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bioRxiv2020

The rates of introgression and barriers to genetic exchange between hybridizing species: sex chromosomes vs. autosomes

Interspecific crossing experiments have shown that sex chromosomes play a major role in reproductive isolation between many pairs of species. However, their ability to act as reproductive barriers, which hamper interspecific genetic exchange, has rarely been evaluated quantitatively compared to Autosomes. This genome-wide limitation of gene flow is essential for understanding the complete separation of species, and thus speciation. Here, we develop a mainland-island model of secondary contact between hybridizing species of an XY (or ZW) sexual system. We obtain theoretical predictions for the frequency of introgressed alleles, and the strength of the barrier to neutral gene flow for the two types of chromosomes carrying multiple interspecific barrier loci. Theoretical predictions are obtained for scenarios where introgressed alleles are rare. We show that the same analytical expressions apply for sex chromosomes and autosomes, but with different sex-averaged effective parameters. The specific features of sex chromosomes (hemizygosity and absence of recombination in the heterogametic sex) lead to reduced levels of introgression on the X (or Z) compared to autosomes. This effect can be enhanced by certain types of sex-biased forces, but it remains overall small (except when alleles causing incompatibilities are recessive). We discuss these predictions in the light of empirical data comprising model-based tests of introgression and cline surveys in various biological systems.

evolutionary biology↗

bioRxiv2026

ExocubeBio: an in-situ fluidic platform for microbial exposure on the International Space Station

Space-based platforms currently represent the most accurate means to experimentally assess the influence of the space environment on biological systems. However, performing such experiments remains technically challenging and requires highly specialized instrumentation. This study describes the current development and hardware qualification of ExocubeBio, a miniaturized experimental platform for in-situ biological space exposure. This experiment is scheduled for installation on the exterior of the International Space Station in 2027, as part of Exobio, the European Space Agencys new generation exobiology exposure facility. ExocubeBio will expose live microbial samples to the low Earth orbit environment, and combine autonomous in-situ optical density and fluorescence measurements, with the capacity to return preserved samples to Earth. Achieving these experimental goals requires a specialized, robust and reliable hardware system. The ExocubeBio hardware testing described here includes assessment of material biocompatibility and durability, functional validation of the miniaturized fluidic system, and optimization of the integrated optical subsystem for optical density and fluorescence measurements. These results demonstrate that the ExocubeBio experimental hardware components can each execute their core functional and operational requirements; subsystems allow for sample exposure, in-situ measurements of microbial cultures, and the chemical preservation of samples for post-flight analysis. As ExocubeBio transitions from hardware development to mission readiness, the results presented here validate the overall design and engineering approaches utilized. By combining the strengths of in-situ monitoring and sample return, ExocubeBio represents a significant advancement in space-based experimentation, and will provide new insights into microbial responses to the space environment.

microbiology↗

bioRxiv2017

System-level studies of a cell-free transcription-translation platform for metabolic engineering

Current methods for assembling biosynthetic pathways in microorganisms require a process of repeated trial and error and have long design-build-test cycles. We describe the use of a cell-free transcription-translation (TX-TL) system as a biomolecular breadboard for the rapid engineering of the 1,4-butanediol (BDO) pathway. We demonstrate the reliability of TX-TL as a platform for engineering biological systems by undertaking a careful characterization of its transcription and translation capabilities and provide a detailed analysis of its metabolic output. Using TX-TL to survey the design space of the BDO pathway enables rapid tuning of pathway enzyme expression levels for improved product yield. Leveraging TX-TL to screen enzyme variants for improved catalytic activity accelerates design iterations that can be directly applied to in vivo strain development.

bioengineering↗

bioRxiv2023

Configurable Full Subtractor and Adder by Lego-like Mixing and Matching of Engineered Bacteria

One of the long-term goals of synthetic bioengineering is to create configurable and programmable biological systems by just mixing and matching "LEGO"-like bio-modules. Here, we introduce a configurable and modular multi-cellular system where, from a small library of nine discrete engineered bacterial cells, a full subtractor and a full adder can be built on demand by just mixing and matching seven appropriate cell types in a culture. Here, each set of engineered bacteria was modelled as an artificial neuro-synapse that, in a co-culture, formed a single layer artificial neural network (ANN) type architecture that worked as a biochemical full subtractor or full adder. The system is configurable with interchangeable cellular modules, whereby through simply interchanging two cell types in the subtractor culture, a full adder can be built and vice versa. This Lego-like mix and match system is mathematically predictive, and provide a flexible and scalable means to build complex cellular functions. This work may have significance in biocomputer technology development, multi-cellular synthetic biology, and cellular hardware for ANN.

synthetic biology↗

bioRxiv2023

From Pixels to Phenotypes: Integrating Image-Based Profiling with Cell Health Data Improves Interpretability

Cell Painting assays generate morphological profiles that are versatile descriptors of biological systems and have been used to predict in vitro and in vivo drug effects. However, Cell Painting features are based on image statistics, and are, therefore, often not readily biologically interpretable. In this study, we introduce an approach that maps specific Cell Painting features into the BioMorph space using readouts from comprehensive Cell Health assays. We validated that the resulting BioMorph space effectively connected compounds not only with the morphological features associated with their bioactivity but with deeper insights into phenotypic characteristics and cellular processes associated with the given bioactivity. The BioMorph space revealed the mechanism of action for individual compounds, including dual-acting compounds such as emetine, an inhibitor of both protein synthesis and DNA replication. In summary, BioMorph space offers a more biologically relevant way to interpret cell morphological features from the Cell Painting assays and to generate hypotheses for experimental validation. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=163 SRC="FIGDIR/small/549031v1_ufig1.gif" ALT="Figure 1"> View larger version (33K): org.highwire.dtl.DTLVardef@1a6169forg.highwire.dtl.DTLVardef@11757eforg.highwire.dtl.DTLVardef@18e3fc2org.highwire.dtl.DTLVardef@1fc0a90_HPS_FORMAT_FIGEXP M_FIG C_FIG IN BRIEFSeal et al. used machine learning models and feature selection approaches to group cell morphological features from Cell Painting assays and to describe the shared role of these morphological features in various cell health phenotypes. The resulting BioMorph space improves the ability to understand the mechanism of action and toxicity of compounds and to generate hypotheses to guide future experiments. HIGHLIGHTSO_LICombining Cell Painting and Cell Health imaging data defines the BioMorph space. C_LIO_LIBioMorph space allows detecting less common mechanisms for bioactive compounds. C_LIO_LIBioMorph space can generate MOA hypotheses to guide experimental validation. C_LIO_LIBioMorph space is more biologically relevant and interpretable than Cell Painting features. C_LI

cell biology↗

bioRxiv2024

A modular system for programming multistep activation of endogenous genes in stem cells

Although genomes encode instructions for mammalian cell differentiation with rich syntactic relationships, existing methods for genetically programming cells have modest capabilities for stepwise regulation of genes. Here, we developed a sequential genetic system that enables transcriptional activation of endogenous genes in a preprogrammed, stepwise manner. The system relies on the removal of an RNA polymerase III termination signal to induce both the transcriptional activation and the DNA endonuclease activities of a Cas9-VPR protein to effect stepwise progression through cascades of gene activation events. The efficiency of the cascading system enables a new dimension for cellular programming by allowing the manipulation of the sequential order of gene activation for directing the differentiation of human stem cells. One-Sentence SummaryDevelopment of a synthetic biology system for preprogrammed, stepwise activation of endogenous genes.

synthetic biology↗

bioRxiv2016

Measurements of translation initiation from all 64 codons in E. coli

Our understanding of translation is one cornerstone of molecular biology that underpins our capacity to engineer living matter. The canonical start codon (AUG) and a few near-cognates (GUG, UUG) are typically considered as the \"start codons\" for translation initiation in Escherichia coli (E. coli). Translation is typically not thought to initiate from the 61 remaining codons. Here, we systematically quantified translation initiation in E. coli from all 64 triplet codons. We detected protein synthesis above background initiating from at least 46 codons. Translation initiated from these non-canonical start codons at levels ranging from 0.01% to 2% relative to AUG. Translation initiation from non-canonical start codons may contribute to the synthesis of peptides in both natural and synthetic biological systems

Synthetic Biology↗

bioRxiv2020

The Oft-Overlooked Massively Parallel Reporter Assay: Where, When, and Which Psychiatric Genetic Variants are Functional?

Neuropsychiatric phenotypes have been long known to be influenced by heritable risk factors. The past decade of genetic studies have confirmed this directly, revealing specific common and rare genetic variants enriched in disease cohorts. However, the early hope for these studies--that only a small set of genes would be responsible for a given disorder--proved false. The picture that has emerged is far more complex: a given disorder may be influenced by myriad coding and noncoding variants of small effect size, and/or by rare but severe variants of large effect size, many de novo. Noncoding genomic sequences harbor a large portion of these variants, the molecular functions of which cannot usually be inferred from sequence alone. This creates a substantial barrier to understanding the higher-order molecular and biological systems underlying disease risk. Fortunately, a proliferation of genetic technologies--namely, scalable oligonucleotide synthesis, high-throughput RNA sequencing, CRISPR, and CRISPR derivatives--have opened novel avenues to experimentally identify biologically significant variants en masse. These advances have yielded an especially versatile technique adaptable to large-scale functional assays of variation in both untranscribed and untranslated regulatory features: Massively Parallel Reporter Assays (MPRAs). MPRAs are powerful molecular genetic tools that can be used to screen tens of thousands of predefined sequences for functional effects in a single experiment. This approach has several ideal features for psychiatric genetics, but remains underutilized in the field to date. To emphasize the opportunities MPRA holds for dissecting psychiatric polygenicity, we review here its applications in the literature, discuss its ability to test several biological variables implicated in psychiatric disorders, illustrate this flexibility with a proof-of-principle, in vivo cell-type specific implementation of the assay, and envision future outcomes of applying MPRA to both computational and experimental neurogenetics.

genetics↗

bioRxiv2024

Spatial Profiling of Metals through Matrix-Assisted Laser Desorption Ionization Mass Spectrometry Imaging

The spatial-omic analysis of biomolecules such as nucleic acids, lipids, metabolites, and proteins is advancing the study of biological systems and processes in a physio-pathological context. Here, we describe an innovative matrix-assisted laser desorption ionization mass spectrometry imaging (MALDI MSI) method to detect metals within biological tissues using instrumentation that is widely available in research and clinical laboratories. We characterize the spatial distribution of metals in diverse settings including mouse embryogenesis, genetic disorders leading to abnormal metal accumulation, and preclinical testing for improved platinum-based chemotherapy delivery through focused ultrasound across the blood-brain barrier. Spatial metal profiling will advance research studies and the clinical analysis of metal-related diseases, enabling more precise use of metal-based therapies and advances in diverse scientific fields beyond biomedicine. One-Sentence SummarySpatial metallomic profiling maps native metals or those coordinated to xenobiotics, antibodies, and biomolecules in tissues.

cancer biology↗

bioRxiv2025

Multiobjective learning and design of bacteriophage specificity

To better understand and design proteins, it is crucial to consider the multifunctional landscapes on which all proteins exist. Proteins are often optimized for single functions during design and engineering, without considering the countless other functionalities that may contribute to or interfere with the intended outcome. In this work, we apply deep learning to understand and design the multifunctional host-targeting landscape of the T7 bacteriophage receptor binding protein for enhanced infectivity, pre-defined specificity, and high generality in virulence toward unseen strains. We compare several different model architectures and design approaches and experimentally characterize designed phages optimized for 26 diverse tasks. We demonstrate that with multiobjective machine learning, it is possible to design complex specificities at success rates that can enable low-throughput validation of predicted hits. Our results show that the targeting capabilities of T7 are highly plastic, with opposite specificities often separated by only a few mutations. This level of tunability underscores how models trained on multifunctional data can uncover key principles of phage biology and specificity. The same modeling framework can be applied to guide the multiobjective design of other proteins or mutable biological systems, offering a general strategy for navigating multifunctional landscapes.

synthetic biology↗

bioRxiv2019

Cortical and Cardiovascular Regulation Support Adaptation to Ambient Sound Environment

Adaptation in complex biological systems is a consequence of sensitivity to environmental changes. We investigate how responses to unattended background sound changes are influenced by the interaction of external (pitch sequence context) and internal factors (biological regulation). Cortical responses (electroencephalograph Mismatch Negativity) demonstrated increased intensity and prolonged latency in unattended pitch changes in the context of the ascending pitch sequence compared with the descending pitch sequence. Moreover, ascending pitch context is associated with more activation of anterior cingula cortex and insula, suggesting arousal effect and internal regulation. However, the intensified and prolonged responses were supported by cortical and cardiovascular biomarkers for regulation respectively. These findings suggest that biological regulation may be associated with environmental context and play different roles in response sensitivity in terms of intensity and speed.

neuroscience↗

bioRxiv2026

Quantum Hamiltonian Learning using Time-Resolved Measurement Data and its Application to Gene Regulatory Network Inference

We present a new Hamiltonian-learning framework based on time-resolved measurement data from a fixed local IC-POVM and its application to inferring gene regulatory networks. We introduce the quantum Hamiltonian-based gene-expression model (QHGM), in which gene interactions are encoded as a parameterized Hamiltonian that governs gene expression evolution over pseudotime. We derive finite-sample recovery guarantees and establish upper bounds on the number of time and measurement samples required for accurate parameter estimation with high probability, scaling polynomially with system size. To recover the QHGM parameters, we develop a scalable variational learning algorithm based on empirical risk minimization. Our method recovers network structure efficiently on synthetic benchmarks and reveals novel, biologically plausible regulatory connections in Glioblastoma single-cell RNA sequencing data, highlighting its potential in cancer research. This framework opens new directions for applying quantum-like modeling to biological systems beyond the limits of classical inference.

bioinformatics↗

bioRxiv2026

Benchmarking zero-shot single-cell foundation model embeddings for cellular dynamics reconstruction

Reconstructing cellular trajectories from time-resolved single-cell transcriptomics is fundamental to understanding processes from embryonic development to cancer progression. While single-cell foundation models (scFMs) promise universal biological representations through large-scale pretraining, their capacity to capture the non-linear dynamics governing cell-fate decisions remains uncharacterized. Here we systematically benchmark multiple scFMs across challenging biomedical scenarios involving branching lineages and continuous state transitions. By coupling zero-shot scFM embeddings with dynamic optimal transport, we evaluated their performance against a traditional highly variable gene (HVG) baseline in backtracking progenitor states, interpolating transition intermediates, and extrapolating future fates. We find that zero-shot scFM embeddings underperform the HVG baseline across diverse biological systems, particularly in recovering the distributional complexity of unobserved cells. Mechanistic analysis reveals that current scFM architectures tend to over-compress subtle temporal signals, causing an artificial "linearization" of branched biological structures that may obscure critical divergence points in disease progression. Our findings suggest that while scFMs provide unified cell-state views, the HVG baseline remains more robust for trajectory inference, identifying a fundamental "temporal-compression" bottleneck that must be addressed to develop next-generation, dynamics-aware foundation models.

bioinformatics↗

bioRxiv2023

Theoretical understanding of evolutionary dynamics on inhomogeneous networks

Evolution is the main feature of all biological systems that allows populations to change their characteristics over successive generations. A powerful approach to understand evolutionary dynamics is to investigate fixation probabilities and fixation times of novel mutations on networks that mimic biological populations. It is now well established that the structure of such networks can have dramatic effects on evolutionary dynamics. In particular, there are population structures that might amplify the fixation probabilities while simultaneously delaying the fixation events. However, the microscopic origins of such complex evolutionary dynamics remain not well understood. We present here a theoretical investigation of the microscopic mechanisms of mutation fixation processes on inhomogeneous networks. It views evolutionary dynamics as a set of stochastic transitions between discrete states specified by different numbers of mutated cells. By specifically considering star networks, we obtain a comprehensive description of evolutionary dynamics. Our approach allows us to employ physics-inspired free-energy landscape arguments to explain the observed trends in fixation times and fixation probabilities, providing a better microscopic understanding of evolutionary dynamics in complex systems.

biophysics↗

bioRxiv2026

Directional heritability and the geometry of multivariate constraint

Evolutionary responses to selection depend on how additive genetic variance is distributed across trait combinations. We focus on directional heritability--the fraction of phenotypic variance that is additive genetic along a given selection gradient--and treat its distribution across directions as a central object for describing multivariate constraint. Using a geometric transformation that rescales trait space so that phenotypic variance is isotropic, we show that directional heritability becomes a quadratic form in a whitened genetic matrix G* = P-1/2GP-1/2. Under uniformly distributed selection directions, the squared coefficient of variation of directional heritability satisfies CV2(h2) = (2/(p + 2)) Vrel(G*), where Vrel(G*) is the relative eigenvalue variance of the whitened matrix and p is the number of traits. Simulations show that alignment between the eigenvector systems of G and P has a larger effect on the spread of directional heritability than correlation strength. Analyses of 55 empirical G-P matrix pairs from 11 studies reveal wide variation across biological systems in how often selection encounters low-heritability directions: over two thirds of the populations examined had more than 25% of phenotypic directions with h2 < 0.25. The eigenvalue spectrum of G* provides a sufficient summary for characterising how matrix geometry shapes evolutionary constraint on the phenotypic scale.

evolutionary biology↗

bioRxiv2019

Custom built scanner and simple image processing pipeline enables low-cost, high-throughput phenotyping of maize ears

High-throughput phenotyping systems are becoming increasingly powerful, dramatically changing our ability to document, measure, and detect phenomena. Unfortunately, taking advantage of these trends can be difficult for scientists with few resources, particularly when studying nonstandard biological systems. Here, we describe a powerful, cost-effective combination of a custom-built imaging platform and open-source image processing pipeline. Our maize ear scanner was built with off-the-shelf parts for <$80. When combined with a cellphone or digital camera, videos of rotating maize ears were captured and digitally flattened into projections covering the entire surface of the ear. Segregating GFP and anthocyanin seed markers were clearly distinguishable in ear projections, allowing manual annotation using ImageJ. Using this method, statistically powerful transmission data can be collected for hundreds of maize ears, accelerating the phenotyping process.

plant biology↗

bioRxiv2023

Phosphoprotein dynamics of interacting tumor and T cells by HySic

Functional interactions between cytotoxic T cells and tumor cells are central to anti-cancer immunity. Some of the proteins involved, particularly immune checkpoints expressed by T cells, serve as promising clinical targets in immunotherapy. However, our understanding of the complexity and dynamics of the interactions between tumor cells and T cells is only rudimentary. Here we present HySic (for Hybrid quantification of SILAC (Stable Isotope Labelling by Amino acids in Cell culture)-labeled interacting cells) as an innovative method to quantify protein and phosphorylation dynamics between and within physically interacting (heterotypic) cells. We show that co-cultured HLA/antigen-matched tumor and T cells engage in physical and stable interactions, allowing for in-depth HySic analysis. This method does not require physical separation of the two cell types for subsequent MS proteome and phosphoproteome measurement using label free quantification (LFQ). We demonstrate that HySic can be used to unravel proteins contributing to functional T cell:tumor cell interactions. We validated HySic with established interactions, including those mediating IFN{gamma} signaling. Using HySic we identified the RHO/RAC/PAK1 signaling pathway to be activated upon interaction of T cells and tumor cells. Pharmacologic inhibition of PAK1 sensitized tumor cells to T cell killing. Thus, HySic is an innovative and simple method to study short-term protein signaling dynamics in physically interacting cells, which can be easily extended to other biological systems.

cancer biology↗

bioRxiv2021

Automatic Consistency Assurance for Literature-based Gene Ontology Annotation

BackgroundLiterature-based gene ontology (GO) annotation is a process where expert curators use uniform expressions to describe gene functions reported in research papers, creating computable representations of information about biological systems. Manual assurance of consistency between GO annotations and the associated evidence texts identified by expert curators is reliable but time-consuming, and is infeasible in the context of rapidly growing biological literature. A key challenge is maintaining consistency of existing GO annotations as new studies are published and the GO vocabulary is updated. MethodIn this work, we introduce a formalisation of biological database annotation inconsistencies, identifying four distinct types of inconsistency. We propose a novel and efficient method using state-of-the-art text mining models to automatically distinguish between consistent GO annotation and the different types of inconsistent GO annotation. We evaluate this method using a synthetic dataset generated by directed manipulation of instances in an existing corpus, BC4GO. Results and ConclusionTwo models built using our method for distinct annotation consistency identification tasks achieved high precision and were robust to updates in the GO vocabulary. We provide detailed error analysis for demonstrating that the method achieves high precision on more confident predictions. Our approach demonstrates clear value for human-in-the-loop curation scenarios. Data availabilityThe synthetic dataset, and the code for generating it are available at https://github.com/jiyuc/BioConsistency.

bioinformatics↗