Continuous-time Markov chains are frequently used to model the stochastic dynamics of (bio)chemical reaction networks. However, except in very special cases, they cannot be analyzed exactly. Additionally, simulation can be computationally intensive. An approach to address these challenges is to consider a more tractable diffusion approximation. Leite and Williams (Ann. Appl. Prob. 29, 2019) proposed a reflected diffusion as an approximation for (bio)chemical reaction networks, which they called the constrained Langevin approximation (CLA) as it extends the usual Langevin approximation beyond the first time some chemical species becomes zero in number. Further explanation and examples of the CLA can be found in Anderson et al. (SIAM Multiscale Modeling Simul. 17, 2019).

In this paper, we extend the approximation of Leite and Williams to (nearly) density-dependent Markov chains, as a first step to obtaining error estimates for the CLA when the diffusion state space is one-dimensional, and we provide a bound for the error in a strong approximation. We discuss some applications for chemical reaction networks and epidemic models, and illustrate these with examples. Our method of proof is designed to generalize to higher dimensions, provided there is a Lipschitz Skorokhod map defining the reflected diffusion process. The existence of such a Lipschitz map is an open problem in dimensions more than one.

Trauma is a common cause of morbidity and mortality in humans and companion animals. Recent efforts in procedural development, training, quality systems, data collection, and research have positively impacted patient outcomes; however, significant unmet need still exists. Coordinated efforts by collaborative, translational, multidisciplinary teams to advance trauma care and improve outcomes have the potential to benefit both human and veterinary patient populations. Strategic use of veterinary clinical trials informed by expertise along the research spectrum (i.e., benchtop discovery, applied science and engineering, large laboratory animal models, clinical veterinary studies, and human randomized trials) can lead to increased therapeutic options for animals while accelerating and enhancing translation by providing early data to reduce the cost and the risk of failed human clinical trials. Active topics of collaboration across the translational continuum include advancements in resuscitation (including austere environments), acute traumatic coagulopathy, trauma-induced coagulopathy, traumatic brain injury, systems biology, and trauma immunology. Mechanisms to improve funding and support innovative team science approaches to current problems in trauma care can accelerate needed, sustainable, and impactful progress in the field. This review article summarizes our current understanding of veterinary and human trauma, thereby identifying knowledge gaps and opportunities for collaborative, translational research to improve multispecies outcomes. This translational trauma group of MDs, PhDs, and DVMs posit that a common understanding of injury patterns and resulting cellular dysregulation in humans and companion animals has the potential to accelerate translation of research findings into clinical solutions.

The idea that plants would be efficient, frugal or optimised echoes the recurrent semantics of ‘blueprint’ and ‘program’ in molecular genetics. However, when analysing plants with quantitative approaches and systems thinking, we instead find that plants are the results of stochastic processes with many inefficiencies, incoherence or delays fuelling their robustness. If one had to highlight the main value of quantitative biology, this could be it: plants are robust systems because they are not efficient. Such systemic insights extend to the way we conduct plant research and opens plant science publication to a much broader framework.

The precision nutrition paradigm is based on the premise that substantial variation exists between human subjects in terms of diet-related disease risk and response to dietary interventions. In terms of better defining, ‘the right diet for the right person at the right time’ may be more appropriate than ‘one-diet-fits-all’. This review will explore how systems biology and nutrigenomics approaches have advanced the precision nutrition paradigm. We will draw upon a number of elegant mechanistic studies that have enhanced our understanding with respect to the complex biology and inter-organ crosstalk, relating to inflammation and metabolism, that underpin cardio-metabolic health. Also, this review will explore the extent to which more targeted, precision nutrition approaches may attenuate adverse risk factors associated with cardio-metabolic disease. We will focus on the key characteristics or ‘metabotypes’ of high- v. low-risk individuals and response v. non-response to interventions, to generate greater insights with respect to risk stratification and therapeutic interventions to enhance disease prevention. The goal is to utilise systems biology to enhance understanding by underpinning more targeted nutritional approaches, which may improve efficacy of personalised nutrition interventions.

The Expanded Program for Immunization Consortium – Human Immunology Project Consortium study aims to employ systems biology to identify and characterize vaccine-induced biomarkers that predict immunogenicity in newborns. Key to this effort is the establishment of the Data Management Core (DMC) to provide reliable data and bioinformatic infrastructure for centralized curation, storage, and analysis of multiple de-identified “omic” datasets. The DMC established a cloud-based architecture using Amazon Web Services to track, store, and share data according to National Institutes of Health standards. The DMC tracks biological samples during collection, shipping, and processing while capturing sample metadata and associated clinical data. Multi-omic datasets are stored in access-controlled Amazon Simple Storage Service (S3) for data security and file version control. All data undergo quality control processes at the generating site followed by DMC validation for quality assurance. The DMC maintains a controlled computing environment for data analysis and integration. Upon publication, the DMC deposits finalized datasets to public repositories. The DMC architecture provides resources and scientific expertise to accelerate translational discovery. Robust operations allow rapid sharing of results across the project team. Maintenance of data quality standards and public data deposition will further benefit the scientific community.

Advances in genomics generated the concept that a better understanding of individual characteristics, e.g. genotype, will lead to improved tailoring of pharmaceutical and nutritional therapies. Subsequent developments in proteomics and metabolomics, in addition to wearable technologies for tracking parameters, such as dietary intakes, physical activity, heart rate and blood glucose, have further driven this idea. Alongside these innovations, there has been a rapid rise in companies offering direct-to-consumer genetic and/or microbiome testing, in combination with the marketing of personalised nutrition services. Key scientific questions include how disparate datasets are integrated, how accurate are current predictions and how these may be developed in the future. In this regard, lessons can be learned from systems biology, which aims both to integrate data from different levels of organisation (e.g. genomic, proteomic and metabolomic) and predict the emergent behaviours of biological systems or organisms as a whole. The present paper reviews the origins and recent advancement of ‘big data’ and systems approaches in medicine and nutrition. Conclusions are that systems integration of multiple technologies has generated mechanistic insights and informed the evolution of precision medicine and personalised nutrition. Pertinent ethical issues include who is entitled to access new technologies and how commercial companies are storing, using and/or re-mining consumer data. Questions about efficacy (both long-term behavioural change and health outcomes), cost-benefit and impacts on health inequalities remain to be fully addressed.

G-protein-coupled receptor 142 (GPR142) belongs to rhodopsin family. GPR142 and GPR119, both Gq-coupled receptors, are expressed in pancreatic β cells of pancreas; their activation eventually leads to triggering of insulin secretion. In this paper, through a systems and synthetic biology approach, the effect of a common hit compound has been investigated in GPR142 and GPR119 pathways. This hit that has the potential to be developed as a lead for nanodrug was obtained through high-throughput virtual screening. The hit compound was further docked with nanoparticles (GOLD, SPION, and CeO2). The probable effect of this potential hit on insulin secretion in type 2 diabetes and its dynamic behavior was explored. Kinetic simulation was performed for cross-validation of its role in both the pathways. This study opens up a probable avenue in therapy of type 2 diabetes through regulation of GPR142 and GPR119 receptors. The biological circuit constructed may further have an application as a modulator to control the up- and downregulation of the biochemical pathway and can be implemented as sensors or nanochips for therapy.

Modern high-throughput molecular and analytical tools offer exciting opportunities to gain a mechanistic understanding of unique traits of weeds. During the past decade, tremendous progress has been made within the weed science discipline using genomic techniques to gain deeper insights into weedy traits such as invasiveness, hybridization, and herbicide resistance. Though the adoption of newer “omics” techniques such as proteomics, metabolomics, and physionomics has been slow, applications of these omics platforms to study plants, especially agriculturally important crops and weeds, have been increasing over the years. In weed science, these platforms are now used more frequently to understand mechanisms of herbicide resistance, weed resistance evolution, and crop–weed interactions. Use of these techniques could help weed scientists to further reduce the knowledge gaps in understanding weedy traits. Although these techniques can provide robust insights about the molecular functioning of plants, employing a single omics platform can rarely elucidate the gene-level regulation and the associated real-time expression of weedy traits due to the complex and overlapping nature of biological interactions. Therefore, it is desirable to integrate the different omics technologies to give a better understanding of molecular functioning of biological systems. This multidimensional integrated approach can therefore offer new avenues for better understanding of questions of interest to weed scientists. This review offers a retrospective and prospective examination of omics platforms employed to investigate weed physiology and novel approaches and new technologies that can provide holistic and knowledge-based weed management strategies for future.

Antigenic variation in malaria was discovered in Plasmodium knowlesi studies involving longitudinal infections of rhesus macaques (M. mulatta). The variant proteins, known as the P. knowlesi Schizont Infected Cell Agglutination (SICA) antigens and the P. falciparum Erythrocyte Membrane Protein 1 (PfEMP1) antigens, expressed by the SICAvar and var multigene families, respectively, have been studied for over 30 years. Expression of the SICA antigens in P. knowlesi requires a splenic component, and specific antibodies are necessary for variant antigen switch events in vivo. Outstanding questions revolve around the role of the spleen and the mechanisms by which the expression of these variant antigen families are regulated. Importantly, the longitudinal dynamics and molecular mechanisms that govern variant antigen expression can be studied with P. knowlesi infection of its mammalian and vector hosts. Synchronous infections can be initiated with established clones and studied at multi-omic levels, with the benefit of computational tools from systems biology that permit the integration of datasets and the design of explanatory, predictive mathematical models. Here we provide an historical account of this topic, while highlighting the potential for maximizing the use of P. knowlesi – macaque model systems and summarizing exciting new progress in this area of research.

The evolution of glyphosate resistance in weedy species places an environmentally benign herbicide in peril. The first report of a dicot plant with evolved glyphosate resistance was horseweed, which occurred in 2001. Since then, several species have evolved glyphosate resistance and genomic information about nontarget resistance mechanisms in any of them ranges from none to little. Here, we report a study combining iGentifier transcriptome analysis, cDNA sequencing, and a heterologous microarray analysis to explore potential molecular and transcriptomic mechanisms of nontarget glyphosate resistance of horseweed. The results indicate that similar molecular mechanisms might exist for nontarget herbicide resistance across multiple resistant plants from different locations, even though resistance among these resistant plants likely evolved independently and available evidence suggests resistance has evolved at least four separate times. In addition, both the microarray and sequence analyses identified non–target-site resistance candidate genes for follow-on functional genomics analysis.

The genetic basis of weedy and invasive traits and their evolution remain poorly understood, but genomic approaches offer tremendous promise for elucidating these important features of weed biology. However, the genomic tools and resources available for weed research are currently meager compared with those available for many crops. Because genomic methodologies are becoming increasingly accessible and less expensive, the time is ripe for weed scientists to incorporate these methods into their research programs. One example is next-generation sequencing technology, which has the advantage of enhancing the sequencing output from the transcriptome of a weedy plant at a reduced cost. Successful implementation of these approaches will require collaborative efforts that focus resources on common goals and bring together expertise in weed science, molecular biology, plant physiology, and bioinformatics. We outline how these large-scale genomic programs can aid both our understanding of the biology of weedy and invasive plants and our success at managing these species in agriculture. The judicious selection of species for developing weed genomics programs is needed, and we offer up choices, but no Arabidopsis-like model species exists in the world of weeds. We outline the roadmap for creating a powerful synergy of weed science and genomics, given well-placed effort and resources.

CVD accounted for 27 % of all deaths in the UK in 2014, and was responsible for 1·7 million hospital admissions in 2013/2014. This condition becomes increasingly prevalent with age, affecting 34·1 and 29·8 % of males and females over 75 years of age respectively in 2011. The dysregulation of cholesterol metabolism with age, often observed as a rise in LDL-cholesterol, has been associated with the pathogenesis of CVD. To compound this problem, it is estimated by 2050, 22 % of the world's population will be over 60 years of age, in culmination with a growing resistance and intolerance to pre-existing cholesterol regulating drugs such as statins. Therefore, it is apparent research into additional therapies for hypercholesterolaemia and CVD prevention is a growing necessity. However, it is also imperative to recognise this complex biological system cannot be studied using a reductionist approach; rather its biological uniqueness necessitates a more integrated methodology, such as that offered by systems biology. In this review, we firstly discuss cholesterol metabolism and how it is affected by diet and the ageing process. Next, we describe therapeutic strategies for hypercholesterolaemia, and finally how the systems biology paradigm can be utilised to investigate how ageing interacts with complex systems such as cholesterol metabolism. We conclude by emphasising the need for nutritionists to work in parallel with the systems biology community, to develop novel approaches to studying cholesterol metabolism and its interaction with ageing.

Toxoplasmosis affects a third of the global population and is of particular concern for immunologically compromised individuals. Toxoplasmosis induces host physiological events ranging from immunological to metabolic responses across multiple biological compartments. To understand the sequence of host responses during acute and chronic Toxoplasma gondii infection, eight male BALB/c mice were infected with 2000 T. gondii ME49 tachyzoites with a further eight uninfected mice used as controls. Plasma cytokines status, urinary metabolic profiling and fecal microbial profiles were characterized to monitor temporal variation related to T. gondii infection. The results showed elevated serum interferon-γ (IFN-γ), interleukin-12p40 and  necrosis factor-α during acute phase of infection with concomitant perturbation in host energy metabolism and host-gut microbiome co-metabolism of phenolics and a shift in microbial composition. However, the differences were less pronounced during the putative chronic phase of infection with elevated IFN-γ, differences in urinary N-acetyls and O-acetyls of glycoproteins with no shift in gut microbial composition. Structural equation modelling on the current data showed host immune responses as the main driver for changes observed in urinary metabolites and gut microbial composition. Such an approach can be applied to other models of infectious diseases to aid understanding of host–pathogen interactions and potential biomarker discovery.

The principles embodied by the Developmental Origins of Health and Disease (DOHaD) view of ‘life history’ trajectory are increasingly underpinned by biological data arising from molecular-based epigenomic and transcriptomic studies. Although a number of ‘omic’ platforms are now routinely and widely used in biology and medicine, data generation is frequently confounded by a frequency distribution in the measurement error (an inherent feature of the chemistry and physics of the measurement process), which adversely affect the accuracy of estimation and thus, the inference of relationships to other biological measures such as phenotype. Based on empirical derived data, we have previously derived a probability density function to capture such errors and thus improve the confidence of estimation and inference based on such data. Here we use published open source data sets to calculate parameter values relevant to the most widely used epigenomic and transcriptomic technologies Then by using our own data sets, we illustrate the benefits of this approach by specific application, to measurement of DNA methylation in this instance, in cases where levels of methylation at specific genomic sites represents either (1) a response variable or (2) an independent variable. Further, we extend this formulation to consideration of the ‘bivariate’ case, in which the co-dependency of methylation levels at two distinct genomic sites is tested for biological significance. These tools not only allow greater accuracy of measurement and improved confidence of functional inference, but in the case of epigenomic data at least, also reveal otherwise cryptic information.

A fundamental goal of systems biology is to create models that describe relationships between biological components. Networks are an increasingly popular approach to this problem. However, a scientist interested in modeling biological (e.g., gene expression) data as a network is quickly confounded by the fundamental problem: how to construct the network? It is fairly easy to construct a network, but is it the network for the problem being considered? This is an important problem with three fundamental issues: How to weight edges in the network in order to capture actual biological interactions? What is the effect of the type of biological experiment used to collect the data from which the network is constructed? How to prune the weighted edges (or what cut-off to apply)? Differences in the construction of networks could lead to different biological interpretations.

Indeed, we find that there are statistically significant dissimilarities in the functional content and topology between gene co-expression networks constructed using different edge weighting methods, data types, and edge cut-offs. We show that different types of known interactions, such as those found through Affinity Capture-Luminescence or Synthetic Lethality experiments, appear in significantly varying amounts in networks constructed in different ways. Hence, we demonstrate that different biological questions may be answered by the different networks. Consequently, we posit that the approach taken to build a network can be matched to biological questions to get targeted answers. More study is required to understand the implications of different network inference approaches and to draw reliable conclusions from networks used in the field of systems biology.

Thrombus formation in flowing blood is a complex time- and space-dependent process ofcell adhesion and fibrin gel formation controlled by huge intricate networks ofbiochemical reactions. This combination of complex biochemistry, non-Newtonianhydrodynamics, and transport processes makes thrombosis difficult to understand. That iswhy numerous attempts to use mathematical modeling for this purpose were undertaken duringthe last decade. In particular, recent years witnessed something of a transition from the“systems biology” to the “systems pharmacology/systems medicine” stage: computationalmodeling is being increasingly applied to practical problems such as drug development,investigation of particular events underlying disease, analysis of the mechanism(s) ofdrug’s action, determining an optimal dosing protocols, etc. Here we review recentadvances and challenges in our understanding of thrombus formation.