The dawn of virtual cell biology

Towards multimodal foundation models in molecular cell biology

The rapid advent of high-throughput omics technologies has created an exponential growth in biological data, often outpacing our ability to derive molecular insights. Large-language models have shown a way out of this data deluge in natural language processing by integrating massive datasets into a joint model with manifold downstream use cases. Here we envision developing multimodal foundation models, pretrained on diverse omics datasets, including genomics, transcriptomics, epigenomics, proteomics, metabolomics and spatial profiling. These models are expected to exhibit unprecedented potential for characterizing the molecular states of cells across a broad continuum, thereby facilitating the creation of holistic maps of cells, genes and tissues. Context-specific transfer learning of the foundation models can empower diverse applications from novel cell-type recognition, biomarker discovery and gene regulation inference, to in silico perturbations. This new paradigm could launch an era of artificial intelligence-empowered analyses, one that promises to unravel the intricate complexities of molecular cell biology, to support experimental design and, more broadly, to profoundly extend our understanding of life sciences.

Exploiting Noise, Non-Linearity, and Feedback for Differential Control of Multiple Synthetic Cells with a Single Optogenetic Input

Synthetic biology seeks to develop modular biocircuits that combine to produce complex, controllable behaviors. These designs are often subject to noisy fluctuations and uncertainties, and most modern synthetic biology design processes have focused to create robust components to mitigate the noise of gene expression and reduce the heterogeneity of single-cell responses. However, a deeper understanding of noise can achieve control goals that would otherwise be impossible. We explore how an "Optogenetic Maxwell Demon" could selectively amplify noise to control multiple cells using single-input-multiple-output (SIMO) feedback. Using data-constrained stochastic model simulations and theory, we show how an appropriately selected stochastic SIMO controller can drive multiple different cells to different user-specified configurations irrespective of initial conditions. We explore how controllability depends on cells' regulatory structures, the amount of information available to the controller, and the accuracy of the model used. Our results suggest that gene regulation noise, when combined with optogenetic feedback and non-linear biochemical auto-regulation, can achieve synergy to enable precise control of complex stochastic processes.

Discrimination of the SARS-CoV-2 strains using of coloured s-LASCA-imaging of GB-speckles, developed for the gene "S" nucleotide sequences

Background: A recent bioinformatics technique involves changing nucleotide sequences into 2D speckles. This technique produces speckles called GB-speckles (Gene Based speckles). All classical strategies of speckle-optics, namely speckle-interferometry, subtraction of speckle-images as well as speckle-correlometry have been inferred for processing of GB-speckles. This indicates the considerable improvement in the present tools of bioinformatics.   Methods: Colour s-LASCA imaging of virtual laser GB-speckles, a new method of high discrimination and typing of pathogenic viruses, has been developed. This method has been adapted to the detecting of natural mutations in nucleotide sequences, related to the spike glycoprotein (coding the gene «S») of SARS-CoV-2 gene as the molecular target.    Results: The rate of the colouring images of virtual laser GB-speckles generated by s-LASCA can be described by the specific value of R. If the nucleotide sequences compared utilizing this approach the relevant images are completely identical, then the three components of the resulting colour image will be identical, and therefore the value of R will be equal to zero. However, if there are at least minimal differences in the matched nucleotide sequences, then the value of R will be positive.    Conclusion: The high effectiveness of an application of the colour images of GB-speckles that were generated by s-LASCA- has been demonstrated for discrimination between different variants of the SARS-CoV-2 spike glycoprotein gene.

Discrimination of the SARS-CoV-2 strains using of coloured s-LASCA-imaging of GB-speckles, developed for the gene "S" nucleotide sequences

Background: A recent bioinformatics technique involves changing nucleotide sequences into 2D speckles. This technique produces speckles called GB-speckles (Gene Based speckles). All classical strategies of speckle-optics, namely speckle-interferometry, subtraction of speckle-images as well as speckle-correlometry have been inferred for processing of GB-speckles. This indicates the considerable improvement in the present tools of bioinformatics.   Methods: Colour s-LASCA imaging of virtual laser GB-speckles, a new method of high discrimination and typing of pathogenic viruses, has been developed. This method has been adapted to the detecting of natural mutations in nucleotide sequences, related to the spike glycoprotein (coding the gene «S») of SARS-CoV-2 gene as the molecular target.    Results: The rate of the colouring images of virtual laser GB-speckles generated by s-LASCA can be described by the specific value of R. If the nucleotide sequences compared utilizing this approach the relevant images are completely identical, then the three components of the resulting colour image will be identical, and therefore the value of R will be equal to zero. However, if there are at least minimal differences in the matched nucleotide sequences, then the value of R will be positive.    Conclusion: The high effectiveness of an application of the colour images of GB-speckles that were generated by s-LASCA- has been demonstrated for discrimination between different variants of the SARS-CoV-2 spike glycoprotein gene.

Discrimination of the SARS-CoV-2 strains using of coloured s-LASCA-imaging of GB-speckles, developed for the gene "S" nucleotide sequences

Background: A recent bioinformatics technique involves changing nucleotide sequences into 2D speckles. This technique produces speckles called GB-speckles (Gene Based speckles). All classical strategies of speckle-optics, namely speckle-interferometry, subtraction of speckle-images as well as speckle-correlometry have been inferred for processing of GB-speckles. This indicates the considerable improvement in the present tools of bioinformatics.   Methods: Colour s-LASCA imaging of virtual laser GB-speckles, a new method of high discrimination and typing of pathogenic viruses, has been developed. This method has been adapted to the detecting of natural mutations in nucleotide sequences, related to the spike glycoprotein (coding the gene «S») of SARS-CoV-2 gene as the molecular target.    Results: The rate of the colouring images of virtual laser GB-speckles generated by s-LASCA can be described by the specific value of R. If the nucleotide sequences compared utilizing this approach the relevant images are completely identical, then the three components of the resulting colour image will be identical, and therefore the value of R will be equal to zero. However, if there are at least minimal differences in the matched nucleotide sequences, then the value of R will be positive.    Conclusion: The high effectiveness of an application of the colour images of GB-speckles that were generated by s-LASCA- has been demonstrated for discrimination between different variants of the SARS-CoV-2 spike glycoprotein gene.

Discrimination of the SARS-CoV-2 strains using of coloured s-LASCA-imaging of GB-speckles, developed for the gene "S" nucleotide sequences

Background: A recent bioinformatics technique involves changing nucleotide sequences into 2D speckles. This technique produces speckles called GB-speckles (Gene Based speckles). All classical strategies of speckle-optics, namely speckle-interferometry, subtraction of speckle-images as well as speckle-correlometry have been inferred for processing of GB-speckles. This indicates the considerable improvement in the present tools of bioinformatics.   Methods: Colour s-LASCA imaging of virtual laser GB-speckles, a new method of high discrimination and typing of pathogenic viruses, has been developed. This method has been adapted to the detecting of natural mutations in nucleotide sequences, related to the spike glycoprotein (coding the gene «S») of SARS-CoV-2 gene as the molecular target.    Results: The rate of the colouring images of virtual laser GB-speckles generated by s-LASCA can be described by the specific value of R. If the nucleotide sequences compared utilizing this approach the relevant images are completely identical, then the three components of the resulting colour image will be identical, and therefore the value of R will be equal to zero. However, if there are at least minimal differences in the matched nucleotide sequences, then the value of R will be positive.    Conclusion: The high effectiveness of an application of the colour images of GB-speckles that were generated by s-LASCA- has been demonstrated for discrimination between different variants of the SARS-CoV-2 spike glycoprotein gene.

Could LASCA-imaging of GB-speckles be applied for a high discrimination and typing of pathogenic bacteria?

In this article, the method of analysis of GB-speckles (gene-based speckles) has been adapted to the problem of detecting the differences in a group of genes (usually 5-7), used in Multi Locus Sequence Typing (MLST). This method is based on s-LASCA imaging (spatial Laser Speckle Contrast Analysis) of virtual GB-speckle and on the technique of RGB coordinates for GB-speckles, processed by the s-LASCA method. A very high sensitivity and accuracy of the new method for detecting gene polymorphism as a great alternative to classical MLST has been demonstrated. The analysis of GB-speckles, obtained for the concatenated sequences of seven genes (gatA, gidA, enoA, fumC, hemN, hflX, oppA) of three different Chlamydia trachomatis strains (E/Bour, ST94; G/9301, ST95; G/11222, ST94) has been applied as the model. The high efficiency of usage of s-LASCA-imaging of GB-speckles has been shown. The data obtained represent a significant progress in digital biology as a whole and improvements in the bio-digitalization of bacterial DNA.

Effect of recombinant erythropoietin on inflammatory markers in patients with affective disorders: A randomised controlled study

AIM

This study investigated the effect of repeated infusions of recombinant human erythropoietin (EPO) on markers of inflammation in patients with affective disorders and whether any changes in inflammatory markers were associated with improvements on verbal memory.

METHODS

In total, 83 patients were recruited: 40 currently depressed patients with treatment-resistant depression (TRD) (Hamilton Depression Rating Scale-17 items (HDRS-17) score >17) (sub-study 1) and 43 patients with bipolar disorder (BD) in partial remission (HDRS-17 and Young Mania Rating Scale (YMRS)⩽14) (sub-study 2). In both sub-studies, patients were randomised in a double-blind, parallel-group design to receive eight weekly intravenous infusions of EPO (Eprex; 40,000IU/ml) or saline (0.9% NaCl). Plasma concentrations of interleukin 6 (IL-6), interleukin 18 (IL-18) and high sensitive c-reactive protein (hsCRP) were measured at week 1 (baseline) and weeks 5, 9 and 14. HDRS-17 and neuropsychological function was assessed at weeks 1, 9 and 14 using a test battery including the RAVLT Auditory Verbal Learning Test (primary depression and primary cognition outcomes in the original trial).

RESULTS

EPO had no cumulative effect on plasma levels of IL-6 or IL-18 but increased hsCRP levels in patients with TRD (mean±SD change in ng/L: EPO: 0.43±1.64; Saline: -0.90±2.43; F(1,39)=4.78, p=0.04). EPO had no effects on inflammatory markers in BD. There was no correlation between change in inflammatory markers and change in verbal memory.

CONCLUSIONS

Repeated EPO infusions had no effect on IL-6 and IL-18 levels but produced a modest increase in hsCRP levels in patients with TRD. Changes over time in inflammatory markers were not correlated with changes in cognition suggesting that modulation of the inflammatory pathway is not a putative mechanism of the EPO-associated improvement of cognition in affective disorders.

An 'Omics' Perspective on Cardiomyopathies and Heart Failure

Pathological enlargement of the heart, represented by hypertrophic cardiomyopathy (HCM) and dilated cardiomyopathy (DCM), occurs in response to many genetic and non-genetic factors. The clinical course of cardiac hypertrophy is remarkably variable, ranging from lifelong absence of symptoms to rapidly declining heart function and sudden cardiac death (SCD). Unbiased omics studies have begun to provide a glimpse into the molecular framework underpinning altered mechanotransduction, mitochondrial energetics, oxidative stress, and extracellular matrix in the heart undergoing physiological and pathological hypertrophy. Omics analyses indicate that post-transcriptional regulation of gene expression plays an overriding role in the normal and diseased heart. Studies to date highlight a need for more effective bioinformatics to better integrate patient omics data with their comprehensive clinical histories.

Integration of 'omics' data in aging research: from biomarkers to systems biology

Age is the strongest risk factor for many diseases including neurodegenerative disorders, coronary heart disease, type 2 diabetes and cancer. Due to increasing life expectancy and low birth rates, the incidence of age-related diseases is increasing in industrialized countries. Therefore, understanding the relationship between diseases and aging and facilitating healthy aging are major goals in medical research. In the last decades, the dimension of biological data has drastically increased with high-throughput technologies now measuring thousands of (epi) genetic, expression and metabolic variables. The most common and so far successful approach to the analysis of these data is the so-called reductionist approach. It consists of separately testing each variable for association with the phenotype of interest such as age or age-related disease. However, a large portion of the observed phenotypic variance remains unexplained and a comprehensive understanding of most complex phenotypes is lacking. Systems biology aims to integrate data from different experiments to gain an understanding of the system as a whole rather than focusing on individual factors. It thus allows deeper insights into the mechanisms of complex traits, which are caused by the joint influence of several, interacting changes in the biological system. In this review, we look at the current progress of applying omics technologies to identify biomarkers of aging. We then survey existing systems biology approaches that allow for an integration of different types of data and highlight the need for further developments in this area to improve epidemiologic investigations.

Cell-based screening: extracting meaning from complex data

Unbiased discovery approaches have the potential to uncover neurobiological insights into CNS disease and lead to the development of therapies. Here, we review lessons learned from imaging-based screening approaches and recent advances in these areas, including powerful new computational tools to synthesize complex data into more useful knowledge that can reliably guide future research and development.

Erythropoietin and organ protection: lessons from negative clinical trials

Based on its pleiotropic effects, erythropoietin can decrease inflammation, oxidative stress, and apoptosis. Erythropoietin provides organ protection for the heart, brain, and kidney in diverse preclinical animal studies, especially models that include ischemia–reperfusion injury and/or inflammation. However, large clinical studies in coronary reperfusion, heart failure, stroke, acute kidney injury, and chronic renal disease have failed to demonstrate improved outcomes. A study in a previous issue of Critical Care examining the ability of erythropoietin to prevent or ameliorate acute kidney injury in patients undergoing complex valvular heart surgery is similarly negative. The failure of erythropoietin in clinical studies may be due to an inadequate dose, since the receptors responsible for organ protection may require higher concentrations than those responsible for erythropoiesis. However, as has occurred in studies in sepsis and acute respiratory distress syndrome, the negative studies probably reflect an inadequate understanding of the complexity of the underlying processes with multiple redundant and interacting pathways that may differ among the large number of different cell types involved. As tools to understand this complexity and integrate it on an organismal basis continue to evolve, we will develop the ability to use erythropoietin and related nonhematopoietic agents for organ protection.

Three-dimensional morphometric comparison of normal and apoptotic endothelial cells based on laser scanning confocal microscopy observation

Three-dimensional (3D) morphometric analysis of cellular and subcellular structures provides an effective method for spatial cell biology. Here, 3D cellular and nuclear morphologies are reconstructed to quantify and compare morphometric differences between normal and apoptotic endothelial cells. Human umbilical vein endothelial cells (HUVECs) are treated with 60 μM H2 O2 to get apoptotic cell model and then a series of sectional images are acquired from laser scanning confocal microscopy. The 3D cell model containing plasma membrane and cell nucleus is reconstructed and fused utilizing three sequential softwares or packages (Mimics, Geomagic, and VTK). The results reveal that H2 O2 can induce apoptosis effectively by regulating the activity of apoptosis-related biomolecules, including pro-apoptotic factors p53 and Bax, and anti-apoptotic factor Bcl-2. Compared with the normal HUVECs, the apoptotic cells exhibit significant 3D morphometric parameters (height, volume and nucleus-to-cytoplasm ratio) variation. The present research provides a new perspective on comparative quantitative analysis associated with cell apoptosis and points to the value of LSCM as an objective tool for 3D cell reconstruction.

Systems biology for molecular life sciences and its impact in biomedicine

Modern systems biology is already contributing to a radical transformation of molecular life sciences and biomedicine, and it is expected to have a real impact in the clinical setting in the next years. In this review, the emergence of systems biology is contextualized with a historic overview, and its present state is depicted. The present and expected future contribution of systems biology to the development of molecular medicine is underscored. Concerning the present situation, this review includes a reflection on the "inflation" of biological data and the urgent need for tools and procedures to make hidden information emerge. Descriptions of the impact of networks and models and the available resources and tools for applying them in systems biology approaches to molecular medicine are provided as well. The actual current impact of systems biology in molecular medicine is illustrated, reviewing two cases, namely, those of systems pharmacology and cancer systems biology. Finally, some of the expected contributions of systems biology to the immediate future of molecular medicine are commented.

Compartmentalization and metabolic channeling for multienzymatic biosynthesis: practical strategies and modeling approaches

: The construction of efficient enzyme complexes for multienzymatic biosynthesis is of increasing interest in order to achieve maximum yield and to minimize the interference due to shortcomings that are typical for straightforward one-pot multienzyme catalysis. These include product or intermediate feedback inhibition, degeneration, and diffusive losses of reaction intermediates, consumption of co-factors, and others. The main mechanisms in nature to tackle these effects in transient or stable protein associations are the formation of metabolic channeling and microcompartments, processes that are desirable also for multienzymatic biosynthesis in vitro. This chapter provides an overview over two main aspects. First, numerous recent strategies for establishing compartmentalized multienzyme associations and constructed synthetic enzyme complexes are reviewed. Second, the computational methods at hand to investigate and optimize such associations systematically, especially with focus on large multienzyme complexes and metabolic channeling, are discussed. Perspectives on future studies of multienzymatic biosynthesis concerning compartmentalization and metabolic channeling are presented.