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Autocatalysis underlies the ability of chemical and biochemical systems to replicate. Recently, Blokhuis et al. (PNAS 117(41):25230–25236, 2020) gave a stoechiometric definition of autocatalysis for reaction networks, stating the existence of a combination of reactions such that the balance for all autocatalytic species is strictly positive, and investigated minimal autocatalytic networks, called autocatalytic cores. By contrast, spontaneous autocatalysis—namely, exponential amplification of all species internal to a reaction network, starting from a diluted regime, i.e. low concentrations—is a dynamical property. We introduce here a topological condition (Top) for autocatalysis, namely: restricting the reaction network description to highly diluted species, we assume existence of a strongly connected component possessing at least one reaction with multiple products (including multiple copies of a single species). We find this condition to be necessary and sufficient for stoechiometric autocatalysis. When degradation reactions have small enough rates, the topological condition further ensures dynamical autocatalysis, characterized by a strictly positive Lyapunov exponent giving the instantaneous exponential growth rate of the system. The proof is generally based on the study of auxiliary Markov chains. We provide as examples general autocatalytic cores of Type I and Type III in the typology of Blokhuis et al. (PNAS 117(41):25230–25236, 2020) . In a companion article (Unterberger in Dynamical autocatalysis for autocatalytic cores, 2021), Lyapunov exponents and the behavior in the growth regime are studied quantitatively beyond the present diluted regime .

Recent advances in gene editing can produce large genotype–fitness maps for targeted genes, yet predicting the effects of mutations between genes remains challenging. Indeed, biochemical models require knowledge of underlying parameters and interactions, whereas machine learning methods typically lack interpretability, as they do not link model parameters to biological quantities. We introduce D-LIM, a neural network that infers low-dimensional fitness landscapes directly from mutation–fitness data. The distinctive feature of D-LIM is that it assumes genes act through independent gene-specific molecular phenotypes whose nonlinear interactions determine fitness. When this assumption holds, the model yields accurate predictions and interpretable effective phenotypes. Conversely, failure reveals that a low-dimensional model is insufficient. Applied to deep mutational scanning of metabolic pathways, protein–protein interactions, and yeast environmental adaptation, D-LIM achieves state-of-the-art predictive accuracy. The inferred phenotype–fitness landscapes reveal whether epistatic interactions can be captured by a low-dimensional continuous model and identify potential trade-offs. Moreover, D-LIM estimates mutational effects on the effective phenotypes, enabling weak extrapolation beyond the training domain. D-LIM demonstrates how simple structure constraints in a neural network can help inference and hypothesis generation in biology.

Synergistic drug combination screening is a promising strategy in drug discovery, but it involves navigating a costly and complex search space. While AI, particularly deep learning, has advanced synergy predictions, its effectiveness is limited by the low occurrence of synergistic drug pairs. Active learning, which integrates experimental testing into the learning process, has been proposed to address this challenge. In this work, we explore the key components of active learning to provide recommendations for its implementation. We find that molecular encoding has a limited impact on performance, while the cellular environment features significantly enhance predictions. Additionally, active learning can discover 60% of synergistic drug pairs with only exploring 10% of combinatorial space. The synergy yield ratio is observed to be even higher with smaller batch sizes, where dynamic tuning of the exploration-exploitation strategy can further enhance performance. The code can be found at https://github.com/LBiophyEvo/DrugSynergy

The inherent stochasticity in metabolic reactions is a potent source of phenotypic heterogeneity in cell populations, with potentially fundamental implications for cancer research. Natural instability in cellular metabolism Molecular fluctuations in individual metabolic reactions are widely thought to have little effect on cell growth, because of averaging over the many biochemical reactions involved. Now Sander Tans and colleagues use time-lapse microscopy to accurately determine the growth rate of single bacterial cells while also monitoring individual enzyme levels, and find that elemental molecular noise does propagate and cause variation in growth. Conversely, they observe that growth fluctuations propagate back to perturb gene expression, so that molecular noise in one gene can influence unrelated genes via growth. The results demonstrate that the inherent variability in metabolic reactions is a potent source of phenotypic heterogeneity in cell populations, with fundamental implications for cancer research. Elucidating the role of molecular stochasticity 1 in cellular growth is central to understanding phenotypic heterogeneity 2 and the stability of cellular proliferation 3 . The inherent stochasticity of metabolic reaction events 4 should have negligible effect, because of averaging over the many reaction events contributing to growth. Indeed, metabolism and growth are often considered to be constant for fixed conditions 5 , 6 . Stochastic fluctuations in the expression level 1 , 7 , 8 , 9 of metabolic enzymes could produce variations in the reactions they catalyse. However, whether such molecular fluctuations can affect growth is unclear, given the various stabilizing regulatory mechanisms 10 , 11 , 12 , the slow adjustment of key cellular components such as ribosomes 13 , 14 , and the secretion 15 and buffering 16 , 17 of excess metabolites. Here we use time-lapse microscopy to measure fluctuations in the instantaneous growth rate of single cells of Escherichia coli , and quantify time-resolved cross-correlations with the expression of lac genes and enzymes in central metabolism. We show that expression fluctuations of catabolically active enzymes can propagate and cause growth fluctuations, with transmission depending on the limitation of the enzyme to growth. Conversely, growth fluctuations propagate back to perturb expression. Accordingly, enzymes were found to transmit noise to other unrelated genes via growth. Homeostasis is promoted by a noise-cancelling mechanism that exploits fluctuations in the dilution of proteins by cell-volume expansion. The results indicate that molecular noise is propagated not only by regulatory proteins 18 , 19 but also by metabolic reactions. They also suggest that cellular metabolism is inherently stochastic, and a generic source of phenotypic heterogeneity.

With the molecular revolution in Biology, a mechanistic understanding of the genotype–phenotype relationship became possible. Recently, advances in DNA synthesis and sequencing have enabled the development of deep mutational scanning assays, capable of scoring comprehensive libraries of genotypes for fitness and a variety of phenotypes in massively parallel fashion. The resulting empirical genotype–fitness maps pave the way to predictive models, potentially accelerating our ability to anticipate the behaviour of pathogen and cancerous cell populations from sequencing data. Besides from cellular fitness, phenotypes of direct application in industry (e.g. enzyme activity) and medicine (e.g. antibody binding) can be quantified and even selected directly by these assays. This review discusses the technological basis of and recent developments in massively parallel genetics, along with the trends it is uncovering in the genotype–phenotype relationship (distribution of mutation effects, epistasis), their possible mechanistic bases and future directions for advancing towards the goal of predictive genetics.

Discovering autocatalytic chemistries that can evolve is a major goal in systems chemistry and a critical step towards understanding the origin of life. Autocatalytic networks have been discovered in various chemistries, but we lack a general understanding of how network topology controls the Darwinian properties of variation, differential reproduction, and heredity, which are mediated by the chemical composition. Using barcoded sequencing and droplet microfluidics, we establish a landscape of thousands of networks of RNAs that catalyze their own formation from fragments, and derive relationships between network topology and chemical composition. We find that strong variations arise from catalytic innovations perturbing weakly connected networks, and that growth increases with global connectivity. These rules imply trade-offs between reproduction and variation, and between compositional persistence and variation along trajectories of network complexification. Overall, connectivity in reaction networks provides a lever to balance variation (to explore chemical states) with reproduction and heredity (persistence being necessary for selection to act), as required for chemical evolution. Autocatalytic networks may have started evolution during the origin of life. Here, the authors establish a landscape of thousands of RNA networks by barcoded sequencing and microfluidics, and derive relationships between topology and Darwinian properties such as variation and differential reproduction.

The appearance of molecular replicators (molecules that can be copied) was probably a critical step in the originz of life. However, parasitic replicators would take over and would have prevented life from taking off unless the replicators were compartmentalized in reproducing protocells. Paradoxically, control of protocell reproduction would seem to require evolved replicators. We show here that a simpler population structure, based on cycles of transient compartmentalization (TC) and mixing of RNA replicators, is sufficient to prevent takeover by parasitic mutants. TC tends to select for ensembles of replicators that replicate at a similar rate, including a diversity of parasites that could serve as a source of opportunistic functionality. Thus, TC in natural, abiological compartments could have allowed life to take hold.

Sustained autocatalysis coupled to compartment growth and division is a key step in the origin of life, but an experimental demonstration of this phenomenon in an artificial system has previously proven elusive. We show that autocatalytic reactions within compartments—when autocatalysis, and reactant and solvent exchange outpace product exchange—drive osmosis and diffusion, resulting in compartment growth. We demonstrate, using the formose reaction compartmentalized in aqueous droplets in an emulsion, that compartment volume can more than double. Competition for a common reactant (formaldehyde) causes variation in droplet growth rate based on the composition of the surrounding droplets. These growth rate variations are partially transmitted after selective division of the largest droplets by shearing, which converts growth-rate differences into differences in droplet frequency. This shows how a combination of properties of living systems (growth, division, variation, competition, rudimentary heredity and selection) can arise from simple physical–chemical processes and may have paved the way for the emergence of evolution by natural selection. The coupling of autocatalysis to compartment growth and division is a key step in the origin of life. Now it has been shown that compartmentalizing the formose reaction in emulsion droplets leads to several crucial properties of living and evolving systems (growth, division, variation, competition, rudimentary heredity and selection).

Prostaglandin receptors are pharmacologically validated targets with implications in several medical indications, including glaucoma, cardiac cyanotic disease, pulmonary hypertension, oncology, and various rare diseases. In this study, we developed a ligand-based machine learning (ML) model to classify chemical compounds as either active or inactive against the prostaglandin receptor EP2. From an initial set of 1,826 descriptors, 20 were selected to train random forest algorithms, yielding an area under the curve score (AUC) of > 0.8 for compound classification in the test set. Our resulting ML classifier showed an overall accuracy of 88.9% towards newly experimentally tested EP2 ligands. This adaptable and tractable workflow can be extended to other EP receptors and possibly other similar targets.

Estimating the plausibility of RNA self-reproduction is central to origin-of-life scenarios. However, this property has been shown in only a handful of catalytic RNAs. Here, we compare models for their generative power in diversifying a reference ribozyme, based on statistical covariation and secondary structure prediction, and experimentally test model predictions using high-throughput sequencing. Leveraging statistical physics methods, we compute the number of ribozymes capable of autocatalytic self-reproduction from oligonucleotide fragments to be over 10 39 , with sequences found up to 65 mutations from the original sequence and 99 mutations away from each other, far beyond the 10 mutations achieved by deep mutational scanning. The findings demonstrate an efficient method for exploring RNA sequence space, and provide quantitative data on self-reproducing RNA that further illuminates the potential pathways to abiogenesis. The spontaneous emergence of autocatalytic RNAs is central to origin of life. Here, the authors use machine learning, high-throughput screening and statistical physics to explore large neutral space of catalytic RNAs.