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bioRxiv2026

An amyloid-β/APP regulatory layer in human brown adipocyte recruitment identifies repurposable drugs for thermogenic fat expansion

Brown adipose tissue (BAT) increases energy expenditure but is scarce in adults, yet most strategies focus on activating existing thermogenic adipocytes rather than recruiting new ones. We integrate human single-nucleus transcriptomics with protein-interaction and knowledge-graph analyses to identify stage-specific, druggable programmes governing brown adipogenesis. This pipeline resolved four ordered transcriptional states and three transitions, revealing an amyloid-{beta}/APP regulatory layer and nominating APP as a central signalling hub. Most prioritised compounds altered differentiation or metabolic phenotype, several substantially. Phenserine (APP-lowering) and sacubitril (neprilysin inhibitor) promoted differentiation through distinct mechanisms and stages and could be combined sequentially for benefit. Phenserine increased differentiation across three human models and, in mice, expanded BAT mass and increased thermogenic capacity. Together, these findings reveal targetable control points in human brown adipocyte recruitment, nominate repurposable drugs for BAT expansion, and establish a generalisable framework coupling single-cell transcriptomics with network-based drug repurposing across staged biological systems.

cell biology↗

bioRxiv2020

Voltage-gated ion channels mediate the electrotaxis of glioblastoma cells in a hybrid PMMA/PDMS microdevice

Transformed astrocytes in the most aggressive form cause glioblastoma, the most common cancer in central nervous system with high mortality. The physiological electric field by neuronal local field potentials and tissue polarity may guide the infiltration of glioblastoma cells through the electrotaxis process. However, microenvironments with multiplex gradients are difficult to create. In this work, we have developed a hybrid microfluidic platform to study glioblastoma electrotaxis in controlled microenvironments with high through-put quantitative analysis by a machine learning-powered single cell tracking software. By equalizing the hydrostatic pressure difference between inlets and outlets of the microchannel, uniform single cells can be seeded reliably inside the microdevice. The electrotaxis of two glioblastoma models, T98G and U-251MG, require optimal laminin-containing extracellular matrix and exhibits opposite directional and electro-alignment tendencies. Calcium signaling is a key contributor in glioblastoma pathophysiology but its role in glioblastoma electrotaxis is still an open question. Anodal T98G electrotaxis and cathodal U-251MG electrotaxis require the presence of extracellular calcium cations. U-251MG electrotaxis is dependent on the P/Q-type voltage-gated calcium channel (VGCC) and T98G is dependent on the R-type VGCC. U-251MG and T98G electrotaxis are also mediated by A-type (rapidly inactivating) voltage-gated potassium channels and acid-sensing sodium channels. The involvement of multiple ion channels suggests that the glioblastoma electrotaxis is complex and patient-specific ion channel expression can be critical to develop personalized therapeutics to fight against cancer metastasis. The hybrid microfluidic design and machine learning-powered single cell analysis provide a simple and flexible platform for quantitative investigation of complicated biological systems.

cell biology↗

bioRxiv2023

Differential allelic representation (DAR) identifies candidate eQTLs and improves transcriptome analysis

In comparisons between mutant and wild-type genotypes, transcriptome analysis can reveal the direct impacts of a mutation, together with the homeostatic responses of the biological system. Recent studies have highlighted that, when homozygous mutations are studied in non-isogenic backgrounds, genes from the same chromosome as a mutation often appear over-represented among differentially expressed (DE) genes. One hypothesis suggests that DE genes chromosomally linked to a mutation may not reflect true biological responses to the mutation but, instead, result from differences in representation of expression quantitative trait loci (eQTLs) between sample groups selected on the basis of mutant or wild-type genotype. This is problematic when inclusion of spurious DE genes in a functional enrichment study results in incorrect inferences of mutation effect. Here we show that chromosomally co-located differentially expressed genes (CC-DEGs) can also be observed in analyses of dominant mutations in heterozygotes. We define a method and a metric to quantify, in RNA-sequencing data, localised differential allelic representation (DAR) between groups of samples subject to differential expression analysis. We show how the DAR metric can predict regions prone to eQTL-driven differential expression, and how it can improve functional enrichment analyses through gene exclusion or weighting of gene-level rankings. Advantageously, this improved ability to identify probable eQTLs also reveals examples of CC-DEGs that are likely to be functionally related to a mutant phenotype. This supports a long-standing prediction that selection for advantageous linkage disequilibrium influences chromosome evolution. By comparing the genomes of zebrafish (Danio rerio) and medaka (Oryzias latipes), a teleost with a conserved ancestral karyotype, we find possible examples of chromosomal aggregation of CC-DEGs during evolution of the zebrafish lineage. The DAR metric provides a solid foundation for addressing the eQTL issue in new and existing datasets because it relies solely on RNA-sequencing data.

genetics↗

bioRxiv2019

Loop-closure Kinetics Reveal a Stable, Right-handed DNA Intermediate in Cre Recombination

In Cre site-specific recombination, the synaptic intermediate is a recombinase homotetramer containing a pair of DNA target sites. The strand-exchange mechanism proceeds via a Holliday-junction (HJ) intermediate; however, the geometry of the DNA segments in the synapse has remained highly controversial. In particular, all crystallographic structures are consistent with an achiral planar Holliday-junction (HJ) structure, whereas topological assays based on Cre-mediated knotting of plasmid DNAs are consistent with a right-handed chiral junction. Here we use the kinetics of loop closure involving closely spaced (131-151 bp), directly repeated loxP sites to investigate the in-aqueo ensemble of conformations for the longest-lived looped DNA intermediate. Fitting the experimental site-spacing dependence of the loop-closure probability, J, to a statistical-mechanical theory of DNA looping provides evidence for substantial out-ofplane HJ distortion. This result unequivocally stands in contrast to the square-planar intermediate geometry determined from crystallographic data for the Cre-loxP system and other int-superfamily recombinases. J measurements carried out with an isomerization-deficient Cre mutant suggest that the apparent geometry of the wild-type complex may result from the temporal averaging of diverse right-handed and achiral structures. Applied to Cre recombinase, and other biological systems, our approach bridges the static pictures provided by crystal structures and the natural dynamics of macromolecules in vivo. This approach thus advances a more comprehensive dynamic analysis of large nucleoprotein structures and their mechanisms.

biophysics↗

bioRxiv2026

Image-scanning light-sheet microscopy for high-speed volumetric imaging of complex biological dynamics

Volumetric fluorescence microscopy is a powerful method for studying complex biological systems because it enables comprehensive observation of structural and physiological dynamics. In particular, light-sheet microscopy (LSM) is a leading option for real-time volumetric fluorescence imaging as it combines high imaging speed, low phototoxicity, minimal photobleaching, high spatiotemporal resolution, and low computational burden. To capture fast biological events, various efforts have been made to improve the imaging speed of volumetric fluorescence microscopy, including LSM. However, existing approaches entail significant trade-offs that make routine volumetric imaging at and beyond video rates challenging under practical conditions. Here, we introduce image-scanning LSM, a method that substantially increases the volumetric imaging speed achievable with LSM while preserving key performance metrics, such as spatial resolution and photon efficiency, as well as accessibility. Our implementation, termed image-scanning oblique plane (ISOP) microscopy, enables volumetric fluorescence imaging at up to 1,000 volumes per second with submicrometer lateral spatial resolution. We demonstrate the broad utility of ISOP microscopy by recording and analyzing the dynamics of behaving and rapidly moving organisms.

bioengineering↗

bioRxiv2025

High-throughput single cell -omics using semi-permeable capsules

Biological systems are inherently complex and heterogeneous. Deciphering this complexity increasingly relies on high-throughput analytical methods and tools that efficiently probe the cellular phenotype and genotype. While recent advancements have enabled various single-cell -omics assays, their broader applications are inherently limited by the challenge of efficiently conducting multi-step biochemical assays while retaining various biological analytes. Extending on our previous work (1) here we present a versatile technology based on semi-permeable capsules (SPCs), tailored for a variety of high-throughput nucleic acid assays, including digital PCR, genome sequencing, single-cell RNA-sequencing (scRNA-Seq) and FACS-based isolation of individual transcriptomes based on nucleic acid marker of interest. Being biocompatible, the SPCs support single-cell cultivation and clonal expansion over long periods of time - a fundamental limitation of droplet microfluidics systems. Using SPCs we perform scRNA-Seq on white blood cells from patients with hematopoietic disorders and demonstrate that capsule-based sequencing approach (CapSeq) offers superior transcript capture, even for the most challenging cell types. By applying CapSeq on acute myeloid leukemia (AML) samples, we uncover notable changes in transcriptomes of mature granulocytes and monocytes associated with blast and progenitor cell phenotypes. Accurate representation of the entirety of the cellular heterogeneity of clinical samples, driving new insights into the malfunctioning of the innate immune system, and ability to clonally expand individual cells over long periods of time, positions SPC technology as customizable, highly sensitive and broadly applicable tool for easy-to-use, scalable single-cell -omics applications.

genomics↗

bioRxiv2025

The inhibitory cascade model and evolution in segmentally organized tissues

The inhibitory cascade model (ICM) of morphogenesis is an effort to link development to the production of variation, which can influence evolutionary trajectories. The ICM proposes that serially developing features, such as molar teeth, is governed by the relative magnitudes of one activating and one inhibiting developmental process. The statistical expectations of the ICM are typically expressed and analyzed on a first-element standardized scale and seem to be a good predictor of molar proportions. However, the ICM has been applied to traits that occur in series but do not develop in sequence and still recovers as good a fit as when applied to serially developing traits. Such an undiscriminating result raises questions about whether the fit of the ICM is an artifact of standardization. The mathematical rendition of the ICM do not correspond with the verbal descriptions of the developmental argument. Applying our novel re-articluation of the ICM to biological, non-biological, and simulated data, we demonstrate that the apparent goodness of fit of the ICM to many biological systems is an artifact of scaling correlated values with a common denominator. There is little evidence supporting the ICM at the developmental, variational, or evolutionary levels.

evolutionary biology↗

bioRxiv2025

A harmonized ovarian cancer scRNA-seq atlas to dissect disease heterogeneity underlying metastatization and chemoresistance

The advent of single cell technology has enabled researchers with the ability to achieve unprecedented resolution in the characterization of biological systems. The consequent increasing availability of single cell dataset brought about the possibility to obtain comprehensive reference atlases, to shed light on the molecular and cellular foundations of healthy and diseased tissues. This process has highlighted the need to integrate datasets from different sources while being able to distinguish true biological signal from technical confounders. While this issue is widespread to most biological settings, it holds especially true for cancer samples, which are characterized by a diffused inter- and intra-patient phenotypic heterogeneity. To address this issue, here we developed a novel integration method tailored to highly heterogeneous single cell transcriptomic data and applied it to one of the quintessential heterogeneous cancer type, namely high-grade serous ovarian cancer, to generate the first reference atlas for this disease. By identifying patient-specific cell populations and deriving metacells, we were able to preserve inter-patient biological variability. Using a variational autoencoder, we integrated metacell data, revealing an evolving landscape of cell states along disease progression and treatment for each of the main cell types constituting the dataset. Also, we showed the potential of this resource by identifying diffused and tissue/treatment-specific cell-to-cell interactions. Finally, the generated integration model allowed to expand the atlas with additional data, granting iterative refinement over time of this disease reference. Our strategy now provides a valuable resource for the cancer research community, facilitating the investigation of tumor heterogeneity towards the development of novel therapeutic strategies.

cancer biology↗

bioRxiv2025

Actin network heterogeneity tunes activator-inhibitor dynamics at the cell cortex

Biological systems can display diverse patterns of self-organization, even when built on conserved networks of interaction between molecular species. In these cases, reaction-diffusion equations provide a valuable tool to learn how new dynamics could emerge from quantitative tuning of parameters. Bringing these models into quantitative correspondence with biological data remains an outstanding challenge, especially when the data manifest heterogeneities that are difficult to account for mathematically. One particular example occurs in cell biology, where the membrane-bound regulatory protein RhoA interacts with the filamentous actin cortex in an activator-inhibitor loop. Though this core biochemical circuit is conserved across multiple cell types in different organisms, it produces different patterns of RhoA activity in different contexts, from traveling waves in starfish to transient pulses in C. elegans. To understand how this variation emerges, we develop an activator-inhibitor model that accounts explicitly for actin assembly and heterogeneity. By fitting the model to summary statistics of experimental data, subject to known parameter constraints, we show that F-actin assembly dynamics tune the spatiotemporal patterns of RhoA activity. A minimal representation of these dynamics reveals how directional transport (via polymerization) combines with stochasticity in F-actin number and orientation to produce the observed patterns. This work sheds light on how phenotypic diversity arises from heterogeneity and anisotropy, with important implications for the next generation of activator-inhibitor models. SignificanceTo divide, move, and polarize, cells must self-organize their constituent proteins into large-scale patterns with varied spatiotemporal character. The design principles of this process remain poorly understood, primarily because quantitatively matching mathematical models to experimental data is difficult. Here we consider pattern formation from two constituents on the cell cortex: the regulatory protein RhoA and actin filaments. Using a mathematical model, constrained quantitatively by data from multiple organisms, we show how diversity in RhoA activity can arise from intra- and inter-organismal changes in actin filament architecture and assembly dynamics. Our results reveal general principles for pattern formation at the cortex, and our combination of data analysis, modeling, and parameter inference provides a broadly-applicable, interdisciplinary methodology to unravel mechanisms of self-organization.

cell biology↗

bioRxiv2026

Nematic order in cellular tissues: a standardized framework and anomalous defect dynamics

Cellular monolayers often exhibit orientational order, with nematic alignment of cell shape and cytoskeletal structures governing tissue-scale collective dynamics. Despite extensive studies, a unified analysis framework for characterizing active nematics in living systems remains partial, and key discrepancies with theory persist. Here, we present a systematic and comparative analysis of nematic order and tissue flow dynamics across twelve distinct cell types. We quantify the impact of analysis parameters and provide data-driven guidelines to improve reproducibility and cross-study comparability. Across all nematic systems, we uncover remarkably consistent static properties, supporting the universality of nematic behavior in living tissues. By combining orientation-field analysis with velocity-field measurements and numerical simulations, we show that all examined systems display contractile active nematic signatures, with characteristic flow structures around topological defects. However, direct tracking of individual defects reveals subdiffusive dynamics, in stark contrast with the superdiffusive, self-propelled motion predicted by the hydrodynamic theory of active nematics. Our results establish a standardized framework for nematic analysis in biological systems and highlight fundamental limitations of current active nematic models in describing defect dynamics in living tissues.

biophysics↗

bioRxiv2015

Inferring protein-protein interaction networks from inter-protein sequence co-evolution

Interaction between proteins is a fundamental mechanism that underlies virtually all biological processes. Many important interactions are conserved across a large variety of species. The need to maintain interaction leads to a high degree of co-evolution between residues in the interface between partner proteins. The inference of protein-protein interaction networks from the rapidly growing sequence databases is one of the most formidable tasks in systems biology today. We propose here a novel approach based on the Direct-Coupling Analysis of the co-evolution between inter-protein residue pairs. We use ribosomal and trp operon proteins as test cases: For the small resp. large ribosomal subunit our approach predicts protein-interaction partners at a true-positive rate of 70% resp. 90% within the first 10 predictions, with areas of 0.69 resp. 0.81 under the ROC curves for all predictions. In the trp operon, it assigns the two largest interaction scores to the only two interactions experimentally known. On the level of residue interactions we show that for both the small and the large ribosomal subunit our approach predicts interacting residues in the system with a true positive rate of 60% and 85% in the first 20 predictions. We use artificial data to show that the performance of our approach depends crucially on the size of the joint multiple sequence alignments and analyze how many sequences would be necessary for a perfect prediction if the sequences were sampled from the same model that we use for prediction. Given the performance of our approach on the test data we speculate that it can be used to detect new interactions, especially in the light of the rapid growth of available sequence data.

Bioinformatics↗

bioRxiv2017

HIVs Feedback Circuit Breaks The Fundamental Limit On Noise Suppression To Stabilize Fate

Diverse biological systems utilize gene-expression fluctuations ( noise) to drive lineage-commitment decisions1-5. However, once a commitment is made, noise becomes detrimental to reliable function6,7 and the mechanisms enabling post-commitment noise suppression are unclear. We used time-lapse imaging and mathematical modeling, and found that, after a noise-driven event, human immunodeficiency virus (HIV) strongly attenuated expression noise through a non-transcriptional negative-feedback circuit. Feedback is established by serial generation of RNAs from post-transcriptional splicing, creating a precursor-product relationship where proteins generated from spliced mRNAs auto-deplete their own precursor un-spliced mRNAs. Strikingly, precursor auto-depletion overcomes the theoretical limits on conventional noise suppression--minimizing noise far better than transcriptional auto-repression--and dramatically stabilizes commitment to the active-replication state. This auto-depletion feedback motif may efficiently suppress noise in other systems ranging from detained introns to non-sense mediated decay.

cell biology↗

bioRxiv2022

Generalized Hypergeometric Distributions Generated by Birth-Death Process in Bioinformatics

Modern high-throughput biological systems detection methods generate empirical frequency distributions (EFD) which exhibit complex forms and have long right-side tails. Such EFD are often observed in normal and pathological processes, of which the probabilistic properties are essential, but the underlying probability mechanisms are poorly understood. To better understand the probability mechanisms driving biological complexity and the pathological role of extreme values, we propose that the observed skewed discrete distributions are generated by non-linear transition rates of birth and death processes (BDPs). We introduce a (3d+1)-parameter Generalized Gaussian Hypergeometric Probability ((3d+1)-GHP) model with the probabilities defined by a stationary solution of generalized BDP (g-BDP) and represented by generalized hypergeometric series with regularly varying function properties. We study the Regularly Varying 3d-Parameter Generalized Gaussian Hypergeometric Probability (3d-RGHP) functions regular variation properties, asymptotically constant slow varying component, unimodality and upward/ downward convexity which allows us to specify a family of 3d-RGHP models and study their analytical and numerical characteristics. The frequency distribution of unique muta-tions occurring in the human genome of patients with melanoma have been analyzed as an example application of our theory in bioinformatics. The results show that the parameterized model not only fits the heavy tail well, but also the entire EFD taken on the complete experimental outcome space. Our model provides a rigorous and flexible mathematical framework for analysis and application of skewed distributions generated by BDPs which often occur in bioinformatics and big data science.

bioinformatics↗

bioRxiv2025

Integrating Recombinase-Based Feedback and Feedforward Control for Optimal Resource Decoupling

Resource competition disrupts circuit modularity by introducing unintended coupling between otherwise independent gene modules, thereby compromising genetic circuit function. While various control strategies have been explored, their complexity or limited efficacy has hindered broader application. Here, we present the Re-NF-FF-Controller, a recombinase-based strategy that integrates negative feedback and feedforward regulation via promoter flipping to mitigate resource competition. Computational modeling and experimental validation demonstrate that Re-NF-FF-Controller effectively reduces resource coupling, ensuring robust gene expression and modularity. Moreover, its tunability allows for performance optimization through straightforward adjustments of recombinase enzyme levels. This strategy offers a versatile and easily implementable solution for designing reliable synthetic biological systems. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=78 SRC="FIGDIR/small/653855v1_ufig1.gif" ALT="Figure 1"> View larger version (15K): org.highwire.dtl.DTLVardef@6b960forg.highwire.dtl.DTLVardef@148bc3borg.highwire.dtl.DTLVardef@3df13borg.highwire.dtl.DTLVardef@a865f2_HPS_FORMAT_FIGEXP M_FIG C_FIG

synthetic biology↗

bioRxiv2026

Behavioral hierarchy without a hierarchical brain

Behavior is inherently hierarchical. The prevailing view holds that hierarchical behavior arises from the brains hierarchical organization. However, this view is primarily derived from experiments in highly controlled laboratory settings, and it remains unknown whether the same principles apply during natural, freely moving behavior. Here, we show that, under naturalistic and freely moving conditions, hierarchical mouse behavior can emerge from localized cortical dynamics alone, independent of anatomical or functional brain hierarchies. We first established a naturalistic neuroethological platform that enables quantitative characterization of hierarchical behavior alongside its corresponding neural activity. Using this platform, we find that localized cortical dynamics encode multiple levels of behavioral hierarchy. Specifically, low-dimensional neural dynamics encode high-level behavioral composites, whereas higher-dimensional neural dynamics encode low-level behavioral kinematics. Finally, by selectively perturbing high-dimensional components of cortical dynamics using optogenetics, we observe a selective reduction in low-level kinematic behaviors, providing causal evidence for this relationship. Together, these findings challenge a central assumption in systems neuroscience by demonstrating that hierarchical behavior does not require a hierarchically organized brain. Instead, hierarchy emerges from the dimensional organization of localized cortical dynamics, revealing a previously unrecognized principle by which complex behavior can arise from relatively simple neural substrates. This dynamic principle offers a unifying framework for understanding how biological systems achieve behavioral flexibility under natural conditions and suggests new directions for artificial intelligence that rely on adaptive local dynamics rather than increasingly deep architectures.

neuroscience↗

bioRxiv2023

How do stochastic processes and genetic threshold effects explain incomplete penetrance and inform causal disease mechanisms?

Incomplete penetrance is the rule rather than the exception in Mendelian disease. In syndromic monogenic disorders, phenotypic variability can be viewed as the combination of incomplete penetrance for each of multiple independent clinical features. Within genetically identical individuals, such as isogenic model organisms, stochastic variation at molecular and cellular levels is the primary cause of incomplete penetrance according to a genetic threshold model. By defining specific probability distributions of causal biological readouts and genetic liability values, stochasticity and incomplete penetrance provide information about threshold values in biological systems. Ascertainment of threshold values has been achieved by simultaneous scoring of relatively simple phenotypes and quantitation of molecular readouts at the level of single cells. However, this is much more challenging for complex morphological phenotypes using experimental and reductionist approaches alone, where cause and effect are separated temporally and across multiple biological modes and scales. Here I consider how causal inference, which integrates observational data with high confidence causal models, might be used to quantify the relative contribution of different sources of stochastic variation to phenotypic diversity. Collectively, these approaches could inform disease mechanisms, improve predictions of clinical outcomes and prioritise gene therapy targets across modes and scales of gene function.

genetics↗

bioRxiv2017

Bioty: A cloud-based development toolkit for programming experiments and interactive applications with living cells

Recent advancements in life-science instrumentation and automation enable entirely new modes of human interaction with microbiological processes and corresponding applications for science and education through biology cloud labs. A critical barrier for remote life-science experimentation is the absence of suitable abstractions and interfaces for programming living matter. To this end we conceptualize a programming paradigm that provides stimulus control functions and sensor control functions for realtime manipulation of biological (physical) matter. Additionally, a simulation mode facilitates higher user throughput, program debugging, and biophysical modeling. To evaluate this paradigm, we implemented a JavaScript-based web toolkit, Bioty, that supports realtime interaction with swarms of phototactic Euglena cells hosted on a cloud lab. Studies with remote users demonstrate that individuals with little to no biology knowledge and intermediate programming knowledge were able to successfully create and use scientific applications and games. This work informs the design of programming environments for controlling living matter in general and lowers the access barriers to biology experimentation for professional and citizen scientists, learners, and the lay public.\n\nSignificance StatementBiology cloud labs are an emerging approach to lower access barriers to life-science experimentation. However, suitable programming approaches and user interfaces are lacking, especially ones that enable the interaction with the living matter itself - not just the control of equipment. Here we present and implement a corresponding programming paradigm for realtime interactive applications with remotely housed biological systems, and which is accessible and useful for scientists, programmers and lay people alike. Our user studies show that scientists and non-scientists are able to rapidly develop a variety of applications, such as interactive biophysics experiments and games. This paradigm has the potential to make first-hand experiences with biology accessible to all of society and to accelerate the rate of scientific discovery.

bioengineering↗

bioRxiv2018

LIPEA: Lipid Pathway Enrichment Analysis

MotivationAnalyzing associations among multiple omic variables to infer mechanisms that meaningfully link them is a crucial step in systems biology. Gene Set Enrichment Analysis (GSEA) was conceived to pursue this aim in computational genomics, unveiling significant pathways associated to certain gene signatures under investigation. Lipidomics is a rapidly growing omic field, and absolute quantification of lipid abundance by shotgun mass spectrometry is generating high-throughput datasets that depict lipid metabolism in a plethora of conditions and organisms. In addition, high-throughput lipidomics represents a new important ally to develop personalized medicine approaches, investigate the causes and predict effective biomarkers in metabolic diseases, and not only.\n\nResultsHere, we present Lipid Pathway Enrichment Analysis (LIPEA), a web-tool for over-representation analysis of lipid signatures and detection of the biological pathways in which they are enriched. LIPEA is a new valid resource for biologists and physicians to mine pathways significantly associated to a set of lipids, helping them to discover whether common and collective mechanisms are hidden behind those lipids. LIPEA was extensively tested and we provide two examples where our system gave successfully results related with Major Depression Disease (MDD) and insulin re-sistance.\n\nAvailabilityThe tool is available as web platform at https://lipea.biotec.tu-dresden.de.

bioinformatics↗