A modular minimal cell model: purine and pyrimidine transport and metabolism

Data integration strategies for whole-cell modeling

Data makes the world go round-and high quality data is a prerequisite for precise models, especially for whole-cell models (WCM). Data for WCM must be reusable, contain information about the exact experimental background, and should-in its entirety-cover all relevant processes in the cell. Here, we review basic requirements to data for WCM and strategies how to combine them. As a species-specific resource, we introduce the Yeast Cell Model Data Base (YCMDB) to illustrate requirements and solutions. We discuss recent standards for data as well as for computational models including the modeling process as data to be reported. We outline strategies for constructions of WCM despite their inherent complexity.

Effect of Sodium Selenite on the Metabolite Profile of Epichloë sp. Mycelia from Festuca sinensis in Solid Culture

Selenium (Se) is an essential micronutrient with many beneficial effects for humans and other living organisms. Numerous microorganisms in culture systems enrich and convert inorganic selenium to organic selenium. In this study, Epichloë sp. from Festuca sinensis was exposed to increasing Na2SeO3 concentrations (0, 0.1, 0.2, 0.3, and 0.4 mmol/L) in Petri dishes with potato dextrose agar (PDA) for 8 weeks. Epichloë sp. mycelia were immediately collected after mycelial diameters were measured at 4, 5, 6, 7, and 8 weeks of cultivation, respectively. Gas chromatography-mass spectrometer (GC-MS) analysis was performed on different groups of Epichloë sp. mycelia. Different changes were observed as Epichloë sp. was exposed to different selenite conditions and cultivation time. The colony diameter of Epichloë sp. decreased in response to increased selenite concentrations, whereas the inhibitory effects diminished over time. Seventy-two of the 203 identified metabolites did not differ significantly across selenite treatments within the same time point, while 82 compounds did not differ significantly between multiple time points of the same Se concentration. However, the relative levels of 122 metabolites increased the most under selenite conditions. Specifically, between the 4th and 8th weeks, there were increases in 2-keto-isovaleric acid, uridine, and maltose in selenite treatments compared to controls. Selenium increased glutathione levels and exhibited antioxidant properties in weeks 4, 5, and 7. Additionally, we observed that different doses of selenite could promote the production of carbohydrates such as isomaltose, cellobiose, and sucrose; fatty acids such as palmitoleic acid, palmitic acid, and stearic acid; and amino acids such as lysine and tyrosine in Epichloë sp. mycelia. Therefore, Epichloë sp. exposed to selenite stress may benefit from increased levels of some metabolite compounds.

Models predict planned phosphorus load reduction will make Lake Erie more toxic

Harmful cyanobacteria are a global environmental problem, yet we lack actionable understanding of toxigenic versus nontoxigenic strain ecology and toxin production. We performed a large-scale meta-analysis including 103 papers and used it to develop a mechanistic, agent-based model of Microcystis growth and microcystin production. Simulations for Lake Erie suggest that the observed toxigenic-to-nontoxigenic strain succession during the 2014 Toledo drinking water crisis was controlled by different cellular oxidative stress mitigation strategies (protection by microcystin versus degradation by enzymes) and the different susceptibility of those mechanisms to nitrogen limitation. This model, as well as a simpler empirical one, predicts that the planned phosphorus load reduction will lower biomass but make nitrogen and light more available, which will increase toxin production, favor toxigenic cells, and increase toxin concentrations.

Dynamic carbon flux network of a diverse marine microbial community

The functioning of microbial ecosystems has important consequences from global climate to human health, but quantitative mechanistic understanding remains elusive. The components of microbial ecosystems can now be observed at high resolution, but interactions still have to be inferred e.g., a time-series may show a bloom of bacteria X followed by virus Y suggesting they interact. Existing inference approaches are mostly empirical, like correlation networks, which are not mechanistically constrained and do not provide quantitative mass fluxes, and thus have limited utility. We developed an inference method, where a mechanistic model with hundreds of species and thousands of parameters is calibrated to time series data. The large scale, nonlinearity and feedbacks pose a challenging optimization problem, which is overcome using a novel procedure that mimics natural speciation or diversification e.g., stepwise increase of bacteria species. The method allows for curation using species-level information from e.g., physiological experiments or genome sequences. The product is a mass-balancing, mechanistically-constrained, quantitative representation of the ecosystem. We apply the method to characterize phytoplankton-heterotrophic bacteria interactions via dissolved organic matter in a marine system. The resulting model predicts quantitative fluxes for each interaction and time point (e.g., 0.16 µmolC/L/d of chrysolaminarin to Polaribacter on April 16, 2009). At the system level, the flux network shows a strong correlation between the abundance of bacteria species and their carbon flux during blooms, with copiotrophs being relatively more important than oligotrophs. However, oligotrophs, like SAR11, are unexpectedly high carbon processors for weeks into blooms, due to their higher biomass. The fraction of exudates (vs. grazing/death products) in the DOM pool decreases during blooms, and they are preferentially consumed by oligotrophs. In addition, functional similarity of phytoplankton i.e., what they produce, decouples their association with heterotrophs. The methodology is applicable to other microbial ecosystems, like human microbiome or wastewater treatment plants.

Transforming the study of organisms: Phenomic data models and knowledge bases

The rapidly decreasing cost of gene sequencing has resulted in a deluge of genomic data from across the tree of life; however, outside a few model organism databases, genomic data are limited in their scientific impact because they are not accompanied by computable phenomic data. The majority of phenomic data are contained in countless small, heterogeneous phenotypic data sets that are very difficult or impossible to integrate at scale because of variable formats, lack of digitization, and linguistic problems. One powerful solution is to represent phenotypic data using data models with precise, computable semantics, but adoption of semantic standards for representing phenotypic data has been slow, especially in biodiversity and ecology. Some phenotypic and trait data are available in a semantic language from knowledge bases, but these are often not interoperable. In this review, we will compare and contrast existing ontology and data models, focusing on nonhuman phenotypes and traits. We discuss barriers to integration of phenotypic data and make recommendations for developing an operationally useful, semantically interoperable phenotypic data ecosystem.

Computer-Aided Whole-Cell Design: Taking a Holistic Approach by Integrating Synthetic With Systems Biology

Computer-aided design (CAD) for synthetic biology promises to accelerate the rational and robust engineering of biological systems. It requires both detailed and quantitative mathematical and experimental models of the processes to (re)design biology, and software and tools for genetic engineering and DNA assembly. Ultimately, the increased precision in the design phase will have a dramatic impact on the production of designer cells and organisms with bespoke functions and increased modularity. CAD strategies require quantitative models of cells that can capture multiscale processes and link genotypes to phenotypes. Here, we present a perspective on how whole-cell, multiscale models could transform design-build-test-learn cycles in synthetic biology. We show how these models could significantly aid in the design and learn phases while reducing experimental testing by presenting case studies spanning from genome minimization to cell-free systems. We also discuss several challenges for the realization of our vision. The possibility to describe and build whole-cells in silico offers an opportunity to develop increasingly automatized, precise and accessible CAD tools and strategies.

The Evolution of Cell Free Biomanufacturing

Cell-free systems are a widely used research tool in systems and synthetic biology and a promising platform for manufacturing of proteins and chemicals. In the past, cell-free biology was primarily used to better understand fundamental biochemical processes. Notably, E. coli cell-free extracts were used in the 1960s to decipher the sequencing of the genetic code. Since then, the transcription and translation capabilities of cell-free systems have been repeatedly optimized to improve energy efficiency and product yield. Today, cell-free systems, in combination with the rise of synthetic biology, have taken on a new role as a promising technology for just-in-time manufacturing of therapeutically important biologics and high-value small molecules. They have also been implemented at an industrial scale for the production of antibodies and cytokines. In this review, we discuss the evolution of cell-free technologies, in particular advancements in extract preparation, cell-free protein synthesis, and cell-free metabolic engineering applications. We then conclude with a discussion of the mathematical modeling of cell-free systems. Mathematical modeling of cell-free processes could be critical to addressing performance bottlenecks and estimating the costs of cell-free manufactured products.

Toward a genome scale sequence specific dynamic model of cell-free protein synthesis in Escherichia coli

In this study, we developed a dynamic mathematical model of E. coli cell-free protein synthesis (CFPS). Model parameters were estimated from a dataset consisting of glucose, organic acids, energy species, amino acids, and protein product, chloramphenicol acetyltransferase (CAT) measurements. The model was successfully trained to simulate these measurements, especially those of the central carbon metabolism. We then used the trained model to evaluate the performance, e.g., the yield and rates of protein production. CAT was produced with an energy efficiency of 12%, suggesting that the process could be further optimized. Reaction group knockouts showed that protein productivity was most sensitive to the oxidative phosphorylation and glycolysis/gluconeogenesis pathways. Amino acid biosynthesis was also important for productivity, while overflow metabolism and TCA cycle affected the overall system state. In addition, translation was more important to productivity than transcription. Finally, CAT production was robust to allosteric control, as were most of the predicted metabolite concentrations; the exceptions to this were the concentrations of succinate and malate, and to a lesser extent pyruvate and acetate, which varied from the measured values when allosteric control was removed. This study is the first to use kinetic modeling to predict dynamic protein production in a cell-free E. coli system, and could provide a foundation for genome scale, dynamic modeling of cell-free E. coli protein synthesis.

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Protein production is biphasic, powered initially by glucose and later by pyruvate.

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Protein is produced with an energy efficiency of only 12%.

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Protein productivity is most sensitive to oxidative phosphorylation and glycolysis.

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Protein production is robust to allosteric control.

Circadian clock helps cyanobacteria manage energy in coastal and high latitude ocean

The circadian clock coordinates cellular functions over the diel cycle in many organisms. The molecular mechanisms of the cyanobacterial clock are well characterized, but its ecological role remains a mystery. We present an agent-based model of Synechococcus (harboring a self-sustained, bona fide circadian clock) that explicitly represents genes (e.g., kaiABC), transcripts, proteins, and metabolites. The model is calibrated to data from laboratory experiments with wild type and no-clock mutant strains, and it successfully reproduces the main observed patterns of glycogen metabolism. Comparison of wild type and no-clock mutant strains suggests a main benefit of the clock is due to energy management. For example, it inhibits glycogen synthesis early in the day when it is not needed and energy is better used for making the photosynthesis apparatus. To explore the ecological role of the clock, we integrate the model into a dynamic, three-dimensional global circulation model that includes light variability due to seasonal and diel incident radiation and vertical extinction. Model output is compared with field data, including in situ gene transcript levels. We simulate cyanobaceria with and without a circadian clock, which allows us to quantify the fitness benefit of the clock. Interestingly, the benefit is weakest in the low latitude open ocean, where Prochlorococcus (lacking a self-sustained clock) dominates. However, our attempt to experimentally validate this testable prediction failed. Our study provides insights into the role of the clock and an example for how models can be used to integrate across multiple levels of biological organization.

From Genes to Ecosystems in Microbiology: Modeling Approaches and the Importance of Individuality

Models are important tools in microbial ecology. They can be used to advance understanding by helping to interpret observations and test hypotheses, and to predict the effects of ecosystem management actions or a different climate. Over the past decades, biological knowledge and ecosystem observations have advanced to the molecular and in particular gene level. However, microbial ecology models have changed less and a current challenge is to make them utilize the knowledge and observations at the genetic level. We review published models that explicitly consider genes and make predictions at the population or ecosystem level. The models can be grouped into three general approaches, i.e., metabolic flux, gene-centric and agent-based. We describe and contrast these approaches by applying them to a hypothetical ecosystem and discuss their strengths and weaknesses. An important distinguishing feature is how variation between individual cells (individuality) is handled. In microbial ecosystems, individual heterogeneity is generated by a number of mechanisms including stochastic interactions of molecules (e.g., gene expression), stochastic and deterministic cell division asymmetry, small-scale environmental heterogeneity, and differential transport in a heterogeneous environment. This heterogeneity can then be amplified and transferred to other cell properties by several mechanisms, including nutrient uptake, metabolism and growth, cell cycle asynchronicity and the effects of age and damage. For example, stochastic gene expression may lead to heterogeneity in nutrient uptake enzyme levels, which in turn results in heterogeneity in intracellular nutrient levels. Individuality can have important ecological consequences, including division of labor, bet hedging, aging and sub-optimality. Understanding the importance of individuality and the mechanism(s) underlying it for the specific microbial system and question investigated is essential for selecting the optimal modeling strategy.

NMR-based metabolomics reveals the metabolite profiles of Vibrio parahaemolyticus under ferric iron stimulation

Vibrio parahaemolyticus is a halophilic bacterium endemic to coastal areas, and its pathogenicity has caused widespread seafood poisoning. In our previous research, the protein expression of V. parahaemolyticus in Fe³⁺ medium was determined using isobaric tags for relative and absolute quantitation (iTRAQ). Here, nuclear magnetic resonance (NMR) was used to detect changes in the V. parahaemolyticus metabolome. NMR spectra were obtained using methanol-water extracts of intracellular metabolites from V. parahaemolyticus under various culture conditions, and 62 metabolites were identified, including serine, arginine, alanine, ornithine, tryptophan, glutamine, malate, NAD⁺, NADP⁺, oxypurinol, xanthosine, dCTP, uracil, thymine, hypoxanthine, and betaine. Among these, 21 metabolites were up-regulated after the stimulation of the cells by ferric iron, and 9 metabolites were down-regulated. These metabolites are involved in amino acid and protein synthesis, energy metabolism, DNA and RNA synthesis and osmolality. Based on these results, we conclude that Fe³⁺ influences the metabolite profiles of V. parahaemolyticus.

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.

The MYpop toolbox: Putting yeast stress responses in cellular context on single cell and population scales

Systems biology holds the promise to integrate multiple sources of information in order to build ever more complete models of cellular function. To do this, the field must overcome two significant challenges. First, the current strategy to model average cells must be replaced with population based models accounting for cell-to-cell variability. Second, models must be integrated with each other and with basic cellular function. This requires a core model of cellular physiology as well as a multiscale simulation platform to support large-scale simulation of culture or tissues from single cells. Here, we present such a simulation platform with a core model of yeast physiology as scaffold to integrate and simulate SBML models. The software automates this integration helping users simulate their model of choice in context of the cell division cycle. We benchmark model merging, simulation and analysis by integrating a minimal model of osmotic stress into the core model and analyzing it. We characterize the effect of single cell differences on the dynamics of osmoadaptation, estimating when normal cell growth is resumed and obtaining an explanation for experimentally observed glycerol dynamics based on population dynamics. Hence, the platform can be used to reconcile single cell and population level data.

Rational design of efficient modular cells

The modular cell design principle is formulated to devise modular (chassis) cells. These cells can be assembled with exchangeable production modules in a plug-and-play fashion to build microbial cell factories for efficient combinatorial biosynthesis of novel molecules, requiring minimal iterative strain optimization steps. A modular cell is designed to be auxotrophic, containing core metabolic pathways that are necessary but insufficient to support cell growth and maintenance. To be functional, it must tightly couple with an exchangeable production module containing auxiliary metabolic pathways that not only complement cell growth but also enhance production of targeted molecules. We developed a MODCELL (modular cell) framework based on metabolic pathway analysis to implement the modular cell design principle. MODCELL identifies genetic modifications and requirements to construct modular cell candidates and their associated exchangeable production modules. By defining the degree of similarity and coupling metrics, MODCELL can evaluate which exchangeable production module(s) can be tightly coupled with a modular cell candidate. We first demonstrated how MODCELL works in a step-by-step manner for example metabolic networks, and then applied it to design modular Escherichia coli cells for efficient combinatorial biosynthesis of five alcohols (ethanol, propanol, isopropanol, butanol and isobutanol) and five butyrate esters (ethyl butyrate, propyl butyrate, isopropyl butyrate, butyl butyrate and isobutyl butyrate) from pentose sugars (arabinose and xylose) and hexose sugars (glucose, mannose, and galactose) under anaerobic conditions. We identified three modular cells, MODCELL1, MODCELL2 and MODCELL3, that can couple well with Group 1 of modules (ethanol, isobutanol, butanol, ethyl butyrate, isobutyl butyrate, butyl butyrate), Group 2 (isopropanol, isopropyl butyrate), and Group 3 (propanol, isopropanol), respectively. We validated the design of MODCELL1 for anaerobic production of ethanol, butanol, and ethyl butyrate using experimental data available in literature.

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.

Escherichia coli adapts to tetracycline resistance plasmid (pBR322) by mutating endogenous potassium transport: in silico hypothesis testing

Antibiotic resistance exerts a metabolic cost on bacteria and presumably a fitness disadvantage in the absence of antibiotics. However, several studies have shown that bacteria can evolve to eliminate this cost. Escherichia coli can adapt to the plasmid pBR322 carrying the tetA tetracycline-resistance gene (codes for the TetA efflux pump) by a chromosome mutation, which requires an intact tetA gene on the plasmid. The TetA pump can mediate potassium uptake. Here, the hypothesis that TetA replaces the endogenous K(+) uptake system Trk is explored using a multi-level modeling approach that explicitly resolves relevant intracellular processes (e.g., metabolism and K(+) uptake) and simulates individual bacteria in competition. The general behavior of the model is consistent with observations from the literature (e.g., growth rate and K(+) limitation). In competition experiments without tetracycline, the model correctly predicts the fitness advantage of naive susceptible over naive resistant, evolved resistant over naive resistant and evolved resistant over evolved susceptible strains. Trk takes up about 10 times the K(+) required, which costs energy. TetA takes up less K(+) , which is more efficient and leads to the evolution of the Trk mutant. The evolved Trk mutant relies on TetA to take up K(+) , and thus, carrying the plasmid is advantageous even in the absence of the antibiotic.

A whole-cell computational model predicts phenotype from genotype

Understanding how complex phenotypes arise from individual molecules and their interactions is a primary challenge in biology that computational approaches are poised to tackle. We report a whole-cell computational model of the life cycle of the human pathogen Mycoplasma genitalium that includes all of its molecular components and their interactions. An integrative approach to modeling that combines diverse mathematics enabled the simultaneous inclusion of fundamentally different cellular processes and experimental measurements. Our whole-cell model accounts for all annotated gene functions and was validated against a broad range of data. The model provides insights into many previously unobserved cellular behaviors, including in vivo rates of protein-DNA association and an inverse relationship between the durations of DNA replication initiation and replication. In addition, experimental analysis directed by model predictions identified previously undetected kinetic parameters and biological functions. We conclude that comprehensive whole-cell models can be used to facilitate biological discovery.

The dawn of virtual cell biology

To fulfill systems biology's promise of providing fundamental new insights will require the development of quantitative and predictive models of whole cells. In this issue, Karr et al. present the first integrated and dynamic computational model of a bacterium that accounts for all of its components and their interactions.

Modeling a minimal cell

One important aim of synthetic biology is to develop a self-replicating biological system capable of performing useful tasks. A mathematical model of a synthetic organism would greatly enhance its value by providing a platform in which proposed modifications to the system could be rapidly prototyped and tested. Such a platform would allow the explicit connection of genomic sequence information to physiological predictions. As an initial step toward this aim, a minimal cell model (MCM) has been formulated. The MCM is defined as a model of a hypothetical cell with the minimum number of genes necessary to grow and divide in an optimally supportive culture environment. It is chemically detailed in terms of genes and gene products, as well as physiologically complete in terms of bacterial cell processes (e.g., DNA replication and cell division). A mathematical framework originally developed for modeling Escherichia coli has been used to build the platform MCM. A MCM with 241 product-coding genes (those which produce protein or stable RNA products) is presented. This gene set is genomically complete in that it codes for all the functions that a minimal chemoheterotrophic bacterium would require for sustained growth and division. With this model, the hypotheses behind a minimal gene set can be tested using a chemically detailed, dynamic, whole-cell modeling approach. Furthermore, the MCM can simulate the behavior of a whole cell that depends on the cell's (1) metabolic rates and chemical state, (2) genome in terms of expression of various genes, (3) environment both in terms of direct nutrient starvation and competitive inhibition leading to starvation, and (4) genomic sequence in terms of the chromosomal locations of genes.

Metabolic ensemble modeling for strain engineers

Previous mathematical modeling efforts have made significant contributions to the development of systems biology for predicting biological behavior quantitatively. However, dynamic metabolic model construction remains challenging due to uncertainties in mechanistic structures and parameters. In addition, parameter estimation and model validation often require designated experiments conducted only for purpose of modeling. Such difficulties have hampered the progress of modeling in biology and biotechnology. To circumvent these problems, ensemble approaches have been used to account for uncertainties in model structure and parameters. Specifically, this review focuses on approaches that utilize readily available fermentation data for parameter screening and model validation. Time course data for metabolite measurements, if available, can further calibrate the model. The basis for this approach is explained in non-mathematical terms accessible to experimentalists. Information gained from such an approach has been shown to be useful in designing Escherichia coli strains for metabolic engineering and synthetic biology.

Considerations for the design and construction of a synthetic platform cell for biotechnological applications

The design and construction of an artificial bacterial cell could revolutionize biotechnological processes and technologies. A functional platform cell that can be easily customized for a pre-defined task would be useful for applications from producing therapeutics to decontaminating waste streams. The platform cell must be robust and highly efficient. A biotechnological platform cell is related to the concept of a minimal cell, but several factors beyond those necessary for a minimal cell must be considered for a synthetic organism designed for biotechnological applications. Namely, a platform cell must exhibit robust cell reproduction, decreased genetic drift, a physically robust cell envelope, efficient and simplified transcription and translation controls, and predictable metabolic interactions. Achieving a biotechnological platform cell will benefit from insights acquired from a minimal cell, but an approach of minimizing an existing organism's genome may be a more practical experimental approach. Escherichia coli possess many of the desired characteristics of a platform cell and could serve as a useful model organism for the design and construction of a synthetic platform organism. In this article we review briefly the current state of research in this field and outline specific characteristics that will be important for a biotechnologically relevant synthetic cell that has a minimized genome and efficient regulatory structure.

Mathematical modeling of a minimal protocell with coordinated growth and division

Self-replication is an essential attribute of life but the molecular-level mechanisms involved are not well understood. Cellular self-replication requires not only duplicating all cellular components and doubling volume and membrane area, but also replicating cellular geometry. A whole-cell modeling framework is presented in which an assumed reaction network determines both concentration changes of cellular components and cell geometry. Cell shape is calculated by minimizing membrane-bending energy. Using this framework, simultaneous doubling of volume, surface area, and all components was found to be insufficient to provide mid-cell "pinching" of the parental cell to form two daughter cells. This prompted the design of a minimal protocell that includes a growing shell, a cell-cycle engine, and a contractile ring to enforce cytokinesis. Kinetic parameters were found such that the system exhibited periodic behavior with fundamental aspects of self-replication. This involved simultaneous doubling of all cellular components during a cell cycle, doubling cell volume and membrane area, achieving periodic changes in surface/volume ratio, and forming daughter cells that were geometrically equivalent to each other and to the "newborn" parental cell. The results presented here impact the design of laboratory protocells and the development of a modular strategy for constructing a comprehensive in silico whole-cell model.

Incorporating genome-wide DNA sequence information into a dynamic whole-cell model of Escherichia coli: application to DNA replication

The advent of thousands of annotated genomes, detailed metabolic reconstructions and databases within the flourishing field of systems biology necessitates the development of functionally complete computer models of whole cells and cellular systems. Such models would realistically describe fundamental properties of living systems such as growth, division and chromosome replication. This will inevitably bridge bioinformatic technologies with ongoing mathematical modelling efforts and would allow for in silico prediction of important dynamic physiological events. To demonstrate a potential for the anticipated merger of bioinformatic genome-wide data with a whole-cell computer model, the authors present here an updated version of a dynamic model of Escherichia coli, including a module that correctly describes the initiation and control of DNA replication by nucleoprotein DnaA-ATP molecules. Specifically, a rigorous mathematical approach used to explicitly include the genome-wide distribution of DnaA-binding sites on the replicating chromosome into a computer model of a bacterial cell is discussed. A new simple deterministic approximation of the complex stochastic process of DNA replication initiation is also provided. It is shown for the first time that reasonable assumptions about the mechanism of DNA replication initiation can be implemented in a deterministic whole-cell model to make predictions about the timing of chromosome replication. Furthermore, it is proposed that a large increase in the concentration of DnaA-binding boxes will result in a decreased steady-state growth rate in E. coli.

On the way towards ‘basic autonomous agents’: Stochastic simulations of minimal lipid–peptide cells

In this paper, we apply a recently developed stochastic simulation platform to investigate the dynamic behaviour of minimal 'self-(re-)producing' cellular systems. In particular, we study a set of preliminary conditions for appearance of the simplest forms of autonomy in the context of lipid vesicles (more specifically, lipid-peptide vesicles) that enclose an autocatalytic/proto-metabolic reaction network. The problem is approached from a 'bottom-up' perspective, in the sense that we try to show how relatively simple cell components/processes could engage in a far-from-equilibrium dynamics, staying in those conditions thanks to a rudimentary but effective control of the matter-energy flow through it. In this general scenario, basic autonomy and, together with it, minimal agent systems would appear when (hypothetically pre-biological) cellular systems establish molecular trans-membrane mechanisms that allow them to couple internal chemical reactions with transport processes, in a way that they channel/transform external material-energetic resources into their own means and actively regulate boundary conditions (e.g., osmotic gradients, inflow/outflow of different compounds, ...) that are critical for their constitution and persistence as proto-metabolic cells. The results of our simulations indicate that, before that stage is reached, there are a number of relevant issues that have to be carefully analysed and clarified: especially the immediate effects that the insertion of peptide chains (channel precursors) in the lipid bilayer may have in the structural properties of the membrane (elasticity, permeability, ...) and in the overall dynamic behaviour of the cell.

Stochastic simulations of minimal self-reproducing cellular systems

This paper is a theoretical attempt to gain insight into the problem of how self-assembling vesicles (closed bilayer structures) could progressively turn into minimal self-producing and self-reproducing cells, i.e. into interesting candidates for (proto)biological systems. With this aim, we make use of a recently developed object-oriented platform to carry out stochastic simulations of chemical reaction networks that take place in dynamic cellular compartments. We apply this new tool to study the behaviour of different minimal cell models, making realistic assumptions about the physico-chemical processes and conditions involved (e.g. thermodynamic equilibrium/non-equilibrium, variable volume-to-surface relationship, osmotic pressure, solute diffusion across the membrane due to concentration gradients, buffering effect). The new programming platform has been designed to analyse not only how a single protometabolic cell could maintain itself, grow or divide, but also how a collection of these cells could 'evolve' as a result of their mutual interactions in a common environment.

A genomically/chemically complete module for synthesis of lipid membrane in a minimal cell

A minimal cell is a hypothetical cell defined by the essential functions required for life. We have developed a module for the synthesis of membrane precursors for a mathematical minimal cell model. This module describes, with chemical and genomic detail the production of the constituents required to build a cell membrane and identifies the corresponding essential genes. Membranes allow selective nutrient passage, harmful substance exclusion, and energy generation. Bacterial membrane components range from lipids to fatty acids with embedded proteins and are structurally similar to eukaryotic cell membranes. Membranes are dynamic structures and experimental analyses show great variations in bacterial membrane composition. The flexibility of the model is such that different membrane compositions could be obtained in response to simulated changes in culture conditions. The model's predictions are in close agreement with the observed biological trends. The model's predictions correspond well with the experimental values of total lipid content in cells grown in chemostat culture, but less well with data from batch growth. Cell shape and size results agree especially well for data for growth rate relative to maximum growth rate larger than 0.5; and DNA, RNA, and protein predictions are consistent with experimental observations. A better understanding of the simplest bacterial membrane should lead to insights on the more complex behavior of membranes of higher species as well as identification of potential targets for antimicrobials.

Cytomics as a new potential for drug discovery

At the single-cell level in conjunction with data-pattern analysis, high-content screening by image analysis or flow cytometry of clinical cell- or tissue-section samples provides differential molecular profiles for the personalized prediction of therapy-dependent disease progression in patients. The molecular reverse-engineering of these molecular profiles, which is the exploration of molecular pathways, backwards, to the origin of the observed molecular differentials, by systems biology has the potential to detect new drug targets in knowledge spaces, typically inaccessible to traditional hypotheses. Furthermore, predictive medicine, by cytomics in stratified patient groups, opens a new way for personalized (or individualized) medicine, as well as for the early detection of adverse drug reactions in patients.

OptStrain: a computational framework for redesign of microbial production systems

This paper introduces the hierarchical computational framework OptStrain aimed at guiding pathway modifications, through reaction additions and deletions, of microbial networks for the overproduction of targeted compounds. These compounds may range from electrons or hydrogen in biofuel cell and environmental applications to complex drug precursor molecules. A comprehensive database of biotransformations, referred to as the Universal database (with >5700 reactions), is compiled and regularly updated by downloading and curating reactions from multiple biopathway database sources. Combinatorial optimization is then used to elucidate the set(s) of non-native functionalities, extracted from this Universal database, to add to the examined production host for enabling the desired product formation. Subsequently, competing functionalities that divert flux away from the targeted product are identified and removed to ensure higher product yields coupled with growth. This work represents an advancement over earlier efforts by establishing an integrated computational framework capable of constructing stoichiometrically balanced pathways, imposing maximum product yield requirements, pinpointing the optimal substrate(s), and evaluating different microbial hosts. The range and utility of OptStrain are demonstrated by addressing two very different product molecules. The hydrogen case study pinpoints reaction elimination strategies for improving hydrogen yields using two different substrates for three separate production hosts. In contrast, the vanillin study primarily showcases which non-native pathways need to be added into Escherichia coli. In summary, OptStrain provides a useful tool to aid microbial strain design and, more importantly, it establishes an integrated framework to accommodate future modeling developments.