Biology subjects

Kempes, C. P.

Publications and source records attributed to Kempes, C. P..

16 recordsLinked to original sources

bioRxiv2026

Microbial Ecosystems Reveal a Universal Signature of Ecological Assembly

Microbial communities obey universal macroecological scaling laws, but which sub-processes generate them remains debated across competing theoretical frameworks. Here we calibrate a modified Yule-Simon model to metagenomic data from 11 distinct microbial environments, revealing that microbial ecosystems occupy a qualitatively distinct region of a two-parameter mechanistic space, characterized by near-neutral recruitment (i.e., linear preferential attachment) and broad diversification strategies, unlike any previously studied complex system, including prokaryotic proteomes and urban economies. This distinctive position, confirmed analytically and validated against sparse-data robustness tests, provides both a mechanistic explanation for observed self-similarities in microbial rank-frequency distributions and a quantitative signature of ecological assembly.

ecology↗

bioRxiv2025

A database of biological thermal performances

We built a database of thermal performance curves that includes different variables --mostly population growth rates --for species in representative groups across major domains, including viruses, bacteria, archaea, unicellular eukaryotes (such as fungi), and multicellular eukaryotes (including plants and both invertebrates and vertebrates). To build the database, we integrated previous databases and compiled additional data from individual studies, all of which were normalized to the same units when corresponding. Our database contains data for 1300 curves across 1125 different species and 38 variable types. The database consists of four (.csv) tables and an R code that can be explored through an R package or a Shiny app. Applications of our dataset include providing a resource to studies in thermal ecology and integration with other types of data, such as geographic temperatures. This would allow us to make further predictions, such as potential species distributions based on their thermal tolerances and their changes with climate warming.

bioinformatics↗

bioRxiv2025

Scaling laws in biological thermal performances

Understanding the extent to which genetic correlations change in response to environmental factors, such as temperature, is a poorly explored question, despite the importance of understanding how different processes will change with climate warming. Despite correlations between thermal performance traits having been reported in the literature for a few taxa and performance tasks, such as population growth rate, a comprehensive global analysis of the entire tree of life and multiple performance tasks remains an open challenge. To advance in this open question, we compile a database of 1,300 thermal response curves, encompassing 38 variable types related to individuals performance (including per capita population growth rate, photosynthetic rate, among others) and 1,125 different species, ranging from viruses to mammals, encompassing all major lineages of the tree of life. Our analysis reveals that among all possible relationships between traits and optimal performance, four traits form a line with a high goodness-of-fit, while the remaining traits exhibit a polygonal pattern, either a triangle or a tetrahedron. We derive a thermodynamic framework that explains the relationships described by a curve or line (as opposed to a surface or polygon), highlighting the linear relationship between maximum and minimum temperatures, as well as between maximum and optimum temperatures. We also discuss other generic trait evolution models, which could account for the other significant sublinear relationships, as well as the more general model, Pareto optimality theory, which could account for relationships in the form of lines or polygons. Our theoretical framework and empirical evidence suggest that, based on a single data point (e.g., minimum temperature), all critical temperature limits and maximum performance boundaries can be predicted using the estimated parameter from this study. Our results reveal universal scaling relationships in thermal performance, which could be useful for predicting changes in performance under scenarios of climate warming.

biophysics↗

bioRxiv2025

A general model for genomic traits evolution

A large body of data indicates that genomic elements, including gene families and entire genomes, exhibit diverse types of evolutionary dynamics. Several models have been developed to describe the dynamics of, for example, genome size; however, a simple deterministic model with interpretable parameters that could be useful for fitting a variety of data remains an open problem. Here, we show that (a continuous form of) the Breeders equation for the evolution of quantitative traits/characters leads to a general model that explains the dynamics of genomic elements generated by indel mutations (duplications, insertions, and deletions) and selection. Our framework consists of a general exponential-linear model that predicts at least six different types of dynamical behaviors, including exponential growth and decay. The equations fit data, such as evolution experiments across generations, field observational data of genome sizes across time, and data from phylogenetic reconstructions of ancestral states of genome size across millions of years of a variety of taxa, including viruses, bacteria, fungi, unicellular eukaryotes, plants, and animals, at both intra- and interspecific levels. To test the universality of the dynamics, we derived a dimensionless equation that enables the (re)scaling and subsequent collapse of all data for growth or decay onto a single universal curve. Thus, our model provides a general basis not only for explaining the dynamics in the size of gene families or whole genomes of populations across taxonomic and environmental scales, but also other traits, providing a foundation for further theoretical developments.

evolutionary biology↗

bioRxiv2025

Convergent Functional Constraints Make Biochemical Reaction Diversity Predictable

The predictability of biochemical evolution remains highly debated, as it is unknown if biological function can be predicted a priori. Here, we explore predictability of function not in individual reactions, but in ensembles. Sampling from 13,777 bacterial, 371 archaeal, and 203 eukaryotic genomes, we use Enzyme Commission (EC) classification to hierarchically group enzyme-catalyzed reactions that perform similar transformations into functional equivalence classes. We show that organisms partition their reaction diversity among these functional equivalence classes in a predictable way, identifying over 120 new, system-size dependent functional scaling relationships. We find distantly related lineages, acting on distinct molecular substrates, can display similar functional scaling, demonstrating that convergence is not driven by phylogeny or common reactions. We demonstrate how transitions in functional scaling can be identified with physiological shifts, using as case studies O2-utilizing oxidoreductases and hexosyltransferases. Taken together, our results open novel avenues for predicting global features of evolving enzyme populations, independent of protein structures. These functional constraints may have broad implications, including for predicting biochemical diversity, designing synthetic organisms, and modeling the evolution of reaction diversity in cases where the exact identity of catalysts is not known, such as during the emergence of life and for potential alternative forms of biochemistry.

biochemistry↗

bioRxiv2024

An algorithm for predicting per-cell proteomic properties

Proteomic studies have traditionally focused on population-level analyses with an emphasis on the relative abundance of various proteins. Such studies have been useful in uncovering physiological differences across diverse species, the physiological response of individual species to distinct environmental conditions, and the function of individual proteins in the context of cellular networks. However, the absolute value of protein abundance in a cell is important for understanding single-cell physiology, detailed biophysical considerations, connecting diverse quantitative data, and for comparisons across species. Such detailed quantification will naturally occur as singlecell proteomics becomes more prevalent, but it is also of current interest to leverage population studies. There are several challenges here. First, most population studies do not measure the quantity of cells associated with a proteome. Second, recent work has shown that cell physiology radically shifts with cell size, and these effects need to be accounted for in going from population to single-cell estimates. Here we develop and implement a method to estimate the basic properties of proteomes, based on well-established scaling relationships among cell components, including genome size, cell size, and proteome volume. Our method estimates similar but higher total proteins per cell compared to previous theoretical and empirical estimations. Our algorithm has applications for interpreting proteomes, analyzing environmental samples, and designing artificial cells. While focusing on prokaryotes, we discuss how the method can be extended to unicellular and multicellular eukaryotes.

bioinformatics↗

bioRxiv2023

Physical constraints during Snowball Earth drive the evolution of multicellularity

Molecular and fossil evidence suggest that complex eukaryotic multicellularity evolved during the late Neoproterozoic era, coincident with Snowball Earth glaciations, where ice sheets covered most of the globe. During this period, environmental conditions--such as sea water temperature and the availability of photosynthetically active light in the oceans--likely changed dramatically. Such changes would have had significant effects on both resource availability and optimal phenotypes. Here, we construct and apply mechanistic models to explore (i) how environmental changes during Snowball Earth and biophysical constraints generated selective pressures and (ii) how these pressures may have had differential effects on organisms with different forms of biological organization. By testing a series of alternate--and commonly debated--hypotheses, we demonstrate how multicellularity was likely acquired differently in eukaryotes and prokaryotes due to selective differences in the biophysical and metabolic regimes they experience: decreasing temperatures and resource-availability instigated by the onset of glaciations generated selective pressures towards smaller sizes in organisms in a diffusive regime and towards larger sizes in motile heterotrophs. These results suggest that changing environmental conditions during Snowball Earth glaciations gave multicellular eukaryotes an evolutionary advantage, paving the way for the complex multicellular lineages that followed.

evolutionary biology↗

bioRxiv2023

Observations of Elemental Composition of Enceladus Consistent with Generalized Models of Theoretical Ecosystems

Recent analysis of data from the Cassini Cosmic Dust Analyzer confirm geochemical modeling work that shows that the ocean of Enceladus contains considerable quantities of inorganic phosphorus as well as ammonium [55]. Technological advancement in flight instrumentation will continue to yield increasingly detailed data about the relative elemental and molecular composition of life detection candidates. Apart from speculating about threshold concentrations of bioactive compounds to support ecosystems, metabolic and ecological theory can provide a powerful interpretative lens to assess whether extraterrestrial environments are compatible with living ecosystems. Using multiple levels of ecological analysis, spanning from assuming strictly Earth-like organismal physiology to more agnostic understandings of putative biochemistries, we compare the proposed N:P stoichiometries of the Enceledus ocean to possible ecologies. We use chemostat models to predict potentially supportable biomass concentrations under different energy and matter flux regimes, macromolecular allometric theory to compare hypothetical biomass N:P ratios to possible environmental N:P supply ratios, and finally take a data-driven biogeochemical approach to predict possible biomass N:P ratios from the observed dissolved ratios. All three of our modeling approaches suggest marginal compatibility of an ecosystem with the ranges of dissolved N and P concentrations in the Enceledean ocean. Based on our analysis, we suggest two main priorities for further research into terrestrial analogs to improve our ability to interpret geochemical ratios as a life detection instrument.

microbiology↗

bioRxiv2023

Accelerated physiology and increased energy expenditure in animals and humans with mitochondrial defects: A meta-analysis

Mitochondria are key energy transforming organelles in mammalian cells. However, how defects in oxidative phosphorylation (OxPhos) and other mitochondrial functions influence whole-body energy expenditure (EE) has not been rigorously studied. Cellular and organismal responses to OxPhos defects likely involve a combination of functional downregulation to conserve energy and compensatory upregulation of stress responses. If the energy cost of compensatory responses exceeds the potential energy savings of functional downregulation, as recent work suggests, the result would be an increase in total EE. To address the hypothesis that OxPhos defects increase the energetic cost of living, we performed a meta-analysis of available studies reporting EE in animal models with mitochondrial gene defects. Of all reported experimental conditions (n = 91, from 29 studies), 51% reported a >10% elevation in EE relative to control animals, compared to 11% reporting <10% reduction in EE (p<0.0001, Chi-square). Of the experimental conditions where locomotor activity was also quantified, 39% showed that OxPhos-deficient animals had elevated EE despite reduced locomotor activity, which would be expected to decrease EE. To extend this finding in humans, we re-analyzed a high-quality clinical and multi-omics dataset (Sharma et al. 2021) of mitochondrial disease patients with the m.3243A>G mutation. This analysis similarly indicates an upregulation of energetically costly physiological, immune, and metabolic parameters in people with OxPhos deficiency. These results suggest that animals and humans with mitochondrial defects must expend more energy to sustain life, a state clinically called hypermetabolism. High-quality human energetics studies are needed to understand the magnitude, mechanisms, and modifiability of hypermetabolism in mitochondrial disorders.

cell biology↗

bioRxiv2022

Bacterial Strategies for Damage Management

Organisms are able to partition resources adaptively between growth and repair. The precise nature of optimal partitioning and how this emerges from cellular dynamics including insurmountable trade-offs remains an open question. We construct a mathematical framework to estimate optimal partitioning and the corresponding maximal growth rate constrained by empirical scaling laws. We model a biosynthesis tradeoff governing the partitioning of the ribosome economy between replicating functional proteins and replicating the ribosome pool, and also an energy tradeoff arising from the finite energy budget of the cell. Through exact analytic calculations we predict limits on the range of values partitioning ratios take while sustaining growth. We calculate how partitioning and cellular composition scale with protein and ribosome degradation rates and organism size. These results reveal different classes of optimizing strategies corresponding to phenotypically distinct bacterial lifestyles. We summarize these findings in a quadrant-based taxonomy including: a "greedy" strategy maximally prioritizing growth, a "prudent" strategy maximally prioritizing the management of damaged pools, and "strategically limited" intermediates.

biophysics↗

bioRxiv2022

Accelerating the clock: Interconnected speedup of energetic and molecular dynamics during aging in cultured human cells

To understand how organisms age, we need reliable multimodal molecular data collected at high temporal resolution, in specific cell types, across the lifespan. We also need interpretative theory that connects aging with basic mechanisms and physiological tradeoffs. Here we leverage a simple cellular replicative aging system combined with mathematical theory to address organismal aging. We used cultured primary human fibroblasts from multiple donors to molecularly and energetically profile entire effective lifespans of up to nine months. We generated high-density trajectories of division rates, telomere shortening, DNA methylation, RNAseq, secreted proteins/cytokines and cell-free DNA, in parallel with bioenergetic trajectories of ATP synthesis rates derived from both mitochondrial oxidative phosphorylation and glycolysis, reflecting total cellular mass-specific metabolic rate (MR). By comparing our cell culture data to data from cells in the body we uncover three fundamental speedups, or rescalings, of MR and molecular aging markers. To explain these rescalings we deploy the allometric theory of metabolism which predicts that the rate of biological aging is related to an organisms size, MR, and the partitioning of energetic resources between growth and maintenance processes. Extending this theory we report three main findings: 1) human cells isolated from the body with faster rates of growth allocate a substantially smaller fraction of their energy budget to maintenance, and correspondingly age 50-300x faster based on multiple molecular markers. 2) Over the course of the cellular lifespan, primary human fibroblasts acquire a >100-fold hypermetabolic phenotype characterized by increased maintenance costs, and associated with increased mtDNA genome density, upregulation of senescence-associated extracellular secretion, and induction of maintenance-related transcriptional programs. 3) Finally, manipulating MR with mitochondria-targeted metabolic, genetic, and pharmacological perturbations predictably altered the molecular rate of aging, providing experimental evidence for the interplay of MR and aging in a human system. These data highlight the key role that the partitioning of energetic resources between growth and maintenance/repair processes plays in cellular aging, and converge with predictions of cross-species metabolic theory indicating that energy metabolism governs how human cells age. Significance StatementHow cells age is of fundamental importance to understanding the diversity of mammalian lifespans and the wide variation in human aging trajectories. By aging primary human fibroblasts over several months in parallel with multi-omics and energetic profiling, we find that as human cells age and progressively divide more slowly, surprisingly, they progressively consume energy faster. By manipulating cellular metabolic rates, we confirm that the higher the cellular metabolic rate, the faster cells experience telomere shortening and epigenetic aging - a speedup phenotype consistent with allometric scaling theory. By modeling robust energetic and molecular aging trajectories across donors and experimental conditions, we find that independent of cell division rates, molecular aging trajectories are predicted by the partitioning of the energy budget between growth and maintenance processes. These results integrate molecular and energetic drivers of aging and therefore have important long-term implications to understand biological aging phenomena ranging from cellular senescence to human longevity.

cell biology↗

bioRxiv2022

Metabolic compatibility and the rarity of prokaryote endosymbioses

The evolution of the mitochondria was a significant event that gave rise to the eukaryotic lineage and most large complex life. Central to the origins of the mitochondria was an endosymbiosis between prokaryotes. Yet, despite the potential benefits that can stem from a prokaryotic endosymbiosis, their modern occurrence is exceptionally rare. While many factors may contribute to their rarity, we lack methods for estimating the extent to which they constrain the appearance of a prokaryotic endosymbiosis. Here, we address this knowledge gap by examining the role of metabolic compatibility between a prokaryotic host and endosymbiont. We use genome-scale metabolic models from three different databases (AGORA, KBase, and CarveMe) to assess the viability, fitness, and adaptability of potential prokaryotic endosymbioses. We find that while more than half of host-endosymbiont pairings are metabolically viable, the resulting endosymbioses have reduced growth rates compared to their ancestral metabolisms and are unlikely to gain mutations to overcome these fitness differences. In spite of these challenges, we do find that they may be more robust in the face of environmental perturbations at least in comparison with the ancestral host metabolism lineages. Our results provide a critical set of null models and expectations for understanding the forces that shape the structure of prokaryotic life.

evolutionary biology↗

bioRxiv2021

A general model for temperature-dependence in biology

At present, there is no simple, complete, and first principles-based model for quantitatively describing the full range of observed biological temperature responses. Here, we derive a theory exhibiting these features based on the Eyring-Evans-Polanyi theory governing chemical reaction rates, and which is applicable across all scales from the micro to the macro. Assuming only that the conformational entropy of molecules changes with temperature, we derive a theory for the temperature dependence which takes the form of an exponential function modified by a power-law. Our framework leads to six deductions applicable to any biological trait that depends on temperature, and elucidates novel aspects of universal temperature responses across the tree of life, from quantum to classical scales. All predictions are well supported by data for a wide variety of biological rates and steady states, from molecular to ecological scales and across multiple taxonomic groups. In addition, we provide novel explanations of several empirical relationships including optimal values in temperature response curves. One-Sentence SummaryWe derive a simple and universal formulae to characterize temperature responses of biological processes across the tree of life.

ecology↗

bioRxiv2021

Role of protein degradation in growth laws

Growing cells adopt common basic strategies to achieve optimal resource allocation under limited resource availability. Our current understanding of such "growth laws" neglects degradation, assuming that it occurs slowly compared to the cell cycle duration. Here we argue that this assumption cannot hold at slow growth, leading to important consequences. We propose a simple framework showing that at slow growth protein degradation is balanced by a fraction of "maintenance" ribosomes. Consequently, active ribosomes do not drop to zero at vanishing growth, but as growth rate diminishes, an increasing fraction of active ribosomes performs maintenance. Through a detailed analysis of compiled data, we show that the predictions of this model agree with data from E. coli and S. cerevisiae. Intriguingly, we also find that protein degradation increases at slow growth, which we interpret as a consequence of active waste management and/or recycling. Our results highlight protein turnover as an underrated factor for our understanding of growth laws across kingdoms. Author summaryThe idea that simple quantitative relationships relate cell physiology to cellular composition dates back to the 1950s, but the recent years saw a leap in our understanding of such "growth laws", with relevant implications regarding the interdependence between growth, metabolism and biochemical networks. However, recent works on nutrient-limited growth mainly focused on laboratory conditions that are favourable to growth. Thus, our current mathematical understanding of the growth laws neglects protein degradation, under the argument that it occurs slowly compared to the timescale of the cell cycle. Instead, at slow growth the timescales of mass loss from protein degradation and dilution become comparable. In this work, we propose that protein degradation shapes the quantitative relationships between ribosome allocation and growth rate, and determines a fraction of ribosomes that do not contribute to growth and need to remain active to balance degradation.

systems biology↗

bioRxiv2021

Scaling laws in enzyme function reveal a new kind of biochemical universality

All life on Earth is unified by its use of a shared set of component chemical compounds and reactions, providing a detailed model for universal biochemistry. However, this notion of universality is specific to currently observed biochemistry and does not allow quantitative predictions about examples not yet observed. Here we introduce a more generalizable concept of biochemical universality, more akin to the kind of universality discussed in physics. Using annotated genomic datasets including an ensemble of 11955 metagenomes and 1282 archaea, 11759 bacteria and 200 eukaryotic taxa, we show how four of the major enzyme functions - the oxidoreductases, transferases, hydrolases and ligases - form universality classes with common scaling behavior in their relative abundances observed across the datasets. We verify these universal scaling laws are not explained by the presence of compounds, reactions and enzyme functions shared across all known examples of life. We also demonstrate how a consensus model for the last universal common ancestor (LUCA) is consistent with predictions from these scaling laws, with the exception of ligases and transferases. Our results establish the existence of a new kind of biochemical universality, independent of the details of the component chemistry, with implications for guiding our search for missing biochemical diversity on Earth, or other for any biochemistries that might deviate from the exact chemical make-up of life as we know it, such as at the origins of life, in alien environments, or in the design of synthetic life.

systems biology↗

bioRxiv2017

The dynamics of starvation and recovery

The eco-evolutionary dynamics of species are fundamentally linked to the energetic constraints of its constituent individuals. Of particular importance is the interplay between reproduction and the dynamics of starvation and recovery. To elucidate this interplay, we introduce a nutritional state-structured model that incorporates two classes of consumer: nutritionally replete, reproducing consumers, and undernourished, non-reproducing consumers. We obtain strong constraints on starvation and recovery rates by deriving allometric scaling relationships and find that population dynamics are typically driven to a steady state. Moreover, these rates fall within a refuge in parameter space, where the probability of population extinction is minimized. We also show that our model provides a natural framework to predict maximum mammalian body size by determining the relative stability of an otherwise homogeneous population to a competing population with altered percent body fat. This framework provides a principled mechanism for a selective driver of Copes rule.

ecology↗