Current Opinion in Systems Biology最新文献

The ability to reprogram mammalian cells with tight spatiotemporal control over gene expression and cell response has provided a powerful means to address biomedical challenges. To provide safer synthetic biology products, RNA has recently emerged as an alternative to DNA to deliver transgenes into mammalian cells. In this review, we discuss recent tools implemented to engineer programmable RNA-based synthetic circuits in mammalian cells. We examine the limitations of RNA-encoded gene delivery, and we highlight significant studies that successfully improved payloads expression and persistence and maximized RNA delivery efficiency. Finally, we conclude by discussing examples of RNA-based therapeutics and future perspectives.

Bacteria constantly monitor their environment to adapt their inner makeup. Beyond providing chemical sustenance, metabolism provides most of the feedback on the cellular environment via metabolite binding to regulatory proteins or mRNA. Although first metabolite-protein interactions were discovered more than 60 years ago, identification of new interactions is still technically challenging and time-consuming. Here, we compiled and quantified the current knowledge on metabolite-protein interactions and review recent advances in the identification of interactions and in understanding how metabolites act as signals to transcription factors, two-component systems, protein kinases, and riboswitches. New systematic methods of metabolite-protein identification and omics integration will accelerate the pace of discovery, a remaining challenge is understanding of functionality and the coordination of local and global metabolic signals across different regulatory layers.

The ability to engineer genetic circuits in living cells has tremendous potential in many applications, from health, to energy, to bio-manufacturing. Although substantial efforts have gone into design approaches that make circuits robust to variable cellular context, context dependence of genetic circuits remains a significant hurdle. We review intra-cellular resource competition, one culprit of context dependence, and summarize recent efforts toward design approaches to mitigate it. We classify these approaches into two main groups: global control and local control. In the former, the pool of resources is regulated to meet the demand, and in the latter, individual modules are regulated to be robust to variability in the pool of resources. Within each group, we highlight both feedback and feedforward implementations.

Clinical approvals of gene and cell therapies in recent years, and advances in our ability to engineer complex cellular functions using synthetic biology have fueled interest in merging these two approaches to develop and deploy ever more sophisticated treatments. One area of interface between synthetic biology tools and therapeutics comprises synthetic gene circuits that ‘compute’ a response in a programmable fashion using multiple biomolecular inputs. The potential therapeutic utility of such circuits hinges on their ability to perform logical integration of inputs linked to the human cell phenotype. AND logic increases response specificity, OR logic enables targeting heterogeneous cell populations, and NOT logic provides additional safety. We review recent efforts to implement input sensing and logical integration capabilities in cell, gene, RNA, and microbiome-based therapies. With therapeutic candidates using biomolecular computation already in clinical trials, the approach is poised to revolutionize the field of advanced therapies in the years to come.

Protein complexes are ubiquitous in living systems and have a range of biotechnological applications. However, building protein structures from scratch can be a difficult and laborious process. Here, we review recent developments in protein self-assembly, including a range of tools for covalent and non-covalent assembly of protein structures with user-defined architectures. Key achievements in covalent protein assembly include the development of systems with fast reaction rates and nM affinities. Non-covalent assembly methods have lagged because of the complexity of natural interactions governing protein assembly; but recent developments have created modular methods that are more broadly applicable. On the horizon, we foresee an increasing role for computational protein design tools as key in cementing the role of applications, as opposed to methodology, as the main driving force of research in this field.

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.