Current Opinion in Systems Biology最新文献

One of the most remarkable features of biological systems is their ability to adapt to the constantly changing environment. By harnessing principles of control theory, synthetic biologists are starting to mimic this adaptation in regulatory gene circuits. Doing so allows for the construction of systems that perform reliably under non-optimal conditions. Furthermore, making a system adaptive can make up for imperfect knowledge of the underlying biology and, hence, avoid unforeseen complications in the implementation. Here, we review recent developments in the analysis and implementation of adaptive regulatory networks in synthetic biology with a particular focus on genetic circuits that can realize perfect adaptation.

Antibiotic resistance is a growing public health problem. To gain a fundamental understanding of resistance evolution, a combination of systematic experimental and theoretical approaches is required. Evolution experiments combined with next-generation sequencing techniques, laboratory automation, and mathematical modeling are enabling the investigation of resistance development at an unprecedented level of detail. Recent work has directly tracked the intricate stochastic dynamics of bacterial populations in which resistant mutants emerge and compete. In addition, new approaches have enabled measuring how prone a large number of genetically perturbed strains are to evolve resistance. Based on advances in quantitative cell physiology, predictive theoretical models of resistance are increasingly being developed. Taken together, a new strategy for observing, predicting, and ultimately controlling resistance evolution is emerging.

Optogenetics uses light as a traceless inducer to remotely control cellular behavior with high safety and spatiotemporal precision, and its implementation for therapeutic synthetic biology enable customizable user-defined remedial outputs to be generated from suitably engineered cells. Here, we focus on non-neural optogenetics, describing the tools and strategies available to engineer light-responsive, therapeutic mammalian designer cells and highlighting recent advances in design and translational applications, including cell and gene therapies. We also discuss current limitations in engineering genetically encoded light-sensitive systems and suggest some possible solutions.

As metabolomics datasets are becoming larger and more complex, there is an increasing need for model-based data integration and analysis to optimally leverage these data. Dynamic models of metabolism allow for the integration of heterogeneous data and the analysis of dynamic phenotypes. Here, we review recent efforts in using dynamic metabolic models for data integration, focusing on approaches based on ordinary differential equations that are applicable to both time-resolved and steady-state measurements and that do not require flux distributions as inputs. Furthermore, we discuss recent advances and current challenges. We conclude that much progress has been made in various areas, such as the development of scalable simulation tools, and although challenges remain, dynamic modeling is a powerful tool for metabolomics data analysis that is not yet living up to its full potential.

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.