Current Opinion in Systems Biology最新文献

Synthetic biology increasingly enables the construction of sophisticated functions in mammalian cells. A particularly promising frontier combines concepts drawn from industrial process control engineering — which is used to confer and balance properties such as stability and efficiency — with understanding as to how living systems have evolved to perform similar tasks with biological components. In this review, we first survey the state-of-the-art for both technologies and strategies available for genetic programming in mammalian cells. We then discuss recent progress in implementing programming objectives inspired by engineered and natural control mechanisms. Finally, we consider the transformative role of model-guided design in the present and future construction of customized mammalian cell functions for applications in biotechnology, medicine, and fundamental research.

Agent-based modelling has proven its usefulness in several biomedical projects by explaining and uncovering mechanisms in diseases. Nevertheless, the scenarios addressed in these models usually consider a small number of cells, lack cell-specific characterisation and dynamic interactions and have a simplistic environment description. Tools that enable scalable, real-sized simulations of biological systems that require complex setups are needed to have simulations closer to biomedical scenarios that can capture cell-to-cell heterogeneity and system-wide emerging properties. To deliver simulations at the giga-scale (10⁹ cells), different tools have implemented technologies to run in high-performance computing clusters. We hereby review these efforts and detail the main areas of improvement the field needs to focus on to have simulations that are a step closer to having digital twins.

Billions of functionally distinct blood cells emerge from a pool of hematopoietic stem cells in our bodies every day. This progressive differentiation process is hierarchically structured and remarkably robust. We provide an introductory review to mathematical approaches addressing the functional aspects of how lineage choice is potentially implemented on a molecular level. Emerging from studies on the mutual repression of key transcription factors, we illustrate how those simple concepts have been challenged in recent years and subsequently extended. Especially, the analysis of omics data on the single-cell level with computational tools provides descriptive insights on a yet unknown level, while their embedding into a consistent mechanistic and mathematical framework is still incomplete.

When engineering microbes for bioproduction, one is necessarily confronted with the existing tradeoff between efficient bioproduction and maintenance of the cell physiology and growth. Moreover, because cellular processes at the single-cell level are coupled with population dynamics via selection mechanisms, this question should be investigated at the population level. Identifying the temporal induction profile that maximizes production in the long term is highly challenging. External control allows to dynamically adapt the strength of the induction from the outside based on intracellular readouts. It allows benchmarking various regulation functions and, coupled with modeling approaches, identifying and applying optimal strategies. In this review, we describe recent advances using quantitative approaches, modeling, and control theory that pave the way to compute external stimulations maximizing long-term production.

Discrete, logic-based models are increasingly used to describe biological mechanisms. Initially introduced to study gene regulation, these models evolved to cover various molecular mechanisms, such as signaling, transcription factor cooperativity, and even metabolic processes. The abstract nature and amenability of discrete models to robust mathematical analyses make them appropriate for addressing a wide range of complex biological problems. Recent technological breakthroughs have generated a wealth of high-throughput data. Novel, literature-based representations of biological processes and emerging algorithms offer new opportunities for model construction. Here, we review up-to-date efforts to address challenging biological questions by incorporating omic data into logic-based models and discuss critical difficulties in constructing and analyzing integrative, large-scale, logic-based models of biological mechanisms.

Contemporary single-cell experiments produce vast amounts of data, but the interpretation of these data is far from straightforward. In particular, understanding mechanisms and sources of cell-to-cell variability, given highly complex and nonlinear cellular networks, precludes intuitive interpretation. It requires careful computational and mathematical analysis instead. Here, we discuss different types of single-cell data and computational, model-based methods currently used to analyze them. We argue that mechanistic models incorporating subpopulation or cell-specific parameters can help to identify sources of variation and to understand experimentally observed behaviors. We highlight how data types and qualities, together with the nonlinearity of single-cell dynamics, make it challenging to identify the correct underlying biological mechanisms and we outline avenues to address these challenges.

Synthetic Biology enables the construction of new genetic circuits with the final goal of controlling cellular behaviour. However, the noisy nature of biomolecular interactions renders a fine regulation of such circuits necessary for their correct operation. A possible solution is cybergenetics, a new discipline merging the tools of Synthetic biology with those of control theory. Biomolecular controllers can be classified into three different categories (i) embedded, in which the controller is implemented as a genetic circuit co-existing in the same cell with the process to be controlled; (ii) external, where the controller is implemented as a software in a computer; (iii) multicellular, in which the controller and the process to be controlled are in two different cell populations. Here, we describe the advantages and drawbacks of each one of the approaches, expounding their main advantages, limitations, and applications.

In nature, quorum sensing is one of the mechanisms bacterial populations use to communicate with their own species or across species to coordinate behaviours. For the last 20 years, synthetic biologists have recognised the remarkable properties of quorum sensing to build genetic circuits responsive to population density. This has led to progress in designing dynamic, coordinated and sometimes multicellular systems for bioproduction in metabolic engineering and for increased spatial and temporal complexity in synthetic biology. In this review, we highlight recent works focused on using quorum sensing to engineer cell–cell behaviour.

Cancer-associated fibroblasts (CAFs) play many roles in supporting tumour growth and progression, and metabolic rewiring is known to be a hallmark of CAF activation. How to effectively target CAF metabolism is still an open question, however. Recent research shows that CAFs and cancer cells engage in complex metabolic crosstalk, which may offer strategies to metabolically target both tumour and stroma. CAF metabolic rewiring also regulates intrinsic CAF protumourigenic functions, by inducing epigenetic changes to maintain CAF activation and by promoting hallmarks of CAFs such as extracellular matrix (ECM) production and immunosuppression. Finally, the emerging field of CAF subpopulations has opened up possibilities for metabolically targeting specific protumourigenic subgroups and raises new questions about how we define and target CAFs.

Each of us is a unique individual despite carrying almost the same genetic information as anyone else. Zooming in into our body, we find the same pattern: all cells share the same genome, and yet they look and behave differently. The process of gene expression is the reason behind this fascinating phenomenon whereby the same genome is translated in different sets of molecules being present in the cell at any given time. The ability to precisely control this process endows researchers with great power, not only for basic science purposes but also for biotechnological and biomedical applications. In this review, we will discuss the current arsenal of tools that consent to control gene expression using light as the external trigger. These tools are, in most cases, preferable to those based on chemical triggers owing to the many favorable properties of light, foremost its spatial confineability and easy removal.