of spatial structure lies in the overall shape of the group of cells. However, the question of how cells can be programmed, and how complex the rules need to be, to achieve a desired tissue morphology has received less attention. In this paper, we attempt to address these questions by developing a mathematical model to study how cells can use diffusion-mediated local rules to grow into clusters with different shapes. Within our model, cells are allowed to secrete diffusible chemicals which can either directly regulate the growth rate of cells ( growth regulator), or indirectly affect growth by changing the secretion rate or the effect of other growth regulators. We find that (1) a single growth inhibitor can be used to grow a rod-like structure, (2) multiple growth regulators are required to grow multiple protrusions, and (3) the length and shape of each protrusion can be controlled using growth-threshold regulators. Based on these regulatory schemes, we also postulate how the range of achievable structures scales with the number of signals: (A) the maximum possible number of protrusions increases exponentially with the number of growth inhibitors involved, and (B) to control the growth of each set of protrusions, it is necessary to have an independent threshold regulator. Together, these experimentally-testable findings illustrate how our approach can be used to guide the design of regulatory circuits for achieving a desired target structure.

systems biology↗

bioRxiv2020

Self-assembly based post-translational protein oscillators

Recent advances in synthetic post-translational protein circuits are significantly impacting the landscape of biomimicry engineering. However, designing sustained dynamic phenomena in these circuits remains an outstanding challenge. Inspired by the KaiABC system regulating the circadian clock in cyanobacteria, we develop two experimentally realizable post-translational oscillators. The oscillators rely on a small number of components interacting only through reversible binding and phosphorylation/dephosphorylation reactions.

systems biology↗

bioRxiv2018

RNA structure prediction including pseudoknots through direct enumeration of states

The accurate prediction of RNA secondary structure from primary sequence has had enormous impact on research from the past forty years. While many algorithms are available to make these predictions, the inclusion of non-nested loops, termed pseudoknots, still poses challenges. Here, we describe a new method to compute the entire free energy landscape of secondary structures of RNA resulting from a primary RNA sequence, by combining a polymer physics model for the entropy of pseudoknots with exhaustive enumeration of the set of possible structures. Our polymer physics model can address arbitrarily complex pseudoknots and has only two free loop entropy parameters that correspond to concrete physical quantities, over an order of magnitude fewer than even the sparsest state-of-the-art algorithms. Our model outperforms previously published methods in predicting pseudoknots, while performing on par with current methods in the prediction of non-pseudoknotted structures. For RNA sequences of ~ 45 nucleotides, or ~ 90 with minimal heuristics, the complet-e enumeration of possible secondary structures can be accomplished quickly despite the NP-complete nature of the problem.

biophysics↗