Biology subjects

Bianco, S.

Publications and source records attributed to Bianco, S..

At least 19 recordsLinked to original sources

bioRxiv2026

Interplay of NuA4/TIP60 and PRC2 Complex Activities in Fusion Driven Endometrial Stromal Sarcoma

Low-grade endometrial stromal sarcoma (LGESS) exhibits frequent chromosomal translocations that fuse various subunits of the NuA4/TIP60 co-activator complex to subunits of the Polycomb Repressive Complex 2 (PRC2) complex. LGESS fusion proteins, such as the commonly occurring JAZF1-SUZ12, have been shown to upregulate genes through mislocalization of NuA4/TIP60 activity to Polycomb target genes. In this study, we characterized an interesting recurrent fusion protein in LGESS that fused a NuA4/TIP60 component, MBTD1, to EZHIP. EZHIP is a recently described vertebrate protein that enzymatically inhibits methyltransferase activity of the PRC2 complex and is a potent oncogene. The MBTD1-EZHIP fusion protein forms a chimeric TIP60-PRC2.1 complex and drastically reduces H3K27me3 levels at Polycomb target genes, similar to EZHIP-overexpressing cancers. However, unlike EZHIP overexpression, MBTD1-EZHIP requires mislocalization of NuA4/TIP60 activity through the MBTD1 protein to upregulate oncogenes. Despite differences in the finer molecular mechanisms, MBTD1-EZHIP and the common JAZF1-SUZ12 fusion protein upregulated similar sets of genes in cell lines, showing a convergence of oncogenic gene expression. Surprisingly, unlike in cellular models, JAZF1-SUZ12 translocated patient samples showed not only upregulation of genes driven by increased H4K8ac and decreased H3K27me3 but also downregulation of specific genes due to accumulated H3K27me3, revealing an additional oncogenic mechanism in LGESS.

cancer biology↗

bioRxiv2026

Deep learning of dynamic signatures resolves mechanistic ambiguity in complex signaling networks

Sparse experimental data often yields vast mechanistic hypothesis spaces with numerous equally probable models. Traditional model selection metrics like the Akaike Information Criterion fall short because they reduce models nonlinear dynamics to a scalar score that masks crucial mechanistic details. Here, we introduce an AI-driven framework treating model dynamics as learnable signatures. Using deep learning autoencoders, we embed the dynamic signatures of thousands of competing models into a low-dimensional latent space. Iterative clustering and physiological constraints systematically refine this space to a manageable number of testable hypotheses. Applying this to the Integrated Stress Response, a fundamental cellular homeostasis mechanism, we converged on 12 compelling mechanistic hypotheses from over 12,000 candidates. Notably, this framework confidently rejected structure-derived kinetic assumptions regarding higher-order PKR activation that traditional metrics could not resolve. Ultimately, this method provides a rigorous, full-state alternative to scalar metrics, accelerating the discovery of driving principles in complex biological systems.

systems biology↗

bioRxiv2026

A Physics-based Model Reveals Mechanisms of Epigenetic Memory

In multi-cellular eukaryotic organisms, cell type and specific functional identity are defined by the epigenetic patterning of chemical modifications to DNA and chromatin that modulate the expression and silencing of specific genes. When a cell divides, histones containing important epigenetic marks are distributed between the two daughter strands leading to a temporary dilution of epigenetic information and cell identity. In this work we introduce a physics-based model of epigenetic memory that explains how cells restore and maintain H3K9me3 and H3K27me3 histone methylation patterning after cell division. We demonstrate that emergence and maintenance of the epigenetic program is driven by an evolved mechanism that makes use of the biophysics of polymers, phase condensates and enzymatic activity. We validate our model via genome-wide epigenetic time-course simulation and comparison to experimental epigenetic data from multiple donors, multiple cell types, and for multiple epigenetic marks. Finally, we use our model as a conceptual framework to understand cellular reprogramming by hypothesizing that these processes first contend with and later utilize somatic epigenetic maintenance programs.

biophysics↗

bioRxiv2025

Unveiling the temporal impact: Exploring dynamic changes in the paediatric solid tumour immune microenvironment through time

The composition of the tumour immune microenvironment (TIME) influences tumour evolution and responsiveness to immunotherapy. While longitudinal changes in TIME have been well-characterized in adult cancers, its dynamics in childhood cancers remain poorly documented, limiting our ability to predict treatment responses and tailor immunotherapeutic strategies. This study aimed to evaluate the plasticity of TIME in paediatric solid tumours, investigate its longitudinal evolution, and identify time-dependent immune alterations. Transcriptomic data from longitudinal samples of 27 paediatric patients (<21 years old) with relapsed or refractory solid tumours were analysed, encompassing 70 timepoints: 16 diagnoses and 54 successive relapses. TIME plasticity was assessed using gene expression clustering and immune cell infiltration enumeration. Patient-adjusted longitudinal analyses were performed using generalised linear mixed models (glmmSeq), adjusted for age and sex. Temporal associations of immune changes were further explored using dynamic regression models. Thirteen patients exhibited significant changes in their TIME profile, indicating high TIME plasticity. Over time, the TIME shifted toward a tolerogenic and immunosuppressive state, characterised by decreased activity in immune pathways (e.g., T cell receptor signalling) and enrichment of tolerogenic (e.g., macrophage differentiation) and oncogenic pathways (e.g., IL6-JAK-STAT3). The core enrichment of upregulated pathways contained key immunosuppressive factors: immune checkpoints (CTLA-4), tumour-associated macrophage activators (CSF1/CSF1R), T-regulatory cell activators (TGFB1), and immunosuppressive genes (IL10RA). This study provides evidence that the TIME in paediatric solid tumours is plastic and remodels towards immune depletion and tolerogenicity. This evolution may underlie treatment resistance and disease progression, underscoring the need for TIME-informed therapeutic approaches in paediatric oncology. Significance StatementThis article demonstrates the plasticity of the tumour immune micro-environment (TIME) of paediatric solid tumours throughout disease evolution. Longitudinal transcriptomic analyses of 70 tumour samples from 27 patients showed a progressive remodelling towards tolerogenicity and immune depletion. Key immunosuppressive factors, including immune checkpoints and tumour-associated macrophages, were identified as potential contributors to immune escape. These findings support the relevance of longitudinal immune monitoring in paediatric oncology and may inform future strategies for immunotherapeutic interventions.

cancer biology↗

bioRxiv2025

Concomitant acetylation and loading of H2A.Z by NuA4/TIP60 regulate target gene transcription

The human NuA4/TIP60 complex is a multi-subunit, dual enzymatic epigenetic factor and gene regulator. It bears histone acetyltransferase (HAT) activity towards the canonical histones H2A and H4 and the histone variant H2A.Z, a function that has been linked to gene activation. It also acts as a chromatin remodeling enzyme through ATP-dependent exchange of nucleosomal H2A-H2B dimers with H2A.Z-H2B, leading to incorporation of H2A.Z into chromatin at gene regulatory elements. NuA4/TIP60 is unique in merging two enzymatic activities targeting H2A.Z. Both NuA4/TIP60-dependent H2A.Z acetylation and remodeling have been linked to several physiological functions and pathologies, but studies have only focused on either one or the other enzymatic activity, and insights on functional coordination between them are lacking. Here, we leverage our EP400 rapid depletion system to explore and untangle the intricate links between H2A.Z acetylation by Tip60 (the HAT subunit) and loading on chromatin by EP400 (the remodeling subunit) through functional genomic and biochemical analyses. Our data support a mechanism in which H2A.Z is first pre-acetylated to allow for H2A.Zac-H2B dimer association with the complex before incorporation into chromatin, particularly at gene promoters to positively regulate transcription. As both H2A.Z-targeted enzymatic functions of NuA4/TIP60 have been linked to disease, albeit separately, our findings hold important implications for therapeutic intervention, where combinatorial targeting is a promising avenue.

molecular biology↗

bioRxiv2025

Physical principles of phase-separation action on chromatin looping associated to pathogenic gene activation

Phase-separation of chimeric proteins resulting from genetic mutations has been shown to trigger aberrant chromatin looping, contributing to disease development, including cancer. However, the physical mechanisms regulating these processes are not yet fully understood. In this study, we employ polymer physics models of chromatin and numerical simulations to investigate the relationship between phase-separation of proteins and chromatin structure. We demonstrate that a simple model, including only protein-protein and protein-chromatin interactions, effectively explains the aberrant looping observed around oncogenes, such as PBX3, in cells expressing the NUP98-HOXA9 chimeric protein, which is associated with leukemia. In this scenario, looping occurs through a switch-like mechanism controlled by the concentration of the chimera and its affinity with chromatin. Moreover, when incorporating the presence of extruding factors in a more complex model, similar results are observed, indicating a mild dependence of this looping mechanism on loop-extrusion activity. Finally, leveraging our numerical simulations, we propose potential strategies to inhibit the formation of enhancer-gene loops by directly targeting the chimeric protein with interfering molecules.

biophysics↗

bioRxiv2024

Coagulative Granular Hydrogels with an Enzyme Catalyzed Fibrin Network for Endogenous Tissue Regeneration

Granular hydrogels, composed of densely packed microgels, are an emerging class of injectable microporous scaffolds that provide interstitial porosity for endogenous cell recruitment and tissue repair. However, weak bonding interactions between constituent microgels compromises the mechanical integrity of these biomaterials, limiting their scope and effectiveness for in vivo application where structural support is required. To address this challenge, we introduce a new bioinspired stabilization method and a novel class of regenerative biomaterial: coagulative granular hydrogels, assembled from thrombin-functionalized gelatin methacryloyl microgels. The surface-bound thrombin is enzymatically active and catalyzes the conversion of fibrinogen into a fibrin hydrogel that extends throughout the interstitial voids of the granular hydrogel. This secondary network acts as a biological glue to stabilize the granular hydrogel, yielding shear and compressive properties comparable to bulk hydrogel controls. Furthermore, the interstitial fibrin network provides a favorable microenvironment for the adhesion, proliferation, and invasion of endothelial cells, highlighting the potential of the biomaterial to support endogenous tissue repair. This innovative stabilization mechanism provides a responsive biomaterial system, and future studies will seek to explore how the biomaterial is annealed in vivo by the presence of blood plasma fibrinogen.

bioengineering↗

bioRxiv2024

Functional interaction between transcription factor Sfp1 and the NuA4 complex in response to nutrient availability

Ribosome biogenesis is a crucial process requiring enormous transcriptional output. In budding yeast, the expression of 138 ribosomal protein (RP) genes and over 200 ribosome biogenesis (RiBi) genes is regulated by an intricate network of factors, including the nutrient-sensitive transcription activator Sfp1 and the NuA4 coactivator/acetyltransferase complex. Nutrient starvation or inhibition of TORC1 by rapamycin leads to repression of RP and RiBi genes, in part through blocking Sfp1 nuclear localization and NuA4-dependent chromatin acetylation. Here, we demonstrate that Sfp1 physically interacts with NuA4 in a TORC1-dependent manner. Our results indicate that Sfp1, along with NuA4, regulate the transcription of RiBi and RP genes via distinct mechanisms depending on promoter architectures. Sfp1 promotes histone acetylation at the promoters without affecting NuA4 recruitment. In contrast, NuA4 does impact Sfp1 binding but specifically at two classes of RP genes. Importantly, NuA4 acetylates Sfp1 at lysines 655 and 657, regulating its function. Cells expressing Sfp1 with acetyl-mimicking mutations exhibit increased expression of RiBi genes while RP genes remain stable. However, the same mutants lead to the loss of Sfp1 binding/activity at RiBi genes when cells are under non-optimal growth conditions. Mimicking constitutive acetylation of Sfp1 also limits the transcriptional burst of RP genes upon addition of glucose. Altogether, these results draw an intricate functional relationship between Sfp1 and NuA4 to control ribosome biogenesis, fine-tuning transcription output in different growth conditions.

molecular biology↗

bioRxiv2024

A single dose of cocaine rewires the 3D genome structure of midbrain dopamine neurons

Midbrain dopamine neurons (DNs) respond to a first exposure to addictive drugs and play key roles in chronic drug usage1-3. As the synaptic and transcriptional changes that follow an acute cocaine exposure are mostly resolved within a few days4,5, the molecular changes that encode the long-term cellular memory of the exposure within DNs remain unknown. To investigate whether a single cocaine exposure induces long-term changes in the 3D genome structure of DNs, we applied Genome Architecture Mapping and single nucleus transcriptomic analyses in the mouse midbrain. We found extensive rewiring of 3D genome architecture at 24 hours past exposure which remains or worsens by 14 days, outlasting transcriptional responses. The cocaine-induced chromatin rewiring occurs at all genomic scales and affects genes with major roles in cocaine-induced synaptic changes. A single cocaine exposure triggers extensive long-lasting changes in chromatin condensation in post-synaptic and post-transcriptional regulatory genes, for example the unfolding of Rbfox1 which becomes most prominent 14 days post exposure. Finally, structurally remodeled genes are most expressed in a specific DN sub-type characterized by low expression of the dopamine auto-receptor Drd2, a key feature of highly cocaine-sensitive cells. These results reveal an important role for long-lasting 3D genome remodelling in the cellular memory of a single cocaine exposure, providing new hypotheses for understanding the inception of drug addiction and 3D genome plasticity.

genomics↗

bioRxiv2023

Multiscale modelling of chromatin 4D organization in SARS-CoV-2 infected cells

SARS-CoV-2 is able to re-structure chromatin organization and alters the epigenomic landscape of the host genome, though the mechanisms that produce such changes are still poorly understood. Here, we investigate with polymer physics chromatin re-organization of the host genome, in space and time upon SARS-CoV-2 viral infection. We show that re-structuring of A/B compartments is well explained by a re-modulation of intra-compartment homotypic affinities, which leads to the weakening of A-A interactions and enhances A-B mixing. At TAD level, re-arrangements are physically described by a general reduction of the loop extrusion activity coupled with an alteration of chromatin phase-separation properties, resulting in more intermingling between different TADs and spread in space of TADs themselves. In addition, the architecture of loci relevant to the antiviral interferon (IFN) response, such as DDX58 or IFIT, results more variable within the 3D single-molecule population of the infected model, suggesting that viral infection leads to a loss of chromatin structural specificity. Analysis of time trajectories of pairwise gene-enhancer and higher-order contacts reveals that such variability derives from a more fluctuating dynamics in infected case, suggesting that SARS-CoV-2 alters gene regulation by impacting the stability of the contact network in time. Overall, our study provides the first polymer-physics based 4D reconstruction of SARS-CoV-2 infected genome with mechanistic insights on the consequent gene mis-regulation.

biophysics↗

bioRxiv2023

Phase separation of ecDNA aggregates establishes in-trans contact domains boosting selective MYC regulatory interactions

Extrachromosomal DNAs (ecDNAs) are found in the nucleus of an array of human cancer cells where they can form clusters that were associated to oncogene overexpression, as they carry genes and cis-regulatory elements. Yet, the mechanisms of aggregation and gene amplification beyond copy-number effects remain mostly unclear. Here, we investigate, at the single molecule level, MYC-harboring ecDNAs of COLO320-DM colorectal cancer cells by use of a minimal polymer model of the interactions of ecDNA BRD4 binding sites and BRD4 molecules. We find that BRD4 induces ecDNAs phase separation, resulting in the self-assembly of clusters whose predicted structure is validated against HiChIP data (Hung et al., 2021). Clusters establish in-trans associated contact domains (I-TADs) enriched, beyond copy number, in regulatory contacts among specific ecDNA regions, encompassing its PVT1-MYC fusions but not its other canonical MYC copy. That explains why the fusions originate most of ecDNA MYC transcripts (Hung et al., 2021), and shows that ecDNA clustering per se is important but not sufficient to amplify oncogene expression beyond copy-number, reconciling opposite views on the role of clusters (Hung et al., 2021; Zhu et al., 2021; Purshouse et al. 2022). Regulatory contacts become strongly enriched as soon as half a dozen ecDNAs aggregate, then saturate because of steric hindrance, highlighting that even cells with few ecDNAs can experience pathogenic MYC upregulations. To help drug design and therapeutic applications, with the model we dissect the effects of JQ1, a BET inhibitor. We find that JQ1 reverses ecDNA phase separation hence abolishing I-TADs and extra regulatory contacts, explaining how in COLO320-DM cells it reduces MYC transcription (Hung et al., 2021).

molecular biology↗

bioRxiv2022

A tradeoff between enterovirus A71 particle stability and cell entry

A central role of viral capsids is to protect the viral genome from the harsh extracellular environment while facilitating initiation of infection when the virus encounters a target cell. Viruses are thought to have evolved an optimal equilibrium between particle stability and efficiency of cell entry. In this study, we genetically perturbed this equilibrium in a non-enveloped virus, enterovirus A71 to determine its structural basis. We isolated a single-point mutation variant with increased particle thermotolerance and decreased efficiency of cell entry. Using cryo-electron microscopy and molecular dynamics simulations, we determined that the thermostable native particles have acquired an expanded conformation that results in a significant increase in protein dynamics. Examining the uncoating intermediate states of the thermostable variant suggests a pathway, where the lipid pocket factor is released first, followed by internal VP4 and finally the viral RNA.

biophysics↗

bioRxiv2022

Phenotype to genotype mapping using supervised and unsupervised learning

The relationship between the genotype, the genetic instructions encoded into a genome, and phenotype, the macroscopic realization of such instructions, remains mostly uncharted. In addition, tools able to uncover the connection between the phenotype with a specific set of responsible genes are still under definition. In this work, we focus on yeast organelles called vacuoles, which are cell membrane compartments that vary size and shape in response to various stimuli, and we develop a framework relating changes of cellular morphology to genetic modification. The method is a combination of convolutional neural network (CNN) and an unsupervised learning pipeline, which employs a deep-learning based segmentation, classification, and anomaly detection algorithm. From the live 3D fluorescence vacuole images, we observe that different genetic mutations generate distinct vacuole phenotypes and that the same mutation might correspond to more than one vacuole morphology. We trained a Unet architecture to segment our cellular images and obtain precise, quantitative information in 2D depth-encoded images. We then used an unsupervised learning approach to cluster the vacuole types and to establish a correlation between genotype and vacuole morphology. Using this procedure, we obtained 4 phenotypic groups. We extracted a set of 131 morphological features from the segmented vacuoles images, reduced to 50 after a tree-based feature selection. We obtained a purity of 85% adopting a Fuzzy K-Means based algorithm on a random subset of 880 images, containing all the detected phenotypic groups. Finally, we trained a CNN on the labels assigned during clustering. The CNN has been used for prediction of a large dataset (6942 images) with high accuracy (80%). Our approach can be applied extensively for live fluorescence image analysis and most importantly can unveil the basic principles relating genotype to vacuole phenotype in yeast cell, which can be thought as a first step for inferring cell designing principles to generate organelles with a specific, desired morphology.

bioinformatics↗

bioRxiv2022

Predicting Epitope Candidates for SARS-CoV-2

Epitopes are short amino acid sequences that define the antigen signature to which an antibody binds. In light of the current pandemic, epitope analysis and prediction is paramount to improving serological testing and developing vaccines. In this paper, we leverage known epitope sequences from SARS-CoV, SARS-CoV-2 and other Coronaviridae and use those known epitopes to identify additional antigen regions in 62k SARS-CoV-2 genomes. Additionally, we present epitope distribution across SARS-CoV-2 genomes, locate the most commonly found epitopes, discuss where epitopes are located on proteins, and how epitopes can be grouped into classes. We also discuss the mutation density of different regions on proteins using a big data approach. We find that there are many conserved epitopes between SARS-CoV-2 and SARS-CoV, with more diverse sequences found in Nucleoprotein and Spike Glycoprotein.

bioinformatics↗

bioRxiv2022

Exploring the rules of chimeric antigen receptor phenotypic output using combinatorial signaling motif libraries and machine learning

Chimeric antigen receptor (CAR) costimulatory domains steer the phenotypic output of therapeutic T cells. In most cases these domains are derived from native immune receptors, composed of signaling motif combinations selected by evolution. To explore if non-natural combinations of signaling motifs could drive novel cell fates of interest, we constructed a library of CARs containing [~]2,300 synthetic costimulatory domains, built from combinations of 13 peptide signaling motifs. The library produced CARs driving diverse fate outputs, which were sensitive to motif combinations and configurations. Neural networks trained to decode the combinatorial grammar of CAR signaling motifs allowed extraction of key design rules. For example, the non-native combination of TRAF- and PLC{gamma}1-binding motifs was found to simultaneously enhance cytotoxicity and stemness, a clinically desirable phenotype associated with effective and durable tumor killing. The neural network accurately predicts that addition of PLC{gamma}1-binding motifs improves this phenotype when combined with TRAF-binding motifs, but not when combined with other immune signaling motifs (e.g. PI3K-or Grb2-binding motifs). This work shows how libraries built from the minimal building blocks of signaling, combined with machine learning, can efficiently guide engineering of receptors with desired phenotypes. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=60 SRC="FIGDIR/small/474985v2_ufig1.gif" ALT="Figure 1"> View larger version (17K): org.highwire.dtl.DTLVardef@8dbf97org.highwire.dtl.DTLVardef@1d8300org.highwire.dtl.DTLVardef@1838ba0org.highwire.dtl.DTLVardef@143d907_HPS_FORMAT_FIGEXP M_FIG C_FIG

synthetic biology↗

bioRxiv2021

Aberrant paracrine signalling for bone remodelling underlies the mutant histone-driven giant cell tumour of bone.

Oncohistones represent compelling evidence for a causative role of epigenetic perturbations in cancer. Giant cell tumours of bone (GCTs) are characterised by a mutated histone H3.3 as the sole genetic driver present in bone-forming osteoprogenitor cells but absent from abnormally large bone-resorbing osteoclasts which represent the hallmark of these neoplasms. While these striking features imply a pathogenic interaction between mesenchymal and myelomonocytic lineages during GCT development, the underlying mechanisms remain unknown. We show that the changes in the transcriptome and epigenome in the mesenchymal cells caused by the H3.3-G34W mutation contribute to increase osteoclast recruitment in part via reduced expression of the TGF{beta}-like soluble factor, SCUBE3. In turn, osteoclasts secrete unregulated amounts of SEMA4D enhancing proliferation of mutated osteoprogenitors and arresting their maturation. These findings provide a mechanism by which GCTs undergo differentiation upon denosumab treatment, a drug that depletes osteoclasts. In contrast, gain of hTERT activity, commonly found in malignant GCT, makes neoplastic cells insensitive to osteoclasts, predicting the unresponsiveness to denosumab. We provide a mechanism for GCT initiation and its response to current treatment, the basis of which is dysfunctional cross-talk between bone-forming and bone-resorbing cells, emphasising the importance of tumor/microenvironment bidirectional interactions in tumorigenesis.

cancer biology↗

bioRxiv2021

Loop-extrusion and polymer phase-separation can co-exist at the single-molecule level to shape chromatin folding

Loop-extrusion and phase-separation have been proposed as mechanisms that shape chromosome large-scale spatial organization. It is unclear, however, how they perform relative to each other in explaining chromatin architecture data and whether they compete or co-exist at the single-molecule level. Here, we compare models of polymer physics based on loop-extrusion and phase-separation, as well as models where both mechanisms act simultaneously in a single molecule, against multiplexed FISH data available in human loci in IMR90 and HCT116 cells. We find that the different models recapitulate bulk Hi-C and average microscopy data. Single-molecule chromatin conformations are also well captured, especially by phase-separation based models that better reflect the experimentally reported segregation in globules of the considered genomic loci and their cell-to-cell structural variability. Such a variability is consistent with two main concurrent causes: single-cell epigenetic heterogeneity and an intrinsic thermodynamic conformational degeneracy of folding. Overall, the model combining loop-extrusion and polymer phase-separation provides a very good description of the data, particularly higher-order contacts, showing that the two mechanisms can co-exist in shaping chromatin architecture in single cells.

molecular biology↗

bioRxiv2021

AI-assisted Biology: Predict the Conditional Probability Distributions from Noisy Measurements

Biological systems are inherently noisy so that two genetically identical cells in the exact same environment will sometimes behave in dramatically different ways. This imposes a big challenge in building traditional supervised machine learning models that can only predict determined phenotypic variables or categories per specific input condition. Furthermore, biological noise has been proven to play a crucial role in gene regulation mechanisms. The prediction of the average value of a given phenotype is not always sufficient to fully characterize a given biological system. In this study, we develop a deep learning algorithm that can predict the conditional probability distribution of a phenotype of interest with a small number of observations per input condition. The key innovation of this study is that the deep neural network automatically generates the probability distributions based on only few (10 or less) noisy measurements for each input condition, with no prior knowledge or assumption of the probability distributions. This is extremely useful for exploring unknown biological systems with limited measurements for each input condition, which is linked not only to a better quantitative understanding of biological systems, but also to the design of new ones, as it is in the case of synthetic biology and cellular engineering.

systems biology↗