Streptomycetes are producers of a wide range of specialized metabolites of great medicinal and industrial importance, such as antibiotics, antifungals, or pesticides. Having been the drivers of the golden age of antibiotics in the 1950s and 1960s, technological advancements over the last two decades have revealed that very little of their biosynthetic potential has been exploited so far. Given the great need for new antibiotics due to the emerging antimicrobial resistance crisis, as well as the urgent need for sustainable biobased production of complex molecules, there is a great renewed interest in exploring and engineering the biosynthetic potential of streptomycetes. Here, we describe the Design-Build-Test-Learn (DBTL) cycle for metabolic engineering experiments in streptomycetes and how it can be used for the discovery and production of novel specialized metabolites.

Recent efforts on the characterization of long non-coding RNAs (lncRNAs) revealed their functional roles in modulating diverse cellular processes. These include pluripotency maintenance, lineage commitment, carcinogenesis, and pathogenesis of various diseases. By interacting with DNA, RNA and protein, lncRNAs mediate multifaceted mechanisms to regulate transcription, RNA processing, RNA interference and translation. Of more than 173000 discovered lncRNAs, the majority remain functionally unknown. The cell type-specific expression and localization of the lncRNA also suggest potential distinct functions of lncRNAs across different cell types. This highlights the niche of identifying functional lncRNAs in different biological processes and diseases through high-throughput (HTP) screening. This review summarizes the current work performed and perspectives on HTP screening of functional lncRNAs where different technologies, platforms, cellular responses and the downstream analyses are discussed. We hope to provide a better picture in applying different technologies to facilitate functional annotation of lncRNA efficiently.

The extracellular matrix (ECM) is a complex assembly of hundreds of proteins that constitutes the scaffold of multicellular organisms. In addition to providing architectural and mechanical support to the surrounding cells, it conveys biochemical signals that regulate cellular processes including proliferation and survival, fate determination, and cell migration. Defects in ECM protein assembly, decreased ECM protein production or, on the contrary, excessive ECM accumulation, have been linked to many pathologies including cardiovascular and skeletal diseases, cancers, and fibrosis. The ECM thus represents a potential reservoir of prognostic biomarkers and therapeutic targets. However, our understanding of the global protein composition of the ECM and how it changes during pathological processes has remained limited until recently. In this mini-review, we provide an overview of the latest methodological advances in sample preparation and mass spectrometry-based proteomics that have permitted the profiling of the ECM of now dozens of normal and diseased tissues, including tumors and fibrotic lesions.

In the past 15 years, cell-based microscopy has evolved its focus from observing cell function to aiming to predict it. In particular—powered by breakthroughs in computer vision, large-scale image analysis and machine learning—high-throughput and high-content microscopy imaging have enabled to uniquely harness single-cell information to systematically discover and annotate genes and regulatory pathways, uncover systems-level interactions and causal links between cellular processes, and begin to clarify and predict causal cellular behaviour and decision making. Here we review these developments, discuss emerging trends in the field, and describe how single-cell ‘omics and single-cell microscopy are imminently in an intersecting trajectory. The marriage of these two fields will make possible an unprecedented understanding of cell and tissue behaviour and function.

Producing ‘designer cells’ with specific functions is potentially feasible in the near future. Recent developments, including whole-cell models, genome design algorithms and gene editing tools, have advanced the possibility of combining biological research and mathematical modelling to further understand and better design cellular processes. In this review, we will explore computational and experimental approaches used for metabolic and genome design. We will highlight the relevance of modelling in this process, and challenges associated with the generation of quantitative predictions about cell behaviour as a whole: although many cellular processes are well understood at the subsystem level, it has proved a hugely complex task to integrate separate components together to model and study an entire cell. We explore these developments, highlighting where computational design algorithms compensate for missing cellular information and underlining where computational models can complement and reduce lab experimentation. We will examine issues and illuminate the next steps for genome engineering.

Orchestrating vertebrate genomes require a complex interplay between the linear composition of the genome and its 3D organization inside the nucleus. This requires the function of specialized proteins, able to tune various aspects of genome organization and gene regulation. The CCCTC-binding factor (CTCF) is a DNA binding factor capable of regulating not only the 3D genome organization, but also key aspects of gene expression, including transcription activation and repression, RNA splicing, and enhancer/promoter insulation. A growing body of evidence proposes that CTCF, together with cohesin contributes to DNA loop formation and 3D genome organization. CTCF binding sites are mutation hotspots in cancer, while mutations in CTCF itself lead to intellectual disabilities, emphasizing its importance in disease etiology. In this review we cover various aspects of CTCF function, revealing the polyvalence of this factor as a highly diversified tool for vertebrate genome organization and transcription regulation.

In this issue of Essays in Biochemistry , biochemistry meets systems biology—a blind date that may hold all the promises, pitfalls and failures of a relationship where a new discipline has been sprung upon a well-established one. As the articles in this issue show, the blind date in this case has great potential to develop into a long-term relationship, where both partners share common values but can benefit from different complementary approaches. Together this partnership is well poised to address and solve some of the major challenges in modern biology.

At genome scale, it is not yet possible to devise detailed kinetic models for metabolism because data on the in vivo biochemistry are too sparse. Predictive large-scale models for metabolism most commonly use the constraint-based framework, in which network structures constrain possible metabolic phenotypes at steady state. However, these models commonly leave many possibilities open, making them less predictive than desired. With increasingly available –omics data, it is appealing to increase the predictive power of constraint-based models (CBMs) through data integration. Many corresponding methods have been developed, but data integration is still a challenge and existing methods perform less well than expected. Here, we review main approaches for the integration of different types of –omics data into CBMs focussing on the methods’ assumptions and limitations. We argue that key assumptions – often derived from single-enzyme kinetics – do not generally apply in the context of networks, thereby explaining current limitations. Emerging methods bridging CBMs and biochemical kinetics may allow for –omics data integration in a common framework to provide more accurate predictions.

An unprecedented opportunity to integrate ~100 years of meticulous in vitro biomolecular research is currently provided in the light of recent advances in methods to visualize closer-to-native architectures of biomolecular machines, and metabolic enzymes in particular. Traditional views of enzymes, namely biomolecular machines, only partially explain their role, organization and kinetics in the cellular milieu. Enzymes self- or hetero-associate, form fibers, may bind to membranes or cytoskeletal elements, have regulatory roles, associate into higher order assemblies (metabolons) or even actively participate in phase-separated membraneless organelles, and all the above in a transient, temporal and spatial manner in response to environmental changes or structural/functional changes of their assemblies. Here, we focus on traditional and emerging concepts in cellular biochemistry and discuss new opportunities in bridging structural, molecular and cellular analyses for metabolic pathways, accumulated over the years, highlighting functional aspects of enzymatic complexes discussed across different levels of spatial resolution.

Gene regulatory networks control the cellular phenotype by changing the RNA and protein composition. Despite its importance, the gene regulatory network in higher organisms is only partly mapped out. Here, we investigate the potential of reverse engineering methods to unravel the structure of these networks. Particularly, we focus on modular response analysis (MRA), a method that can disentangle networks from perturbation data. We benchmark a version of MRA that was previously successfully applied to reconstruct a signalling-driven genetic network, termed MLMSMRA, to test cases mimicking various aspects of gene regulatory networks. We then investigate the performance in comparison with other MRA realisations and related methods. The benchmark shows that MRA has the potential to predict functional interactions, but also shows that successful application of MRA is restricted to small sparse networks and to data with a low signal-to-noise ratio.

Systems biology is an integrative discipline connecting the molecular components within a single biological scale and also among different scales (e.g. cells, tissues and organ systems) to physiological functions and organismal phenotypes through quantitative reasoning, computational models and high-throughput experimental technologies. Systems biology uses a wide range of quantitative experimental and computational methodologies to decode information flow from genes, proteins and other subcellular components of signaling, regulatory and functional pathways to control cell, tissue, organ and organismal level functions. The computational methods used in systems biology provide systems-level insights to understand interactions and dynamics at various scales, within cells, tissues, organs and organisms. In recent years, the systems biology framework has enabled research in quantitative and systems pharmacology and precision medicine for complex diseases. Here, we present a brief overview of current experimental and computational methods used in systems biology.

Signal exchange between different cell types is essential for development and function of multicellular organisms, and its dysregulation is causal in many diseases. Unfortunately, most cell-signaling work has employed single cell types grown under conditions unrelated to their native context. Recent technical developments have started to provide the tools needed to follow signaling between multiple cell types, but gaps in the information they provide have limited their usefulness in building realistic models of heterocellular signaling. Currently, only targeted assays have the necessary sensitivity, selectivity, and spatial resolution to usefully probe heterocellular signaling processes, but these are best used to test specific, mechanistic models. Decades of systems biology research with monocultures has provided a solid foundation for building models of heterocellular signaling, but current models lack a realistic description of regulated proteolysis and the feedback processes triggered within and between cells. Identification and understanding of key regulatory processes in the extracellular environment and of recursive signaling patterns between cells will be essential to building predictive models of heterocellular systems.

Communication between and within cells is essential for multicellular life. While intracellular signal transduction pathways are often specified in molecular terms, the information content they transmit remains poorly defined. Here, we review research efforts to merge biological experimentation with concepts of communication that emerge from the engineering disciplines of signal processing and control theory. We discuss the challenges of performing experiments that quantitate information transfer at the molecular level, and we highlight recent studies that have advanced toward a clearer definition of the information content carried by signaling molecules. Across these studies, we emphasize a theme of increasingly well-matched experimental and theoretical approaches to decode the data streams directing cellular behavior.

The cellular abundance of proteins can vary even between isogenic single cells. This variability between single-cell protein levels can have regulatory roles, such as controlling cell fate during apoptosis induction or the proliferation/quiescence decision. Here, we review examples connecting protein levels and their dynamics in single cells to cellular functions. Such findings were made possible by the introduction of antibodies, and subsequently fluorescent proteins, for tracking protein levels in single cells. However, in heterogeneous cell populations, such as tumors or differentiating stem cells, cellular decisions are controlled by hundreds, even thousands of proteins acting in concert. Characterizing such complex systems demands measurements of thousands of proteins across thousands of single cells. This demand has inspired the development of new methods for single-cell protein analysis, and we discuss their trade-offs, with an emphasis on their specificity and coverage. We finish by highlighting the potential of emerging mass-spec methods to enable systems-level measurement of single-cell proteomes with unprecedented coverage and specificity. Combining such methods with methods for quantitating the transcriptomes and metabolomes of single cells will provide essential data for advancing quantitative systems biology.

Cascades of phosphorylation between protein kinases comprise a core mechanism in the integration and propagation of intracellular signals. Although we have accumulated a wealth of knowledge around some such pathways, this is subject to study biases and much remains to be uncovered. Phosphoproteomics, the identification and quantification of phosphorylated proteins on a proteomic scale, provides a high-throughput means of interrogating the state of intracellular phosphorylation, both at the pathway level and at the whole-cell level. In this review, we discuss methods for using human quantitative phosphoproteomic data to reconstruct the underlying signalling networks that generated it. We address several challenges imposed by the data on such analyses and we consider promising advances towards reconstructing unbiased, kinome-scale signalling networks.

Age is the single greatest risk factor for a wide range of diseases, and as the mean age of human populations grows steadily older, the impact of this risk factor grows as well. Laboratory studies on the basic biology of ageing have shed light on numerous genetic pathways that have strong effects on lifespan. However, we still do not know the degree to which the pathways that affect ageing in the lab also influence variation in rates of ageing and age-related disease in human populations. Similarly, despite considerable effort, we have yet to identify reliable and reproducible ‘biomarkers’, which are predictors of one’s biological as opposed to chronological age. One challenge lies in the enormous mechanistic distance between genotype and downstream ageing phenotypes. Here, we consider the power of studying ‘endophenotypes’ in the context of ageing. Endophenotypes are the various molecular domains that exist at intermediate levels of organization between the genotype and phenotype. We focus our attention specifically on proteins and metabolites. Proteomic and metabolomic profiling has the potential to help identify the underlying causal mechanisms that link genotype to phenotype. We present a brief review of proteomics and metabolomics in ageing research with a focus on the potential of a systems biology and network-centric perspective in geroscience. While network analyses to study ageing utilizing proteomics and metabolomics are in their infancy, they may be the powerful model needed to discover underlying biological processes that influence natural variation in ageing, age-related disease, and longevity.

Systems modelling has been successfully used to investigate several key molecular mechanisms of ageing. Modelling frameworks to allow integration of models and methods to enhance confidence in models are now well established. In this article, we discuss these issues and work through the process of building an integrated model for cellular senescence as a single cell and in a simple tissue context.

Microbial diversity and complexity pose challenges in understanding the voluminous genetic information produced from whole-genome sequences, bioinformatics and high-throughput ‘-omics’ research. These challenges can be overcome by a core blueprint of a genome drawn with a minimal gene set, which is essential for life. Systems biology and large-scale gene inactivation studies have estimated the number of essential genes to be ∼300–500 in many microbial genomes. On the basis of the essential gene set information, minimal-genome strains have been generated using sophisticated genome engineering techniques, such as genome reduction and chemical genome synthesis. Current size-reduced genomes are not perfect minimal genomes, but chemically synthesized genomes have just been constructed. Some minimal genomes provide various desirable functions for bioindustry, such as improved genome stability, increased transformation efficacy and improved production of biomaterials. The minimal genome as a chassis genome for synthetic biology can be used to construct custom-designed genomes for various practical and industrial applications.

Biological systems exhibit complex behaviours that emerge at many different levels of organization. These span the regulation of gene expression within single cells to the use of quorum sensing to co-ordinate the action of entire bacterial colonies. Synthetic biology aims to make the engineering of biology easier, offering an opportunity to control natural systems and develop new synthetic systems with useful prescribed behaviours. However, in many cases, it is not understood how individual cells should be programmed to ensure the emergence of a required collective behaviour. Agent-based modelling aims to tackle this problem, offering a framework in which to simulate such systems and explore cellular design rules. In this article, I review the use of agent-based models in synthetic biology, outline the available computational tools, and provide details on recently engineered biological systems that are amenable to this approach. I further highlight the challenges facing this methodology and some of the potential future directions.