Modeling a minimal cell

Testing Theoretical Minimal Genomes Using Whole-Cell Models

The minimal gene set for life has often been theorized, with at least ten produced for Mycoplasma genitalium (M. genitalium). Due to the difficulty of using M. genitalium in the lab, combined with its long replication time of 12-15 h, none of these theoretical minimal genomes have been tested, even with modern techniques. The publication of the M. genitalium whole-cell model provided the first opportunity to test them, simulating the genome edits in silico. We simulated minimal gene sets from the literature, finding that they produced in silico cells that did not divide. Using knowledge from previous research, we reintroduced specific essential and low essential genes in silico; enabling cellular division. This reinforces the need to identify species-specific low essential genes and their interactions. Any genome designs created using the currently incomplete and fragmented gene essentiality information will very likely require in vivo reintroductions to correct issues and produce dividing cells.

A Multi-Scale Approach to Modeling E. coli Chemotaxis

The degree to which we can understand the multi-scale organization of cellular life is tied to how well our models can represent this organization and the processes that drive its evolution. This paper uses Vivarium-an engine for composing heterogeneous computational biology models into integrated, multi-scale simulations. Vivarium's approach is demonstrated by combining several sub-models of biophysical processes into a model of chemotactic E. coli that exchange molecules with their environment, express the genes required for chemotaxis, swim, grow, and divide. This model is developed incrementally, highlighting cross-compartment mechanisms that link E. coli to its environment, with models for: (1) metabolism and transport, with transport moving nutrients across the membrane boundary and metabolism converting them to useful metabolites, (2) transcription, translation, complexation, and degradation, with stochastic mechanisms that read real gene sequence data and consume base pairs and ATP to make proteins and complexes, and (3) the activity of flagella and chemoreceptors, which together support navigation in the environment.

Simultaneous cross-evaluation of heterogeneous E. coli datasets via mechanistic simulation

The extensive heterogeneity of biological data poses challenges to analysis and interpretation. Construction of a large-scale mechanistic model of Escherichia coli enabled us to integrate and cross-evaluate a massive, heterogeneous dataset based on measurements reported by various groups over decades. We identified inconsistencies with functional consequences across the data, including that the total output of the ribosomes and RNA polymerases described by data are not sufficient for a cell to reproduce measured doubling times, that measured metabolic parameters are neither fully compatible with each other nor with overall growth, and that essential proteins are absent during the cell cycle-and the cell is robust to this absence. Finally, considering these data as a whole leads to successful predictions of new experimental outcomes, in this case protein half-lives.

Genome-driven cell engineering review: in vivo and in silico metabolic and genome engineering

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.

Mathematical modelling of microbes: metabolism, gene expression and growth

The growth of microorganisms involves the conversion of nutrients in the environment into biomass, mostly proteins and other macromolecules. This conversion is accomplished by networks of biochemical reactions cutting across cellular functions, such as metabolism, gene expression, transport and signalling. Mathematical modelling is a powerful tool for gaining an understanding of the functioning of this large and complex system and the role played by individual constituents and mechanisms. This requires models of microbial growth that provide an integrated view of the reaction networks and bridge the scale from individual reactions to the growth of a population. In this review, we derive a general framework for the kinetic modelling of microbial growth from basic hypotheses about the underlying reaction systems. Moreover, we show that several families of approximate models presented in the literature, notably flux balance models and coarse-grained whole-cell models, can be derived with the help of additional simplifying hypotheses. This perspective clearly brings out how apparently quite different modelling approaches are related on a deeper level, and suggests directions for further research.

A novel proposal of a simplified bacterial gene set and the neo-construction of a general minimized metabolic network

A minimal gene set (MGS) is critical for the assembly of a minimal artificial cell. We have developed a proposal of simplifying bacterial gene set to approximate a bacterial MGS by the following procedure. First, we base our simplified bacterial gene set (SBGS) on experimentally determined essential genes to ensure that the genes included in the SBGS are critical. Second, we introduced a half-retaining strategy to extract persistent essential genes to ensure stability. Third, we constructed a viable metabolic network to supplement SBGS. The proposed SBGS includes 327 genes and required 431 reactions. This report describes an SBGS that preserves both self-replication and self-maintenance systems. In the minimized metabolic network, we identified five novel hub metabolites and confirmed 20 known hubs. Highly essential genes were found to distribute the connecting metabolites into more reactions. Based on our SBGS, we expanded the pool of targets for designing broad-spectrum antibacterial drugs to reduce pathogen resistance. We also suggested a rough semi-de novo strategy to synthesize an artificial cell, with potential applications in industry.

Systems biology perspectives on minimal and simpler cells

The concept of the minimal cell has fascinated scientists for a long time, from both fundamental and applied points of view. This broad concept encompasses extreme reductions of genomes, the last universal common ancestor (LUCA), the creation of semiartificial cells, and the design of protocells and chassis cells. Here we review these different areas of research and identify common and complementary aspects of each one. We focus on systems biology, a discipline that is greatly facilitating the classical top-down and bottom-up approaches toward minimal cells. In addition, we also review the so-called middle-out approach and its contributions to the field with mathematical and computational models. Owing to the advances in genomics technologies, much of the work in this area has been centered on minimal genomes, or rather minimal gene sets, required to sustain life. Nevertheless, a fundamental expansion has been taking place in the last few years wherein the minimal gene set is viewed as a backbone of a more complex system. Complementing genomics, progress is being made in understanding the system-wide properties at the levels of the transcriptome, proteome, and metabolome. Network modeling approaches are enabling the integration of these different omics data sets toward an understanding of the complex molecular pathways connecting genotype to phenotype. We review key concepts central to the mapping and modeling of this complexity, which is at the heart of research on minimal cells. Finally, we discuss the distinction between minimizing the number of cellular components and minimizing cellular complexity, toward an improved understanding and utilization of minimal and simpler cells.

Developments in the tools and methodologies of synthetic biology

Synthetic biology is principally concerned with the rational design and engineering of biologically based parts, devices, or systems. However, biological systems are generally complex and unpredictable, and are therefore, intrinsically difficult to engineer. In order to address these fundamental challenges, synthetic biology is aiming to unify a “body of knowledge” from several foundational scientific fields, within the context of a set of engineering principles. This shift in perspective is enabling synthetic biologists to address complexity, such that robust biological systems can be designed, assembled, and tested as part of a biological design cycle. The design cycle takes a forward-design approach in which a biological system is specified, modeled, analyzed, assembled, and its functionality tested. At each stage of the design cycle, an expanding repertoire of tools is being developed. In this review, we highlight several of these tools in terms of their applications and benefits to the synthetic biology community.

The future of whole-cell modeling

Integrated whole-cell modeling is poised to make a dramatic impact on molecular and systems biology, bioengineering, and medicine--once certain obstacles are overcome. From our group's experience building a whole-cell model of Mycoplasma genitalium, we identified several significant challenges to building models of more complex cells. Here we review and discuss these challenges in seven areas: first, experimental interrogation; second, data curation; third, model building and integration; fourth, accelerated computation; fifth, analysis and visualization; sixth, model validation; and seventh, collaboration and community development. Surmounting these challenges will require the cooperation of an interdisciplinary group of researchers to create increasingly sophisticated whole-cell models and make data, models, and simulations more accessible to the wider community.

Geptop: a gene essentiality prediction tool for sequenced bacterial genomes based on orthology and phylogeny

Integrative genomics predictors, which score highly in predicting bacterial essential genes, would be unfeasible in most species because the data sources are limited. We developed a universal approach and tool designated Geptop, based on orthology and phylogeny, to offer gene essentiality annotations. In a series of tests, our Geptop method yielded higher area under curve (AUC) scores in the receiver operating curves than the integrative approaches. In the ten-fold cross-validations among randomly upset samples, Geptop yielded an AUC of 0.918, and in the cross-organism predictions for 19 organisms Geptop yielded AUC scores between 0.569 and 0.959. A test applied to the very recently determined essential gene dataset from the Porphyromonas gingivalis, which belongs to a phylum different with all of the above 19 bacterial genomes, gave an AUC of 0.77. Therefore, Geptop can be applied to any bacterial species whose genome has been sequenced. Compared with the essential genes uniquely identified by the lethal screening, the essential genes predicted only by Gepop are associated with more protein-protein interactions, especially in the three bacteria with lower AUC scores (<0.7). This may further illustrate the reliability and feasibility of our method in some sense. The web server and standalone version of Geptop are available at http://cefg.uestc.edu.cn/geptop/ free of charge. The tool has been run on 968 bacterial genomes and the results are accessible at the website.

Variability in minimal genomes: analysis of tandem repeats in the microsporidia Encephalitozoon intestinalis

Microsporidia are ubiquitous fungi with genomes that have undergone a strong reduction to the extreme cases of Encephalitozoon cuniculi and Encephalitozoon intestinalis. Genetic variability within species of the Encephalitozoon genus has been reported, with most of the studies based on the internal transcribed spacer (ITS) of the rDNA. However, in contrast to the picture of E. cuniculi and Encephalitozoon hellem, where different strains have been identified, no genetic variability has yet been observed in E. intestinalis. We have analysed tandem repeats included in putative coding sequences which could be used as polymorphic markers in E. intestinalis. Eight candidate loci (M2, M2A, M3, M5, M7, M7A, M8 and PTP1) were established and 9 E. intestinalis cultured strains from North America, South America and Europe were analysed. M2, M7 and PTP1 nucleotide sequences were identical among the different strains and the GenBank sequence. In contrast, we observed variants in 4 markers (M2A, M3, M7A and M8) which did not correspond to their respective reference sequences. The most noticeable finding was that with the M5 marker two genotypes were defined among the different strains studied, demonstrating genotypic variability of E. intestinalis. Although the diversity described is certainly not high, which can be explained by a lower chance of genetic variability in its minimal genome, we have demonstrated that polymorphisms actually exist in E. intestinalis. Epidemiological studies using this genetic marker should now be conducted to elucidate the genetic variability in E. intestinalis and improve our knowledge of the epidemiology of this microsporidia.