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bioRxiv2024

A differentiable Gillespie algorithm for simulating chemical kinetics, parameter estimation, and designing synthetic biological circuits

The Gillespie algorithm is commonly used to simulate and analyze complex chemical reaction networks. Here, we leverage recent breakthroughs in deep learning to develop a fully differentiable variant of the Gillespie algorithm. The differentiable Gillespie algorithm (DGA) approximates discontinuous operations in the exact Gillespie algorithm using smooth functions, allowing for the calculation of gradients using backpropagation. The DGA can be used to quickly and accurately learn kinetic parameters using gradient descent and design biochemical networks with desired properties. As an illustration, we apply the DGA to study stochastic models of gene promoters. We show that the DGA can be used to: (i) successfully learn kinetic parameters from experimental measurements of mRNA expression levels from two distinct E. coli promoters and (ii) design nonequilibrium promoter architectures with desired input-output relationships. These examples illustrate the utility of the DGA for analyzing stochastic chemical kinetics, including a wide variety of problems of interest to synthetic and systems biology.

biophysics↗

bioRxiv2020

Functional Sensitivity and Mutational Robustness of Proteins

Sensitivity and robustness appear to be contrasting concepts. However, natural proteins are robust enough to tolerate random mutations, meanwhile be susceptible enough to sense environmental signals, exhibiting both high functional sensitivity (i.e., plasticity) and mutational robustness. Uncovering how these two aspects are compatible is a fundamental question in the protein dynamics and genotype-phenotype relation. In this work, a general framework is established to analyze the dynamics of protein systems under both external and internal perturbations. We introduce fluctuation entropy for the functional sensitivity and the spectrum entropy for the mutational robustness. The compatibility of sensitivity and robustness is analyzed by the optimization of two entropies, which leads to the power-law vibration spectrum of proteins. These power-law behaviors are confirmed extensively by protein data, as a hallmark of criticality. Moreover, the dependence of functional sensitivity and mutational robustness on the protein size suggests a general evolutionary constraint for proteins with different chain lengths. This framework can also establish a general link of the criticality with robustness-plasticity compatibility, both of which are ubiquitous features in biological systems.

biophysics↗

bioRxiv2024

Universality of representation in biological and artificial neural networks

Many artificial neural networks (ANNs) trained with ecologically plausible objectives on naturalistic data align with behavior and neural representations in biological systems. Here, we show that this alignment is a consequence of convergence onto the same representations by high-performing ANNs and by brains. We developed a method to identify stimuli that systematically vary the degree of inter-model representation agreement. Across language and vision, we then showed that stimuli from high-and low-agreement sets predictably modulated model-to-brain alignment. We also examined which stimulus features distinguish high-from low-agreement sentences and images. Our results establish representation universality as a core component in the model-to-brain alignment and provide a new approach for using ANNs to uncover the structure of biological representations and computations.

neuroscience↗

bioRxiv2026

AlphaFold-Driven Structural Proteomics Reveals Extensive Cellulosome Machinery in Human Ruminococcal Symbionts

Cellulosomes are large, surface-displayed enzyme complexes that enable anaerobic bacteria to degrade recalcitrant plant polysaccharides, yet cellulosome-expressing bacteria are thought to be rare in the human gut. Here we show that extensive sequence divergence obscures the detection of many ruminococcal cellulosomes by conventional sequence homology-based methods. Using proteome-scale AlphaFold2 structural predictions, we uncovered a substantially expanded set of cellulosome-producing Ruminococcus species, including six previously unrecognized human symbionts. Structure-based clustering identifies several novel cohesin families that retain conserved folds despite extreme sequence divergence and define distinct, phylogenetically conserved cellulosome architectures. The analysis reveals R. callidus and related human symbionts encode elaborate cellulosomes that are invisible to sequence-based annotation. Similarly, R. difficilis, a human gut symbiont, is found to produce an atypical cohesin-based assembly enriched in amylases and related starch-binding proteins that may enable this microbe to degrade resistant starches that evade digestion in the upper gastrointestinal tract. Together, these findings reveal that ruminococcal cellulosomes are far more prevalent and diverse than previously appreciated and demonstrate the power of structural proteomics to uncover deeply divergent functional systems in the gut microbiome. Significance StatementPlant cell wall polysaccharides are a major dietary carbon source, yet their degradation relies on rare, highly specialized microbial enzyme assemblies known as cellulosomes, which have long appeared uncommon in the human gut. Using proteome-scale structure prediction combined with experimental validation, we show that cellulosomes are far more widespread and structurally diverse in human-associated Ruminococcus species than previously appreciated. We identify multiple new cohesin families and reveal distinct cellulosome architectures likely adapted to degrade different dietary substrates. Together, these findings redefine the distribution and evolution of cellulosomes in gut microbes and demonstrate the power of structural proteomics to uncover deeply diverged biological systems.

microbiology↗

bioRxiv2025

Mining thermophile photosynthesis genes: a synthetic operon expressing Chloroflexota species reaction center genes in Rhodobacter sphaeroides

Photosynthesis is the foundation of the vast majority of life systems, and therefore the most important bioenergetic process on earth, and the greatest diversity in photosynthetic systems are found in microorganisms. However, understanding of the biophysical and biochemical processes that transduce light to chemical energy has derived from the relatively small subset of proteins from microbes that are amenable to cultivation, in contrast to the huge number of microbial DNA sequences encoding proteins that catalyze the initial photochemical reactions that has been deposited in databases, such as from metagenomics. We describe the use of a Rhodobacter sphaeroides laboratory strain for expression of heterologous photosynthesis genes to demonstrate the feasibility of mining this resource, focusing on hot spring Chloroflexota gene sequences. Using a synthetic operon of genes, we produced a photochemically active complex of reaction center proteins in our biological system. We also present bioinformatic analyses of anoxygenic type II reaction center sequences from metagenomic samples collected from hot (42-90{degrees} C) springs available through the JGI IMG database, to generate a resource of diverse sequences that potentially are adapted to photosynthesis at such temperatures. These data provide a view into the natural diversity of anoxygenic photosynthesis, through a lens focused on high-temperature environments. The approach we took to express such genes can be applied for potential biotechnology purposes as well as for studies of fundamental catalytic properties of these heretofore inaccessible protein complexes.

microbiology↗

bioRxiv2022

Microphase separation of living cells

Self-organization of cells is central to a variety of biological systems and physical concepts of condensed matter have proven instrumental in deciphering some of their properties. Here we show that microphase separation, long studied in polymeric materials and other inert systems, has a natural counterpart in living cells. When placed below a millimetric film of liquid nutritive medium, a quasi two-dimensional, high-density population of Dictyostelium discoideum cells spontaneously assemble into compact domains. Their typical size of 100 m is governed by a balance between competing interactions: an adhesion acting as a short-range attraction and promoting aggregation, and an effective long-range repulsion stemming from aerotaxis in near anoxic condition. Experimental data, a simple model and cell-based simulations all support this scenario. Our findings establish a generic mechanism for self-organization of living cells and highlight oxygen regulation as an emergent organizing principle for biological matter.

biophysics↗

bioRxiv2022

Breakthrough: A First-In-Class Virtual Simulator for Dose Optimization of ACE Inhibitors in Veterinary Cardiology

AO_SCPLOWBSTRACTC_SCPLOWThe human and canine renin-angiotensin-aldosterone-systems (RAAS) play a central role in the pathophysiology of congestive heart failure (CHF), justifying the use of angiotensin converting enzyme inhibitors inhibitors (ACEi) in this indication. Seminal studies in canine CHF had suggested that the pharmacological action of benazepril was relatively independent of doses > 0.25 mg/kg P.O, thereby providing a rationale for the European label dose of 0.25 mg/kg P.O q24h in dogs with cardiovascular diseases. However, most of these earlier studies on benazepril pharmacodynamics relied on measures of ACE activity - a sub-optimal endpoint to characterize the effect of benazepril on the RAAS. Nonlinear mixed-effects (NLME) modeling is an established framework for characterizing the effect of therapeutics on complex biological systems, such as the RAAS cascade. Importantly for therapeutic schedule optimization, one can use such a model to predict the outcomes of various hypothetical dosing schedules via simulation. The objectives of this study were (i) to expand on previous NLME modeling efforts of the dose-exposure-response relationship of benazepril on biomarkers of the RAAS which are relevant to CHF pathophysiology and disease prognosis {angiotensins I, II, III, IV, (1-7)} by using a quantitative systems pharmacology (QSP) modeling approach; and (ii) to develop a software implementation of the model capable of simulating clinical trials in benazepril in dogs bedside dose optimization. This study expands on previous modeling efforts to characterize the changes in RAAS pharmacodynamics in response to benazepril administration and showcase how QSP modeling can be used for efficient dose optimization of ACEis at the bedside. Our results suggest that 0.5 mg/kg PO q12h of benazepril produced the most robust reduction in AngII and upregulation of RAAS alternative pathway biomarkers. This model will eventually be expanded to include relevant clinical endpoints, which will be evaluated in an upcoming prospective trial in canine patients with CHF. AO_SCPLOWUTHORC_SCPLOW SO_SCPLOWUMMARYC_SCPLOWCongestive heart failure (CHF) is a disease of the heart, common to both dogs and humans, where the heart is not healthy enough to pump blood around the body efficiently. Because the blood isnt moving around the body as efficiently, it tends to get congested in various areas of the body and increases strain on the heart. Benazepril is a drug for CHF used in both dogs and humans to reduce congestion and improve the functioning of the cardiovascular system. Although benazepril is effective, theres evidence that suggests the dosing could be improved if the therapeutic was further studied. In this experiment, we tested benazepril at several safe dosages in well-cared for and healthy dogs to collect data on the relationship between dose size, dosing frequency, and effect on the cardiovascular system. Using this data, we built computer models of benazepril to simulate many clinical trials. By studying these simulations, we were able to make several predictions about the optimal dosing schedule of benazepril in dogs. Weve also built a web-app version of the computer model for veterinary researchers to use, modify, and study. This work also provides a platform and roadmap for optimizing benazepril dosages in human CHF.

pharmacology and toxicology↗

bioRxiv2023

Deep learning of genomic contexts predicts protein co-regulation and function

Deciphering the relationship between a gene and its genomic context is fundamental to understanding and engineering biological systems. Machine learning has shown promise in learning latent relationships underlying the sequence-structure-function paradigm from massive protein sequence datasets. However, to date, limited attempts have been made in extending this continuum to include higher order genomic context information. Evolutionary processes dictate the specificity of genomic contexts in which a gene is found across phylogenetic distances, and these emergent genomic patterns can be leveraged to uncover functional relationships between gene products. Here, we trained a genomic language model (gLM) on millions of metagenomic scaffolds to learn the latent functional and regulatory relationships between genes. gLM learns contextualized protein embeddings that capture the genomic context as well as the protein sequence itself, and encode biologically meaningful and functionally relevant information (e.g. enzymatic function, taxonomy). Our analysis of the attention patterns demonstrates that gLM is learning co-regulated functional modules (i.e. operons). Our findings illustrate that gLMs unsupervised deep learning of the metagenomic corpus is an effective and promising approach to encode functional semantics and regulatory syntax of genes in their genomic contexts and uncover complex relationships between genes in a genomic region.

bioinformatics↗

bioRxiv2026

Controlling metal-carbonate phase, form, and function through de novo protein design

Biomineralization enables living systems to construct hybrid materials by controlling the location, orientation, and polymorph of inorganic crystals with proteins and other biomolecules. Despite decades of study, the molecular principles underlying these processes remain difficult to harness in engineered materials, in part because native biomineralization proteins are often intrinsically disordered, heterogeneous, or insoluble. Here we show that de novo designed protein interfaces can be assembled into reconfigurable two-dimensional arrays which template calcite nanocrystals. By fine-tuning RFdiffusion2 on repeat protein scaffolds, we further enable the design of protein architectures which selectively form aragonite, a metastable polymorph of calcium carbonate, in nucleation conditions that otherwise result in a mixture of phases. Extending beyond inorganics found in biological systems, we show that lattice-matched protein designs template cobalt carbonate formation: a flat helical repeat protein interface promotes unconfined growth, whereas soluble D3 cage assemblies yield more homogenous cobalt carbonate nanocrystals confined to the interior of the cage. These protein-cage cobalt carbonate hybrid materials function as electrocatalysts for alkaline water splitting. Our results demonstrate the potential of deep learning-based methods to unlock the structural and functional activity of protein-mineral composites.

biochemistry↗

bioRxiv2021

Measuring optimality of a neural system by a logic gate model

The nematode worm Caenorhabditis elegans has a relatively simple neural system for analysis of information transmission from sensory organ to muscle fiber. Therefore, an example of a neural circuit is analyzed that originates in the nematode worm, and a method is applied for measuring its information flow efficiency by use of a model of logic gates. This model-based approach is useful where the assumptions of a logic gate design are applicable. It is also an useful approach where there are competing mathematical models for explaining the role of a neural circuit since the logic gate model can estimate the computational complexity of a network, and distinguish which of the mathematical models require fewer computations. In addition, for generalization of the concept of information optimality in biological systems, there is an extensive discussion of its role in the genetic-based pathways of organisms.

neuroscience↗

bioRxiv2023

Calling Cards: a customizable platform to longitudinally record protein-DNA interactions over time in cells and tissues

Calling Cards is a platform technology to record a cumulative history of transient protein-DNA interactions in the genome of genetically targeted cell types. The record of these interactions is recovered by next generation sequencing. Compared to other genomic assays, whose readout provides a snapshot at the time of harvest, Calling Cards enables correlation of historical molecular states to eventual outcomes or phenotypes. To achieve this, Calling Cards uses the piggyBac transposase to insert self-reporting transposon (SRT) "Calling Cards" into the genome, leaving permanent marks at interaction sites. Calling Cards can be deployed in a variety of in vitro and in vivo biological systems to study gene regulatory networks involved in development, aging, and disease. Out of the box, it assesses enhancer usage but can be adapted to profile specific transcription factor binding with custom transcription factor (TF)-piggyBac fusion proteins. The Calling Cards workflow has five main stages: delivery of Calling Card reagents, sample preparation, library preparation, sequencing, and data analysis. Here, we first present a comprehensive guide for experimental design, reagent selection, and optional customization of the platform to study additional TFs. Then, we provide an updated protocol for the five steps, using reagents that improve throughput and decrease costs, including an overview of a newly deployed computational pipeline. This protocol is designed for users with basic molecular biology experience to process samples into sequencing libraries in 1-2 days. Familiarity with bioinformatic analysis and command line tools is required to set up the pipeline in a high-performance computing environment and to conduct downstream analyses. Basic Protocol 1: Preparation and delivery of Calling Cards reagents Basic Protocol 2: Sample preparation Basic Protocol 3: Sequencing library preparation Basic Protocol 4: Library pooling and sequencing Basic Protocol 5: Data analysis

genomics↗

bioRxiv2025

A sulfatide-centered ultra-high resolution magnetic resonance MALDI imaging benchmark dataset for MS1-based lipid annotation tools

Spatial omics techniques are indispensable for studying complex biological systems and for the discovery of spatial biomarkers. While several current matrix-assisted laser desorption/ionization (MALDI) mass spectrometry imaging (MSI) instruments are capable of localizing numerous metabolites at high spatial and spectral resolution, the majority of MSI data is acquired at the MS1 level only. Assigning molecular identities based on MS1 data presents significant analytical and computational challenges, as the inherent limitations of MS1 data preclude confident annotations beyond the sum formula level. To enable future advancements of computational lipid annotation tools, well-characterized benchmark - or ground truth - datasets are crucial, which exceed the scope of synthetic data or data derived from mimetic tissue models. To this end, we provide two sulfatide-centered, biology-driven magnetic resonance MSI (MR-MSI) datasets at different mass resolving powers that characterize lipids in a mouse model of human metachromatic dystrophy. This data includes an ultra-high-resolution (R [~]1,230,000) quantum cascade laser mid-infrared imaging-guided MR-MSI dataset that enables isotopic fine structure analysis and therefore enhances the level of confidence substantially. To highlight the usefulness of the data, we compared 118 manual sulfatide annotations with the number of decoy database-controlled sulfatide annotations performed in Metaspace (67 at FDR < 10%). Overall, our datasets can be used to benchmark annotation algorithms, validate spatial biomarker discovery pipelines, and serve as a reference for future studies that explore sulfatide metabolism and its spatial regulation.

biochemistry↗

bioRxiv2019

Cellular crowding guides and debundles the microtubule cytoskeleton

Cytoplasm is densely packed with macromolecules causing cellular crowding, which alters interactions inside cells and differs between biological systems. Here we investigate the impact of crowding on microtubule cytoskeleton organization. Using mathematical modelling, we find that only anisotropic crowding affects the mean microtubule direction, but any crowding reduces the number of microtubules that form bundles. We validate these predictions in vivo using Drosophila follicular epithelium. Since cellular components are transported along microtubules, our results identify cellular crowding as a novel regulator of this transport and cell organization.

cell biology↗

bioRxiv2025

Super-resolving particle diffusion heterogeneity in porous hydrogels via high-speed 3D active-feedback single-particle tracking microscopy

Nanoparticle diffusion in 3D porous structures is critical to understanding natural and synthetic systems but remains underexplored due to limitations in traditional microscopy methods. Here, we use 3D Single-Molecule Active-feedback Real-time Tracking (3D-SMART) microscopy to resolve nanoparticle dynamics in agarose gels with unprecedented spatiotemporal resolution. We highlight hopping diffusion, where particles intermittently escape confinement pockets, providing insights into hydrogel microstructure. Long, highly sampled trajectories enable extraction of kinetic parameters, confinement sizes, and thermodynamic barriers. This study demonstrates 3D-SMARTs ability to probe particle-environment interactions at super-resolution ([~]10 nm in XY and [~]30 nm in Z) in 3D, offering new perspectives on nanoparticle diffusion and the structural dynamics of porous materials, with implications for drug delivery, material science, and biological systems. ToC figure O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=182 SRC="FIGDIR/small/643103v3_ufig1.gif" ALT="Figure 1"> View larger version (45K): org.highwire.dtl.DTLVardef@7c11c4org.highwire.dtl.DTLVardef@1725c22org.highwire.dtl.DTLVardef@fecc4forg.highwire.dtl.DTLVardef@175d33_HPS_FORMAT_FIGEXP M_FIG C_FIG

biophysics↗

bioRxiv2025

A food chain of ribozymes built on an ancestral strand

Molecular life forms in the prebiotic earth were capable of self-reproduction from basic building blocks. However, scarcity of building blocks likely led to the evolution of heterotrophic, predatory species. In this work we reconstruct a simple model of such interactions between RNA molecules, based on RNA strands found in sinkhole mineral pools in the Dead Sea. These RNA strands exhibited significant stability in this divergent chemical environment, and contained sequence motifs reminiscent of two ribozyme species, suggesting they could be ancestral to both. We built a system in which a single ancestral RNA strand serves as a limiting resource forming two different species of ribozymes that compete over it. One of these species is capable of metabolizing the other in order to free more of this resource, driving the assembly of additional copies of itself. This interaction resembles a rudimentary food chain, the dynamics of which is sensitive to environmental factors such as temperature and magnesium concentrations, with competitive advantage shifting between the species. Taken together, these findings highlight a hypothetical feature of prebiotic life, and hint at the possibility that heterotrophic RNA is a phenomenon that might still occur today in some unique geochemical niches, and even in the overlooked background of modern biological systems.

biophysics↗

bioRxiv2026

Handwritten Digit classification with neural cultures is influenced by neural architecture, network dynamics, and decoding methods

As silicon-based computing approaches fundamental physical limits, neurocomputing offers an energy-efficient alternative by leveraging the intrinsic non-linear dynamics of biological systems. To harness these dynamics, it is vital to understand the structure-function relationship governing how neural cultures process complex spatio-temporal information and how to appropriately decode the resulting neural electrophysiological activity. We investigated this utilizing a closed-loop electrophysiology platform, the CL1, to implement reservoir computing in human iPSC-derived neuronal networks. To systematically evaluate the variables driving neurocomputational capacity, we explored how cellular composition (cortical vs. hippocampal lineages), and the physical architecture (unstructured monolayers, 3D neural organoids, and modular networks confined by microfluidic devices) influenced electrophysiological properties and interacted with different decoding methodologies. Using a spatio-temporal version of a handwritten digit pattern recognition task (MNIST), we analyzed how these biological and analytical factors influenced classification accuracy. To ensure robust interpretation this required us to first demonstrated that reservoir computing decoding methods require strict artifact control and trial-based cross-validation to distinguish network computation from artifactual signal separability or temporal data leakage. Applying this validated frequency-domain pipeline, we suggest a clear functional hierarchy where structural modularity acts as a vital functional regularizer. Modular cortical cultures significantly outperformed unconstrained monolayers and organoids on MNIST. Furthermore, decoding frequency information from raw signals proved superior to typical time-bin decoding implementations. These findings establish that maximizing the computational potential of Synthetic Biological Intelligence, while avoiding false positives, requires a synergistic optimization of cellular identity, structural governance, and rigorous decoding logic. In doing so, this work provides a critical base establishing the criteria under which to evaluate neurocomputing implementations.

neuroscience↗

bioRxiv2016

Information theory and the phenotypic complexity of evolutionary adaptations and innovations

Two main lines of research link information theory to evolutionary biology. The first focuses on organismal phenotypes, and on the information that organisms acquire about their environment. The second connects information-theoretic concepts to genotypic change. The genotypic and phenotypic level can be linked by experimental high-throughput genotyping and computational models of genotype-phenotype relationships. I here use a simple information-theoretic framework to compute a phenotypes information content (its phenotypic complexity), and the information gain or change that comes with a new phenotype. I apply this framework to experimental data on DNA-binding phenotypes of multiple transcription factors. Low phenotypic complexity is associated with a biological systems ability to discover novel phenotypes in evolution. I show that DNA duplications lower phenotypic complexity, which illustrates how information theory can help explain why gene duplications accelerate evolutionary adaptation. I also demonstrate that with the right experimental design, sequencing data can be used to infer the information gain associated with novel evolutionary adaptations, for example in laboratory evolution experiments. Information theory can help quantify the evolutionary progress embodied in the discovery of novel adaptive phenotypes.

Evolutionary Biology↗

bioRxiv2021

Dichotomous Feedback: A Signal Sequestration-based Feedback Mechanism for Biocontroller Design

We introduce a new design framework for implementing negative feedback regulation in Synthetic Biology, which we term dichotomous feedback. Our approach is different from current methods, in that it sequesters existing fluxes in the process to be controlled, and in this way takes advantage of the processs architecture to design the control law. This signal sequestration mechanism appears in many natural biological systems and can potentially be easier to realise than molecular sequestration and other comparison motifs that are nowadays common in biomolecular feedback control design. The loop is closed by linking the strength of signal sequestration to the process output. Our feedback regulation mechanism is motivated by two-component signalling systems, where we introduce a second response regulator competing with the natural response regulator thus sequestering kinase activity. Here, dichotomous feedback is established by increasing the concentration of the second response regulator as the level of the output of the natural process increases. Extensive analysis demonstrates how this type of feedback shapes the signal response, attenuates intrinsic noise while increasing robustness and reducing crosstalk.

synthetic biology↗