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Results for “Synthetic Biology”

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bioRxiv2026

Engineering an Enzymatically Active Granular Matrix for On-Chip Modeling of Bone-Like Mineralization

Controlled biomineralization is central to engineering physiologically relevant hard-tissue models, yet achieving spatially organized, three-dimensional (3D) mineral deposition in microfluidic on-chip systems remains challenging. While cell-based bone-on-chip platforms offer biological complexity, they intrinsically couple mineral initiation to confounding factors such as matrix remodeling and paracrine signaling, obscuring the earliest biochemical drivers of nucleation. Drawing inspiration from bottom-up synthetic biology, we engineered an enzymatically active granular matrix that recapitulates a key osteogenic function within a perfusable 3D microenvironment. Alkaline phosphatase (ALP), the key driver of native bone formation, was covalently conjugated to poly(ethylene glycol)-based (PEG) microgels via thiol-ene photochemistry, retaining over 90% enzymatic activity after 48 h. These monodisperse microgels were assembled into a jammed, perfusable matrix within an on-chip chamber, enabling independent control over enzyme loading and substrate delivery. The system supported rapid in situ mineralization (24-48 h), yielding a carbonated, calcium-deficient, apatite-like phase characteristic of early-stage bone mineral. We demonstrate that the spatial 3D localization of enzymatic activity to discrete microscale compartments, coupled with interstitial perfusion, enables localized and near-physiological mineral formation. This mechanistically defined, acellular platform provides a programmable foundation for investigating ALP-driven 3D mineralization and establishes a modular route toward hybrid biosynthetic models of (patho)physiological tissue mineralization.

bioengineering↗

bioRxiv2025

Robust Prediction of Enzyme Variant Kinetics with RealKcat

Predicting enzyme kinetics directly from sequence remains a central challenge in computational biology, particularly in resolving the effects of mutations at catalytically essential residues. Existing models frequently overlook the functional consequences of such perturbations, often defaulting to wild-type predictions even in cases of substantial activity loss, thereby limiting their reliability for enzyme design and mechanistic inference. Here, we introduce RealKcat, a machine learning framework trained on KinHub-27k, a rigorously curated dataset of 27,176 experimentally reported enzyme-substrate entries consolidated from BRENDA, SABIO-RK, and UniProt and verified across 2,158 primary sources. To ensure biochemical realism, kinetic parameters were collapsed into order-of-magnitude bins, enabling predictions that are tolerant to experimental noise yet sensitive to functional shifts. RealKcat integrates ESM embeddings for enzyme sequences with ChemBERTa embeddings of affiliated substrate, producing a unified feature space of the chemical conversion that supports robust multi-class classification of both catalytic turnover (kCat) and substrate affinity (KM). Across cross-validation, hold-out, out-of-distribution, and few-shot evaluations--including a dense mutational landscape of alkaline phosphatase (PafA)--RealKcat consistently capturead the direction and magnitude of mutation-induced changes, while preserving discrimination in both wild-type and mutant contexts. Importantly, structural descriptors were deliberately excluded, as naive integration of structural features has been shown to impair model generalization, underscoring the primacy of rigorous dataset curation, biologically informed task formulation, and balanced evaluation metrics. RealKcat establishes a scalable and mutation-sensitive framework for enzyme kinetics prediction, offering a biologically grounded platform for enzyme engineering, metabolic modeling, and therapeutic design. Significance StatementEnzymes catalyze biochemical reactions that sustain life, and accurate measurement of their efficiency--expressed through turnover number (kCat) and substrate affinity (KM)--is fundamental to biotechnology, synthetic biology, and even pharmaceutical innovation. Yet experimental assays remain prohibitive, time-intensive, and sensitive to conditions such as pH, temperature, and ionic strength of the assay buffer, while existing computational approaches often lack sensitivity to catalytic-site mutations and are constrained by inconsistencies in public databases. RealKcat addresses these gaps by introducing a rigorously curated dataset (KinHub-27k) derived from manual review of 2,158 articles and augmented with 5,278 synthetic catalytic variants generated through alanine substitution at annotated catalytic residues. Leveraging protein and substrate embeddings and a classification scheme based on order-of-magnitude kinetic bins, RealKcat achieves state-of-the-art functional e-accuracy and, critically, demonstrates sensitivity to catalytic perturbations. By adopting e-accuracy--a performance metric that evaluates predictions within {+/-}1 order of magnitude, aligning with the practical utility of enzyme kinetics--RealKcat provides biologically meaningful assessments that conventional metrics often obscure. This work establishes a robust, mutation-aware predictive platform that advances computational enzyme design and extends applicability to biomanufacturing, metabolic engineering, and precision medicine.

systems biology↗

bioRxiv2022

EVMP: Enhancing machine learning models for synthetic promoter strength prediction by Extended Vision Mutant Priority framework

MotivationIn metabolic engineering and synthetic biology applications, promoters with appropriate strengths are critical. However, it is time-consuming and laborious to annotate promoter strength by experiments. Nowadays many machine learning (ML) methods are applied to synthetic promoter strength prediction, but existing models are limited by the excessive proximity between synthetic promoters. ResultsIn order to enhance ML models to better predict the synthetic promoter strength, we propose EVMP(Extended Vision Mutant Priority), a universal framework which utilize mutation information more effectively. In EVMP, synthetic promoters are equivalently transformed into base promoter and corresponding k-mer mutations, which are input into BaseEncoder and VarEncoder respectively. In Trc synthetic promoter library, EVMP was applied to multiple ML models and the model effect was enhanced to varying extents, up to 61.30%, while the SOTA(state-of-the-art) record was improved by 15.25%. EVMP also provides optional data augmentation based on multiple base promoters, which further improved the model performance by 17.95% compared with non-EVMP SOTA record. In further study, extended vision is shown to be essential for EVMP. We also found that EVMP can alleviate the over-smoothing phenomenon, which may contributes to its effectiveness. Our work suggests that EVMP can highlight the mutation information of synthetic promoters and significantly improve the prediction accuracy of strength. Availability and implementationThe source code is publicly available on github: https://github.com/Tiny-Snow/EVMP. Contacthuangrr@sdu.edu.cn Supplementary informationAppendix is available at bioRxiv online.

bioinformatics↗

bioRxiv2026

Purinergic signaling disrupts emergent patterns of multicellular coordination in basal Xenobots.

The ability of self-organizing systems to display emergent, adaptive capabilities is a fundamental feature of biological life. Understanding the mechanisms by which cells co-ordinate at the micro-scale to produce macro-scale structures and behaviors is a fundamental problem in developmental biology. Moreover, it is an important goal of biomedicine to identify triggers that re-wire the physiological patterns of information flow and control signals. Here, we use a recently developed, synthetic biology platform known as basal Xenobots to explore how patterns of information flow and multi-cellular integration are regulated by chemical signaling pathways. Basal Xenobots are modified, organoid-like systems constructed from embryos, and have previously been shown to display complex patterns of information flows. In this study, we recorded calcium signals before and after exposing basal Xenobots to extra-cellular adenosine triphosphate (eATP), and used statistics from multivariate information theory to explore the differences in global patterns of information flow across the cells in each state. We found that eATP resulted in a dramatic reconfiguration of global information processing dynamics, characterized by a global decrease in multi-cellular co-ordination, reduced information transfer and integration, and a decrease in the global entropy rate of the basal Xenobots. These results provide evidence that purinergic signaling may play a key role in the regulation of multi-cellular self-organization, with implications for a variety of clinical disorders thought to involve aberrant purinergic signaling. These results also suggest the possibility that bioengineers may be able to "tune" the degree of self-organizing capacity in living systems via pharmacological intervention.

systems biology↗

bioRxiv2023

A Molecular Communication model for cellular metabolism

Understanding cellular engagement with its environment is essential to control and monitor metabolism. Molecular Communication theory (MC) offers a computational means to identify environmental perturbations that direct or signify cellular behaviors by quantifying the information about a molecular environment that is transmitted through a metabolic system. We developed an model that integrates conventional flux balance analysis metabolic modeling (FBA) and MC to mechanistically expand the scope of MC, and thereby uniquely blends mechanistic biology and information theory to understand how substrate consumption is captured reaction activity, metabolite excretion, and biomass growth. This is enabled by defining several channels through which environmental information transmits in a metabolic network. The information flow in bits that is calculated through this workflow further determines the maximal metabolic effect of environmental perturbations on cellular metabolism and behaviors, since FBA simulates maximal efficiency of the metabolic system. We exemplify this method on two intestinal symbionts - Bacteroides thetaiotaomicron and Methanobrevibacter smithii - and visually consolidated the results into constellation diagrams that facilitate interpretation of information flow from given environments and thereby cultivate the design of controllable biological systems. The unique confluence of metabolic modeling and information theory in this model advances basic understanding of cellular metabolism and has applied value for the Internet of Bio-Nano Things, synthetic biology, microbial ecology, and autonomous laboratories.

bioinformatics↗

bioRxiv2024

SARITA: A Large Language Model for Generating the S1 Subunit of the SARS-CoV-2 Spike Protein

The COVID-19 pandemic has profoundly impacted global health, economics, and daily life, with over 776 million cases and 7 million deaths from December 2019 to November 2024. Since the original SARS-CoV-2 Wuhan strain emerged, the virus has evolved into variants such as Alpha, Beta, Gamma, Delta, and Omicron, all characterized by mutations in the Spike glycoprotein, critical for viral entry into human cells via its S1 and S2 subunits. The S1 subunit, binding to the ACE2 receptor and mutating frequently, affects infectivity and immune evasion; the more conserved S2, on the other hand, facilitates membrane fusion. Predicting future mutations is crucial for developing vaccines and treatments adaptable to emerging strains, enhancing preparedness and intervention design. Generative Large Language Models (LLMs) are becoming increasingly common in the field of genomics, given their ability to generate realistic synthetic biological sequences, including applications in protein design and engineering. Here we present SARITA, an LLM with up to 1.2 billion parameters, based on GPT-3 architecture, designed to generate high-quality synthetic SARS-CoV-2 Spike S1 sequences. SARITA is trained via continuous learning on the pre-existing protein model RITA. When trained on Alpha, Beta, and Gamma variants (data up to February 2021 included), SARITA correctly predicts the evolution of future S1 mutations, including characterized mutations of Delta, Omicron and Iota variants. Furthermore, we show how SARITA outperforms alternative approaches, including other LLMs, in terms of sequence quality, realism, and similarity with real-world S1 sequences. These results indicate the potential of SARITA to predict future SARS-CoV-2 S1 evolution, potentially aiding in the development of adaptable vaccines and treatments.

bioinformatics↗

bioRxiv2021

Emergent spatiotemporal population dynamics with cell-length control of synthetic microbial consortia

AO_SCPLOWBSTRACTC_SCPLOWIncreased complexity of engineered microbial biocircuits highlights the need for distributed cell functionality due to concomitant increases of metabolic and regulatory burdens imposed on single-strain topologies. Distributed systems, however, introduce additional challenges since consortium composition and spatiotemporal dynamics of constituent strains must be robustly controlled to achieve desired circuit behaviors. Here, we address these challenges with a modeling-based investigation of emergent spatiotemporal population dynamics that result from cell-length control of monolayer, two-strain bacterial consortia. We demonstrate that with dynamic control of a strains division length, nematic cell alignment in close-packed monolayers can be destabilized. We found this destabilization conferred an emergent, competitive advantage on smaller-length strains--but by mechanisms that differed depending on the spatial patterns of the population. We used complementary modeling approaches to elucidate underlying mechanisms: an agent-based model to simulate detailed mechanical and signaling interactions between the competing strains and a reductive, stochastic lattice model to represent cell-cell interactions with a single rotational parameter. Our modeling suggests that spatial strain-fraction oscillations can be generated when cell-length control is coupled to quorum-sensing signaling in negative feedback topologies. Our research employs novel methods of population control and points the way to programming strain fraction dynamics in consortial synthetic biology. Engineered microbial collectives are more versatile and robust than single strain populations. However, the function of such collectives is sensitive to their spatiotemporal organization. Here, we demonstrate control of the spatiotemporal composition of synthetic microbial consortia by dynamically modulating the average cell length of constituent strains. Such modulation confers an emergent "mechanical fitness" advantage upon the shorter length strain. We used both a biophysically realistic agent-based model to test the impact of cell shape on spatiotemporal dynamics and a conceptually simpler stochastic lattice model to explain the essential mechanisms driving the dynamics.

biophysics↗

bioRxiv2023

A zinc finger transcription factor tunes social behaviors by controlling transposable elements and immune response in prefrontal cortex

The neurobiological origins of social behaviors are incompletely understood. Here we utilized synthetic biology approaches to reprogram the function of ZFP189, a transcription factor whose expression and function in the rodent prefrontal cortex was previously determined to be protective against stress-induced social deficits. We created novel synthetic ZFP189 transcription factors including ZFP189VPR, which activates the transcription of target genes and therefore exerts opposite functional control from the endogenous, transcriptionally repressive ZFP189WT. Upon viral delivery of these synthetic ZFP189 transcription factors to mouse prefrontal cortex, we observe that ZFP189-mediated transcriptional control promotes mature dendritic spine morphology on transduced pyramidal neurons. Interestingly, dysregulation of ZFP189-mediated transcription in this brain area, achieved by delivery of synthetic ZFP189VPR, precipitates social behavioral deficits in terms of social interaction, motivation, and the cognition necessary for the maintenance of social hierarchy, without other observable behavioral deficits. By performing RNA sequencing in virally manipulated prefrontal cortex tissues, we discover that ZFP189 transcription factors of opposing regulatory function have opposite influence on the expression of genetic transposable elements as well as genes that participate in immune functions. Collectively, this work reveals that ZFP189 function in the prefrontal cortex coordinates structural and transcriptional neuroadaptations necessary for social behaviors by binding transposable element-rich regions of DNA to regulate immune-related genes. Given the evidence for a co-evolution of social behavior and the brain immune response, we posit that ZFP189 may have evolved to augment brain transposon-associated immune function as a way of enhancing an animals capacity for functioning in social groups.

neuroscience↗

bioRxiv2024

KinPFN: Bayesian Approximation of RNA FoldingKinetics using Prior-Data Fitted Networks

AO_SCPLOWBSTRACTC_SCPLOWRNA is a dynamic biomolecule crucial for cellular regulation, with its function largely determined by its folding into complex structures, while misfolding can lead to multifaceted biological sequelae. During the folding process, RNA traverses through a series of intermediate structural states, with each transition occurring at variable rates that collectively influence the time required to reach the functional form. Understanding these folding kinetics is vital for predicting RNA behavior and optimizing applications in synthetic biology and drug discovery. While in silico kinetic RNA folding simulators are often computationally intensive and time-consuming, accurate approximations of the folding times can already be very informative to assess the efficiency of the folding process. In this work, we present KinPFN, a novel approach that leverages prior-data fitted networks to directly model the posterior predictive distribution of RNA folding times. By training on synthetic data representing arbitrary prior folding times, KinPFN efficiently approximates the cumulative distribution function of RNA folding times in a single forward pass, given only a few initial folding time examples. Our method offers a modular extension to existing RNA kinetics algorithms, promising significant computational speed-ups orders of magnitude faster, while achieving comparable results. We showcase the effectiveness of KinPFN through extensive evaluations and real-world case studies, demonstrating its potential for RNA folding kinetics analysis, its practical relevance, and generalization to other biological data.

bioinformatics↗

bioRxiv2019

SOLQC : Synthetic Oligo Library Quality Control Tool.

MotivationRecent years have seen a growing number and a broadening scope of studies using synthetic oligo libraries for a range of applications in synthetic biology. As experiments are growing by numbers and complexity, analysis tools can facilitate quality control and help in assessment and inference. ResultsWe present a novel analysis tool, called SOLQC, which enables fast and comprehensive analysis of synthetic oligo libraries, based on NGS analysis performed by the user. SOLQC provides statistical information such as the distribution of variant representation, different error rates and their dependence on sequence or library properties. SOLQC produces graphical descriptions of the analysis results. The results are reported in a flexible report format. We demonstrate SOLQC by analyzing literature libraries. We also discuss the potential benefits and relevance of the different components of the analysis. Availabilityhttps://app.gitbook.com/@yoav-orlev/s/solqc/

bioinformatics↗

bioRxiv2025

Advancing Codon Language Modeling with Synonymous Codon Constrained Masking

Codon language models offer a promising framework for modeling protein-coding DNA sequences, yet current approaches often conflate codon usage with amino acid semantics, limiting their ability to capture DNA-level biology. We introduce SynCodonLM, a codon language model that enforces a biologically grounded constraint: masked codons are only predicted from synonymous options, guided by the known protein sequence. This design disentangles codon-level from protein-level semantics, enabling the model to learn nucleotide-specific patterns. The constraint is implemented by masking non-synonymous codons from the prediction space prior to softmax. Unlike existing models, which cluster codons by amino acid identity, SynCodonLM clusters by nucleotide properties, revealing structure aligned with DNA-level biology. Furthermore, SynCodonLM outperforms existing models on 6 of 7 benchmarks sensitive to DNA-level features, including mRNA and protein expression. Our approach advances domain-specific representation learning and opens avenues for sequence design in synthetic biology, as well as deeper insights into diverse bioprocesses.

bioinformatics↗

bioRxiv2023

Methodology for inference of intercellular gene interactions

To govern organ size, shape, and function, cell-secreted diffusible molecules called morphogens spatially pattern cell differentiation, gene expression, and proliferation. Local morphogen concentration governs cell differentiation through gene regulatory networks (GRN). Previous inference methodologies tackle intercellular GRN inference between cells of one type. This is insufficient, as many developmental systems consist of cells of different types interacting with each other. Inference methodologies of GRNs between different cell types assume knowledge of diffusible morphogen identity and concentration. This makes their applicability limited in real biological systems. Here, we develop a computational methodology to infer the intercellular GRN derived from experiments that use fluorescence from reporter proteins for gene expression measurements. For validation, we demonstrate the methodology in silico using three case studies based on developmental and synthetic biology. The results show that, barring practical identifiability limitations, the methodology successfully infers the intercellular GRNs.

systems biology↗

bioRxiv2023

CATHI: An interactive platform for comparative genomics and homolog identification

AO_SCPLOWBSTRACTC_SCPLOWBioinformatics has established itself as a central pillar of modern biology. Specifically, comparative genomics enables scientists to study a vast number of genomes efficiently. These comparative analyses shed light on the evolution and potential function of genomes and genes, but are also increasingly used as a key tool for metabolic engineering and synthetic biology by identifying appropriate targets for modification. While numerous sophisticated tools for comparative genomics and homolog identification exist, those tools predominantly target highly skilled bioinformatics users. Consequently, many biologists either defer such analyses to their more versed bioinformatic collaborators or resort to suboptimal tools. Here, we present an intuitive solution available on all major operating systems, easily accessed through common web browsers. CATHI - Comparative Analysis Tool for Homolog Identification - integrates a suite of best-practice bioinformatic tools, encompassing BLAST for homology searches, MAFFT for multiple sequence alignment, FastTree2 for phylogeny reconstruction, and clinker for synteny analysis. Specifically tailored to biologists, CATHI orchestrates predefined settings and automated pipelines, obviating the need for programming expertise. This platform empowers researchers to confidently engage in detailed comparative genomics studies by streamlining the analytical process. The interactive framework provides users with a plethora of options. This includes real-time execution and progress monitoring, facilitates dynamic result tracking, and a set of search functions across NCBI databases like CDD or ProtFam. Users can interactively engage in data exploration, filtering, and visualization through CATHIs intuitive interface. Furthermore, the seamless export of project data in standard formats (FASTA, Newick, CSV, and HTML) facilitates the integration with further third-party tools such as TreeViewer and Jalview. To benchmark CATHI, we revisited the comparative analysis of cyanobacterial circadian clock proteins conducted by Schmelling et al. in 2017, revealing consistent global patterns among identified homologs, while also highlighting individual variations attributed to the expansion of available databases.

bioinformatics↗

bioRxiv2026

The hydrophobicity of peptides limits their availability to bind to interacting domains

A challenge in synthetic biology is to transfer the knowledge acquired in vitro to in vivo applications. For instance, binding affinity between peptide binding domains (PBDs) and their binding peptides is usually measured in simple buffers and in the absence of the cell machinery responsible for synthesis and proteostasis. Consequently, there is often a discrepancy between interaction quantified in vitro and in vivo. To identify strongly binding PBD-peptide pairs for in vivo applications, we aim to better understand the determinants of synthetic interaction in living yeast cells. We designed libraries of peptides expected to bind to PBDs based on data collected by phage-display. Using an in vivo protein interaction assay, we assessed the interaction strength of >6,500 PBD-peptide variants in yeast. We found that most substitutions disrupt binding, but that 3.4% increased binding strength to levels higher than expected from the phage-display data. We asked if, by using our mutational data, it was possible to optimize peptide sequences to gain even more binding strength and show additivity effect up to four substitutions. We found that PBD specificity is consistent between in vitro and in vivo assays. We hypothesized that in vivo peptide abundance could contribute to the discrepancy between the PCA and the phage-display results. We performed assays to detect in vivo peptide availability, reporting on peptide abundance, and we found that peptides with a stronger binding also show a higher availability in cells. We show that peptide availability, which seems mainly defined by peptide hydrophobicity, is key for the design of strong synthetic interactions.

molecular biology↗

bioRxiv2021

Reconstitution of ultrawide DNA origami pores in liposomes for transmembrane transport of macromolecules

Molecular traffic across lipid membranes is a vital process in cell biology that involves specialized biological pores with a great variety of pore diameters, from fractions of a nanometer to >30 nm. Creating artificial membrane pores covering similar size and complexity will aid the understanding of transmembrane molecular transport in cells, while artificial pores are also a necessary ingredient for synthetic cells. Here, we report the construction of DNA origami nanopores that have an inner diameter as large as 30 nm. We developed new methods to successfully insert these ultrawide pores into the lipid membrane of giant unilamellar vesicles (GUVs) by administering the pores concomitantly with vesicle formation in an inverted-emulsion cDICE technique. The reconstituted pores permit the transmembrane diffusion of large macromolecules such as folded proteins, which demonstrates the formation of large membrane-spanning open pores. The pores are size selective as dextran molecules with a diameter up to 22 nm can traverse the pores, whereas larger dextran molecules are blocked. By FRAP measurements and modelling of the GFP influx rate, we find that up to hundreds of pores can be functionally reconstituted into a single GUV. Our technique bears great potential for applications across different fields from biomimetics, synthetic biology, to drug delivery.

biophysics↗

bioRxiv2026

Programmable CRISPRtune dissects the transcriptional repressive activity of MeCP2

The ability to control the expression of human genes is a major goal in synthetic biology, enables dissection of gene function, and can be harnessed for therapeutic applications. Advances in genome editing and transcriptional engineering often result in complete gene inactivation or full transcriptional repression. However, programmable tools to dial transcription at intermediate levels remain challenging. Here, we present CRISPRtune - a synthetic fusion of MeCP2 to catalytically dead dCas9 that tunes down transcription of endogenous genes in human cells by harnessing the mild repressor activity of MeCP2. Using pooled genome-scale CRISPR screens, we tune the expression of thousands of endogenous genes and define the targeting rules of CRISPRtune in human cells. With a platform to target MeCP2 at defined genomic sites, we show the direct epigenetic changes induced by MeCP2 at gene promoters and we identify its genetic dependency partners for productive transcriptional repression. Rett syndrome-associated mutations of MeCP2 show defects for transcriptional repression due to their failure to remodel the local epigenetic landscape of target genes. Together, we present a programmable method for transcriptional tuning in mammalian cells and offer an orthogonal platform to dissect the mechanistic function of chromatin regulators in living cells.

genomics↗

bioRxiv2025

Evaluating the Limitations of Bayesian Metabolic Control Analysis

Bayesian Metabolic Control Analysis (BMCA) has emerged as a promising framework for inferring metabolic control coefficients in data-limited scenarios by integrating Bayesian inference with linlog rate laws. However, its predictive accuracy and limitations remain underexplored. This study systematically evaluates BMCAs ability to infer elasticity values, flux control coefficients (FCCs), and concentration control coefficients (CCCs) under varying data availability conditions using three synthetic metabolic network models. Our findings highlight the strengths and weaknesses of BMCA, guiding its application in metabolic engineering and emphasizing the need for methodological refinements. Author summaryUnderstanding how enzymes control metabolic pathways is crucial for optimizing biomanufacturing and synthetic biology applications. Bayesian Metabolic Control Analysis (BMCA) is a promising computational method that integrates Bayesian inference with metabolic control analysis to estimate key control parameters, even in cases with limited experimental data. However, the accuracy and limitations of BMCA remain unclear. In this study, we systematically evaluate BMCA using three synthetic metabolic networks to determine how different types of physiological data impact its predictive performance. We find that BMCA requires flux and enzyme concentration data for accurate predictions, while external metabolite concentrations contribute little. Additionally, BMCA fails to predict elasticity values beyond a magnitude of 1.5 and reliably infer allosteric regulation, even when strong regulatory interactions exist. In addition, BMCA does not accurately rank metabolic control points, which may limit its utility in identifying key enzymes in engineered pathways. Our work provides practical insights into when and how BMCA can be applied, guiding future research in metabolic modeling and control analysis.

systems biology↗

bioRxiv2024

Optimal network sizes for most robust Turing patterns

Many cellular patterns exhibit a reaction-diffusion component, suggesting that Turing instability may contribute to pattern formation. However, biological gene-regulatory pathways are more complex than simple Turing activator-inhibitor models and generally do not require fine-tuning of parameters as dictated by the Turing conditions. To address these issues, we employ random matrix theory to analyze the Jacobian matrices of larger networks with robust statistical properties. Our analysis reveals that Turing patterns are more likely to occur by chance than previously thought and that the most robust Turing networks have an optimal size, surprisingly consisting only of a handful of molecular species, thus significantly increasing their identifiability in biological systems. This optimal size emerges from a tradeoff between the highest stability in small networks and the greatest instability with diffusion in large networks. Furthermore, we find that with multiple immobile nodes, differential diffusion ceases to be important for Turing patterns. Our findings may inform future synthetic biology approaches and provide insights into bridging the gap to complex developmental pathways.

biophysics↗