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bioRxiv2025

Intermittent attachments form three-dimensional cell aggregates with emergent fluid properties

In living systems across developmental and cancer biology, populations of cells on surfaces organize themselves into aggregates that mediate function and disease. Recent experimental studies have identified that such aggregates can have emergent fluid-like properties such as surface tension, yet the physical origin of these properties is not clear. Here, we develop a minimal cell-based model in which cell-cell and cell-substrate interactions are governed by active intermittent attachments. We explain the transition of cells from a dilute population to a dense aggregate, and quantify the emergent material properties underpinning this transition. We use our model to interpret experiments on dewetting of aggregates of MDA-MDB-231 cancer cells and shape fluctuations of surface-associated OVCAR3 cell aggregates. Finally, we show how spatial heterogeneity in attachments governs collective chemotaxis of cell aggregates. Together, these results reveal how active intermittent attachments generate cell aggregates with emergent material properties, with broad implications for development and cancer.

biophysics↗

bioRxiv2024

Data driven refinement of gene expression signatures for enrichment analysis

Gene set enrichment methods measure biological process or pathway activation in gene expression data by testing coordinate up- or down-regulation of pathway members in a ranked list of genes. These methods rely on curated, annotated gene sets whose members coordinate expression is an indicator of a process or state. We therefore developed the Molecular Signatures Database (MSigDB), a collection of expertly annotated gene sets. While using, enhancing, and expanding MSigDB, we have observed that some gene sets can lack coordinate expression, especially those derived from canonical pathways. To address this challenge, we developed gene set refinement (GSR), a data-driven approach leveraging large-scale multi-omics compendia to extract context-specific sets, deconvolve heterogeneity, and reveal multiple downstream signaling. We applied this method to address cancer biology questions, and demonstrated successful, targeted refinement of existing MSigDB gene sets.

bioinformatics↗

bioRxiv2024

CLEAR-IT: Contrastive Learning Enabled Accurate Registration of Immune and Tumor cells from multiplexed images with limited labels in a platform-independent manner

Accurate phenotyping of cells in the tumor microenvironment is crucial for understanding cancer biology and developing effective therapies. However, current methods require precise cell segmentations and struggle to generalize across different imaging modalities, limiting their utility in digital pathology. Here, we show that Contrastive Learning Enabled Accurate Registration of Immune and Tumor Cells (CLEAR-IT) overcomes these limitations, providing a robust and versatile tool for cell phenotyping. CLEAR-IT accurately phenotypes cells comparable to state-of-the-art methods, generalizes across multiplex imaging modalities, maintains high performance even with limited number of labels, and enables the extraction of prognostic markers. Additionally, CLEAR-IT can be combined with existing methods to boost their performance, whereas its lack of need for precise cell segmentations significantly reduces training efforts. This method enhances the robustness and efficiency of digital pathology workflows, making it a valuable tool for cancer research and diagnostics.

bioinformatics↗

bioRxiv2025

A Fully Automated, Data-Driven Approach for Dimensionality Reduction and Clustering in Single-Cell RNA-seq Analysis

Single-cell RNA sequencing (scRNA-seq) provides deep insights into cellular heterogeneity but demands robust dimensionality reduction (DR) and clustering to handle high-dimensional, noisy data. Many DR and clustering approaches rely on user-defined parameters, undermining reliability. Even automated clustering methods like ChooseR and MultiK still employ fixed principal component defaults, limiting their full automation. To overcome this limitation, we propose a fully automated clustering approach by integrating scLENS--a method for optimal PC selection--with these tools. Our fully automated approach improves clustering performance by [~]14% for ChooseR and [~]10% for MultiK and identifies additional cell subtypes, highlighting the advantages of adaptive, data-driven DR. HighlightsO_LIFully automated, data-driven clustering pipeline for scRNA-seq analysis. C_LIO_LIAddresses the limitation of fixed principal component defaults in DR. C_LIO_LIData-driven pipeline improves clustering performance by 10-14%. C_LIO_LIPerformance gains are most pronounced on high-sparsity, high-skewness data. C_LI Author SummaryA fundamental goal in modern biology is to create detailed cellular maps of complex tissues to understand their function in health and disease. Achieving this level of detail is now possible through single-cell sequencing, a technology that generates massive, high-dimensional, and noisy datasets of individual cells genetic profiles. However, accurately identifying cell-types from these datasets remains a major analytical hurdle, as current computational methods rely on subjective parameters that compromise reliability and reproducibility. To address this challenge, we developed a fully automated pipeline that integrates scLENS, an adaptive noise filtering method, with automated cell grouping tools. By providing optimal parameters for noise filtering and cell grouping, our pipeline eliminates their reliance on subjective, fixed parameters, thereby significantly improving accuracy in cell-type classification. This tool, characterized by its high clustering efficiency, can accelerate discoveries in fields like cancer biology and immunology by providing a robust and reproducible analytical platform.

bioinformatics↗

bioRxiv2023

Comparative Genomics Exploration of Shared lncRNA-coding genes in Human and All Annotated Plant Genomes: Potential Implications for Cancer Research

Since their discovery in 1990, lncRNAs have been heavily investigated. It is already known that the genomes of plants and animals express large numbers of lncRNAs. In humans, many lncRNA are considered essential players in cancer biology, mainly acting as gene expression regulators in processes like cell proliferation, apoptosis, migration, invasion and stemness. In plants, lncRNAs are involved in vegetative growths, reproduction and stress responses. Yet, in the entirety of the animal and plant kingdoms, the functions and mechanisms of action for the majority of lncRNAs remain largely undiscovered, as do their orthology relationships. The poor conservation of the DNA sequences encoding for these transcripts is hindering the identification of lncRNAs orthologs especially among very distant evolutionary species. In this short study, using an uncomplicated, but creative bioinformatic approach, I searched for sequence homologies between human DNA and 175 annotated plant genomes from NCBI. Using stringent filtering, I found 20 human lncRNA-encoding genes overlapping plant genomic features, including lncRNAs. Evenmore, 3 of these human lncRNA-encoding genes with a potential plant ortholog are reported in multiple databases for lncRNA involved in cancers. This study opens the road for investigating the tumorigenesis in a deep homology context of lncRNAs.

bioinformatics↗

bioRxiv2026

Differential reconfiguration of the RNA-interactome during low glucose stress in memory and cytotoxic T cells

Cellular adaptation to nutrient fluctuation is a fundamental biological process, crucial to cell fate, function, and survival. The choice between aerobic glycolysis and oxidative phosphorylation does not merely satisfy energetic requirements but actively shapes the cellular stress responses. Here we report that during the complex metabolic programming of CD8+ T cells, glucose utilization pathways correlate with the speed and nature of the response to glucose withdrawal. By quantitating systematic RNA-protein interactions in response to glucose withdrawal, we found that effector T cells mount acute transcriptional and post-transcriptional responses to the stress, while memory T cells exhibit slower, more limited responses. The functional dichotomy observed in T cells - between highly glycolytic cytotoxic effector cells and respiratory memory cells - exemplifies how distinct glucose utilization pathways impact immunological fate and function. Understanding the intricate interplay between metabolic modality, glucose pathways, and post-transcriptional control is crucial for deciphering environment adaptations and developing interventions in contexts ranging from immunotherapy to cancer biology.

cell biology↗

bioRxiv2026

Shape, Shrink, Spheroid: A DIY High-throughput Spheroid Generation Device

3D spheroids, which closely replicate three-dimensional cell-cell and cell-extracellular matrix interactions, offer superior predictive capabilities compared to conventional 2D monolayer cultures, positioning them as forward-looking platforms in drug testing, cancer biology, and regenerative medicine. However, high-throughput generation of uniform sized spheroids is still a technological challenge. In one hand, the use of conventional ultra-low attachment (ULA) multiwell plates for this purpose is labour intensive and complex. On the other hand, the use of microfabricated facilities demands cutting-edge infrastructure such as clean room, photolithography, and microfluidic setup which are often unavailable for the resource constrained laboratories. In this study, we addressed these problems by developing a low-cost Do-It-Yourself (DIY), polydimethylsiloxane (PDMS) and agarose-based spheroid generation device, capable of producing and maintaining hundreds of spheroids with minimal user intervention. We have demonstrated two variants based on their size, termed here as S1 and S2 devices which fit into 6-well and 12-well plates, and can generate 600 and 1200 uniform-sized spheroids respectively. We validated our device with various cell lines including primary and cancerous cell lines. We further demonstrated the drug testing capabilities of the device by estimating the IC50 value of the anticancer drug Temozolomide on U87-MG. The value was comparable with the same obtained from the spheroids generated using conventional ULA plates. Additional attachment of a perfusion system made the device suitable for long-term spheroid culture without much user intervention. Furthermore, the devices can also be used for the production of spheroids with gradually changing diameters in a controlled manner, resembling a size gradient. This feature is useful for checking the effect of drugs on different-sized spheroids and for co-culturing spheroids with varying cell densities, mimicking the disease architecture. We have co-cultured two types of the placental trophoblast cells, i.e., extravillous trophoblast (HTR-8) and syncytiotrophoblast (BeWo) with varying densities. In summary, this paper demonstrates a unique DIY method for a high-throughput uniform-sized spheroid generation at a fraction of cost which can be deployed to resource-constrained labs.

bioengineering↗

bioRxiv2020

PhyDOSE: Design of Follow-up Single-cell Sequencing Experiments of Tumors

The combination of bulk and single-cell DNA sequencing data of the same tumor enables the inference of high-fidelity phylogenies that form the input to many important downstream analyses in cancer genomics. While many studies simultaneously perform bulk and single-cell sequencing, some studies have analyzed initial bulk data to identify which mutations to target in a follow-up single-cell sequencing experiment, thereby decreasing cost. Bulk data provide an additional untapped source of valuable information, composed of candidate phylogenies and associated clonal prevalence. Here, we introduce PhyDOSE, a method that uses this information to strategically optimize the design of follow-up single cell experiments. Underpinning our method is the observation that only a small number of clones uniquely distinguish one candidate tree from all other trees. We incorporate distinguishing features into a probabilistic model that infers the number of cells to sequence so as to confidently reconstruct the phylogeny of the tumor. We validate PhyDOSE using simulations and a retrospective analysis of a leukemia patient, concluding that PhyDOSEs computed number of cells resolves tree ambiguity even in the presence of typical single-cell sequencing errors. We also conduct a retrospective analysis on an acute myeloid leukemia cohort, demonstrating the potential to achieve similar results with a significant reduction in the number of cells sequenced. In a prospective analysis, we demonstrate that only a small number of cells suffice to disambiguate the solution space of trees in a recent lung cancer cohort. In summary, PhyDOSE proposes cost-efficient single-cell sequencing experiments that yield high-fidelity phylogenies, which will improve downstream analyses aimed at deepening our understanding of cancer biology. Author summaryCancer development in a patient can be explained using a phylogeny -- a tree that describes the evolutionary history of a tumor and has therapeutic implications. A tumor phylogeny is constructed from sequencing data, commonly obtained using either bulk or single-cell DNA sequencing technology. The accuracy of tumor phylogeny inference increases when both types of data are used, but single-cell sequencing may become prohibitively costly with increasing number of cells. Here, we propose a method that uses bulk sequencing data to guide the design of a follow-up single-cell sequencing experiment. Our results suggest that PhyDOSE provides a significant decrease in the number of cells to sequence compared to the number of cells sequenced in existing studies. The ability to make informed decisions based on prior data can help reduce the cost of follow-up single cell sequencing experiments of tumors, improving accuracy of tumor phylogeny inference and ultimately getting us closer to understanding and treating cancer.

bioinformatics↗

bioRxiv2023

A novel Bayesian model for assessing intratumor heterogeneity of tumor infiltrating leukocytes with multi-region gene expression sequencing

Intratumor heterogeneity (ITH) of tumor-infiltrated leukocytes (TILs) is an important phenomenon of cancer biology with potentially profound clinical impacts. Multiregion gene expression sequencing data provide a promising opportunity that allows for explorations of TILs and their intratumor heterogeneity for each subject. Although several existing methods are available to infer the proportions of TILs, considerable methodological gaps exist for evaluating intratumor heterogeneity of TILs with multi-region gene expression data. Here, we develop ICeITH, immune cell estimation reveals intratumor heterogeneity, a Bayesian hierarchical model that borrows cell type profiles as prior knowledge to decompose mixed bulk data while accounting for the within-subject correlations among tumor samples. ICeITH quantifies intratumor heterogeneity by the variability of targeted cellular compositions. Through extensive simulation studies, we demonstrate that ICeITH is more accurate in measuring relative cellular abundance and evaluating intratumor heterogeneity compared with existing methods. We also assess the ability of ICeITH to stratify patients by their intratumor heterogeneity score and associate the estimations with the survival outcomes. Finally, we apply ICeITH to two multi-region gene expression datasets from lung cancer studies to classify patients into different risk groups according to the ITH estimations of targeted TILs that shape either pro- or anti-tumor processes. In conclusion, ICeITH is a useful tool to evaluate intratumor heterogeneity of TILs from multi-region gene expression data.

bioinformatics↗

bioRxiv2019

Mechanosensitive regulation of FGFR1 through the MRTF-SRF pathway

Controlling basic fibroblast growth factor (bFGF) signaling is important for both tissue-engineering purposes, controlling proliferation and differentiation potential, and for cancer biology, influencing tumor progression and metastasis. Here, we observed that human mesenchymal stromal cells (hMSCs) no longer responded to soluble or covalently bound bFGF when cultured on microfibrillar substrates, while fibroblasts did. This correlated with a downregulation of FGF receptor 1 (FGFR1) expression of hMSCs on microfibrillar substrates, compared to hMSCs on conventional tissue culture plastic (TCP). hMSCs also expressed less SRF on ESP scaffolds, compared to TCP, while fibroblasts maintained high FGFR1 and SRF expression. Inhibition of actin-myosin tension or the MRTF/SRF pathway decreased FGFR1 expression in hMSCs, fibroblasts and MG63 osteosarcoma cells. This downregulation was functional, as hMSCs became irresponsive to bFGF in the presence of MRTF/SRF inhibitor. Together, our data show that hMSCs, but not fibroblasts, are irresponsive to bFGF when cultured on microfibrillar susbtrates by downregulation of FGFR1 through the MRTF/SRF pathway. This is the first time FGFR1 expression has been shown to be mechanosensitive and adds to the sparse literature on FGFR1 regulation. These results could open up new targets for cancer treatments and could aid designing tissue engineering constructs that better control cell proliferation.

bioengineering↗

bioRxiv2026

A translational checkpoint couples proline sensing to mitochondrial proline catabolism in Candida glabrata

Proline catabolism represents a central metabolic and regulatory hub integrating nutrient sensing, stress adaptation, and energy production across diverse organisms. Despite its biological importance, the regulatory mechanisms controlling proline catabolism remain poorly understood in eukaryotic microbes. Here, we describe the transcriptional and translational coordination to catabolize the proline to maintain the cellular homeostasis under stress in the human fungal pathogen Candida glabrata. We identified proline utilisation trigger global translation repression to activates the stress-sensing kinase Gcn2, which phosphorylates eIF2, thereby promoting the activation of the transcription factor Gcn4. Activated Gcn4 upregulates the transcription factor Put3 and the proline transporter Put4. Put3 orchestrates expression of the mitochondrial catabolic enzymes Put1 and Put2, ensuring efficient proline utilization, mitochondrial function, and redox balance. Genetic disruption of PUT3 abolishes proline utilization, impairs mitochondrial function, and severely compromises cellular fitness. Importantly, Put3-mediated proline catabolism is also critical for C. glabrata survival within macrophages and for virulence in systemic infection models. These findings reveal a mechanistic link between proline catabolism, translational regulation, and amino acid sensing in C. glabrata. We propose a regulatory cascade wherein Gcn2-Gcn4-Put3 signaling aligns translational reprogramming with metabolic demands to optimize proline utilization. Thus, this study establishes proline catabolism as a signaling-driven adaptive mechanism essential for fungal metabolism and persistence, rather than merely a nutritional pathway. AUTHOR SUMMARYProline is a versatile amino acid that is essential for cellular metabolism, signaling, stress adaptation, and redox equilibrium. Proline catabolism has been implicated in cancer biology and is increasingly recognized as a key determinant of virulence in diverse pathogens. Although the enzymatic processes of proline use are well characterized, the regulatory mechanisms that sense proline availability and coordinate its metabolic integration remain poorly understood. Here, we identify a hitherto unknown regulatory axis linking proline catabolism to translational reprogramming in Candida glabrata, which seems to be similarly present in numerous fungi. We demonstrate that proline utilization triggers global translational repression via Gcn2-dependent phosphorylation of eIF2, thereby activating the transcription factor Gcn4. Gcn4 is essential for proline utilization, as it controls the expression of PUT3 and PUT4. Notably, PUT3 has no known human counterpart. Our findings establish proline as a metabolic signal that couples translational control to virulence, revealing new opportunities for antifungal intervention.

microbiology↗

bioRxiv2020

Anti-cancer Potential of Moringa oleifera on BRCA1 Gene: Systems Biology

Breast Cancer has always been a global challenge that is prevalent among women. There is a continuous increase in the high number of women mortality rates as a result of breast cancer and affecting countries at all levels of modernization. Women with high-risk factors including hereditary, obesity, and menopause have the possibility of developing breast cancer. With the advent of radiotherapy, chemotherapy, hormone therapy, and surgery in the treatment of breast cancer, there has an increased number of breast cancer survivors. Also, the design and development of drugs targeting therapeutic enzymes are helping to effectively treat the tumor cells at an early stage. However, long term use of anti-cancer drugs has been linked to severe side effects. This research aims to develop potential drug candidates from Moringa oleifera which could serve as anti-cancer agents. In silico analysis using Schrodinger Molecular Drug Discovery Suite and SWISS ADME was employed to determine the therapeutic potential of phytochemicals from M. oleifera against breast cancer via molecular docking, pharmacokinetic parameters, and drug-like properties. The result shows that Rutin, Vicenin-2, and Quercetin-3-O-glucoside have the highest binding energy of -7.522, -6.808, -6.635kcal/mol respectively in the active site of BRCA1. The essential amino acids involved in the protein-ligand interaction following active site analysis are ASN 1678, ASN 1774, GLY 1656, LEU 1657, GLN 1779, LYS 1702, SER 1655, PHE 1662, ARG 1699, GLU 1698, and VAL 1654. Thus, we propose that bioactive compounds from M. oleifera may be potential hit drug candidates against breast cancer.

systems biology↗

bioRxiv2019

Finding a suitable library size to call variants in RNA-seq

BackgroundRNA-Seq allows the study of both gene expression changes and transcribed mutations, providing a highly effective way to gain insight into cancer biology. When planning the sequencing of a large cohort of samples, library size is a fundamental factor affecting both the overall cost and the quality of the results. While several studies analyse the effect that library size has on differential expression analyses, sensitivity analysis for variant detection has received far less attention. ResultsWe simulated shallower sequencing depths by downsampling 45 AML samples that are part of the Leucegene project, which were originally sequenced at high depth. We compared the sensitivity of six methods of recovering validated mutations on the same samples. The methods compared are a combination of three popular callers (MuTect, VarScan, and VarDict) and two filtering strategies. We observed an incremental loss in sensitivity when simulating libraries of 80M, 50M, 40M, 30M and 20M fragments, with the largest loss detected with less than 30M fragments (below 90%). The sensitivity in recovering indels varied markedly between callers, with VarDict showing the highest sensitivity (60%). Single nucleotide variant sensitivity is relatively consistent across methods, apart from MuTect, whose default filters need adjustment when using RNA-Seq. We also analysed 136 RNA-Seq samples from the TCGA-LAML cohort, assessing the change in sensitivity between the initial libraries (average 59M fragments) and after downsampling to 40M fragments. When considering single nucleotide variants in recurrently mutated myeloid genes we found a comparable performance, with a 3% average loss in sensitivity using 40M fragments. ConclusionsBetween 30M and 40M fragments are needed to recover 90%-95% of the initial variants on recurrently mutated myeloid genes. To extend this result to another cancer type, an exploration of the characteristics of its mutations and gene expression patterns is suggested.

bioinformatics↗

bioRxiv2016

Personalized characterization of diseases using sample-specific networks

A complex disease generally results not from malfunction of individual molecules but from dysfunction of the relevant system or network, which dynamically changes with time and conditions. Thus, estimating a condition-specific network from a sample is crucial to elucidating the molecular mechanisms of complex diseases at the system level. However, there is currently no effective way to construct such an individual-specific network by expression profiling of a single sample because of the requirement of multiple samples for computing correlations. We developed here with a statistical method, i.e., a sample-specific network method, which allows us to construct individual-specific networks based on molecular expression of a single sample. Using this method, we can characterize various human diseases at a network level. In particular, such sample-specific networks can lead to the identification of individual-specific disease modules as well as driver genes, even without gene sequencing information. Extensive analysis by using the Cancer Genome Atlas data not only demonstrated the effectiveness of the method, but also found new individual-specific driver genes and network patterns for various cancers. Biological experiments on drug resistance further validated one important advantage of our method over the traditional methods, i.e., we even identified those drug resistance genes that actually have no clearly differential expression between samples with and without the resistance, due to the additional network information.

Systems Biology↗

bioRxiv2016

Rapidly evolving homing CRISPR barcodes

We present here an approach for engineering evolving DNA barcodes in living cells. The methodology entails using a homing guide RNA (hgRNA) scaffold that directs the Cas9-hgRNA complex to target the DNA locus of the hgRNA itself. We show that this homing CRISPR-Cas9 system acts as an expressed genetic barcode that diversifies its sequence and that the rate of diversification can be controlled in cultured cells. We further evaluate these barcodes in cultured cell populations and show that they can record lineage history and and that their RNA can be assayed as single molecules in situ. This integrated approach will have wide ranging applications, such as in deep lineage tracing, cellular barcoding, molecular recording, dissecting cancer biology, and connectome mapping.

Synthetic Biology↗

bioRxiv2016

Identification of outcome-related driver mutations in cancer using conditional co-occurrence distributions

The methods proposed for the detection of cancer driver mutations are based on the estimation of background mutation rate, impact on protein function, or network influence. Instead, we focus on those influencing patient survival. For this, an approximation of the log-rank test has been systematically applied even though it assumes a large and similar number of patients in both risk groups, which is violated in cancer genomics. Here, we propose VALORATE, a novel algorithm for the estimation of the null distribution for the log-rank test independently of the number of mutations. VALORATE is based on conditional distributions of the co-occurrences between events and mutations. The results using simulations, comparisons with other methods, TCGA and ICGC cancer datasets, and validations, suggests that VALORATE is accurate, fast, and can identify known and novel gene mutations. Our proposal and results may have important implications in cancer biology, in bioinformatics analyses, and ultimately in precision medicine.

Bioinformatics↗

bioRxiv2017

Prominent Features Of The Amino Acid Mutation Landscape In Cancer

Cancer can be viewed as a set of different diseases with distinctions based on tissue origin, driver mutations, and genetic signatures. Accordingly, each of these distinctions have been used to classify cancer subtypes and to reveal common features. Here, we present a different analysis of cancer based on amino acid mutation signatures. Non-negative Matrix Factorization and principal component analysis of 29 cancers revealed six amino acid mutation signatures, including four signatures that were dominated by either arginine to histidine (Arg>His) or glutamate to lysine (Glu>Lys) mutations. Sample-level analyses reveal that while some cancers are heterogeneous, others are largely dominated by one type of mutation. Using a non-overlapping set of samples from the COSMIC somatic mutation database, we validate five of six mutation signatures, including signatures with prominent arginine to histidine (Arg>His) or glutamate to lysine (Glu>Lys) mutations. This suggests that our classification of cancers based on amino acid mutation patterns may provide avenues of inquiry pertaining to specific protein mutations that may generate novel insights into cancer biology.

bioinformatics↗

bioRxiv2020

Loss of Tet2 affects proliferation and drug sensitivity through altered dynamics of cell-state transitions

A persistent puzzle in cancer biology is how mutations, which neither alter canonical growth signaling pathways nor directly interfere with drug mechanism, can still recur and persist in tumors. One notable example is the loss-of-function mutation of the DNA demethylase Tet2 in acute myeloid leukemias (AMLs) that frequently persists from diagnosis through remission and relapse (Rothenberg-Thurley et al., 2018; Corces-Zimmerman et al., 2014; Nibourel et al., 2010), but whose fitness advantage in the setting of anti-leukemic chemotherapy is unclear. Here we use paired isogenic human AML cell lines to show that Tet2 loss-of-function alters the dynamics of transitions between differentiated and stem-like states. Mathematical modeling and experimental validation reveal that these altered cell-state dynamics can benefit the cell population by slowing population decay during drug treatment and lowering the number of survivor cells needed to re-establish the initial population. These studies shed light on the functional and phenotypic effects of a Tet2 loss-of-function in AML, illustrate how a single gene mutation can alter a cells phenotypic plasticity, and open up new avenues in the development of strategies to combat AML relapse.

systems biology↗