Biology subjects

Giaffar, H.

Publications and source records attributed to Giaffar, H..

7 recordsLinked to original sources

bioRxiv2026

Nucleotide-binding motifs nucleated folding of the first enzymes

The earliest stages of protein evolution remain a mystery: the nature of the first protein forms, their roles in emergent biological systems, and the forces that shaped them are largely unknown. Here, we combine insights from metabolic modelling, the organization of protein structure space, and protein folding mechanisms to probe the emergence of two ubiquitous cofactor- binding folds: Rossmanns and P-loop NTPases. While both folds are essential for contemporary life, we show that Rossmanns catalyze reactions deeper within the metabolic core and are more central in structure space than P-loop NTPases. Folding mechanism analysis further reveals that, whereas P-loop NTPases may require non-local interactions to fold, Rossmann folding can be nucleated by a structural module at the heart of the fold that contains a nucleotide-binding motif. Because this motif also directly mediates biochemical activity, this result suggests how early proteins may have compactly satisfied both folding and biochemical activity. Our results imply that folding constraints favored early enzymatic forms with compact binding motifs and modest catalytic roles. We conclude that the early emergence of the Rossmann fold reflects the chemical and physical constraints of protein folding, explaining both its profound antiquity and sustained longevity.

biochemistry↗

bioRxiv2025

The anterior olfactory nucleus mediates curious exploration evoked by novel odors

The spontaneous exploratory reaction to novel stimuli reflects a fundamental form of curiosity, which is widely observed in the animal kingdom. How sensory systems mediate the recognition of novel stimuli to evoke exploration is not well understood. To address this question, we presented novel and familiar olfactory stimuli to head-restrained mice, while measuring novelty-evoked exploratory behaviors. In parallel, we recorded neural activity in primary olfactory cortical structures, the anterior olfactory nucleus (AON) or the anterior piriform cortex (aPCx). Novelty strongly modulated odor responses in the AON, but only weakly in the aPCx. Pharmacological and chemogenetic inactivation of the AON but not the aPCx disrupted exploratory responses. During long-term habituation over multiple days, sensory representations were drifting in the AON whereas they became stable within one day in the aPCx. Our findings suggest that AON and aPCx play distinct roles in novelty-evoked exploration. While the AON mediates the immediate reaction to novel stimuli, the aPCx exhibits stable stimulus representations, consistent with supporting odor memory.

neuroscience↗

bioRxiv2023

The effective number of shared dimensions: A simple method for revealing shared structure between datasets

A number of recent studies have sought to understand the behavior of artificial and biological neural networks by comparing representations across layers, networks and brain areas. Simultaneously, there has been growing interest in using dimensionality of a dataset as a proxy for computational complexity. At the intersection of these topics, studies exploring the dimensionality of shared computational and representational subspaces have relied on model-based methods, but a standard, model-free measure is lacking. Here we present a candidate measure for shared dimensionality that we call the effective number of shared dimensions (ENSD). The ENSD can be applied to data matrices sharing at least one dimension, reduces to the well-known participation ratio when both data sets are equivalent and has a number of other robust and intuitive mathematical properties. Notably, the ENSD can be written as a similarity metric that is a re-scaled version of centered kernel alignment (CKA) but additionally describes the dimensionality of the aligned subspaces. Unlike methods like canonical correlation analysis (CCA), the ENSD is robust to cases where data is sparse or low rank. We demonstrate its utility and computational efficiency by a direct comparison of CKA and ENSD on across-layer similarities in convolutional neural networks as well as by recovering results from recent studies in neuroscience on communication subspaces between brain regions. Finally, we demonstrate how the ENSD and its constituent statistics allow us to perform a variety of multi-modal analyses of multivariate datasets. Specifically, we use connectomic data to probe the alignment of parallel pathways in the fly olfactory system, revealing novel results in the interaction between innate and learned olfactory representations. Altogether, we show that the ENSD is an interpretable and computationally efficient model-free measure of shared dimensionality and that it can be used to probe shared structure in a wide variety of data types.

neuroscience↗

bioRxiv2023

Emergence of Darwinian evolution in Bayesian collectives

Building on the algorithmic equivalence between finite population replicator dynamics and particle filtering based approximation of Bayesian inference, we design a computational model to demonstrate the emergence of Darwinian evolution over representational units when collectives of units are selected to infer statistics of high-dimensional combinatorial environments. The non-Darwinian starting point is two units undergoing a few cycles of noisy, selection-dependent information transmission, corresponding to a serial (one comparison per cycle), non-cumulative process without heredity. Selection for accurate Bayesian inference at the collective level induces an adaptive path to the emergence of Darwinian evolution within the collectives, capable of maintaining and iteratively improving upon complex combinatorial information. When collectives are themselves Darwinian, this mechanism amounts to a top-down (filial) transition in individuality. We suggest that such a selection mechanism can explain the hypothesized emergence of fast timescale Darwinian dynamics over a population of neural representations within animal and human brains, endowing them with combinatorial planning capabilities. Further possible physical implementations include prebiotic collectives of non-replicating molecules and reinforcement learning agents with parallel policy search.

evolutionary biology↗

bioRxiv2020

Darwinian dynamics over recurrent neural computations for combinatorial problem solving

Efficient search in enormous combinatorial spaces is an essential component of intelligence. Humans, for instance, are often found searching for optimal action sequences, linguistic structures and causal explanations. Is there any computational domain that provides good-enough and fast-enough solutions to such a diverse set of problems, yet can be robustly implemented over neural substrates? Based on previous accounts, we propose that a Darwinian process, operating over sequential cycles of imperfect copying and selection of informational patterns, is a promising candidate. It is, in effect, a stochastic parallel search that i) does not need local gradient-like information and ii) redistributes its computational resources from globally bad to globally good solution candidates automatically. Here we demonstrate these concepts in a proof-of-principle model based on dynamical output states of reservoir computers as units of evolution. We show that a population of reservoir computing units, arranged in one or two-dimensional topologies, is capable of maintaining and continually improving upon existing solutions over rugged combinatorial reward landscapes. We also provide a detailed analysis of how neural quantities, such as noise and topology, translate to evolutionary ones, such as mutation rate and population structure. We demonstrate the existence of a sharp error threshold, a neural noise level beyond which information accumulated by an evolutionary process cannot be maintained. We point at the importance of neural representation, akin to genotype-phenotype maps, in determining the efficiency of any evolutionary search in the brain. Novel analysis methods are developed, including neural firing pattern phylogenies that display the unfolding of the process.

neuroscience↗

bioRxiv2019

Evolutionary implementation of Bayesian computations

The Bayesian framework offers a flexible language for the consistent modular assembly of statistical models used by both minds and machines. Another algorithmic domain capable of adaptation in potentially high-dimensional and uncertain environments is Darwinian evolution. The equivalence of their fundamental dynamical equations, replicator dynamics and Bayesian update, hints at a deeper algorithmic analogy. Here we show, based on a unified mathematical discussion of evolutionary dynamics and statistical learning in terms of Bayesian graphical models, that this is indeed the case. Building blocks of Bayesian computations, such as inference in hierarchical models, filtering in hidden Markov models, gradient likelihood optimization, and expectation-maximization dynamics of mixture models, map naturally to fundamental concepts of evolution: multilevel selection, quasispecies dynamics, phenotypic adaptation and ecological competition, respectively. We believe that these correspondences point towards a more comprehensive understanding of flavors of adaptive computation observed in Nature, as well as suggesting new ways to combine insights from the two domains in engineering applications.

evolutionary biology↗

bioRxiv2018

Primacy model and the evolution of the olfactory receptor repertoire

Understanding sensory processing relies on establishing a consistent relationship between the stimulus space, its neural representation, and perceptual quality. In olfaction, the difficulty in establishing these links lies partly in the complexity of the underlying odor input space and perceptual responses. Based on the recently proposed primacy code for concentration invariant odor identity representation and a few assumptions, we have developed a theoretical framework for mapping the odor input space to the response properties of olfactory receptors. We analyze a geometrical structure containing odor representations in a multidimensional space of receptor affinities and describe its low-dimensional implementation, the primacy hull. We propose the implications of the primacy hull for the structure of feedforward connectivity in early olfactory networks. We test the predictions of our theory by comparing the existing receptor-ligand affinity and connectivity data obtained in the fruit fly olfactory system. We find that the Kenyon cells of the insect mushroom body integrate inputs from the high-affinity (primacy) sets of olfactory receptors in agreement with the primacy theory.

neuroscience↗