Cellular Growth Rate Research Articles

Growth rates of human induced pluripotent stem cells and neural stem cells from attention-deficit hyperactivity disorder patients: a preliminary study

Attention-deficit hyperactivity disorder (ADHD) is a neurodevelopmental polygenic disorder that affects more than 5% of children and adolescents around the world. Genetic and environmental factors play important roles in ADHD etiology, which leads to a wide range of clinical outcomes and biological phenotypes across the population. Brain maturation delays of a 4-year lag are commonly found in patients, when compared to controls of the same age. Possible differences in cellular growth rates might reflect the clinical observations in ADHD patients. However, the cellular mechanisms are still not elucidated. To test this hypothesis, we analysed the proliferation of induced pluripotent stem cells (iPSCs) and neural stem cells (NSCs) derived from male children and adolescents diagnosed with ADHD and with genetic predisposition to it (assessed using polygenic risk scores), as well as their respective matched controls. In the current pilot study, it was noticeable that NSCs from the ADHD group proliferate less than controls, while no differences were seen at the iPSC developmental stage. Our results from two distinct proliferation methods indicate that the functional and structural delays found in patients might be associated with these in vitro phenotypic differences, but start at a distinct neurodevelopmental stage. These findings are the first ones in the field of disease modelling of ADHD and might be crucial to better understand the pathophysiology of this disorder.

The Slowdown of Growth Rate Controls the Single-Cell Distribution of Biofilm Matrix Production via an SinI-SinR-SlrR Network.

In Bacillus subtilis, master regulator Spo0A controls several cell-differentiation pathways. Under moderate starvation, phosphorylated Spo0A (Spo0A~P) induces biofilm formation by indirectly activating genes controlling matrix production in a subpopulation of cells via an SinI-SinR-SlrR network. Under severe starvation, Spo0A~P induces sporulation by directly and indirectly regulating sporulation gene expression. However, what determines the heterogeneity of individual cell fates is not fully understood. In particular, it is still unclear why, despite being controlled by a single master regulator, biofilm matrix production and sporulation seem mutually exclusive on a single-cell level. In this work, with mathematical modeling, we showed that the fluctuations in the growth rate and the intrinsic noise amplified by the bistability in the SinI-SinR-SlrR network could explain the single-cell distribution of matrix production. Moreover, we predicted an incoherent feed-forward loop; the decrease in the cellular growth rate first activates matrix production by increasing in Spo0A phosphorylation level but then represses it via changing the relative concentrations of SinR and SlrR. Experimental data provide evidence to support model predictions. In particular, we demonstrate how the degree to which matrix production and sporulation appear mutually exclusive is affected by genetic perturbations. IMPORTANCE The mechanisms of cell-fate decisions are fundamental to our understanding of multicellular organisms and bacterial communities. However, even for the best-studied model systems we still lack a complete picture of how phenotypic heterogeneity of genetically identical cells is controlled. Here, using B. subtilis as a model system, we employ a combination of mathematical modeling and experiments to explain the population-level dynamics and single-cell level heterogeneity of matrix gene expression. The results demonstrate how the two cell fates, biofilm matrix production and sporulation, can appear mutually exclusive without explicitly inhibiting one another. Such a mechanism could be used in a wide range of other biological systems.

What programs the size of animal cells?

The human body is programmed with definite quantities, magnitudes, and proportions. At the microscopic level, such definite sizes manifest in individual cells - different cell types are characterized by distinct cell sizes whereas cells of the same type are highly uniform in size. How do cells in a population maintain uniformity in cell size, and how are changes in target size programmed? A convergence of recent and historical studies suggest - just as a thermostat maintains room temperature - the size of proliferating animal cells is similarly maintained by homeostatic mechanisms. In this review, we first summarize old and new literature on the existence of cell size checkpoints, then discuss additional advances in the study of size homeostasis that involve feedback regulation of cellular growth rate. We further discuss recent progress on the molecules that underlie cell size checkpoints and mechanisms that specify target size setpoints. Lastly, we discuss a less-well explored teleological question: why does cell size matter and what is the functional importance of cell size control?

Delineation of proteome changes driven by cell size and growth rate.

Increasing cell size drives changes to the proteome, which affects cell physiology. As cell size increases, some proteins become more concentrated while others are diluted. As a result, the state of the cell changes continuously with increasing size. In addition to these proteomic changes, large cells have a lower growth rate (protein synthesis rate per unit volume). That both the cell’s proteome and growth rate change with cell size suggests they may be interdependent. To test this, we used quantitative mass spectrometry to measure how the proteome changes in response to the mTOR inhibitor rapamycin, which decreases the cellular growth rate and has only a minimal effect on cell size. We found that large cell size and mTOR inhibition, both of which lower the growth rate of a cell, remodel the proteome in similar ways. This suggests that many of the effects of cell size are mediated by the size-dependent slowdown of the cellular growth rate. For example, the previously reported size-dependent expression of some senescence markers could reflect a cell’s declining growth rate rather than its size per se. In contrast, histones and other chromatin components are diluted in large cells independently of the growth rate, likely so that they remain in proportion with the genome. Finally, size-dependent changes to the cell’s growth rate and proteome composition are still apparent in cells continually exposed to a saturating dose of rapamycin, which indicates that cell size can affect the proteome independently of mTORC1 signaling. Taken together, our results clarify the dependencies between cell size, growth, mTOR activity, and the proteome remodeling that ultimately controls many aspects of cell physiology.

Effect of Titanium, Silver and Zinc Nanoparticles on Microalgae in the Aquatic Environment

Metallic nanoparticles (MNPs) are commonly incorporated in products found in households, industries, and agriculture. The presence of MNPs in the aquatic environment causes damage to living organisms and pollutes the water body rendering it harmful for human consumption. Several studies have been made on the toxicity of MNPs toward microalgae. Most of these studies reported changes in the cellular structure, growth rate, pigments, proteins, and enzymatic activity of microalgae. This review paper focuses on the toxic effects of titanium, zinc, and silver nanoparticles on microalgae in the aquatic environment. A better understanding of the behavior of MNPs in the ecosystem will allow scientists to produce environmentally safe MNPs.

Validated Growth Rate-Dependent Regulation of Lipid Metabolism in Yarrowia lipolytica.

Given the strong potential of Yarrowia lipolytica to produce lipids for use as renewable fuels and oleochemicals, it is important to gain in-depth understanding of the molecular mechanism underlying its lipid accumulation. As cellular growth rate affects biomass lipid content, we performed a comparative proteomic analysis of Y. lipolytica grown in nitrogen-limited chemostat cultures at different dilution rates. After confirming the correlation between growth rate and lipid accumulation, we were able to identify various cellular functions and biological mechanisms involved in oleaginousness. Inspection of significantly up- and downregulated proteins revealed nonintuitive processes associated with lipid accumulation in this yeast. This included proteins related to endoplasmic reticulum (ER) stress, ER–plasma membrane tether proteins, and arginase. Genetic engineering of selected targets validated that some genes indeed affected lipid accumulation. They were able to increase lipid content and were complementary to other genetic engineering strategies to optimize lipid yield.

A plasmid system with tunable copy number

Plasmids are one of the most commonly used platforms for genetic engineering and recombinant gene expression in bacteria. The range of available copy numbers for cloning vectors is largely restricted to the handful of Origins of Replication (ORIs) that have been isolated from plasmids found in nature. Here, we introduce two systems that allow for the continuous, finely-tuned control of plasmid copy number between 1 and 800 copies per cell: a plasmid with an anhydrotetracycline-controlled copy number, and a parallelized assay that is used to generate a continuous spectrum of 1194 ColE1-based copy number variants. Using these systems, we investigate the effects of plasmid copy number on cellular growth rates, gene expression, biosynthesis, and genetic circuit performance. We perform single-cell timelapse measurements to characterize plasmid loss, runaway plasmid replication, and quantify the impact of plasmid copy number on the variability of gene expression. Using our assay, we find that each plasmid imposes a 0.063% linear metabolic burden on their hosts, hinting at a simple relationship between metabolic burdens and plasmid DNA synthesis. Our systems enable the precise control of gene expression, and our results highlight the importance of tuning plasmid copy number as a powerful tool for the optimization of synthetic biological systems.

Characterization of the Nuclear Proteome of Chlamydomonas in Response to Salt Stress

Microalgae biomass is considered a promising alternative feedstock for biodiesel production due to its high productivity of neutral lipids, especially under abiotic stress conditions. Among the unicellular microalgae that show this characteristic, Chlamydomonas reinhardtii appears as one of the most important model species with increased lipid production under abiotic stress conditions. In this study, we show that C. reinhardtii cells cultivated under mixotrophic condition supplemented with 0.1 M of NaCl rapidly raise their intracellular amount of neutral lipids without a reduction in their cellular growth rate, representing a promising condition for biomass production toward bioenergy applications. The nuclear proteome of these cells was investigated, and we identified 323 proteins with an enrichment of almost 60% of nuclear proteins in the total dataset. We found 61 proteins differentially regulated upon salt treatment, including proteins annotated in functional categories related to translation and nucleosome assembly functions. Additionally, we identified transcription factor proteins (TFs) and analyzed their likely transcription factor-binding regulatory elements, identifying target genes related to lipid metabolism and kinase functions, indicating possible regulatory pathways of lipid biosynthesis. Together, these data can help understand regulatory nuclear mechanisms, leading to an increase in lipids in the first 24 h of salt stress.

Experiment verification test of the Artemis I ‘Deep Space Radiation Genomics’ experiment

When the Artemis missions launch, NASA's Orion spacecraft (and crew as of the Artemis II mission) will be exposed to the deep space radiation environment beyond the protection of Earth's magnetosphere. Hence, it is essential to characterize the effects of space radiation, microgravity, and the combination thereof on cells and organisms, i.e., to quantify any correlations between the deep space radiation environment, genetic variation, and induced genetic changes in cells. To address this, the Artemis I mission will include the Peristaltic Laboratory for Automated Science with Multigenerations (PLASM) hardware containing the Deep Space Radiation Genomics (DSRG) experiment. The scientific aims of DSRG are (i) to identify the metabolic and genomic pathways in yeast affected by microgravity, space radiation, and their combination, and (ii) to differentiate between gravity and radiation exposure on single-gene deletion/overexpressing strains' ability to thrive in the spaceflight environment. Yeast is used as a model system because 70% of its essential genes have a human homolog, and over half of these homologs can functionally replace their human counterpart. As part of the experiment preparation towards spaceflight, an Experiment Verification Test (EVT) was performed at the Kennedy Space Center to verify that the experiment design, hardware, and approach to automated operations will enable achieving the scientific aims. For the EVT, fluidic systems were assembled, sterilized, loaded, and acceptance-tested, and subsequently integrated with the engineering parts to produce a flight-like PLASM unit. Each fluidic system consisted of (i) a Media Bag, (ii) four Culture Bags loaded with Saccharomyces cerevisiae (two with deletion series and the remaining two with overexpression series), and (iii) tubing and check valves. The EVT PLASM unit was put under a temperature profile replicating the anticipated different phases of flight, including handover to launch, spaceflight, and splashdown to handover back to the science team, for a 58-day period. At EVT completion, the rate of activation, cellular growth, RNA integrity, and sample contamination were interrogated. All of the experiment's success criteria were satisfied, encouraging our efforts to perform this investigation on Artemis I. This manuscript thus describes the process of spaceflight experiment design maturation with a focus on the EVT, its results, DSRG's preparation for its planned launch on Artemis I in 2022, and how the PLASM hardware can enable other scientific goals on future Artemis missions and/or the Lunar Orbital Platform – Gateway.

The Growth Dependent Design Constraints of Transcription-Factor-Based Metabolite Biosensors.

Metabolite biosensors based on metabolite-responsive transcription factors are key synthetic biology components for sensing and precisely controlling cellular metabolism. Biosensors are often designed under laboratory conditions but are deployed in applications where cellular growth rate differs drastically from its initial characterization. Here we asked how growth rate impacts the minimum and maximum biosensor outputs and the dynamic range, which are key metrics of biosensor performance. Using LacI, TetR, and FadR-based biosensors in Escherichia coli as models, we find that the dynamic range of different biosensors have different growth rate dependencies. We developed a kinetic model to explore how tuning biosensor parameters impact the dynamic range growth rate dependence. Our modeling and experimental results revealed that the effects to dynamic range and its growth rate dependence are often coupled, and the metabolite transport mechanisms shape the dynamic range-growth rate response. This work provides a systematic understanding of biosensor performance under different growth rates, which will be useful for predicting biosensor behavior in broad synthetic biology and metabolic engineering applications.

Abstract P1-05-07: Spatially-resolved single-cell tumor heterogeneity captured by TumorScope biophysical modeling software using MR Imaging

Abstract Background: Dysregulated cellular metabolism is a hallmark of breast cancer, and targeting it has promising implications for improving care and patient outcomes. Specifically, heterogeneity in tumor metabolism is thought to play a role in determining chemotherapy response, the development of resistance, and promoting metastastasis. Despite this, metabolic tumor heterogeneity for individual breast cancer patients has not been characterized completely. Methods: In this study, we used state-of-the-art techniques to characterize metabolic heterogeneity within individual patient tumors by integrating single cell RNA-seq data with genome-scale metabolic modeling. Using SimBioSys’ TumorScope - a commercially available biophysical modeling platform, we compared intra-tumoral metabolic heterogeneity from experimental single cell RNA-seq data to simulated intra tumoral heterogeneity. Results: Using single cell RNA-seq data, we found that intra-tumoral gradients in nutrient availability are widely present within patient tumors (for a single luminal A patient, glucose import flux ranged from 0.19 - 1.25 g/gDW/day, while glutathione import ranged from 0.004 - 0.054 g/gDW/day). We also found that these gradients lead to cellular growth rate gradients within individual tumors (for our representative patient, median SGR = 0.62 %/day +/- 0.33 %/day stdev). Using TumorScope, we found this same gradient behavior within patient tumors. Selecting a similarly growing luminal A patient from our TumorScope simulations resulted in gradients in glucose import (range = 0.17 - 1.26 g/gDW/day), glutathione import (range = 0.024 - 0.058 g/gDW/day), and tumor SGR (median = 0.40 %/day, stdev = 0.42 %/day), which closely match metabolism from single cells (comparing maximum-scaled SGR distributions between single cells and TumorScope yielded a p-value = 0.10). We next examined which nutrients govern heterogeneity in tumor SGR. We found that glucose availability with the tumor microenvironment is more limiting to cell growth than oxygen availability, and this result was consistent between metabolic profiles from both single cell RNA-seq data and TumorScope simulations. TumorScope’s spatially resolved simulations offered the additional insight that gradients in nutrient availability are caused by heterogeneity in the distribution of macro- and micro-vasculature and the composition of the tumor microenvironment. We then used data reduction techniques to compare populations of single cells with differing metabolic phenotypes to identify molecular behavior at the single cells in higher molecular resolution. We found that single cells collected from the clinic co-cluster with single cells from TumorScope simulations, suggesting that a significant amount of intra-tumoral metabolic heterogeneity observed in patients is captured by TumorScope simulations. Conclusion: Accessing tumor heterogeneity has traditionally required specialized equipment, analytic expertise, and invasive procedures, largely limiting its study to large, academic hospitals. Currently, metabolic heterogeneity is only understood in 2D and for few markers (using pathology slides) or in relatively few cells with little or no spatial resolution (for single cell RNA-seq). TumorScope provides a novel approach to simulate metabolic heterogeneity at the single cell scale in 3D across a whole tumor. TumorScope democratizes the study of tumor heterogeneity by making it accessible to clinicians and researchers from MRI data alone. TumorScope has the capability to capture tumor metabolic heterogeneity at a higher scale than previously achievable which will allow for a dramatic increase in our understanding of tumor biology and ultimately improve clinical decision making. Citation Format: Daniel J Cook, John Whitman, Nicole Liadis, John Cole. Spatially-resolved single-cell tumor heterogeneity captured by TumorScope biophysical modeling software using MR Imaging [abstract]. In: Proceedings of the 2021 San Antonio Breast Cancer Symposium; 2021 Dec 7-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2022;82(4 Suppl):Abstract nr P1-05-07.

Mechanisms of antibiotic action shape the fitness landscapes of resistance mutations

Antibiotic-resistant pathogens are a major public health threat. A deeper understanding of how an antibiotic’s mechanism of action influences the emergence of resistance would aid in the design of new drugs and help to preserve the effectiveness of existing ones. To this end, we developed a model that links bacterial population dynamics with antibiotic-target binding kinetics. Our approach allows us to derive mechanistic insights on drug activity from population-scale experimental data and to quantify the interplay between drug mechanism and resistance selection. We find that both bacteriostatic and bactericidal agents can be equally effective at suppressing the selection of resistant mutants, but that key determinants of resistance selection are the relationships between the number of drug-inactivated targets within a cell and the rates of cellular growth and death. We also show that heterogeneous drug-target binding within a population enables resistant bacteria to evolve fitness-improving secondary mutations even when drug doses remain above the resistant strain’s minimum inhibitory concentration. Our work suggests that antibiotic doses beyond this “secondary mutation selection window” could safeguard against the emergence of high-fitness resistant strains during treatment.

Photobiomodulation therapy upregulates the growth kinetics and multilineage differentiation potential of human dental pulp stem cells-an in vitro Study.

This study aims to evaluate the impact of red LED irradiation on the viability, proliferation, colonogenic potential, markers expression along with osteogenic and chondrogenic differentiation of dental pulp stem cells. DPSCs were isolated from sound human permanent teeth using enzymatic digestion method and seeded with regular culture media. Cells at P4 were irradiated using red LED Light (627nm, 2J/cm2) and examined for growth kinetics, and multilineage differentiation using the appropriate differentiation media. The irradiated groups showed an increase in cellular growth rates, cell viability, clonogenic potential, and decrease in population doubling time compared to the control group. Cells of the irradiated groups showed enhanced differentiation towards osteogenic and chondrogenic lineages as revealed by histochemical staining using alizarin red and alcian blue stains. Photobiomodulation is an emerging promising element of tissue engineering triad besides stem cells, scaffolds, and growth factors.

Programming cell growth into different cluster shapes using diffusible signals.

Advances in genetic engineering technologies have allowed the construction of artificial genetic circuits, which have been used to generate spatial patterns of differential gene expression. However, the question of how cells can be programmed, and how complex the rules need to be, to achieve a desired tissue morphology has received less attention. Here, we address these questions by developing a mathematical model to study how cells can collectively grow into clusters with different structural morphologies by secreting diffusible signals that can influence cellular growth rates. We formulate how growth regulators can be used to control the formation of cellular protrusions and how the range of achievable structures scales with the number of distinct signals. We show that a single growth inhibitor is insufficient for the formation of multiple protrusions but may be achieved with multiple growth inhibitors, and that other types of signals can regulate the shape of protrusion tips. These examples illustrate how our approach could potentially be used to guide the design of regulatory circuits for achieving a desired target structure.

Microeconomics of Metabolism: The Warburg Effect as Giffen Behaviour

Metabolic behaviours of proliferating cells are often explained as a consequence of rational optimization of cellular growth rate, whereas microeconomics formulates consumption behaviours as optimization problems. Here, we pushed beyond the analogy to precisely map metabolism onto the theory of consumer choice. We thereby revealed the correspondence between long-standing mysteries in both fields: the Warburg effect, a seemingly wasteful but ubiquitous strategy where cells favour aerobic glycolysis over more energetically efficient oxidative phosphorylation, and Giffen behaviour, the unexpected consumer behaviour where a good is demanded more as its price rises. We identified the minimal, universal requirements for the Warburg effect: a trade-off between oxidative phosphorylation and aerobic glycolysis and complementarity, i.e. impossibility of substitution for different metabolites. Thus, various hypotheses for the Warburg effect are integrated into an identical optimization problem with the same universal structure. Besides, the correspondence between the Warburg effect and Giffen behaviour implies that oxidative phosphorylation is counter-intuitively stimulated when its efficiency is decreased by metabolic perturbations such as drug administration or mitochondrial dysfunction; the concept of Giffen behaviour bridges the Warburg effect and the reverse Warburg effect. This highlights that the application of microeconomics to metabolism can offer new predictions and paradigms for both biology and economics.

Independence of a Marine Unicellular Diazotroph to the Presence of NO3.

Marine nitrogen (N2) fixation was historically considered to be absent or reduced in nitrate (NO3−) rich environments. This is commonly attributed to the lower energetic cost of NO3− uptake compared to diazotrophy in oxic environments. This paradigm often contributes to making inferences about diazotroph distribution and activity in the ocean, and is also often used in biogeochemical ocean models. To assess the general validity of this paradigm beyond the traditionally used model organism Trichodesmium spp., we grew cultures of the unicellular cyanobacterium Crocosphaera watsonii WH8501 long term in medium containing replete concentrations of NO3−. NO3− uptake was measured in comparison to N2 fixation to assess the cultures’ nitrogen source preferences. We further measured culture growth rate, cell stoichiometry, and carbon fixation rate to determine if the presence of NO3− had any effect on cell metabolism. We found that uptake of NO3− by this strain of Crocosphaera was minimal in comparison to other N sources (~2–4% of total uptake). Furthermore, availability of NO3− did not statistically alter N2 fixation rate nor any aspect of cell physiology or metabolism measured (cellular growth rate, cell stoichiometry, cell size, nitrogen fixation rate, nitrogenase activity) in comparison to a NO3− free control culture. These results demonstrate the capability of a marine diazotroph to fix nitrogen and grow independently of NO3−. This lack of sensitivity of diazotrophy to NO3− suggests that assumptions often made about, and model formulations of, N2 fixation should be reconsidered.

The effect of natural selection on the propagation of protein expression noise to bacterial growth.

In bacterial cells, protein expression is a highly stochastic process. Gene expression noise moreover propagates through the cell and adds to fluctuations in the cellular growth rate. A common intuition is that, due to their relatively high noise amplitudes, proteins with a low mean expression level are the most important drivers of fluctuations in physiological variables. In this work, we challenge this intuition by considering the effect of natural selection on noise propagation. Mathematically, the contribution of each protein species to the noise in the growth rate depends on two factors: the noise amplitude of the protein's expression level, and the sensitivity of the growth rate to fluctuations in that protein's concentration. We argue that natural selection, while shaping mean abundances to increase the mean growth rate, also affects cellular sensitivities. In the limit in which cells grow optimally fast, the growth rate becomes most sensitive to fluctuations in highly abundant proteins. This causes abundant proteins to overall contribute strongly to the noise in the growth rate, despite their low noise levels. We further explore this result in an experimental data set of protein abundances, and test key assumptions in an evolving, stochastic toy model of cellular growth.

Production of Soluble and Functional Anti-TNF-α Fab' Fragment in Cytoplasm of E. coli: Investigating the Effect of Process Conditions on Cellular Biomass and Protein Yield Using Response Surface Methodology.

With the increasing dominance of monoclonal antibodies (mAbs) in the biopharmaceutical industry and smaller antibody fragments bringing notable advantages over full-length antibodies, it is of considerable significance to choose the most suitable production system. Although mammalian expression system has been the preferred choice in recent years for mAbs production, E. coli could be the favorable host for non-glycosylated small antibody fragments due to the emergence of new engineered E. coli strains capable of forming disulfide-bonds in their cytoplasm.In this study, non-glycosylated anti-TNF-α Fab' moiety of Certolizumab pegol, produced by periplasmic expression in E. coli in previous studies, was produced in the cytoplasm of E. coli SHuffle strain. The results indicated that it is biologically functional by testing the antigen-binding activity via indirect ELISA and inhibition of TNF-α induced cytotoxicity using MTT test. Major factors affecting protein production and, optimized culture conditions were examined by analyzing growth characteristics and patterns of expression in 24h of post-induction cultivation and, optimization of culture conditions by response surface methodology considering temperature, time of induction and concentration of inducer in small (tube) and shake-flask scale. Based on the results, temperature had the most significant influence on functional protein yield while exerting different impacts in small and shake-flask scales, which indicated that cultivation volume is also an important factor that should be taken into account in optimization process. Furthermore, richness of medium and slower cellular growth rate improved specific cellular yield of functional protein by having a positive effect on the solubility of Fab' antibody.

The Ribosomal Gene Loci-The Power behind the Throne.

Nucleoli form around actively transcribed ribosomal RNA (rRNA) genes (rDNA), and the morphology and location of nucleolus-associated genomic domains (NADs) are linked to the RNA Polymerase I (Pol I) transcription status. The number of rDNA repeats (and the proportion of actively transcribed rRNA genes) is variable between cell types, individuals and disease state. Substantial changes in nucleolar morphology and size accompanied by concomitant changes in the Pol I transcription rate have long been documented during normal cell cycle progression, development and malignant transformation. This demonstrates how dynamic the nucleolar structure can be. Here, we will discuss how the structure of the rDNA loci, the nucleolus and the rate of Pol I transcription are important for dynamic regulation of global gene expression and genome stability, e.g., through the modulation of long-range genomic interactions with the suppressive NAD environment. These observations support an emerging paradigm whereby the rDNA repeats and the nucleolus play a key regulatory role in cellular homeostasis during normal development as well as disease, independent of their role in determining ribosome capacity and cellular growth rates.

Multimodal regularized linear models with flux balance analysis for mechanistic integration of omics data.

High-throughput biological data, thanks to technological advances, have become cheaper to collect, leading to the availability of vast amounts of omic data of different types. In parallel, the in silico reconstruction and modeling of metabolic systems is now acknowledged as a key tool to complement experimental data on a large scale. The integration of these model- and data-driven information is therefore emerging as a new challenge in systems biology, with no clear guidance on how to better take advantage of the inherent multisource and multiomic nature of these data types while preserving mechanistic interpretation. Here, we investigate different regularization techniques for high-dimensional data derived from the integration of gene expression profiles with metabolic flux data, extracted from strain-specific metabolic models, to improve cellular growth rate predictions. To this end, we propose ad-hoc extensions of previous regularization frameworks including group, view-specific and principal component regularization and experimentally compare them using data from 1143 Saccharomyces cerevisiae strains. We observe a divergence between methods in terms of regression accuracy and integration effectiveness based on the type of regularization employed. In multiomic regression tasks, when learning from experimental and model-generated omic data, our results demonstrate the competitiveness and ease of interpretation of multimodal regularized linear models compared to data-hungry methods based on neural networks. All data, models and code produced in this work are available on GitHub at https://github.com/Angione-Lab/HybridGroupIPFLasso_pc2Lasso. Supplementary data are available at Bioinformatics online.