Journal of Biological Engineering

The suspension culture of pluripotent stem (PS) cells in stirred bioreactors poses a delicate balance between maintaining homogeneous cell dispersion and avoiding excessive shear stress that can compromise cell viability and pluripotency. In this study, we used computational fluid dynamics (CFD) coupled with a discrete particle method (DPM) to simulate iPS cell behavior in a 5 mL delta-impeller stirred tank. Our analysis revealed that upward flow at the tank bottom and downward flow at the top are critical for maintaining a stable suspension. To optimize the stirring protocol, we applied Bayesian optimization to identify a time-dependent stirring schedule that begins with a high-speed phase for resuspension, followed by a low-speed phase for sustained suspension with minimal hydrodynamic stress. The optimized schedule demonstrated improved suspension ratio and reduced slip velocity, indicating lower mechanical stress on cells. These findings provide engineering insights into scalable bioreactor operation, contributing to the design of robust iPS cell manufacturing systems.

Standard differentiation assays sample cell states only at discrete time points, while the underlying progression unfolds continuously and heterogeneously across cells. As a result, different combinations of proliferation, commitment, and maturation dynamics can converge to similar endpoint measurements. This many-to-one mapping between latent trajectories and observable readouts constitutes a partially observed inverse problem that limits mechanistic interpretation. Although this ambiguity is inherent to many experimental systems, it is rarely examined using models that connect cell-state dynamics to assay-level quantities. We present OsteoMin, a coarse-grained cellular automaton that links stochastic transitions between pre-osteoblast and osteoblast states to experimentally measurable readouts of alkaline phosphatase activity, collagen deposition, and mineralization. Model parameters were constrained using literature-reported kinetics and evaluated against dexamethasone and menaquinone-4 perturbations. The frame-work reproduces qualitative assay trends and enables systematic analysis of how cell-state progression, matrix maturation, and external perturbations shape differentiation outcomes. Using this framework, we quantify the identifiability limits of endpoint assays and test whether standard measurements can distinguish underlying differentiation mechanisms. Distinct perturbation families often produce similar endpoint responses (macro-F1 {approx} 0.42), indicating limited discriminative power. Incorporating temporal trajectories improves separability (macro-F1 {approx} 0.78), demonstrating that most identifiable information resides in marker dynamics rather than terminal measurements. Sobol analysis shows early markers depend on proliferation timing, whereas late mineralization is governed by nonlinear matrix maturation and parameter interactions. Together, these results show that endpoint assays constrain overall progression but do not uniquely identify underlying mechanisms. OsteoMin provides a framework linking differentiation dynamics to assay observables and a basis for assessing identifiability in endpoint-driven systems.

Human pluripotent stem cells (hPSCs) hold significant promise for regenerative medicine, yet optimizing their expansion in three-dimensional bioreactor systems remains challenging due to complex interactions between mechanical forces, metabolic constraints, and aggregate formation dynamics. This study developed and validated a mechanistic mathematical model to predict hPSC proliferation dynamics in vertical-wheel bioreactor (VWBR) systems, incorporating the effects of shear stress and energy dissipation rate (EDR) on cell growth and aggregate dynamics. Seven model variants employing different kinetic formulations for shear stress and energy dissipation rate effects were systematically evaluated through model selection, identifiability analyses, and experimental validation. Experimental data from six bioreactor conditions varying in initial cell density (2 x 104-15 x 104 cells/mL), agitation rate (30-60 RPM), and working volume (100-500 mL) were used for model calibration and selection. Bayesian Information Criterion analysis identified a model combining Michaelis-Menten kinetics for shear stress inhibition with a EDR-mediated aggregate detachment formulation as the best-performing variant, achieving a Mean Relative Prediction Error of 13.97%, comparable to the experimental variability of 16.29%. Independent validation experiments using leave-out data gathered under different media exchange schedules confirmed model accuracy with prediction errors below 14%, consistent with observed experimental variability around 12%. The validated model was used to optimize the media exchange protocol, leading to a 37.5% reduction in media consumption with only a 13.5% reduction in final cell yield, demonstrating its utility for prospective, quantitative bioprocess design in VWBR systems.

Despite accelerating interest in using human induced pluripotent stem cell (hiPSC)-derived products for disease modeling and therapeutic development, there is substantial evidence that conventional culture approaches do not fully recapitulate natural embryonic nor lineage-committed states. It remains poorly understood how in vitro environmental conditions cause divergence from natural developmental trajectories, and current strategies emphasize restricted characterization of phenotype without appreciating the complexity of biology in maintaining pluripotency and driving differentiation. To address this knowledge gap, we examined hiPSC cell state during short-term culture in stirred-suspension bioprocesses under varying oxygen and agitation conditions. We profiled intracellular metabolic, transcriptional, and proteomic changes to characterize cellular responses to engineered environments and implications for cell phenotype. Using a random forest framework, we modeled population dynamics over time across metabolic and transcriptional programs and mapped those predictions onto hallmark biological signatures. This integrative approach captures and identifies environmentally reinforced programs, offering a framework to guide optimization of pluripotent cell state maintenance and differentiation.

Understanding skeletal muscle metabolism involves real-time monitoring of key cellular parameters, such as calcium ions (Ca2+), adenosine triphosphate (ATP), cyclic adenosine monophosphate (cAMP), and intracellular temperature. Fluorescent protein (FP)-based biosensors are used for live-cell imaging of these signals with high spatiotemporal resolution. Differentiated myotubes are in vitro models used for physiological muscle metabolism research. However, efficient transfection of FP-based biosensors into these cells is challenging. Here, we developed an electroporation-based strategy for delivering recombinant protein biosensors into fully differentiated myotubes. Biosensors for Ca2+, ATP, cAMP, and temperature were recombinantly produced using Escherichia coli and introduced into myotubes using electroporation. Electroporation conditions were optimised to maximise delivery efficiency, preserve cell viability, and minimise cellular damage. We established a robust intracellular delivery system that effectively demonstrated Ca2+, ATP, and temperature dynamics. Furthermore, we achieved the successful co-delivery of two biosensors that enabled dual imaging of Ca2+ and cAMP in response to stimulation.

Matrix-bound nanovesicles (MBVs) are a type of small extracellular vesicle (EV) embedded in the extracellular matrix (ECM) throughout the body. MBVs have been previously isolated from various tissues and in vitro-cultured cell sheets, demonstrating remarkable attributes in regenerative medicine. However, differences between MBVs and conditioned culture medium-derived EVs (liquid-EVs) have yet to be characterized, and the field currently lacks specific protein markers that can identify MBVs from other EV subtypes. Here, we isolate MBVs and liquid-EVs from bone marrow mesenchymal stem cell (MSC) sheets and define differences in size, protein, and zeta potential between these EVs. We show that there is a correlation between cell-driven ECM deposition and MBV and liquid-EV production. We also find that MBVs are smaller, contain less protein per particle, and possess lower zeta potential than liquid-EVs. Interestingly, MBVs also comprise a distinct tetraspanin profile compared to liquid-EVs, with MBVs containing more CD63 and little to no CD81. Finally, we define that CD63, LAMP1, Alix, ITG{beta}1, and GRP94 and their abundance, may be markers specifically used to identify MBVs from liquid-EVs. Our study paves the way for the characteristic differentiation between MBVs from liquid-EVs, elucidates their differences in biogenesis, and reveals a potential connection between EV and ECM production.

Compact tissue-specific promoters are highly desirable for gene therapy because viral vectors possess limited packaging capacity. However, existing promoter engineering strategies rely primarily on rational design or de novo sequence generation and lack efficient approaches for compressing long native promoters while preserving regulatory specificity. Although genome foundation models have substantially improved sequence-to-function prediction, they have not been effectively translated into computational platforms for promoter engineering. Here, we present VirEvo, a computational promoter engineering framework that integrates a virtual dual-luciferase assay (VirDLA), genome-foundation-model-guided genetic evolution, and an orthogonal Pan-Tissue Consistency Filter (PTCF). VirDLA introduces an internal-reference normalization strategy inspired by dual-luciferase reporter assays, enabling relative comparison of promoter activity across tissues without retraining AlphaGenome. Guided by these normalized activity scores, VirEvo iteratively optimizes promoter selectivity, off-target activity, and sequence length. Using the human p16INK4a promoter as a proof of concept, VirEvo evolved a compact synthetic promoter, SRP2M, of only 398 bp, representing an 85.9% reduction in sequence length. Experimental validation using dual-luciferase reporter assays in senescent IMR90 fibroblasts demonstrated that SRP2M retained 77% of wild-type senescence selectivity while reducing basal leakage to 52% of the wild-type level. Together, these results demonstrate the feasibility of genome-foundation-model-guided promoter engineering. VirEvo provides a generalizable framework for designing compact tissue-specific regulatory elements and extends the application of genome foundation models from functional prediction to synthetic regulatory engineering.

We describe a cost-effective Escherichia coli (E. coli)-based platform for producing endotoxin-free cytokines. As a proof of concept, we applied this protocol to purify four representative human and mouse cytokines - mouse leukemia inhibitory factor (mLIF), human interleukin-2 (hIL-2), and human and mouse interleukin-33 (hIL-33 and mIL-33) - and demonstrated their bioactivity to be equivalent to commercial counterparts for direct use in stem cell culture and immune cell activation, both in vitro and in vivo. Reagent costs for producing these proteins are approximately 5%-10% of commercial list prices. This platform is readily adaptable to other costly cytokines and growth factors, providing a scalable and affordable approach to accelerate research in cell biology, tissue engineering, and biomanufacturing.

Co-culturing cells with mismatched densities, where one cell type adheres to surfaces while the other floats, represents a fundamental challenge in cell biology. This is particularly evident in studying macrophage-adipocyte interactions, where macrophages must engage and clear lipid-rich apoptotic adipocytes, a process critical to understanding chronic inflammation in obesity and metabolic disease. The density disparity between macrophages, which sink and adhere to culture surfaces, and adipocytes, which float due to their lipid content, has prevented conventional co-culture approaches from achieving sustained cell-cell contact. To address this challenge, we developed a microfluidic system that confines adipocytes and lipid droplets in close proximity to macrophages. This platform features recessed micro-traps within the upper surface of a microfluidic chamber that trap buoyant objects while allowing media exchange and delivery of reagents for live-cell and immunofluorescence imaging. Time lapse imaging revealed that the dynamic process of macrophages-dead corpse interactions, showing that individual macrophages cannot engulf entire corpses but instead mechanically deform them. Furthermore, the platform successfully recapitulates the formation of Crown-Like Structures (CLS), clusters of macrophages surrounding dead adipocytes that are hallmarks of adipose tissue inflammation. Long-term culture revealed that CLS effectively clear lipids compared to partial macrophage engagement, providing mechanistic insights that were previously unattainable with standard histological approaches. Beyond the macrophage-lipid interaction, this platform has potential for studying interactions between adherent cells and buoyant targets, such as microplastics, opening new avenues for research where density mismatch poses a major barrier.

Recombinant adeno-associated viruses (rAAVs) have gained increasing importance in gene therapy due to their safe and precise gene delivery. However, certain indications require substantially higher vector doses, pushing manufacturing capacity and cost of goods (COG) to its limits. In this study, we present for the first time a continuous twin-column capture process (CaptureSMB) enabling direct purification of rAAV5 from unprocessed perfusion harvest without prior concentration or processing. This approach differs fundamentally from conventional batch workflows which typically mandate clarification and concentration before affinity capture and offers a novel process integration in viral vector manufacturing. A single-column batch capture process was developed first and subsequently compared to continuous CaptureSMB configurations. Optimized CaptureSMB operation achieved consistent yields over four cycles, with recoveries exceeding batch operation (+ 14.3%) with concomitant higher productivity (+ 11.4%) and reduced buffer consumption (- 79.2%). Critical quality attribute analysis showed lower host cell protein levels and lower residual DNA in early CaptureSMB cycles, while full capsid ratios, thermal stability and transduction efficiency of rAAV5 particles remained unaltered across cycles and process modes. These findings highlight that continuous twin-column CaptureSMB directly from perfusion harvest can not only improve yield and manufacturing efficiency but also maintain and in some respects enhance product quality. This novel strategy provides a promising route to address manufacturing capacity and cost challenges in rAAV gene therapy production.

Virus-free genome engineering provides a flexible alternative to viral vectors for generating genetically modified cell models. Here, we establish an integrated biosafety level 1-compatible CRISPR/Cas9 homology-directed repair (HDR) workflow for stable transgene knock-in in neuroblastoma cell lines using non-viral delivery approaches. We systematically evaluated donor cassette architecture and delivery conditions across electroporation-based Cas9 ribonucleoprotein (RNP) delivery and lipid nanoparticle (LNP)-mediated co-delivery of Cas9 mRNA, sgRNA, and donor DNA. Modular AAVS1-targeting donor constructs identified a compact EF1(s)-Donor-Q8-Tag-sPA cassette that consistently yielded the strongest HDR-associated knock-in readouts, achieving up to 60% stable reporter-positive cells following electroporation without HDR enhancers. While LNP-mediated delivery enabled efficient CRISPR cargo co-delivery and generation of genetically modified tumor cell populations, knock-in efficiencies remained lower than those observed with electroporation. Subsequent enrichment approaches enabled generation of highly pure edited cell populations following both delivery strategies. Functional validation demonstrated stable transgene expression in vitro, including in three-dimensional bioprinted tumor models, and in vivo in xenograft mice without impairing tumor growth or viability. Together, these findings establish a practical non-viral HDR platform for stable engineering of solid tumor models and provide a framework for further optimization of genome editing workflows across distinct delivery modalities. Key findings- We establish a complete, virus-free CRISPR/Cas9 HDR workflow that reliably enables stable knock-in in solid tumor cell lines, demonstrated here in two neuroblastoma models under biosafety level 1 conditions. - We establish and evaluate LNP-mediated co-delivery of Cas9 mRNA, gRNA, and donor DNA for non-viral HDR knock-in in solid tumor models, revealing delivery modality-specific differences in editing efficiency, toxicity, and expression dynamics. - By systematically varying donor architectures, we identify a compact HDR template - combining a shortened custom EF1 promoter, the minimal Q8 surface reporter, and a synthetic polyadenylation signal (sPA) - that markedly improves knock-in efficiency in solid tumor cell lines, outperforming conventional cassettes. - Virus-free edited tumor cells generated using this workflow retain stable transgene expression and functional fitness in 3D bioprinted tumor constructs and xenograft mouse models, directly linking in vitro knock-in optimization to in vivo relevance. - The resulting biosafety level 1 compatible, end-to-end pipeline - integrating donor design, digital PCR-based quantification of precise integration, and enrichment strategies-offers a practical and transferable platform for engineering transgenic solid tumor models without viral vectors.

The SCN1A gene encodes NaV1.1, a voltage-gated sodium channel protein that is necessary for neuronal excitability and whose loss-of-function mutations cause Dravet syndrome, a treatment-resistant childhood onset epilepsy. Gene replacement strategies for this syndrome are challenged by the large size of SCN1A and difficulty achieving stable cellular expression. Lentiviral vectors (LVVs) offer sufficient packaging capacity and genomic integration for defective SCN1A gene replacement. Here, we evaluated LVV-mediated delivery of different engineered SCN1A transgene sequences in human cells. LVV-transduced cells expressed full-length NaV1.1 protein that trafficked to the membrane and produced functional sodium currents. However, SCN1A transgene expression declined over time despite stable vector copy number, indicating post-integration regulatory limitations. Expression efficiency varied by SCN1A transgene sequence, with a codon-optimized variant showing higher expression despite lower LVV copy number. Treatment with sodium butyrate, a histone deacetylase inhibitor, significantly enhanced SCN1A transgene expression and partially rescued expression decay in a sequence-dependent manner. Incorporation of a ubiquitous chromatin opening element (UCOE) upstream of the promoter to maintain expression resulted in a trend of increased expression and increased responsiveness to butyrate. These findings demonstrate that sequence-specific and epigenetic factors may influence expression of large transgenes following lentiviral delivery, highlighting key challenges and design considerations for therapeutic SCN1A transgene expression.

Here we introduce OpenEvo, a fully open-source, low-cost turbidostat platform for automated continuous culture and directed evolution experiments. Existing tools are expensive, complex, or lack open-source hardware; OpenEvo addresses this gap. OpenEvo is a complete, fully automated evolution platform with detailed, illustrated construction instructions for beginners, open-source software and firmware, and a single device priced around $300. An optional PC-based version offers enhanced functionality, including remote access, programmable evolution cycles, programmable LED stimulation, and a data visualization tool. OpenEvo can cycle through three types of media for positive, negative, and neutral selection conditions, supporting a wide range of experimental designs. We validate the use of OpenEvo by evolving H. volcanii to grow from 15% to 12% salt over ~150 cycles, ~1,000 hours. Evolved cells grew 36% faster than wild-type at 12% salt. Whole-genome sequencing of adapted cells found SNPs and large deletions. We also demonstrate positive and negative selection using the OpenEvo LEDs to drive optogenetics via a Phytochrome B-based optogenetic tool, with light as the selection stimulus during over 4000 hours of growth. OpenEvo lowers the technical and cost barriers for continuous evolution experiments, serves as a teaching tool, and is designed to grow an open community of users who share modifications.

Advanced in vitro systems such as organoids and microfluidic organ-on-a-chip platforms enable physiologically richer experimentation, but their complexity creates large parameter spaces and makes it difficult to disentangle the mechanistic roles of transport, mechanics, and extracellular microstructure. Agent-based modelling provides a natural computational counterpart to these systems by representing heterogeneous cells as discrete entities coupled through local rules and environmental fields. However, realistic microenvironment models often remain limited by scalability, simplified extracellular matrix representations, and the practical difficulty of calibrating large numbers of parameters. Here we present Cellfoundry, a computational framework built on a FLAMEGPU2-based modelling template for simulating complex cellular microenvironments. The framework integrates multiple interacting agent populations, including cells, fibrous networks, and focal adhesions mediating attachment dynamics and traction-force transmission. It combines mechanically resolved cell-cell and cell-matrix interactions with multi-species diffusion fields that propagate biochemical signals through the extracellular environment and regulate processes such as metabolism, migration, and cell-cycle progression. Cellfoundry also supports customizable behaviours across multiple cell types, enabling the study of heterogeneous multicellular systems within a unified computational setting. To support reproducible model development and calibration, the framework includes a fibre-network generation module, automated performance benchmarking workflows, post-processing and reporting utilities, and an Optuna-based Bayesian optimization pipeline with configurable single- and multi-objective targets. Two showcase examples illustrate these capabilities: a migration assay calibrated against fibroblast motility descriptors and a multi-objective organoid growth scenario reproducing target population composition and expansion dynamics and over time. Together, these examples demonstrate how Cellfoundry can be used to build, calibrate, and extend mechanistically interpretable models of coupled biochemical and mechanical dynamics in advanced in vitro systems. HighlightsO_LIHighly versatile, GPU-accelerated agent-based framework for cellular microenvironments C_LIO_LIExplicit fibrous ECM networks with dynamic remodelling and focal adhesion agents C_LIO_LICoupled mechanics and multi-species diffusion regulate cell behaviour in a highly customizable environment C_LIO_LIModular architecture with automated benchmarking and Bayesian parameter optimization C_LI

Human induced pluripotent stem cells (hiPSCs) are the most accessible source material for derivation of stem-cell-based therapies at scale. However, a disconnect exists between quality characteristics of phenotype in the pluripotent state, and downstream metrics for efficacy and safety. Bridging this gap is a major challenge. Given hiPSC plasticity, environmental conditioning plays a crucial role in guiding phenotype. This work presents a parallelizable scale-down approach, acquiring real-time data to inform hiPSC phenotype throughout biomanufacturing. We developed an optoelectronic instrumentation suite capable of measuring pH, dissolved oxygen, and cell density as important surrogates for phenotype in a scale-down expansion bioprocess. We were successful in obtaining continuous, integrated parametric data throughout cultivation and estimating metabolic characteristics of hiPSC phenotype. This system functions as a proof-of-concept tool for development of predictive models and monitoring strategies around the elucidation of phenotypic dynamics within hiPSC biomanufacturing. We have demonstrated a feasible open-source multivariate continuous monitoring approach at research scale that combines common process parameters with a scattering measurement against aggregate density. The combination of these parameters enables surrogate measurement of a metric for metabolic phenotype. This contribution emphasizes monitoring how the bioprocess influences variables important in the context of cell state, in broader pursuit of better understanding the link to downstream functionality and global optima in hiPSC biomanufacturing for regenerative medicine.

Intestinal organoids are powerful in vitro models, but their use in large-scale analyses remains constrained by the low throughput, labor-intensive handling, and high reagent consumption of conventional Matrigel dome culture. Here, we present Organoid-in-Bead (OrB), a vortex-based compartmentalization workflow that partitions organoid fragments into thousands of discrete Matrigel microbeads, enabling scalable, high-density culture from a single batch preparation. OrB maintains dome-comparable organoid growth and epithelial polarity, supports passaging-based culture expansion, yields more than 5,000 organoids in the final 10 cm dish format, and reduces Matrigel and medium consumption by approximately 70% on a per-organoid basis. OrB therefore provides a practical and scalable upstream workflow for generating screening-scale intestinal organoids. HighlightsO_LIOrB generates Matrigel microcompartments by vortexing without microfluidics C_LIO_LIOrB enables scalable, high-density intestinal organoid culture in one batch C_LIO_LIOrB maintains dome-comparable growth and epithelial polarity and supports passaging C_LIO_LIOrB yields >5,000 organoids per batch with [~]70% less Matrigel/medium per organoid C_LI

The clinical translation of CRISPR gene editing is challenged by the lack of delivery systems that are both safe and efficient in therapeutically relevant cell types such as mesenchymal stem cells (MSCs). Non-viral delivery avoids the immunogenicity and genomic integration risks of viral vectors but faces fundamental trade-offs between editing efficiency and cytotoxicity. Here, we present a comprehensive multiomic analysis of four non-viral CRISPR delivery modalities including cell-penetrating peptide- (CPP), lipid-, and polymer-based nanoparticles and electroporation; across mRNA and ribonucleoprotein (RNP) molecular formats. We systematically evaluate each modality, demonstrating that lipid-based delivery achieved the highest editing rates at the cost of genomic instability risks, interferon pathway activation, and a pro-inflammatory shift in MSC paracrine activity. Alternatively, CPPs yield moderate editing rates while reducing these unintended side-effects, whereas polymers and electroporation consistently yielded the lowest efficiencies. CRISPR molecular format and delivery method interacted in a stress-dependent manner, with RNP delivery reducing editing rates under high-stress systems while improving them in lower-stress modalities such as CPP and electroporation. These findings establish that editing efficiency alone is an insufficient metric for delivery system selection, and that genomic stability, transcriptomic dysregulation, and inflammatory response must be treated as primary design criteria for CRISPR therapies.

Reaction-diffusion circuits generate self-organized spatial patterns through local activation and long-range inhibition, but synthetic implementations in mammalian cells have been limited by the differential-diffusion requirement. Here, we introduce a novel architecture, juxtacrine activation with paracrine inhibition (JAPI), where the activator propagates through cell-cell contacts rather than diffusion. We demonstrate mathematically and numerically that JAPI accesses the same patterning regimes as classical diffusion-based circuits with one fewer free parameter. We then engineer compact synNotch-based JAPI circuits in mammalian fibroblasts and demonstrate their sufficiency for self-organized patterning through tunable, size-limited signal propagation. Functionalized to spatially control morphogen secretion, these circuits perturb feather bud formation on adjacent embryonic chicken epidermis. Finally, we develop a library-based approach to explore coupled, dual-JAPI circuits with tunable cross-inhibition, enabling programmable interactions between patterns and access to a broad morphospace of spatial states. Together, JAPI provides a compact, modular platform for programming self-organized multicellular patterning. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=97 SRC="FIGDIR/small/727552v1_ufig1.gif" ALT="Figure 1"> View larger version (29K): org.highwire.dtl.DTLVardef@174b4b1org.highwire.dtl.DTLVardef@1030baeorg.highwire.dtl.DTLVardef@f4077forg.highwire.dtl.DTLVardef@1184a51_HPS_FORMAT_FIGEXP M_FIG C_FIG

The fast-growing cyanobacterium Synechococcus sp. PCC 11901 is emerging as a promising chassis for photosynthetic biomanufacturing. Here we report recombinant protein production in PCC 11901 via signal peptide-mediated secretion, enabling direct recovery of target proteins from the culture medium without cell disruption. Seven signal peptides spanning both Sec and Tat pathways are screened using eYFP as a reporter, with secretion quantified daily over seven days by fluorescence measurements. FutA, belonging to the Tat pathway from Synechocystis sp. PCC 6803, achieves 92.2% extracellular export by day 7, substantially outperforming all Sec candidates, including the best Sec signal peptide thermitase from Cyanobacterium aponinum PCC 10605 (55.7%). Signal peptide-bearing strains exhibit growth reductions of up to 26% relative to the wild-type, with FutA most affected, indicating a general metabolic cost correlated with secretion efficiency. The best-performing signal peptides from both pathways, FutA and thermitase, are validated with secretion of lichenase. Notably, the rank order of signal peptide performance is reversed for lichenase: thermitase demonstrates 2.6-fold higher extracellular activity than FutA, indicating that optimal signal peptide selection is cargo-dependent. These results establish PCC 11901 as a secretion-competent chassis and provide a rational framework for matching signal peptide pathways to target protein properties.

Temporal gradient sensing is a fundamental capability observed across diverse natural biological systems, contributing to the coordination of their functions. Harnessing this ability is also of significant interest in synthetic biology, particularly for sensing and control applications. In this work, we focus on a biomolecular topology that exemplifies a broader class of signal-differentiating architectures, while introducing a structural variant of it. We examine their behavior under both nominal and non-ideal conditions, accounting for stochastic noise arising from different sources. Our investigation includes scenarios where these topologies operate independently, as well as when embedded within minimal regulatory architectures based on negative as well as positive feedback. We analyze the stability of the resulting macroscopic dynamics--a prerequisite for practical deployment--and quantify stochastic fluctuations in system output, providing comparisons with the corresponding input/unregulated process. Importantly, our results demonstrate that signal differentiation can be effectively implemented in a biomolecular setting without incurring deleterious noise amplification--a major concern in the utilization of derivative action across disciplines.