Biology subjects

Birtwistle, M. R.

Publications and source records attributed to Birtwistle, M. R..

At least 19 recordsLinked to original sources

bioRxiv2026

LFCT: A Benchmark Dataset for Low-Frame-Rate Cell Tracking in Long-Term Live-Cell Microscopy

Cell tracking in time-lapse microscopy is essential for studying dynamic biological processes such as migration, proliferation, and lineage formation. Existing benchmarks primarily focus on high-framerate imaging, where short temporal intervals simplify correspondence between cells across consecutive frames. We present the Low Frame-rate Cell Tracking dataset (LCFT), a benchmark dataset designed specifically for evaluating cell tracking methods under low-frame-rate conditions. The dataset contains multi-day live-cell microscopy sequences from four human cell lines (MCF10A, MDA-MB-231, HEK293T, and U87), acquired at 10x and 20x magnifications using phase-contrast and fluorescence imaging (nucleus). Ground-truth annotations include cell identifications, temporal linking, lineage relationships, and mitosis events. To generate reliable annotations, automated segmentation and tracking were combined with extensive manual curation. LCFT provides a comprehensive resource for developing and benchmarking robust cell tracking algorithms capable of handling sparse temporal sampling and large inter-frame motion in long-term live-cell imaging experiments.

cell biology↗

bioRxiv2025

Serial Imaging of Tumor and microEnvironment (SITE) platform for live-cell ex vivo modeling of primary and metastatic cancer dynamics.

Understanding cancer initiation and progression is extremely challenging, in part due to experimental limitations in measuring and interpreting key signalling and tumour-microenvironment (TME) interactions that determine changes in cell and tissue behaviours over time. Here we developed SITE (Serial Imaging of Tumour and microEnvironment), a spatially-and temporally integrated, modular, ex vivo platform enabling quantitative analysis of TME interaction dynamics, signalling, and cell fate at single cell and tissue scales. Applied to modelling primary and lung metastatic breast cancer, SITE revealed tissue-specific TME interactions and ERK signalling patterns linked to distinct single-cell behaviours. We found that the earliest steps in tumour establishment and metastatic seeding involved active cell protrusion and the establishment of a multicellular niche interfacing tumour and host. Experimental and mathematical modelling showed that ERK signalling was co-influenced by these interactions, where cancer cluster formation increased signalling via the establishment of local signalling circuits. Disruption of these signalling circuits led to tissue-specific impacts on cancer intrinsic and TME interaction dynamics. Here, we modelled breast cancer as a test case, demonstrating the broad utility of SITE for quantitative exploration of TME interaction dynamics- closing a significant gap in experimental capabilities between in vivo models and in vitro systems.

cancer biology↗

bioRxiv2024

Purifying circular RNA by ultrafiltration

Developing messenger RNA (mRNA) vaccines for COVID-19 renewed and intensified the interest in using mRNA for disease prevention and treatment. Despite their efficacy, linear mRNA molecules are short-lived in the human body, primarily due to enzymatic degradation at the free ends. In contrast, circular RNA (circRNA) exhibits enhanced stability and resistance to exonuclease degradation. However, this stability depends highly on purity. Unfortunately, the in vitro transcription/self-splicing reaction products contain a mixture of circular and linear RNAs. Yet, practical methods for purifying circRNA from solutions containing linear RNA contaminants are lacking. In this study, we explored the feasibility of using ultrafiltration to purify protein-encoding circRNA produced by the self-splicing of a precursor RNA (preRNA) during in vitro transcription (IVT). We measured the sieving coefficients, a separation metric, of circRNA, linear precursor RNA, and nicked RNA conformers using polyethersulfone membranes with molecular weight cutoffs from 30 to 300 kDa, analyzing performance as a function of permeate flux. We also estimated the RNA critical fluxes and determined suitable operating conditions for purification. We achieved a purity of 86% with a yield above 50%. By comparison, the purity achieved by size-exclusion high-performance liquid chromatography (SE-HPLC), the leading alterative separation technology, was 41% with a yield of 45%. These findings highlight ultrafiltration as a superior method for purifying circRNA at the research scale. Its scalability suggests that it could play a critical role in enabling the large-scale manufacturing of circRNA-based therapeutics.

bioengineering↗

bioRxiv2024

Genetically-Encoded Fluorescence Barcodes for Single-Cell Analysis

Genetically-encoded, single-cell barcodes are broadly useful for experimental tasks such as lineage tracing or genetic screens. For such applications, a barcode library would ideally have high diversity (many unique barcodes), non-destructive identification (repeated measurements in the same cells or population), and fast, inexpensive readout (many cells and conditions). Current nucleic acid barcoding methods generate high diversity but require destructive and slow/expensive readout, and current fluorescence barcoding methods are non-destructive, fast, and inexpensive to readout but lack high diversity. We recently proposed theory for how fluorescent protein combinations may generate a high-diversity barcode library with non-destructive, fast and inexpensive identification. Here, we present an initial experimental proof-of-concept by generating a library of [~]150 barcodes from two-way combinations of 18 fluorescent proteins, 61 of which are tested experimentally. We use a pooled cloning strategy to generate a barcode library that is validated to contain every possible combination of the 18 fluorescent proteins. Experimental results using single mammalian cells and spectral flow cytometry demonstrate excellent classification performance of individual fluorescent proteins, with the exception of mTFP1, and of most evaluated barcodes, with many true positive rates >99%. The library is compatible with genetic screening for hundreds of genes (or gene pairs) and lineage tracing hundreds of clones. This work lays a foundation for greater diversity libraries (potentially [~]105 and more) generated from hundreds of spectrally-resolvable tandem fluorescent protein probes.

synthetic biology↗

bioRxiv2024

Mechanistic modeling of cell viability assays with in silico lineage tracing

Data from cell viability assays, which measure cumulative division and death events in a population and reflect substantial cellular heterogeneity, are widely available. However, interpreting such data with mechanistic computational models is hindered because direct model/data comparison is often muddled. We developed an algorithm that tracks simulated division and death events in mechanistically-detailed single-cell lineages to enable such a model/data comparison and suggest causes of cell-cell drug response variability. Using our previously developed model of mammalian single-cell proliferation and death signaling, we simulated drug dose response experiments for four targeted anti-cancer drugs (alpelisib, neratinib, trametinib and palbociclib) and compared them to experimental data. Simulations are consistent with data for strong growth inhibition by trametinib (MEK inhibitor) and overall lack of efficacy for alpelisib (PI-3K inhibitor), but are inconsistent with data for palbociclib (CDK4/6 inhibitor) and neratinib (EGFR inhibitor). Model/data inconsistencies suggest that (i) the importance of CDK4/6 for driving the cell cycle may be overestimated, and (ii) the cellular balance between basal (tonic) and ligand-induced signaling is a critical determinant of receptor inhibitor response. Simulations show subpopulations of rapidly and slowly dividing cells in both control and drug-treated conditions. Variations in mother cells prior to drug treatment impinging on ERK pathway activity are associated with the rapidly dividing phenotype and trametinib resistance. This work lays a foundation for the application of mechanistic modeling to large-scale cell viability datasets and better understanding determinants of cellular heterogeneity in drug response. Author SummaryThere is a large amount of data in the public domain for how a variety of cancer cell types respond to a multitude of anti-cancer drugs. However, interpreting such data mechanistically is hindered because of a lack of precise computational tools for model/data comparison. We developed an algorithm that tracks simulated division and death events in mechanistically detailed single-cell lineages to enable such a model/data comparison and suggest causes of drug response variability. We applied this tool to understand four targeted anti-cancer drugs better. Simulations are consistent with data for strong growth inhibition by trametinib and overall lack of efficacy for alpelisib, but are inconsistent with data for palbociclib and neratinib. Model/data inconsistencies suggest where biological understanding may be incomplete. Simulations show subpopulations of rapidly and slowly dividing cells in both control and drug-treated conditions, and analysis thereof suggest mechanisms potentially involved with acute drug resistance. This work lays a foundation for using large-scale datasets to better understand drug response.

systems biology↗

bioRxiv2024

A 96-Well Polyacrylamide Gel for Electrophoresis and Western Blotting

Western blotting is a stalwart technique for analyzing specific proteins and/or their post-translational modifications. However, it remains challenging to accommodate more than ~10 samples per experiment without substantial departure from trusted, established protocols involving accessible instrumentation. Here, we describe a 96-sample western blot that conforms to standard 96-well plate dimensional constraints and has little operational deviation from standard western blotting. The main differences are that (i) submerged polyacrylamide gel electrophoresis is operated horizontally (similar to agarose gels) as opposed to vertically, and (ii) a 6 mm thick gel is used, with 2 mm most relevant for membrane transfer (vs ~1 mm typical). Results demonstrate both wet and semi-dry transfer are compatible with this gel thickness. The major tradeoff is reduced molecular weight resolution, due primarily to less available migration distance per sample. We demonstrate proof-of-principle using gels loaded with molecular weight ladder, recombinant protein, and cell lysates. We expect the 96-well western blot will increase reproducibility, efficiency (cost and time ~8-fold), and capacity for biological characterization relative to established western blots.

biochemistry↗

bioRxiv2024

Network Analyses of Brain Tumor Patients' Multiomic Data Reveals Pharmacological Opportunities to Alter Cell State Transitions

Glioblastoma Multiforme (GBM) remains a particularly difficult cancer to treat, and survival outcomes remain poor. In addition to the lack of dedicated drug discovery programs for GBM, extensive intratumor heterogeneity and epigenetic plasticity related to cell-state transitions are major roadblocks to successful drug therapy in GBM. To study these phenomenon, publicly available snRNAseq and bulk RNAseq data from patient samples were used to categorize cells from patients into four cell states (i.e. phenotypes), namely: (i) neural progenitor-like (NPC-like), (ii) oligodendrocyte progenitor-like (OPC-like), (iii) astrocyte-like (AC-like), and (iv) mesenchymal-like (MES-like). Patients were subsequently grouped into subpopulations based on which cell-state was the most dominant in their respective tumor. By incorporating phosphoproteomic measurements from the same patients, a protein-protein interaction network (PPIN) was constructed for each cell state. These four-cell state PPINs were pooled to form a single Boolean network that was used for in silico protein knockout simulations to investigate mechanisms that either promote or prevent cell state transitions. Simulation results were input into a boosted tree machine learning model which predicted the cell states or phenotypes of GBM patients from an independent public data source, the Glioma Longitudinal Analysis (GLASS) Consortium. Combining the simulation results and the machine learning predictions, we generated hypotheses for clinically relevant causal mechanisms of cell state transitions. For example, the transcription factor TFAP2A can be seen to promote a transition from the NPC-like to the MES-like state. Such protein nodes and the associated signaling pathways provide potential drug targets that can be further tested in vitro and support cell state-directed (CSD) therapy.

systems biology↗

bioRxiv2024

Low-frequency ERK and Akt activity dynamics are predictive of stochastic cell division events

Understanding the dynamics of intracellular signaling pathways, such as ERK1/2 (ERK) and Akt1/2 (Akt), in the context of cell fate decisions is important for advancing our knowledge of cellular processes and diseases, particularly cancer. While previous studies have established associations between ERK and Akt activities and proliferative cell fate, the heterogeneity of single-cell responses adds complexity to this understanding. This study employed a data-driven approach to address this challenge, developing machine learning models trained on a dataset of growth factor-induced ERK and Akt activity time courses in single cells, to predict cell division events. The most effective predictive models were developed by applying discrete wavelet transforms (DWTs) to extract low-frequency features from the time courses, followed by using Ensemble Integration, an effective data integration and predictive modeling framework. The results demonstrated that these models effectively predicted cell division events in MCF10A cells (F-measure=0.524, AUC=0.726). ERK dynamics were found to be more predictive than Akt, but the combination of both measurements further enhanced predictive performance. The ERK models performance also generalized to predicting division events in RPE cells, indicating the potential applicability of these models and our data-driven methodology for predicting cell division across different biological contexts. Interpretation of these models suggested that ERK dynamics throughout the cell cycle, rather than immediately after growth factor stimulation, were associated with the likelihood of cell division. Overall, this work contributes insights into the predictive power of intra-cellular signaling dynamics for cell fate decisions, and highlights the potential of machine learning approaches in unraveling complex cellular behaviors.

cell biology↗

bioRxiv2023

Increasing Signal Intensity of Fluorescent Oligo-Labeled Antibodies

Full-spectrum flow cytometry has increased antibody-based multiplexing, yet further increases remain potentially impactful. We recently proposed how fluorescence Multiplexing using Spectral Imaging and Combinatorics (MuSIC) could do so using tandem dyes and an oligo-based antibody labeling method. In this work, we found that such labeled antibodies had significantly lower signal intensity than conventionally-labeled antibodies in human cell experiments. To improve signal intensity, we tested moving the fluorophores from the original external (ext.) 5 or 3 end-labeled orientation to internal (int.) fluorophore modifications. Cell-free spectrophotometer measurements showed a [~]6-fold signal intensity increase of the new int. configuration compared to the previous ext. configuration. Time-resolved fluorescence and fluorescence correlation spectroscopy showed that [~]3-fold brightness difference is due to static quenching most likely by the oligo or solution in the ext. configuration. Spectral flow cytometry experiments using peripheral blood mononuclear cells show int. MuSIC probe-labeled antibodies (i) retained increased signal intensity while having no significant difference in the estimated % of CD8+ lymphocytes and (ii) labeled with Atto488, Atto647, and Atto488/647 combinations can be demultiplexed in triple-stained samples. The antibody labeling approach is general and can be broadly applied to many biological and diagnostic applications where spectral detection is available.

molecular biology↗

bioRxiv2022

Expanding large-scale mechanistic models with machine learned associations and big datasets

Computational models that can explain and predict complex sub-cellular, cellular, and tissue level drug response mechanisms could speed drug discovery and prioritize patient-specific treatments (i.e., precision medicine). Some models are mechanistic: detailed equations describing known (or supposed) physicochemical processes, while some models are statistical/machine learning-based: descriptive correlations that explain datasets but have no mechanistic or causal guarantees. These two types of modeling are rarely combined, missing the opportunity to explore possibly causal but data-driven new knowledge while explaining what is already known. Here, we explore a combination of machine learning with mechanistic modeling methods to develop computational models that could more fully represent cell-line-specific drug responses. In this proposed framework, machine learning/statistical models built using omics datasets provide high confidence predictions for new interactions between genes and proteins where there is physicochemical uncertainty. These possibly new interactions are used as new connections (edges) in a large-scale mechanistic model (called SPARCED) to better recapitulate the recently released NIH LINCS Consortium large-scale MCF10A dataset. As a test case, we focused on incorporating novel IFN{gamma}/PD-L1 related associations into the SPARCED model to enable description of the cellular response to checkpoint inhibitor immunotherapies. This work is a template for combining big data, machine-learning-inferred interactions with mechanistic models, which could be more broadly applicable towards building multi-scale precision medicine and whole cell models.

systems biology↗

bioRxiv2022

Computational Speed-Up of Large-Scale, Single-Cell Model Simulations Via a Fully-Integrated SBML-Based Format

SummaryLarge-scale and whole-cell modeling has multiple challenges, including scalable model building and module communication bottlenecks (e.g. between metabolism, gene expression, signaling, etc). We previously developed an open-source, scalable format for a large-scale mechanistic model of proliferation and death signaling dynamics, but communication bottlenecks between gene expression and protein biochemistry modules remained. Here, we developed two solutions to communication bottlenecks that speed up simulation by ~4-fold for hybrid stochastic-deterministic simulations and by over 100-fold for fully deterministic simulations. Availability and ImplementationSource code is freely available at https://github.com/birtwistlelab/SPARCED/releases/tag/v1.1.0 implemented in python, and supported on Linux, Windows, and MacOS (via Docker). ContactMarc Birtwistle mbirtwi@clemson.edu Supplementary informationN/A

systems biology↗

bioRxiv2022

Predicting Anti-Cancer Drug Combination Responses with a Temporal Cell State Network Model

Cancer chemotherapy combines multiple drugs, but predicting the effects of drug combinations on cancer cell proliferation remains challenging. We hypothesized that by combining knowledge of single drug dose responses and cell state transition network dynamics, we could predict how a population of cancer cells will respond to drug combinations. We tested this hypothesis here using three targeted inhibitors of different cell cycle states in two different cell lines. We formulated a Markov model to capture temporal cell state transitions between different cell cycle phases, with single drug data constraining how drug doses affect transition rates. This model was able to predict the landscape of all three different pairwise drug combinations across all dose ranges for both cell lines with no additional data. This work shows how currently available or attainable information could be combined to predict how cancer cell populations respond to drug combinations.

systems biology↗

bioRxiv2022

A Theory for High-Throughput Genetic Interaction Screening

Systematic, genome-scale genetic screens have been instrumental for elucidating genotype-phenotype relationships, but approaches for probing genetic interactions have been limited to at most [~]100 pre-selected gene combinations in mammalian cells. Here, we introduce a theory for high-throughput genetic interaction screens. The theory extends our recently developed Multiplexing using Spectral Imaging and Combinatorics (MuSIC) approach to propose [~]105 spectrally unique, genetically-encoded MuSIC barcodes from 18 currently available fluorescent proteins. Simulation studies based on constraints imposed by spectral flow cytometry equipment suggest that genetic interaction screens at the human genome-scale may be possible if MuSIC barcodes can be paired to guide RNAs. While experimental testing of this theory awaits, it offers transformative potential for genetic perturbation technology and knowledge of genetic function. More broadly, the availability of a genome-scale spectral barcode library for non-destructive identification of single-cells could find more widespread applications such as traditional genetic screening and high-dimensional lineage tracing.

biochemistry↗

bioRxiv2022

Multi-Omics Binary Integration via Lasso Ensembles (MOBILE) for identification of context-specific networks and new regulatory mechanisms

Cell phenotypes are dictated by both extra- and intra-cellular contexts, and robust identification of context-specific network features that control phenotypes remains challenging. Here, we developed a multi-omics data integration strategy called MOBILE (Multi-Omics Binary Integration via Lasso Ensembles) to nominate molecular features associated with specific cellular phenotypes. We applied this method to chromatin accessibility, mRNA, protein, and phospho-protein time course datasets and focus on two illustrative use cases after we show MOBILE could recover known biology. First, MOBILE nominated new mechanisms of interferon-{gamma} (IFN{gamma}) regulated PD-L1 expression, where analyses suggested, and literature supported that IFN{gamma}-controlled PD-L1 expression involves BST2, CLIC2, FAM83D, ACSL5, and HIST2H2AA3 genes. Second, we explored differences between the highly similar transforming growth factor-beta 1 (TGF{beta}1) and bone morphogenetic protein 2 (BMP2) and showed that differential cell size and clustering properties induced by TGF{beta}1, but not BMP2, were related to the laminin/collagen pathway activity. Given the ever-growing availability of multi-omics datasets, we envision that MOBILE will be broadly applicable to identify context-specific molecular features associated with cellular phenotypes. Graphical Summary O_FIG O_LINKSMALLFIG WIDTH=162 HEIGHT=200 SRC="FIGDIR/small/501297v1_ufig1.gif" ALT="Figure 1"> View larger version (76K): org.highwire.dtl.DTLVardef@12fa3a5org.highwire.dtl.DTLVardef@a0b6a8org.highwire.dtl.DTLVardef@137efb7org.highwire.dtl.DTLVardef@15aec85_HPS_FORMAT_FIGEXP M_FIG C_FIG Multi-Omics Binary Integration via Lasso Ensembles (MOBILE) pipeline yields statistically robust, context-specific association networksThe MOBILE pipeline integrates omics datasets in a data-driven, biologically-structured manner. The pipeline outputs are gene-level, contextspecific association networks. These association networks nominate differentially enriched pathways, subnetworks, and new connections. Broadly applicable to find condition specific networks using multi-omics datasets.

systems biology↗

bioRxiv2021

Mesowestern Blot: Simultaneous Analysis of Hundreds of Sub-Microliter Lysates

Western blotting is a widely-used technique for molecular-weight-resolved analysis of proteins and their post-translational modifications, but has been refractory to affordable scale-up. Here, we report the Mesowestern blot, which uses a 3D-printable gel-casting mold to enable affordable, high-throughput Western blotting with standard sample preparation and small (<1 uL) sample sizes. The casted polyacrylamide gel contains 336, 0.5 uL micropipette-loadable sample wells arranged within a standard microplate footprint. Polyacrylamide % can be altered to change molecular weight resolution range. Proof-of-concept experiments using both infrared-fluorescent molecular weight protein ladder as well as cell lysate (RIPA buffer) demonstrate protein loaded in Mesowestern gels is amenable to the standard Western blotting steps. The main difference between Mesowestern and traditional Western is that semi-dry horizontal instead of immersed vertical gel electrophoresis is used. The linear range of detection is approximately 2 orders of magnitude, with a limit of detection (for {beta}-actin) of around 30 ng of total protein from mammalian cell lysates (~30-3000 cells). Because the gel mold is 3D-printable, users have significant design freedom for custom layouts, and there are few barriers to adoption by the typical cell and molecular biology laboratory already performing Western blots.

biochemistry↗

bioRxiv2021

Drug specific transcriptomic signatures in healthy human subject iPSC derived cardiomyocytes

Drug-induced gene expression profiles can identify potential mechanisms of toxicity. We focused on obtaining signatures for cardiotoxicity of FDA-approved tyrosine kinase inhibitors (TKIs) in human induced pluripotent stem cell-derived cardiomyocytes. Using bulk transcriptomics profiles, we applied singular value decomposition to identify drug-selective patterns in cell lines obtained from multiple healthy human subjects. Cellular pathways affected by highly cardiotoxic TKIs include energy metabolism, contractile, and extracellular matrix dynamics. Projecting these pathways to single cell expression profiles indicates that TKI responses can be evoked in both cardiomyocytes and fibroblasts. Whole genome sequences of the cell lines, using outlier responses enabled us to correctly reidentify a genomic variant associated with anthracycline cardiotoxicity and predict genomic variants potentially associated with TKI cardiotoxicity. We conclude that mRNA expression profiles when integrated with publicly available genomic, pathway, and single cell transcriptomic datasets, provide multiscale predictive understanding of cardiotoxicity for drug development and patient stratification. One sentence summaryGenes, pathways, and cell types of the human heart associated with antineoplastic drug cardiotoxicity.

systems biology↗

bioRxiv2021

Predicting Individual Cell Division Events from Single-Cell ERK and Akt Dynamics

Predictive determinants of stochastic single-cell fates have been elusive, even for the well-studied mammalian cell cycle. What drives proliferation decisions of single cells at any given time? We monitored single-cell dynamics of the ERK and Akt pathways, critical cell cycle progression hubs and anti-cancer drug targets, and paired them to division events in the same single cells using the non-transformed MCF10A epithelial line. Following growth factor treatment, in cells that divide both ERK and Akt activities are significantly higher within the S-G2 time window ([~]8.5-40 hours). Such differences were much smaller in the pre-S-phase, restriction point window which is traditionally associated with ERK and Akt activity dependence, suggesting unappreciated roles for ERK and Akt in S through G2. Machine learning algorithms show that simple metrics of central tendency in this time window are most predictive for subsequent cell division; median ERK and Akt activities classify individual division events with an AUC=0.76. Surprisingly, ERK dynamics alone predict division in individual cells with an AUC=0.74, suggesting Akt activity dynamics contribute little to the decision driving cell division in this context. We also find that ERK and Akt activities are less correlated with each other in cells that divide. Network reconstruction experiments demonstrated that this correlation behavior was likely not due to crosstalk, as ERK and Akt do not interact in this context, in contrast to other transformed cell types. Overall, our findings support roles for ERK and Akt activity throughout the cell cycle as opposed to just before the restriction point, and suggest ERK activity dynamics are substantially more important than Akt activity dynamics for driving cell division in this non-transformed context. Single cell imaging along with machine learning algorithms provide a better basis to understand cell cycle progression on the single cell level.

systems biology↗

bioRxiv2021

A LINCS microenvironment perturbation resource for integrative assessment of ligand-mediated molecular and phenotypic responses

The phenotype of a cell and its underlying molecular state is strongly influenced by extracellular signals, including growth factors, hormones, and extracellular matrix. While these signals are normally tightly controlled, their dysregulation leads to phenotypic and molecular states associated with diverse diseases. To develop a detailed understanding of the linkage between molecular and phenotypic changes, we generated a comprehensive dataset that catalogs the transcriptional, proteomic, epigenomic and phenotypic responses of MCF10A mammary epithelial cells after exposure to the ligands EGF, HGF, OSM, IFNG, TGFB and BMP2. Systematic assessment of the molecular and cellular phenotypes induced by these ligands comprise the LINCS Microenvironment (ME) perturbation dataset, which has been curated and made publicly available for community-wide analysis and development of novel computational methods (synapse.org/LINCS_MCF10A). In illustrative analyses, we demonstrate how this dataset can be used to discover functionally related molecular features linked to specific cellular phenotypes.

systems biology↗