Hormonal regulation of root growth: integrating local activities into global behaviour

To date, plant researchers have probed the control of root growth by studying the roles of individual regulatory components or cellular processes. However, recent studies in the Arabidopsis (Arabidopsis thaliana) root have shown that different hormones control organ growth by regulating specific growth processes (cell proliferation, differentiation or expansion) in distinct tissues. We discuss key issues raised by these new insights and hypothesise that novel tissue-to-tissue signals exist to coordinate organ growth. We conclude by describing how multiscale models can help probe the interplay between the different scales at which hormones and their regulatory networks operate in different cells and tissues. Such approaches promise to generate new insights into the mechanisms that control root growth.     

Resource-allocation constraint governs structure and function of microbial communities in metabolic modeling

Predictive modeling tools for assessing microbial communities are important for realizing transformative capabilities of microbiomes in agriculture, ecology, and medicine. Constraint-based community-scale metabolic modeling is unique in its potential for making mechanistic predictions regarding both the structure and function of microbial communities. However, accessing this potential requires an understanding of key physicochemical constraints, which are typically considered on a per-species basis. What is needed is a means of incorporating global constraints relevant to microbial ecology into community models. Resource-allocation constraint, which describes how limited resources should be distributed to different cellular processes, sets limits on the efficiency of metabolic and ecological processes. In this study, we investigate the implications of resource-allocation constraints in community-scale metabolic modeling through a simple mechanism-agnostic implementation of resource-allocation constraints directly at the flux level. By systematically performing single-, two-, and multi-species growth simulations, we show that resource-allocation constraints are indispensable for predicting the structure and function of microbial communities. Our findings call for a scalable workflow for implementing a mechanistic version of resource-allocation constraints to ultimately harness the full potential of community-scale metabolic modeling tools.     

Towards Predictive Models of the Human Gut Microbiome

The intestinal microbiota is an ecosystem susceptible to external perturbations such as dietary changes and antibiotic therapies. Mathematical models of microbial communities could be of great value in the rational design of microbiota-tailoring diets and therapies. Here, we discuss how advances in another field, engineering of microbial communities for wastewater treatment bioreactors, could inspire development of mechanistic mathematical models of the gut microbiota. We review the state of the art in bioreactor modeling and current efforts in modeling the intestinal microbiota. Mathematical modeling could benefit greatly from the deluge of data emerging from metagenomic studies, but data-driven approaches such as network inference that aim to predict microbiome dynamics without explicit mechanistic knowledge seem better suited to model these data. Finally, we discuss how the integration of microbiome shotgun sequencing and metabolic modeling approaches such as flux balance analysis may fulfill the promise of a mechanistic model.     

Protein-based models offer mechanistic insight into complex nickel metalloenzymes

There are ten nickel enzymes found across biological systems, each with a distinct active site and reactivity that spans reductive, oxidative, and redox–neutral processes. We focus on the reductive enzymes, which catalyze reactions that are highly germane to the modern-day climate crisis: [NiFe] hydrogenase, carbon monoxide dehydrogenase, acetyl coenzyme A synthase, and methyl coenzyme M reductase. The current mechanistic understanding of each enzyme system is reviewed along with existing knowledge gaps, which are addressed through the development of protein-derived models, as described here. This opinion is intended to highlight the advantages of using robust protein scaffolds for modeling multiscale contributions to reactivity and inspire the development of novel artificial metalloenzymes for other small molecule transformations.     

How can we harness quantitative genetic variation in crop root systems for agricultural improvement?

Root systems are a black box obscuring a comprehensive understanding of plant function,from the ecosystem scale down to the individual. In particular,a lack of knowledge about the genetic mechanisms and environmental effects that condition root system growth hinders our ability to develop the next generation of crop plants for improved agricultural productivity and sustainability. We discuss how the methods and metrics we use to quantify root systems can affect our ability to understand them,how we can bridge knowledge gaps and accelerate the derivation of structurefunction relationships for roots,and why a detailed mechanistic understanding of root growth and function will be important for future agricultural gains.     

Interindividual variation in epigenomic phenomena in humans

Our knowledge of regulatory mechanisms of gene expression and other chromosomal processes related to DNA methylation and chromatin state is continuing to grow at a rapid pace. Understanding how these epigenomic phenomena vary between individuals will have an impact on understanding their broader role in determining variation in gene expression and biochemical, physiological, and behavioural phenotypes. In this review we survey recent progress in this area, focusing on data available from humans. We highlight the role of obligatory (sequence-dependent) epigenomic variation as an important mechanism for generating interindividual variation that could impact our understanding of the mechanistic basis of complex trait architecture.     

Auxin and ethylene: collaborators or competitors?

The individual roles of auxin and ethylene in controlling the growth and development of young seedlings have been well studied. In recent years, these two hormones have been shown to act synergistically to control specific growth and developmental processes, such as root elongation and root hair formation, as well as antagonistically in other processes, such as lateral root formation and hypocotyl elongation. This review examines the growth and developmental processes that are regulated by crosstalk between these two hormones and explores the mechanistic basis for the regulation of these processes. The emerging trend from these experiments is that ethylene modulates auxin synthesis, transport, and signaling with unique targets and responses in a range of tissues to fine-tune seedling growth and development.     

Leaf expansion – an integrating plant behaviour

Leaves expand to intercept light for photosynthesis, to take up carbon dioxide, and to transpire water for cooling and circulation. The extent to which they expand is determined partly by genetic constraints, and partly by environmental conditions signalling the plant to expand more or less leaf surface area. Leaves have evolved sophisticated sensory mechanisms for detecting these cues and responding with their own growth and function as well as influencing a variety of whole-plant behaviours. Leaf expansion itself is an integrating behaviour that ultimately determines canopy development and function, allocation of materials determining relative shoot : root volume, and the onset of reproduction. To understand leaf development, and in particular, how leaf expansion is regulated, we must know at the molecular level which biochemical processes accomplish cell growth. Physiological experimentation focusing on ion fluxes across the plasmamembrane is providing new molecular information on how light stimulates cell expansion in some dicotyledonous species. Genetic analyses in Arabidopsis, corn, and other species are rapidly generating a list of mutations and enzyme activities associated with leaf development and expansion. Combination of these approaches, using informed physiological interpretations of phenotypic variation will allow us in the future to identify genes encoding both the processes causing cell expansion, and the regulators of these events.     

Quantitative models of developmental pattern formation

Pattern formation in developing organisms can be regulated at a variety of levels, from gene sequence to anatomy. At this level of complexity, mechanistic models of development become essential for integrating data, guiding future experiments, and predicting the effects of genetic and physical perturbations. However, the formulation and analysis of quantitative models of development are limited by high levels of uncertainty in experimental measurements, a large number of both known and unknown system components, and the multiscale nature of development. At the same time, an expanding arsenal of experimental tools can constrain models and directly test their predictions, making the modeling efforts not only necessary, but feasible. Using a number of problems in fruit fly development, we discuss how models can be used to test the feasibility of proposed patterning mechanisms and characterize their systems-level properties.     

Cytoneme-mediated cell-to-cell signaling during development

Cell-to-cell communication is vital for animal tissues and organs to develop and function as organized units. Throughout development, intercellular communication is crucial for the generation of structural diversity, mainly by the regulation of differentiation and growth. During these processes, several signaling molecules function as messengers between cells and are transported from producing to receptor cells. Thus, a tight spatial and temporal regulation of signaling transport is likely to be critical during morphogenesis. Despite much experimental and theoretical work, the question as to how these signals move between cells remains. Cell-to-cell contact is probably the most precise spatial and temporal mechanism for the transference of signaling molecules from the producing to the receiving cells. However, most of these molecules can also function at a distance between cells that are not juxtaposed. Recent research has shown the way in which cells may achieve direct physical contact and communication through actin-based filopodia. In addition, increasing evidence is revealing the role of such filopodia in regulating spatial patterning during development; in this context, the filopodia are referred to as cytonemes. In this review, we highlight recent work concerning the roles of these filopodia in cell signaling during development. The processes that initiate and regulate the formation, orientation and dynamics of cytonemes are poorly understood but are potentially extremely important areas for our knowledge of intercellular communication.     

Multiscale Modeling of Metabolism and Macromolecular Synthesis in E. coli and Its Application to the Evolution of Codon Usage

Biological systems are inherently hierarchal and multiscale in time and space. A major challenge of systems biology is to describe biological systems as a computational model, which can be used to derive novel hypothesis and drive experiments leading to new knowledge. The constraint-based reconstruction and analysis approach has been successfully applied to metabolism and to the macromolecular synthesis machinery assembly. Here, we present the first integrated stoichiometric multiscale model of metabolism and macromolecular synthesis for Escherichia coli K12 MG1655, which describes the sequence-specific synthesis and function of almost 2000 gene products at molecular detail. We added linear constraints, which couple enzyme synthesis and catalysis reactions. Comparison with experimental data showed improvement of growth phenotype prediction with the multiscale model over E. coli’s metabolic model alone. Many of the genes covered by this integrated model are well conserved across enterobacters and other, less related bacteria. We addressed the question of whether the bias in synonymous codon usage could affect the growth phenotype and environmental niches that an organism can occupy. We created two classes of in silico strains, one with more biased codon usage and one with more equilibrated codon usage than the wildtype. The reduced growth phenotype in biased strains was caused by tRNA supply shortage, indicating that expansion of tRNA gene content or tRNA codon recognition allow E. coli to respond to changes in codon usage bias. Our analysis suggests that in order to maximize growth and to adapt to new environmental niches, codon usage and tRNA content must co-evolve. These results provide further evidence for the mutation-selection-drift balance theory of codon usage bias. This integrated multiscale reconstruction successfully demonstrates that the constraint-based modeling approach is well suited to whole-cell modeling endeavors.     

Genetic and management approaches to boost UK wheat yields by ameliorating water deficits

Faced with the challenge of increasing global food production, there is the need to exploit all approaches to increasing crop yields. A major obstacle to boosting yields of wheat (an important staple in many parts of the world) is the availability and efficient use of water, since there is increasing stress on water resources used for agriculture globally, and also in parts of the UK. Improved soil and crop management and the development of new genotypes may increase wheat yields when water is limiting. Technical and scientific issues concerning management options such as irrigation and the use of growth-promoting rhizobacteria are explored, since these may allow the more efficient use of irrigation. Fundamental understanding of how crops sense and respond to multiple abiotic stresses can help improve the effective use of irrigation water. Experiments are needed to test the hypothesis that modifying wheat root system architecture (by increasing root proliferation deep in the soil profile) will allow greater soil water extraction thereby benefiting productivity and yield stability. Furthermore, better knowledge of plant and soil interactions and how below-ground and above-ground processes communicate within the plant can help identify traits and ultimately genes (or alleles) that will define genotypes that yield better under dry conditions. Developing new genotypes will take time and, therefore, these challenges need to be addressed now.     

Multiscale Measurements Distinguish Cellular and Interstitial Hindrances to Diffusion In Vivo

Molecular cancer therapy relies on interstitial diffusion for drug distribution in solid tumors. A mechanistic understanding of how tumor components affect diffusion is necessary to advance cancer drug development. Yet, because of limitations in current techniques, it is unclear how individual tissue components hinder diffusion. We developed multiscale fluorescence recovery after photobleaching (MS-FRAP) to address this deficiency. Diffusion measurements facilitated by MS-FRAP distinguish the diffusive hindrance of the interstitial versus cellular constituents in living tissue. Using multiscale diffusion measurements in vivo, we resolved the contributions of these two major tissue components toward impeding diffusive transport in solid tumors and subcutaneous tissue in mice. We further used MS-FRAP in interstitial matrix-mimetic gels and in vivo to show the influence of physical interactions between collagen and hyaluronan on diffusive hindrance through the interstitium. Through these studies, we show that interstitial hyaluronan paradoxically improves diffusion and that reducing cellularity enhances diffusive macromolecular transport in solid tumors.     

Plant multiscale networks: charting plant connectivity by multi-level analysis and imaging techniques

In multicellular and even single-celled organisms, individual components are interconnected at multiscale levels to produce enormously complex biological networks that help these systems maintain homeostasis for development and environmental adaptation. Systems biology studies initially adopted network analysis to explore how relationships between individual components give rise to complex biological processes. Network analysis has been applied to dissect the complex connectivity of mammalian brains across different scales in time and space in The Human Brain Project. In plant science, network analysis has similarly been applied to study the connectivity of plant components at the molecular, subcellular, cellular, organic, and organism levels. Analysis of these multiscale networks contributes to our understanding of how genotype determines phenotype. In this review, we summarized the theoretical framework of plant multiscale networks and introduced studies investigating plant networks by various experimental and computational modalities. We next discussed the currently available analytic methodologies and multi-level imaging techniques used to map multiscale networks in plants. Finally, we highlighted some of the technical challenges and key questions remaining to be addressed in this emerging field.     

Phenotype-centric modeling for rational metabolic engineering

Phenotype-centric modeling enables a paradigm shift in the analysis of mechanistic models. It brings the focus to a network's biochemical phenotypes and their relationship with measurable traits (e.g., product yields, system dynamics, signal amplification factors, etc.) and away from computationally intensive simulation-centric modeling. Here, we explore applications of this new modeling strategy in the field of rational metabolic engineering using the amorphadiene biosynthetic network as a case study. This network has previously been studied using a mechanistic model and the simulation-centric strategy, and thus provides an excellent means to compare and contrast results obtained from these two very different strategies. We show that the phenotype-centric strategy, without values for the parameters, not only identifies beneficial intervention strategies obtained with the simulation-centric strategy, but it also provides an understanding of the mechanistic context for the validity of these predictions. Additionally, we propose a set of hypothetical strains with the potential to outperform reported production strains and to enhance the mechanistic understanding of the amorphadiene biosynthetic network. Further, we identify the landscape of possible intervention strategies for the given model. We believe that phenotype-centric modeling can advance the field of rational metabolic engineering by enabling the development of next generation kinetics-based algorithms and methods that do not rely on a priori knowledge of kinetic parameters but allow a structured, global analysis of system design in the parameter space.     

Modeling the belowground response of plants and soil biota to edaphic and climatic change—What can we expect to gain?

As atmospheric CO₂ concentrations continue to increase, so too will the emphasis placed on understanding the belowground response of plants to edaphic and climatic change. Controlled-exposure studies that address the significance of an increased supply of carbon to roots and soil biota, and the consequences of this to nutrient cycling will play a prominent role in this process. Models will also contribute to understanding the response of plants and ecosystems to changes in the earth's climate by incorporating experimental results into conceptual or quantitative frameworks from which potential feedbacks within the plant-soil system can be identified. Here we present five examples of how models can be used in this analysis and how they can contribute to the development of new hypotheses in the areas of root biology, soil biota, and ecosystem processes. Two examples illustrate the role of coarse and fine roots in nitrogen and phosphorus uptake from soils, the respiratory costs associated with this acquisition of nutrients, and the significance of root architecture in these relationships. Another example focuses on a conceptual model that has helped raise new ideas about the effects of elevated CO₂ on root and microbial biomass, and on nutrient dynamics in the rhizosphere. Difficulties associated with modeling the contribution of mycorrhizal fungi to whole-plant growth are also discussed. Finally, several broad-scale models are used to illustrate the importance of root turnover, litter decomposition, and nitrogen mineralization in determining an ecosystem's response to atmospheric CO₂ enrichment. We conclude that models are appropriate tools for use both in guiding existing studies and in identifying new hypotheses for future research. Development of models that address the complexities of belowground processes and their role in determining plant and ecosystem function within the context of rising CO₂ concentrations and associated climate change should be encouraged.     

Novel Multiscale Modeling Tool Applied to Pseudomonas aeruginosa Biofilm Formation

Multiscale modeling is used to represent biological systems with increasing frequency and success. Multiscale models are often hybrids of different modeling frameworks and programming languages. We present the MATLAB-NetLogo extension (MatNet) as a novel tool for multiscale modeling. We demonstrate the utility of the tool with a multiscale model of Pseudomonas aeruginosa biofilm formation that incorporates both an agent-based model (ABM) and constraint-based metabolic modeling. The hybrid model correctly recapitulates oxygen-limited biofilm metabolic activity and predicts increased growth rate via anaerobic respiration with the addition of nitrate to the growth media. In addition, a genome-wide survey of metabolic mutants and biofilm formation exemplifies the powerful analyses that are enabled by this computational modeling tool.