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bioRxiv2018

Modeling genome-wide evolution of catalytic turnover rates: Strong epistasis shaped modern enzyme kinetics

Systems biology describes cellular phenotypes as properties that emerge from the complex interactions of individual system components. Little is known about how these interactions have affected the evolution of metabolic enzymes. To address this question, we combine genome-scale metabolic modelling with population genetics models to simulate the evolution of enzyme turnover numbers (kcats) from a theoretical ancestor with inefficient enzymes. This systems view of biochemical evolution reveals strong epistatic interactions between metabolic genes that shape evolutionary trajectories and influence the magnitude of evolved kcats. A small number of biophysically constrained enzymes suffice to induce diminishing returns epistasis that prevents enzymes from developing higher kcats in all reactions and keeps the organism far from the potential fitness optimum. In addition, multifunctional enzymes cause synergistic epistasis that slows down adaptation. The resulting fitness landscape is smooth and causes kcat evolution to be convergent. Predicted kcat parameters show a significant correlation with experimental data on in vitro and in vivo turnover rates, validating our modelling approach. Our analysis thus suggests that enzyme evolution can be predicted on a genome scale and reveals the mechanisms by which evolutionary forces shape modern kcats and the whole of cell metabolism.

evolutionary biology↗

bioRxiv2020

Inclusion of processed cell metadata improves single cell sequencing analysis reproducibility and accessibility

Single cell RNA sequencing provides an unprecedented view of cellular diversity of biological systems. Thousands of scRNA-seq datasets have been generated, providing a wealth of biological data on the diversity of cell types across different organisms, developmental stages, and disease states. But while a tremendous number of publications and datasets have been generated using this technology, we found that a minority (< 25%) of studies provide sufficient information to enable direct reuse of their data for further studies. This problem is common across journals, data repositories, and publication dates. The lack of appropriate information not only hinders exploration and knowledge transfer of reported data, but also makes reproducing the original study prohibitively difficult and/or time-consuming. Correcting this problem is not easy but we encourage investigators, reviewers, journals, and data repositories to take steps to improve their standards and ensure proper documentation of these valuable datasets.

bioinformatics↗

bioRxiv2025

RealTrace: Uncovering biological dynamics hidden under measurement noise in time-lapse microscopy data

One of the most powerful approaches for identifying the mechanisms underlying complex biological phenomena is not just to measure bulk populations, or even take single-cell snapshots, but to directly track the behaviors of single cells in time. Indeed, in recent years there has been a steep rise in the use of time-lapse microscopy with fluorescent reporters to quantify single-cell time dynamics in many different biological systems. However, there is a major challenge with the analysis of such data. Since biological changes are inherently small on short time scales, much of the true biological dynamics is hidden under unavoidable measurement noise, but we do not know what functional form the time dynamics may take, and smoothing over neighboring time points simply replaces the true dynamics with a time average. Here, we present RealTrace, a Bayesian method that rigorously solves this challenge using only the assumption that, while true biological fluctuations are correlated on short time scales, measurement errors are uncorrelated. We show that this assumption can be formalized in a Bayesian model using maximum entropy process priors and that this model can be accurately solved by recursively approximating the non-linear dynamics over short time intervals. Given raw input cell size and fluorescence measurements, RealTrace calculates the estimated true sizes, total GFP contents, instantaneous growth rates, and volumic GFP production rates across all cells and time, together with rigorous error bars on all these quantities. We demonstrate the broad applicability of RealTrace by analyzing time-lapse microscopy datasets from E. coli bacteria, mouse embryonic stem cells, and entire C. elegans embryos. To exemplify the subtle dynamical features RealTrace can uncover, we perform an in-depth study of E. coli cells carrying fluorescent reporters for constitutive and ribosomal promoters, across multiple growth conditions. We find growth rates of single cells substantially fluctuate in time and, as the average growth rate decreases, these fluctuations systematically increase in amplitude and duration. Both growth rate and gene expression vary systematically across the cell cycle, but while the pattern of growth rate variation is condition-independent, the systematic variation in gene expression across the cell cycle is both condition and promoter-dependent. Finally, under a sudden change in nutrients, single cells exhibit highly consistent transient changes in growth rate and gene expression. While gene expression rates settle into a new steady-state surprisingly rapidly, the growth rate shows a dramatic drop followed by an overshoot that takes several cell cycles to settle down. All these observations give fundamental new insights into the single-cell physiology of bacteria. RealTrace is a highly versatile method that accurately removes measurement noise from any fluorescence time-lapse microscopy data, enabling the identification and quantification of subtle features in the biological dynamics that would otherwise be hidden under measurement noise.

bioinformatics↗

bioRxiv2025

Cryoprotectants-assisted plunge freezing of thick brain tissue specimens for targeted physiologically relevant cryo-imaging in situ

In situ cryoET (cryoelectron tomography) and cryo-FIB/SEM (cryo-focused ion beam/scanning electron microscopy) volume-EM (electron microscopy) imaging provide spatiotemporal snapshots of biological systems in their near-native aqueous environment. Freezing and subsequent thinning of thick biological specimens prior to cryo-imaging is a time-consuming and challenging task that requires state-of-art methodology. As a result, cryo-imaging reports obtained from non-trivial specimens including mammalian brain tissues are scarce and their physiological relevance remains to be determined. Here, we benchmarked plunge freezing with a variety of cryoprotectants that allow for mouse brain tissue vitrification of up to about 100 microns thick and across several brain regions while keeping the tissue functional. By utilizing the knock-in (KI) mouse model with fluorescent astrocytes we have performed targeted cryo-FIB/SEM volume-EM imaging as well as targeted high-resolution cryoET imaging. Prior to cryoET, we have successfully generated lamellae in a semi-automated fashion on both LMIS (liquid metal ion source)- and plasma-based cryo-FIB/SEM instrumentation thus expanding applicability of our pipeline. We visualized the NVU (neurovascular unit) and astrocytes processes and validated the physiological relevance of our outputs based on the morphology of the corresponding cellular and subcellular features. The pipeline utilizes common vitrification setups and can be potentially extended toward alternative tissue specimens. Ultimately, we expect our approach to become an important step towards democratization of physiologically relevant in situ cryo-imaging studies.

biophysics↗

bioRxiv2025

Dynamic and Catalytic Multiphase Coacervates

Subcellular organization is critical for myriad cellular functions, including spatiotemporal regulation of biocatalysts. Liquid-liquid phase separation contributes to intracellular organization by providing microenvironments that sequester biomolecules and control their activities. Although catalytic reactions in multiphase coacervate systems are important for living cells, provide functionality to artificial systems, and may aid understanding of how metabolism first arose, they are not well understood. Here we report an artificial multiphase system in which catalytically active droplets containing three distinct coexisting coacervate phases undergo reorganization in response to pH, enabling control over chemical reactions. Catalysis is performed by an inner, polyhistidine-dense coacervate phase with inherent nonenzymatic activity. We show how phase composition, in addition to pH, affects catalytic activity, and how the addition of enzymes that alter local pH provides a means to regulate composition and catalysis. By exploiting competitive interplay between intramolecular self-association and intermolecular complexation among polymers within multiphase coacervate systems, it was possible to (re)organize and tune the reactivity of dynamic synthetic biological systems beyond what is possible for single-phase coacervates. Such approaches could prove valuable in designing artificial cells and related microreactor systems capable of catalyzing and controlling complex chemical and biochemical processes.

biochemistry↗

bioRxiv2025

NetFlow: A Framework to Explore Topological Representations of High-Dimensional Biomedical Data

Biomedical datasets are increasingly high-dimensional and multi-modal, theoretically enabling a more comprehensive understanding of biological systems. However, common clustering and dimensionality reduction methods often oversimplify continua and obscure complex, biologically relevant patterns, hindering our ability to meaningfully probe sample relationships. To address this challenge, we developed NetFlow, a computational framework that constructs graph representations of structural relationships between samples by integrating a data-driven, lineage-tracing-inspired pseudo-ordering with sparse similarity networks. NetFlow is suitable for small cohorts, features a flexible, modular design, supports multi-modal data fusion, and provides interactive visualization, enabling nuanced exploration of heterogeneity across biological samples. Applied across uni- and multi-modal cancer datasets, NetFlow refined clinically relevant subtypes, identified biomarker associations, and revealed more informative and structured relational patterns when benchmarked against current approaches. NetFlow thus delivers an interpretable graph representation of sample relatedness that supports precision oncology, hypothesis generation, and general high-dimensional data analysis.

bioinformatics↗

bioRxiv2018

Characterizing Building Blocks of Resource Constrained Biological Networks

Identification of motifs-recurrent and statistically significant patterns-in biological networks is the key to understand the design principles, and to infer governing mechanisms of biological systems. This, however, is a computationally challenging task. This task is further complicated as biological interactions depend on limited resources, i.e., a reaction takes place if the reactant molecule concentrations are above a certain threshold level. This biochemical property implies that network edges can participate in a limited number of motifs simultaneously. Existing motif counting methods ignore this problem. This simplification often leads to inaccurate motif counts (over-or under-estimates), and thus, wrong biological interpretations. In this paper, we develop a novel motif counting algorithm, Partially Overlapping MOtif Counting (POMOC), that considers capacity levels for all interactions in counting motifs. Our experiments on real and synthetic networks demonstrate that motif count using the POMOC method significantly differs from the existing motif counting approaches, and our method extends to large-scale biological networks in practical time. Our results also show that our method makes it possible to characterize the impact of different stress factors on cells organization of network. In this regard, analysis of a S. cerevisiae transcriptional regulatory network using our method shows that oxidative stress is more disruptive to organization and abundance of motifs in this network than mutations of individual genes. Our analysis also suggests that by focusing on the edges that lead to variation in motif counts, our method can be used to find important genes, and to reveal subtle topological and functional differences of the biological networks under different cell states.

bioinformatics↗

bioRxiv2018

A Multimodal Adaptive Super-Resolution and Confocal Microscope

Existing optical microscopy techniques compromise between resolution, photodamage, speed of acquisition and imaging in to deep samples. This often confines a technique to a certain biological system or process. We present a versatile imaging system which can switch between imaging modalities with sub millisecond transition times to adapt to the needs of a wide range of sample types. The imaging modalities provide the minimally invasive but low-resolution epi-fluorescence though increasing invasive but higher resolution confocal and structured illumination until the highest resolution is achieved through the most intrusive, localisation microscopy. The ability of the system to overcome the limitations of conventional single mode microscopy is demonstrated by several biological investigations. The ideas presented in this work allow researchers to move away from the model of a single imaging modality to study a specific process and instead follow those processes using the most suitable method available during the lifetime of the investigation.

biophysics↗

bioRxiv2018

Enhancing biochemical resolution by hyper-dimensional imaging microscopy

Two decades of high-paced innovation have improved the spatial resolution of fluorescence microscopy to enable molecular resolution combined with the low-invasiveness and specificity characteristic of optical microscopy. However, fluorescence microscopy also enables scientists and clinicians to map and quantitate the physico-chemical properties (e.g., analyte concentration, enzymatic activities and protein-protein interactions) of biological samples. But the optimization of the biochemical resolving power in fluorescence microscopy is not as well-developed compared to its spatial resolution. Typical techniques rely on the observation of individual properties of fluorescence thus limiting the opportunities for sensing and multiplexing. Aiming to overcome existing limitations, we demonstrate a new imaging paradigm -- Hyper Dimensional Imaging Microscopy (HDIM) -- that enables the orthogonal properties of fluorescence emission (excited state lifetime, polarization and spectra) in biological samples to be quantified simultaneously and efficiently. Therefore, akin to how multi-dimensional separation in mass-spectroscopy and multi-dimensional spectra in NMR impacted proteomics and structural biology, we envisage that HDIM spectra of unprecedented dimensionality will impact the fields of systems biology and medical diagnostics by maximizing the biochemical resolving power of fluorescence microscopy.

biophysics↗

bioRxiv2019

Dissociation of solid tumour tissues with cold active protease for single-cell RNA-seq minimizes conserved collagenase associated stress responses

BackgroundSingle-cell RNA sequencing (scRNAseq) is a powerful tool for studying complex biological systems, such as tumour heterogeneity and tissue microenvironments. However, the sources of technical and biological variation in primary solid tumour tissues and patient-derived mouse xenografts for scRNAseq, are not well understood. Here, we used low temperature (6{degrees}C) protease and collagenase (37{degrees}C) to identify the transcriptional signatures associated with tissue dissociation across a diverse scRNAseq dataset comprising 128,481 cells from patient cancer tissues, patient-derived breast cancer xenografts and cancer cell lines.\n\nResultsWe observe substantial variation in standard quality control (QC) metrics of cell viability across conditions and tissues. From FACS sorted populations gated for cell viability, we identify a sub-population of dead cells that would pass standard data filtering practices, and quantify the extent to which their transcriptomes differ from live cells. We identify a further subpopulation of transcriptomically \"dying\" cells that exhibit up-regulation of MHC class I transcripts, in contrast with live and fully dead cells. From the contrast between tissue protease dissociation at 37{degrees}C or 6{degrees}C, we observe that collagenase digestion results in a stress response. We derive a core gene set of 512 heat shock and stress response genes, including FOS and JUN, induced by collagenase (37{degrees}C), which are minimized by dissociation with a cold active protease (6{degrees}C). While induction of these genes was highly conserved across all cell types, cell type-specific responses to collagenase digestion were observed in patient tissues. We observe that the yield of cancer and non-cancer cell types varies between tissues and dissociation methods.\n\nConclusionsThe method and conditions of tumour dissociation influence cell yield and transcriptome state and are both tissue and cell type dependent. Interpretation of stress pathway expression differences in cancer single cell studies, including components of surface immune recognition such as MHC class I, may be especially confounded. We define a core set of 512 genes that can assist with identification of such effects in dissociated scRNA-seq experiments.

genomics↗

bioRxiv2026

Complete Mutagenesis of the Genome and Proteome of {Phi}X174

The bacteriophage {Phi}X174 was the first genome to be sequenced and the first to be chemically synthesised. Here we present a complete map of the consequences of changing every nucleotide in the {Phi}X174 genome and every amino acid in the proteome. In total, half of nucleotide and >60% of amino acid changes impair fitness. However, this varies substantially across proteins and non-coding regions. Surprisingly, the most important mechanistic cause of reduced fitness is disruption of protein interaction interfaces, accounting for nearly half of detrimental protein variants. A further one quarter of damaging variants disrupt protein cores but one in four lack a mechanistic explanation. Mutational effects are only moderately-well predicted by state-of-the-art artificial intelligence models, but, combined with structural modelling, provide mechanistic hypotheses and insights into the DNA replication machinery. This complete mutagenesis of a biological system quantifies the limits of our current understanding of, and ability to predict, molecular biology.

genomics↗

bioRxiv2022

Characterization of Integrase and Excisionase Activity in Cell-free Protein Expression System Using a Modeling and Analysis Pipeline

We present a full-stack modeling, analysis, and parameter identification pipeline to guide the modeling and design of biological systems starting from specifications to circuit implementations and parameterizations. We demonstrate this pipeline by characterizing the integrase and excisionase activity in cell-free protein expression system. We build on existing Python tools -- BioCRNpyler, AutoReduce, and Bioscrape -- to create this pipeline. For enzyme-mediated DNA recombination in cell-free system, we create detailed chemical reaction network models from simple high-level descriptions of the biological circuits and their context using BioCRNpyler. We use Bioscrape to show that the output of the detailed model is sensitive to many parameters. However, parameter identification is infeasible for this high-dimensional model, hence, we use AutoReduce to automatically obtain reduced models that have fewer parameters. This results in a hierarchy of reduced models under different assumptions to finally arrive at a minimal ODE model for each circuit. Then, we run sensitivity analysis-guided Bayesian inference using Bioscrape for each circuit to identify the model parameters. This process allows us to quantify integrase and excisionase activity in cell extracts enabling complex-circuit designs that depend on accurate control over protein expression levels through DNA recombination. The automated pipeline presented in this paper opens up a new approach to complex circuit design, modeling, reduction, and parameterization.

synthetic biology↗

bioRxiv2025

Mycoelectronics: Bioprinted Living Fungal Bioelectronics for Artificial Sensation

The intelligence of the human biological system is enabled by the highly distributed sensing receptors on soft skin that can distinguish various stimulations or environmental cues, thus establishing the fundamental logic of sensing and physiological regulation or response. To replicate biological perception, two approaches have emerged: artificial nervous systems that utilize soft electronics as biomimetic receptors to convert external stimuli into frequency-encoded signals, and biohybrid solutions that integrate living cells, plants, or even live animals with electronic components to decode environmental cues for life-like sensations. However, most current biohybrid approaches for artificial sensation are based on eukaryotic cells, which suffer from slow growth, stringent culture conditions, environmental susceptibility, and short lifespans, thus limiting their integration into practical wearables or robotic sensory skins. Here, we introduce fungi-based printable "Mycoelectronics", which are created by additive bioprinting of living fungal mycelium networks onto stretchable electronics, as a practical living thermo-responsive sensory platform. This Mycoelectronics approach leverages fungis capacity for rapid biological responsiveness, cultivability with exponential growth, stability and self-healing in ambient conditions, bioprintability for scalable manufacturing, and mechanical flexibility for seamless integration with soft electronics. Critically, we discovered that the thermal responsiveness of the fungal network arises from intrinsic cellular processes--specifically, heat-induced vacuole remodeling and fusion, which modulate ionic transport and thus the electrical conductivity of the mycelial cells and networks, enabling a rapid temperature response. By bridging the gap between cell biology and soft electronics, the Mycoelectronics device with a living mycelium network functions as a thermal sensation system with rapid response and intrinsic self-healing properties, autonomously restoring sensing capabilities after damage or autonomously establishing sensor pathways in hard-to-reach locations. Furthermore, by integrating fungal thermal sensing with electronic circuits, we established a hybrid bioelectronic reflex arc that can actuate muscles and initiate diverse actions, suggesting promising applications in future neurorobotics and neuroprosthetics.

bioengineering↗

bioRxiv2019

An improved auxin-inducible degron system preserves native protein levels and enables rapid and specific protein depletion

Rapid perturbation of protein function permits the ability to define primary molecular responses while avoiding down-stream cumulative effects of protein dysregulation. The auxin-inducible degron (AID) system was developed as a tool to achieve rapid and inducible protein degradation in non-plant systems. However, tagging proteins at their endogenous loci results in chronic, auxin-independent degradation by the proteasome. To correct this deficiency, we expressed the Auxin Response Transcription Factor (ARF) in an improved inducible degron system. ARF is absent from previously engineered AID systems, but ARF is a critical component of native auxin signaling. In plants, ARF directly interacts with AID in the absence of auxin and we found that expression of the ARF Phox and Bem1 (PB1) domain suppresses constitutive degradation of AID-tagged proteins. Moreover, the rate of auxin-induced AID degradation is substantially faster in the ARF-AID system. To test the ARF-AID system in a quantitative and sensitive manner, we measured genome-wide changes in nascent transcription after rapidly depleting the ZNF143 transcription factor. Transciptional profiling indicates that ZNF143 activates transcription in cis and ZNF143 regulates promoter-proximal paused RNA Polymerase density. Rapidly inducible degradation systems that preserve the target proteins native expression levels and patterns will revolutionize the study of biological systems by enabling specific and temporally defined protein dysregulation.

molecular biology↗

bioRxiv2017

Collateral sensitivity to β-lactam drugs in drug-resistant tuberculosis is driven by the transcriptional wiring of BlaI operon genes

BackgroundThe evolution and spread of antimicrobial resistance is a major global public health threat. In some cases the evolution of resistance to one antimicrobial seemingly results in enhanced sensitivity to another (known as collateral sensitivity). This largely underexplored phenomenon represents a fascinating evolutionary paradigm that opens new therapeutic possibilities for patients infected with pathogens unresponsive to classical treatments. Intrinsic resistance to {beta}-lactams in Mycobacterium tuberculosis (Mtb, the causative agent of tuberculosis) has traditionally curtailed the use of these low-cost and easy-to-administer drugs for tuberculosis treatment. Recently, {beta}-lactam sensitivity has been reported in strains resistant to classical tuberculosis drug therapy, leading to a resurgence of interest in using {beta}-lactams in the clinic. Unfortunately though, there remains a limited understanding of the mechanisms driving {beta}-lactam sensitivity.\n\nMethodsWe used a novel combination of systems biology and computational approaches to characterize the molecular underpinnings of {beta}-lactam sensitivity in Mtb. We performed differential gene expression and coexpression analyses of genes previously associated with {beta}-lactam sensitivity and genes associated with resistance to classical tuberculosis drugs. Protein-protein interaction and gene regulatory network analyses were used to validate regulatory interactions between these genes, and random walks through the networks identified key mediators of these interactions. Further validation was obtained using functional in silico knockout of gene pairs.\n\nResultsOur results reveal up regulation of the key regulatory inhibitor of {beta}-lactamase production, blal, following treatment with classical drugs. Co-expression and network analyses showed direct co-regulation between genes associated with {beta}-lactam sensitivity and those associated with resistance to classical tuberculosis treatment. blal and its downstream genes (sigC and atpH) were found to be key mediators of these interactions.\n\nConclusionsOur results support the hypothesis that Mtb {beta}-lactam sensitivity is a collateral consequence of the evolution of resistance to classical tuberculosis drugs, mediated through changes to transcriptional regulation. These findings support continued exploration of {beta}-lactams for the treatment of tuberculosis, particularly for patients infected with strains resistant to classical therapies that are otherwise difficult to treat. Importantly, this work also highlights the potential of systems-level and network biology approaches to improve our understanding of collateral drug sensitivity.

microbiology↗

bioRxiv2020

Distinct evolutionary trajectories in asexual populations through an interplay of their size, resource availability and mutation rates

Asexually reproducing populations of single cells evolve through mutation, natural selection, and genetic drift to enhance their reproductive fitness. The environment provides the contexts that allow and regulate their fitness dynamics. In this work, we used Avida - a digital evolution framework - to uncover the effect of mutation rates, maximum size of the population, and the relative abundance of resources, on evolutionary outcomes in asexually reproducing populations of digital organisms. We observed that over extended simulations, the population evolved predominantly to one of several discrete fitness classes, each with distinct sequence motifs and/or phenotypes. For a low mutation rate, the organisms acquired either of four fitness values through an enhancement in the rate of genomic replication. Evolution at a relatively higher mutation rate presented a more complex picture. While the highest fitness values at a high mutation rate were achieved through enhanced genome replication rates, a suboptimal one was achieved through organisms sharing information relevant to metabolic tasks with each other. The information sharing capacity was vital to fitness acquisition and frequency of the genotype associated with it increased with greater resource levels and maximum population size. In addition, populations optimizing their fitness through such means exhibited a greater degree of genotypic heterogeneity and metabolic activity than those that improved replication rates. Our results reveal a minimal set of conditions for the emergence of interdependence within evolving populations with significant implications for biological systems in appropriate environmental contexts.

evolutionary biology↗

bioRxiv2021

Representation learning of RNA velocity reveals robust cell transitions

AO_SCPLOWBSTRACTC_SCPLOWRNA velocity is a promising technique to reveal transient cellular dynamics among a heterogeneous cell population and quantify their transitions from single-cell transcriptome experiments. However, the cell transitions estimated from high dimensional RNA velocity are often unstable or inaccurate, partly due to the high technical noise and less informative projection. Here, we present VeloAE, a tailored representation learning method to learn a low-dimensional representation of RNA velocity on which cell transitions can be robustly estimated. From various experimental datasets, we show that VeloAE can both accurately identify stimulation dynamics in time-series designs and effectively capture the expected cellular differentiation in different biological systems. VeloAE therefore enhances the usefulness of RNA velocity for studying a wide range of biological processes.

bioinformatics↗

bioRxiv2024

Computational identification of surface markers for isolating distinct subpopulations from heterogeneous cancer cell populations

Intratumor heterogeneity reduces treatment efficacy and complicates our understanding of tumor progression. There is a pressing need to understand the functions of heterogeneous tumor cell subpopulations within a tumor, yet biological systems to study these processes in vitro are limited. With the advent of single-cell RNA sequencing (scRNA-seq), it has become clear that some cancer cell line models include distinct subpopulations. Heterogeneous cell lines offer a unique opportunity to study the dynamics and evolution of genetically similar cancer cell subpopulations in controlled experimental settings. Here, we present clusterCleaver, a computational package that uses metrics of statistical distance to identify candidate surface markers maximally unique to transcriptomic subpopulations in scRNA-seq which may be used for FACS isolation. clusterCleaver was experimentally validated using the MDA-MB-231 and MDA-MB-436 breast cancer cell lines. ESAM and BST2/tetherin were experimentally confirmed as surface markers which identify and separate major transcriptomic subpopulations within MDA-MB-231 and MDA-MB-436 cells, respectively. clusterCleaver is a computationally efficient and experimentally validated workflow for identification and enrichment of distinct subpopulations within cell lines which paves the way for studies on the coexistence of cancer cell subpopulations in well-defined in vitro systems.

cancer biology↗