Search bioRxiv⌕ Search
Home/Archive/bioRxiv

bioRxiv · 10.1101/2024.11.04.621929

Geometrically balanced model of cell growth

Vazquez, A.·Gedeon, T.
View original source ↗Export metadata ↓

Abstract

The proteome balance constraint in metabolic flux balance analysis asserts that the proteome is constructed by ribosomes, which themselves contain many proteins. This leads to a fundamental question of optimal allocation of limited proteome among different pools of enzymes, which include ribosomes themselves. However, recent work points to additional constraints imposed by the cell geometry. In this paper we deduce the proteogeometric constraint [Formula], where{pi} A,{pi} P and{pi} L are the proteomic fractions allocated to the cell surface area, protein synthesis and cell membrane phospholipids synthesis and [Formula] and{theta} are constants imposed by geometry of the cell. We illustrate the relevance of this constraint using a reduced model of cell metabolism, illuminating the interplay between cell metabolism and cell geometry.

Source connections

Explore related subjects

systems biologyPreprint

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Vazquez, A., Gedeon, T.. 2024-11-06. Geometrically balanced model of cell growth. https://doi.org/10.1101/2024.11.04.621929

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

bioRxiv2026

The impacts of exogenous noise on stochastic disease dynamics

Much of the literature and intuition associated with mathematical epidemiology is driven by deterministic models, which are a reasonable assumption when the population size is large. Stochastic models, especially individual based models, are however considered vital when dealing with small population sizes, especially at times of invasion or extinction. The overwhelming majority of these models (both deterministic and stochastic) assume that the underlying parameters are fixed (or follow a regular seasonal pattern). Here, we consider an analytic framework for dealing with randomly varying parameters through the use of stochastic differential equations - thereby capturing the action of external noisy processes such as weather. In particular, we focus on when the transmission rate, {beta}, varies as the solution to a Cox-Ingersoll-Ross Model, such that {beta} is gamma distributed with autocorrelation. We consider the impact of this parameter variation on a stochastic version of the Susceptible-Infected-Recovered model, and for this 'double-stochastic' model show through simulation and analytical results that exogenous noise increases the impact of stochasticity, potentially leading to more early extinctions, wider variations in the number of cases at equilibrium, but that early growth rate can be faster or slower depending on the precise parameters.

systems biology↗
bioRxiv2026

Site-resolved spatial and structural interactome of a human cell

The spatial and structural arrangement of proteins determine virtually every process in human cells. We combined gentle subcellular fractionation by differential ultracentrifugation with cross-linking mass spectrometry to systematically map this cellular proteome architecture with residue-level evidence, identifying 164,146 residue-to-residue links in HEK293 cells. These links capture spatial protein arrangement at a resolution sufficient to pinpoint protein localizations at sub-organelle level, determine protein orientations within cellular membranes, and identify inter-organelle contact sites. The residue-level information provides evidence for 18,074 direct protein-protein interactions (PPIs), which we integrate into AlphaFold-based pipelines to nominate PPI-mediating short linear motifs and generate assembly models of large protein complexes. Guided by these spatial and structural readouts, we discover new PPIs within the endomembrane system that regulate the compartmental localization of trafficking machinery. Leveraging a network topology-driven strategy, we augment our HEK293 dataset with PPI data from different cell lines and methods, expanding the spatial and structural interactome of human cells.

systems biology↗
bioRxiv2026

Sensory variation and behavioural degeneracy: a framework for interpreting heterogeneity in the gut-brain axis

Gut microbiome differences are frequently interpreted as reflecting underlying biological differences between individuals. When outcomes are mediated by behaviour, however, this mapping may be fundamentally non-unique. This limits causal inference in gut-brain research, where microbiome differences in autism and depression are routinely attributed to intrinsic neurobiology despite highly variable, overlapping findings. I built a minimal agent-based model grounded in the known sensory variation across the autism spectrum. Dietary behaviour emerges from latent sensory traits, including sensory drive, predictability preference, and context sensitivity, through reinforcement learning and environmental interaction. This behaviour shapes gut microbiome composition. Behavioural variation organizes endogenously into a continuum of specialist, opportunist, and explorer strategies that maps onto the autism sensory spectrum. The system is fundamentally degenerate. Similar microbiome states arise from distinct behavioural pathways. Similar dietary patterns emerge from divergent latent traits. This many-to-one mapping reflects the structural interaction of behaviour, learning, and environmental variability, not stochasticity alone. Microbiome similarity therefore does not uniquely identify underlying cause. As such, the model provides a theoretical framework for interpreting heterogeneity in gut microbiome research, particularly in autism, and generalizes to any condition where behaviour mediates between neural processes and ecological outcomes.

systems biology↗