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bioRxiv2022

PhysiCOOL: A generalized framework for model Calibration and Optimization Of modeLing projects

In silico models of biological systems are usually very complex and rely on several parameters describing physical and biological properties that require validation. As such, parameter space exploration is an essential component of computational model development to fully characterize and validate simulation results. Experimental data may also be used to constrain parameter space (or enable model calibration) to enhance the biological relevance of model parameters. One widely used computational platform in the mathematical biology community is PhysiCell which provides a standardized approach to agent-based models of biological phenomena at different time and spatial scales. Nonetheless, one limitation of PhysiCell is that there has not been a generalized approach for parameter space exploration and calibration that can be run without high-performance computing access. Taking this into account, we present PhysiCOOL, an open-source Python library tailored to create standardized calibration and optimization routines of PhysiCell models. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=96 SRC="FIGDIR/small/516671v2_ufig1.gif" ALT="Figure 1"> View larger version (31K): org.highwire.dtl.DTLVardef@16d2601org.highwire.dtl.DTLVardef@da335dorg.highwire.dtl.DTLVardef@1623bd3org.highwire.dtl.DTLVardef@45d9f4_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioengineering↗

bioRxiv2023

Desiccated cyanobacteria serve as efficient plasmid DNA carrier in space flight

Effective transport of biological systems as cargo during space travel is a critical requirement to use synthetic biology and biomanufacturing in outer space. Bioproduction using microbes will drive the extent to which many human needs can be met in environments with limited resources. Vast repositories of biological parts and strains are available to meet this need, but their on-site availability requires effective transport. Here, we explore an approach that allows DNA plasmids, a ubiquitous synthetic biology part, to be safely transported to the International Space Station and back to the Kennedy Space center, without low-temperature or cryogenic stowage. Our approach relied on the cyanobacterium Nostoc punctiforme PC73102, that is naturally tolerant to prolonged desiccation. Desiccated N. punctiforme was able to carry the non-native pSCR119 plasmid as intracellular cargo safely to space and back. Upon return to the laboratory, the extracted plasmid showed no DNA damage or additional mutations and could be used as intended to transform the model Synbio host Escherichia coli to bestow Kanamycin resistance. This proof-of-concept study provides the foundation for a ruggedized transport host for DNA to environments where there is a need to reduce equipment and infrastructure for biological parts stowage and storage.

microbiology↗

bioRxiv2024

Filtering cells with high mitochondrial content removes viable metabolically altered malignant cell populations in cancer single-cell studies

BackgroundSingle-cell transcriptomics has transformed our understanding of cellular diversity in biological systems. However, systematic noise, often introduced by low-quality cells, can obscure biological signals if not properly accounted for. Thus, one of the common quality control steps involves filtering out cells with a high percentage of mitochondrial RNA counts (pctMT), as high pctMT typically indicates cell death. Yet, commonly used filtering thresholds, primarily derived from studies on healthy tissues, may be overly stringent for malignant cells, which often naturally exhibit higher baseline mitochondrial gene expression. We analyzed public single-cell RNA-seq and spatial data to investigate if malignant cells with high pctMT are viable and functionally significant subpopulations. ResultsWe analyzed nine single-cell RNA-seq datasets from uveal melanoma, breast, lung, kidney, head and neck, prostate, and pancreatic cancers, including 439,507 cells from 151 patients. Malignant cells exhibited significantly higher pctMT than nonmalignant cells without a significant increase in dissociation-induced stress signature scores. Malignant cells with high pctMT showed metabolic dysregulation, including increased xenobiotic metabolism, which is implicated in cancer therapeutic response. Our analysis of pctMT in cancer cell lines uncovered associations with resistance and sensitivity to certain classes of drugs. Additionally, we observed a link between pctMT and malignant cell transcriptional heterogeneity as well as patient clinical features. ConclusionsThis study provides a detailed exploration of the functional characteristics of malignant cells with elevated pctMT, challenging current quality control practices in single-cell RNA-seq analyses of tumors. Our findings have the potential to improve data interpretation and refine the biological conclusions of future cancer studies.

bioinformatics↗

bioRxiv2017

OmicsNet: Integration of Multi-Omics Data using Path Analysis in Multilayer Networks

Integrative analysis of heterogeneous omics data is essential to obtain a comprehensive overview of otherwise fragmented information and to better understand dysregulated biological pathways leading to a specific condition. One of the major challenges in systems biology is to develop computational methods for proper integration of multi-omics datasets. We propose OmicsNet that uses a multilayer network for the integration and analysis of multi-omics data of heterogeneous types. Each layer of the multilayer network represents a certain data type: input layers correspond to genotype features and nodes in the output layer correspond to phenotypes, while intermediate layers may represent genesets or biological concepts to facilitate functional interpretation of the data. OmicsNet then calculates the highest coefficient paths in multilayer network from each genomic feature to the phenotype by computing an integrated score along the paths. These paths may indicate the most plausible signalling cascade caused by perturbed genotype features leading to a particular phenotype response. With example applications, we illustrate the potential power of OmicsNet in the functional analysis, biomarker discovery and drug response prediction in personalized medicine using multi-omics data.

bioinformatics↗

bioRxiv2019

Compact attractors of an antithetic integral feedback system have a simple structure

Since its introduction by Briat, Gupta and Khammash, the antithetic feedback controller design has attracted considerable attention in both theoretical and experimental systems biology. The case in which the plant is a two-dimensional linear system (making the closed-loop system a nonlinear four-dimensional system) has been analyzed in much detail. This system has a unique equilibrium but, depending on parameters, it may exhibit periodic orbits. An interesting open question is whether other dynamical behaviors, such as chaotic attractors, might be possible for some parameter choices. This note shows that, for any parameter choices, every bounded trajectory satisfies a Poincare-Bendixson property. The analysis is based on the recently introduced notion of k-cooperative dynamical systems. It is shown that the model is a strongly 2-cooperative system, implying that the dynamics in the omega-limit set of any precompact solution is conjugate to the dynamics in a compact invariant subset of a two-dimensional Lipschitz dynamical system, thus precluding chaotic and other strange attractors.

bioengineering↗

bioRxiv2025

Design-of-Experiments for Nonlinear, Multivariate Biology: Rethinking Experimental Design through Perturb-seq

Design of experiments (DOE) principles are increasingly applied to biological assays, yet it remains unclear whether the optimality of their foundational assumption--orthogonal decomposition--holds in nonlinear biological systems. We addressed this question using Perturb-seq as a case study. By benchmarking a design commonly used in Perturb-seq and related experiments against the orthogonal Plackett-Burman (PB) design via simulations, we uncovered a counter-intuitive phenomenon: while orthogonal designs generally excel, the correlated structure inherent to the common design is functionally robust in systems with significant signal amplification. This challenges the blind application of DOE to biology. Based on these findings, we developed the PB suitability index (PBSI), a simple, parameter-free metric that predicts the optimal design solely from network structure. Our work not only provides practical guidelines for Perturb-seq but also establishes a "biology-oriented DOE" framework, bridging the gap between statistical rigor and biological complexity.

bioinformatics↗

bioRxiv2026

Graph Lens Lite: An interactive biological network viewer for displaying, exploring, and sharing disease pathobiology and drug mechanism of action models

Biological network visualization together with graph-based analyses are key techniques in systems biology and network medicine to detect patterns and generate hypotheses regarding disease pathobiology, drug target identification, biomarker prioritization, and digital drug discovery. Network representations provide an intuitive way to communicate and share research findings. We have developed Graph Lens Lite, a browser-based tool that combines rich visualization with a streamlined interface for exploring and sharing biological networks. It offers an expressive query language, topological network analysis, interactive filtering, visual grouping, customizable layouts, a data editor, fine-grained property-based styling, animated edge-flow visualization, community detection, and a context-aware locally powered AI assistant particularly suited for exploring molecular models of disease pathobiology or drug mechanism of action. We demonstrate its utility on a curated network model of autosomal dominant polycystic kidney disease. Graph Lens Lite is open source, with a live web version available at https://delta4ai.github.io/GraphLensLite/.

bioinformatics↗

bioRxiv2026

Derivation and theoretical validation of fractional quasi-steady state approximation (fQSSA) for target-mediated drug disposition models with memory effects

Standard target-mediated drug disposition (TMDD) models are widely used to describe nonlinear pharmacokinetics driven by high-affinity drug-target interactions. However, their reliance on instantaneous binding limits their ability to capture delayed and history-dependent dynamics observed in vivo. Here, we introduce a fractional TMDD model that incorporates memory effects through a fractional derivative, thereby generalizing the standard TMDD (sTMDD) framework. Although this fractional TMDD (fTMDD) formulation increases modeling flexibility, it also exacerbates parameter identifiability challenges under typical experimental conditions where only drug concentration data are available. To address this limitation, we derive a fractional quasi-steady-state approximation (fQSSA) that reduces model dimensionality while preserving essential nonlinear and memory-dependent pharmacokinetic dynamics. We further establish an explicit validity condition that quantifies the approximation error of both fTMDD and fQSSA without requiring numerical simulation. This condition reveals that the initial drug-to-target ratio is the primary determinant of QSSA validity, whereas the fractional order has a comparatively minor influence. Application of the proposed framework to recombinant human erythropoietin (rhEPO) data demonstrates that fractional dynamics play a population-dependent role, improving model performance in adults but not in infants. Together, this work provides the first systematic derivation of a QSSA framework for fractional TMDD models, along with rigorous and computable applicability conditions. Our results establish a principled foundation for incorporating memory effects into pharmacokinetic modeling and offer a generalizable framework for nonlinear PK-PD systems involving binding-mediated dynamics. Author summaryMany drugs interact strongly with their biological targets, leading to complex and nonlinear pharmacokinetics that are commonly described using target-mediated drug disposition (TMDD) models. However, these models assume that drug-target interactions occur instantaneously, which limits their ability to capture delayed and history-dependent behaviors observed in real biological systems. In this study, we develop a new modeling framework that incorporates such memory effects by extending TMDD models using fractional calculus. To make the model more practical and computationally efficient, we derive a simplified version based on a quasi-steady-state approximation (QSSA) and provide a clear mathematical condition that determines when this simplification is valid. Our analysis shows that the accuracy of the simplified model is primarily controlled by the initial ratio of drug to target, while the influence of memory effects is comparatively smaller. When applied to experimental data for erythropoietin, our model reveals that memory effects are important in adults but negligible in infants, suggesting that these effects may reflect underlying physiological differences. Overall, this work provides a systematic and interpretable framework for incorporating memory effects into pharmacokinetic modeling, with potential applications to a wide range of drug systems involving complex binding dynamics.

pharmacology and toxicology↗

bioRxiv2026

An Enhanced Pipeline for Multi-Omic Integration Based on Topological Data Analysis

The advent of high-throughput sequencing technologies has made it essential to employ advanced tools for data integration and interpretation. In this work, we propose to build upon two existing efforts by expanding their scope and applicability to multi-omic data integration through the development of a pipeline that generates a topologically informed ranking of genes to assess gene relevance to disease. Specifically, we exploit topological data analysis, capitalizing on: its graph-based framework, to refine complex interactions characteristic of biological systems; its intrinsic explainability, to enable interpretation of model outputs in terms of biologically meaningful features; its robustness to small input perturbations, to ensure stable results in the presence of noisy data, as omics are. In particular, we employ Harmonic Persistent Homology (HPH), and propose two relevant advances. First, we adopt an integration and clustering tool (i.e. iNETgrate) as an informed means to reduce data size and integrate multiple omic layers by aggregating methylation beta values from loci to the gene level, to be combined with gene expression values. This enables the application of HPH, otherwise limited by the computational burden. Second, as a non-trivial consequence, we apply HPH to multi-omic molecular data, and not to patients as done so far, enlarging HPH scope. Finally, we follow up on recent efforts toward performance standardization, and validate our results against biomarkers from a well-known TCGA breast cancer benchmark dataset.

bioinformatics↗

bioRxiv2020

Evolution of colonial life history in styelids tunicates involves changes in complexity patterns

Biological complexity is defined as the number of modules that compose an organism or a biological system, the type of interactions between these modules, and new hierarchies that describe these interactions. These patterns in biological complexity are changing during the evolution of life-histories, such as the evolution of coloniality in animals. In relation to coloniality, it is possible to observe an increment in all the aspects defined in the concept of biological complexity. First, in colonial animals, there is an increment in the modules that compound the system (i.e. zooids) compared with a solitary organism in which the multicellular individual a unity. Consequently, this transformation of the multicellular individual, in a component of the modular architecture in colonies, involves an increase in the regulatory processes of colonial system. This is precisely the case of the colonial life history evolution from solitary ancestors in the Styelids tunicates. Therefore, the main question of this study is How is the regulation of the asexual developmental processes that occurred simultaneously in the modules of the colonies? This question was studied, by the research of colonial strategy in the styelid Symplegma. Using in vivo observations of the budding process, description and classification of the extra-corporeal blood vessels system and the blood cells, by cytohistological assays. The conclusion is that the regulation of the simultaneous developmental processes that occurred in Symplegma colonies is mediated by the system of extra-corporeal blood vessels, which maintain physically the cohesion of the individuals, the plasma, and migratory blood cells transport signals between the individuals of the colonies.

developmental biology↗

bioRxiv2023

Structure primed embedding on the transcription factor manifold enables transparent model architectures for gene regulatory network and latent activity inference

The modeling of gene regulatory networks (GRNs) is limited due to a lack of direct measurements of regulatory features in genome-wide screens. Most GRN inference methods are therefore forced to model relationships between regulatory genes and their targets with expression as a proxy for the upstream independent features, complicating validation and predictions produced by modeling frameworks. Separating covariance and regulatory influence requires aggregation of independent and complementary sets of evidence, such as transcription factor (TF) binding and target gene expression. However, the complete regulatory state of the system, e.g. TF activity (TFA) is unknown due to a lack of experimental feasibility, making regulatory relations difficult to infer. Some methods attempt to account for this by modeling TFA as a latent feature, but these models often use linear frameworks that are unable to account for non-linearities such as saturation, TF-TF interactions, and other higher order features. Deep learning frameworks may offer a solution, as they are capable of modeling complex interactions and capturing higher-order latent features. However, these methods often discard central concepts in biological systems modeling, such as sparsity and latent feature interpretability, in favor of increased model complexity. We propose a novel deep learning autoencoder-based framework, StrUcture Primed Inference of Regulation using latent Factor ACTivity (SupirFactor), that scales to single cell genomic data and maintains interpretability to perform GRN inference and estimate TFA as a latent feature. We demonstrate that SupirFactor outperforms current leading GRN inference methods, predicts biologically relevant TFA and elucidates functional regulatory pathways through aggregation of TFs.

systems biology↗

bioRxiv2022

Combining SLA 3D printing and soft lithography for fast, versatile, and accessible high-resolution fabrication of customised multiscale cell culture devices with complex designs

Cell culture devices, such as microwells and microfluidic chips, are designed to increase the complexity of cell-based models whilst retaining control over culture conditions and have become indispensable platforms for biological systems modelling. From microtopography, microwells, plating devices and microfluidic systems to larger constructs for specific applications such as live imaging chamber slides, a wide variety of culture devices with different geometries have become indispensable in biology laboratories. However, while their application in biological projects is increasing exponentially, due to a combination of the techniques and tools required for their manufacture, and the physical science background sometimes needed, the design and fabrication of such devices directly by biological labs remains a relatively high investment in terms of costs, use of facilities, needed collaborations and time. Whilst commercially available systems are available, these are also often costly, and importantly lack the potential for customisation by each single lab. This combination of factors still limits widespread application of microfabricated custom devices in most biological wet labs. Capitalising on recent important advancements in the fields of bioengineering and microfabrication, and taking advantage of low-cost, high-resolution desktop resin 3D printers combined with PDMS soft lithography, we have developed an optimised low-cost and highly reproducible microfabrication pipeline, capable of generating a wide variety of customisable devices for cell culture and tissue engineering in an easy, fast reproducible way for a fraction of the cost of conventional microfabrication or commercial alternatives. This protocol is designed specifically to be a resource for biological labs with little to none prior exposure to these fields technique and enables the manufacture of complex devices across the {micro}m to cm scale. We provide a ready-to-go pipeline for the efficient treatment of resin-based 3D printed constructs for PDMS curing, using a combination of curing steps, washes and surface treatments. Together with the extensive characterisation of the fabrication pipeline, we show the utilization of this system to a variety of applications and use cases relevant to biological experiments, ranging from micro topographies for cell alignments to complex multi-part hydrogel culturing systems. This methodology can be easily adopted by any wet lab, irrespective of prior expertise or resource availability and will enable the wide adoption of tailored microfabricated devices across many fields of biology.

bioengineering↗

bioRxiv2026

Measuring Amorphous Motion: Application of Optical Flow to Three-Dimensional Fluorescence Microscopy Images

Motion is an essential component of any living system. It is rich with information, but it is often challenging to quantitatively extract biologically informative results from the motion apparent in microscopy images. This challenge is exacerbated by the wide variety in biological movement, which often takes the form of difficult-to-segment amorphous structures undergoing complex motion. An image processing technique known as optical flow can capture motion at each pixel in an image, thus bypassing the need for object segmentation or a priori definition of motion types. This makes it a powerful tool for quantitative assessment of biological systems from the protein to organism scale. However, despite its flexibility and strengths for analyzing fluorescence microscopy images, its adoption in the bioimaging community has been limited by the availability of easy-to-use tools and guidance in results interpretation. Here we describe an optical flow tool, OpticalFlow3D, that can be run in Python or MATLAB and is compatible with three-dimensional microscopy images. Using biological examples across length scales, we illustrate how OpticalFlow3D can enable new biological insight.

bioinformatics↗

bioRxiv2022

Efficient computation of adjoint sensitivities at steady-state in ODE models of biochemical reaction networks

Dynamical models in the form of systems of ordinary differential equations have become a standard tool in systems biology. Many parameters of such models are usually unknown and have to be inferred from experimental data. Gradient-based optimization has proven to be effective for parameter estimation. However, computing gradients becomes increasingly costly for larger models, which are required for capturing the complex interactions of multiple biochemical pathways. Adjoint sensitivity analysis has been pivotal for working with such large models, but methods tailored for steady-state data are currently not available. We propose a new adjoint method for computing gradients, which is applicable if the experimental data include steady-state measurements. The method is based on a reformulation of the backward integration problem to a system of linear algebraic equations. The evaluation of the proposed method using real-world problems shows a speedup of total simulation time by a factor of up to 4.4. Our results demonstrate that the proposed approach can achieve a substantial improvement in computation time, in particular for large-scale models, where computational efficiency is critical. Author summaryLarge-scale dynamical models are nowadays widely used for the analysis of complex processes and the integration of large-scale data sets. However, computational cost is often a bottleneck. Here, we propose a new gradient computation method that facilitates the parameterization of large-scale models based on steady-state measurements. The method can be combined with existing gradient computation methods for time-course measurements. Accordingly, it is an essential contribution to the environment of computationally efficient approaches for the study of large-scale screening and omics data, but not tailored to biological applications, and, therefore, also useful beyond the field of computational biology.

systems biology↗

bioRxiv2025

Systems level analysis of B-cell development identifies BDNF as a driver for human B lymphopoiesis

B-cell aplasia is a major consequence of aging, chemotherapy, and B-cell-depleting immunotherapies, compromising immune protection against infections, cancer, and vaccines. Yet, unlike the myeloid and erythroid lineages, no strategy exists to accelerate human B-cell reconstitution. Here, we used an integrative systems biology approach to identify regulators of human B lymphopoiesis in the bone marrow (BM) microenvironment. By combining single-cell transcriptomic analysis of human BM with intercellular communication mapping, we generated an initial set of candidate factors predicted to act on developing B cells. To distinguish biologically meaningful putative regulators from a broad candidate space, we further intersected these findings with orthogonal human datasets capturing age-impaired B lymphopoiesis and protein dynamics associated with B-cell depletion and reconstitution. This convergent prioritization strategy highlighted a focused set of putative regulators, among which brain-derived neurotrophic factor (BDNF) emerged repeatedly as a top putative regulator. Functional interrogation in progenitor BM cells showed that several prioritized putative regulators induced transcriptional programs linked to early immune development, with BDNF consistently promoting pathways associated with B-cell differentiation. Importantly, in a human in vitro BM co-culture system, BDNF enhanced the differentiation of CD34+ hematopoietic progenitors into CD19+ progenitor B cells. Together, these findings identify BDNF as a previously unrecognized regulator of early human B lymphopoiesis and establish a general framework for uncovering functional hematopoietic regulators by integrating single-cell analysis with complementary biological and clinical signals. This approach may support future strategies to improve immune reconstitution in settings of prolonged B-cell depletion.

systems biology↗

bioRxiv2023

The highly rugged yet navigable regulatory landscape of the bacterial transcription factor TetR

Transcription factor binding sites (TFBSs) are important sources of evolutionary innovations. Understanding how evolution navigates the sequence space of such sites can be achieved by mapping TFBS adaptive landscapes. In such a landscape, an individual location corresponds to a TFBS bound by a transcription factor. The elevation at that location corresponds to the strength of transcriptional regulation conveyed by the sequence. We developed an in vivo massively parallel reporter assay to map the landscape of bacterial TFBSs. We applied this assay to the TetR repressor, for which few TFBSs are known. We quantify the strength of transcriptional repression for 17,765 TFBSs and show that the resulting landscape is highly rugged, with 2,092 peaks. Only a few peaks convey stronger repression than the wild type. Non-additive (epistatic) interactions between mutations are frequent. Despite these hallmarks of ruggedness, most high peaks are evolutionarily accessible. They have large basins of attraction and are reached by around 20% of populations evolving on the landscape. Which high peak is reached during evolution is unpredictable and contingent on the mutational path taken. This first in-depth analysis of a prokaryotic gene regulator reveals a landscape that is navigable but much more rugged than the landscapes of eukaryotic regulators. SignificanceUnderstanding how evolution explores the vast space of genotypic possibilities is a fundamental question in evolutionary biology. The mapping of genotypes to quantitative traits (such as phenotypes and fitness) allows us to delineate adaptive landscapes and their topological properties, shedding light on how evolution can navigate such vast spaces. In this study, we focused on mapping a transcription factor binding site (TFBS) landscape to gene expression levels, as changes in gene expression patterns play a crucial role in biological innovation. We developed a massively parallel reporter assay and mapped the first comprehensive in vivo gene regulatory landscape for a bacterial transcriptional regulator, TetR. Surprisingly, this landscape is way more rugged than those observed in eukaryotic regulators. Despite its ruggedness, the landscape remains highly navigable through adaptive evolution. Our study presents the first high-resolution landscape for a bacterial TFBS, offering valuable insights into the evolution of TFBS in vivo. Moreover, it holds promise as a framework for discovering new genetic components for synthetic biological systems.

evolutionary biology↗

bioRxiv2025

The Extended Life Cycle: A Multiscale Modeling Framework for Extended Evolutionary Dynamics

A complete explanation of evolutionary change requires reconciling processes that operate across multiple time scales. Development, the processes by which traits are generated, unfolds over an individuals lifetime; heredity, encompassing the diverse forms of information transmission, occurs across generations; and population and ecological change often take place over even longer temporal horizons. Capturing these layered dynamics in a formal mathematical framework is essential for advancing evolutionary theory in line with recent conceptual developments, such as Extended Evolutionary Theory (EET) or the Extended Evolutionary Synthesis (EES). In this work, we introduce the Extended Life Cycle (ELC), a multiscale mathematical modeling framework that centers the life cycle as the fundamental unit of evolutionary analysis. In contrast to traditional gene-centric approaches, the ELC formalism captures the nested temporal structure and multilevel causal architecture of biological systems by modeling development, heredity, population, and ecological dynamics across distinct but interacting timescales. Each layer is expressed as a stochastic state-space model, enabling feedback across levels and accommodating diverse interaction topologies and inheritance systems. We demonstrate the utility of the ELC through a Bayesian simulation to estimate latent dynamics across three time scales. By formalizing the life cycle as a dynamic scaffold, the ELC offers a flexible foundation for modeling evolution in complex systems.

evolutionary biology↗

bioRxiv2023

A control engineering perspective on the advantages of efference copies

Biological systems have evolved to perform high-speed voluntary movements whilst maintaining robustness and stability. This paper examines a control architecture based on the principles of efference copies found in insect sensorimotor control which we call the fully-separable-degrees-of-freedom (FSDoF) controller. Within a control engineering framework, we benchmark the advantages of this control architecture against two common engineering control schemes: a pure feedback (PFB) controller and a Smith predictor (SP). Our study identifies three advantages of the FSDoF for biology. It is advantageous in controlling systems with sensor delays, and it can effectively handle noise. Thirdly, it allows biological sensors to increase their operating range. We evaluate the robustness of the FSDoF controller and show that it achieves improved performance with equal stability margins and robustness. Finally, we discuss variations of the FSDoF which theoretically provide the same performance.

bioengineering↗