Current Opinion in Systems Biology最新文献

The increasing amount of available high-content data in genomics, proteomics, and metabolomics has significantly improved the predictive power and model accuracy of genome-scale metabolic network models in recent years. We review recent constraint-based modeling approaches that incorporate genomics and proteomics data to form resource allocation models. Different modeling approaches to build resource allocation models and the related enzyme-constrained genome-scale metabolic models are discussed and evaluated with respect to differences regarding model features. In addition, an overview of the data required to construct, simulate and validate models for the different approaches is given, together with a list of relevant databases.

Metabolism is executed by an efficient, interconnected and ancient biochemical system, the metabolic network. Its evolutionary origins are, however, barely understood. We here discuss that because of niche adaptation, the evolutionary selection acting on the metabolic network structure distinguishes modern species and early life forms. Yet, its basic structure remained conserved over more than three billion years of diverging evolution. We speculate that this situation attributes key roles in metabolic network evolution to (i) the reaction properties of central metabolites, (ii) simple catalysts (e.g. metal ions, amino acids) whose importance remained unchanged during evolution, and (iii) the interconnectivity of the network that limits its expansion. The conservation of network structure hence implies that early life forms already used similar metabolic reaction topologies as modern species.

Cellular burden limits the applications of bacterial synthetic biology. Experimental approaches for burden minimisation have recently become available. Tools to identify construct design with low footprint on the host include capacity monitors that quantify cellular capacity, high-throughput approaches and cell-free systems for construct prototyping. Orthogonal ribosomes and feedback controllers are instead useful to seek control of resource allocation and lower burden. Other approaches include genome reduction to increase the available resource pool and synthetic addiction to couple cell fitness and product accumulation. However, controlling the cellular response to exogenous expression is still a challenge, and more tools are needed to widen the applications of synthetic biology. Further effort that combines novel evolutionary data with burden-aware tools can set the foundation to increase the stability and robustness of future genetic systems.

Metabolism is still often regarded as a set of canonical reactions, identical in all organisms, yet that is far from correct. Metabolism and the metabolic networks required for cellular functions vary dramatically even within species. This diversity is also present in bacterial pathogens. This mini-review explores the role of metabolic convergence and divergence in shaping the metabolic network of Mycobacterium tuberculosis and its ability to survive in the host. With the help of a few selected examples, we aim to illustrate the magnitude of changes observed in M. tuberculosis metabolic network.

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.