Population variability in animal health: Influence on dose-exposure-response relationships: Part II: Modelling and simulation

During the 2017 Biennial meeting, the American Academy of Veterinary Pharmacology and Therapeutics hosted a 1‐day session on the influence of population variability on dose‐exposure‐response relationships. In Part I, we highlighted some of the sources of population variability. Part II provides a summary of discussions on modelling and simulation tools that utilize existing pharmacokinetic data, can integrate drug physicochemical characteristics with species physiological characteristics and dosing information or that combine observed with predicted and in vitro information to explore and describe sources of variability that may influence the safe and effective use of veterinary pharmaceuticals

Genome-scale architecture of small molecule regulatory networks and the fundamental trade-off between regulation and enzymatic activity

Metabolic flux is in part regulated by endogenous small molecules that modulate the catalytic activity of an enzyme, e.g., allosteric inhibition. In contrast to transcriptional regulation of enzymes, technical limitations have hindered the production of a genome-scale atlas of small molecule-enzyme regulatory interactions. Here, we develop a framework leveraging the vast, but fragmented, biochemical literature to reconstruct and analyze the small molecule regulatory network (SMRN) of the model organism Escherichia coli, including the primary metabolite regulators and enzyme targets. Using metabolic control analysis, we prove a fundamental trade-off between regulation and enzymatic activity, and we combine it with metabolomic measurements and the SMRN to make inferences on the sensitivity of enzymes to their regulators. Generalizing the analysis to other organisms, we identify highly conserved regulatory interactions across evolutionarily divergent species, further emphasizing a critical role for small molecule interactions in the maintenance of metabolic homeostasis.P30 CA008748 - NCI NIH HHS; R01 GM121950 - NIGMS NIH HH

The silicon trypanosome

African trypanosomes have emerged as promising unicellular model organisms for the next generation of systems biology. They offer unique advantages, due to their relative simplicity, the availability of all standard genomics techniques and a long history of quantitative research. Reproducible cultivation methods exist for morphologically and physiologically distinct life-cycle stages. The genome has been sequenced, and microarrays, RNA-interference and high-accuracy metabolomics are available. Furthermore, the availability of extensive kinetic data on all glycolytic enzymes has led to the early development of a complete, experiment-based dynamic model of an important biochemical pathway. Here we describe the achievements of trypanosome systems biology so far and outline the necessary steps towards the ambitious aim of creating a , a comprehensive, experiment-based, multi-scale mathematical model of trypanosome physiology. We expect that, in the long run, the quantitative modelling enabled by the Silicon Trypanosome will play a key role in selecting the most suitable targets for developing new anti-parasite drugs

Simulation and inference algorithms for stochastic biochemical reaction networks: from basic concepts to state-of-the-art

Stochasticity is a key characteristic of intracellular processes such as gene regulation and chemical signalling. Therefore, characterising stochastic effects in biochemical systems is essential to understand the complex dynamics of living things. Mathematical idealisations of biochemically reacting systems must be able to capture stochastic phenomena. While robust theory exists to describe such stochastic models, the computational challenges in exploring these models can be a significant burden in practice since realistic models are analytically intractable. Determining the expected behaviour and variability of a stochastic biochemical reaction network requires many probabilistic simulations of its evolution. Using a biochemical reaction network model to assist in the interpretation of time course data from a biological experiment is an even greater challenge due to the intractability of the likelihood function for determining observation probabilities. These computational challenges have been subjects of active research for over four decades. In this review, we present an accessible discussion of the major historical developments and state-of-the-art computational techniques relevant to simulation and inference problems for stochastic biochemical reaction network models. Detailed algorithms for particularly important methods are described and complemented with MATLAB implementations. As a result, this review provides a practical and accessible introduction to computational methods for stochastic models within the life sciences community

Analysis of metabolic flux using dynamic labeling and metabolic modeling

Metabolic fluxes and the capacity to modulate them are a crucial component of the ability of the plant cell to react to environmental perturbations. Our ability to quantify them and to attain information concerning the regulatory mechanisms which control them is therefore essential to understand and influence metabolic networks. For all but the simplest of flux measurements labelling methods have proven to be the most informative. Both steady-state and dynamic labelling approaches having been adopted in the study of plant metabolism. Here the conceptual basis of these complementary approaches, as well as their historical application in microbial, mammalian and plant sciences are reviewed and an update on technical developments in label distribution analyses is provided. This is supported by illustrative cases studies involving the kinetic modelling of secondary metabolism. One issue that is particularly complex in the analysis of plant fluxes is the extensive compartmentation of the plant cell. This problem is discussed from both theoretical and experimental perspectives and the current approaches used to address it are assessed. Finally, current limitations and future perspectives of kinetic modelling of plant metabolism are discussed

Polar Microalgae: New Approaches towards Understanding Adaptations to an Extreme and Changing Environment

Polar Regions are unique and highly prolific ecosystems characterized by extreme environmental gradients. Photosynthetic autotrophs, the base of the food web, have had to adapt physiological mechanisms to maintain growth, reproduction and metabolic activity despite environmental conditions that would shut-down cellular processes in most organisms. High latitudes are characterized by temperatures below the freezing point, complete darkness in winter and continuous light and high UV in the summer. Additionally, sea-ice, an ecological niche exploited by microbes during the long winter seasons when the ocean and land freezes over, is characterized by large salinity fluctuations, limited gas exchange, and highly oxic conditions. The last decade has been an exciting period of insights into the molecular mechanisms behind adaptation of microalgae to the cryosphere facilitated by the advancement of new scientific tools, particularly “omics” techniques. We review recent insights derived from genomics, transcriptomics, and proteomics studies. Genes, proteins and pathways identified from these highly adaptable polar microbes have far-reaching biotechnological applications. Furthermore, they may provide insights into life outside this planet, as well as glimpses into the past. High latitude regions also have disproportionately large inputs into global biogeochemical cycles and are the region most sensitive to climate change

CD8 T-cell induction against vascular endothelial growth factor receptor 2 by Salmonella for vaccination purposes against a murine melanoma.

The Salmonella type III secretion system (T3SS) efficiently translocates heterologous proteins into the cytosol of eukaryotic cells. This leads to an antigen-specific CD8 T-cell induction in mice orally immunized with recombinant Salmonella. Recently, we have used Salmonella's T3SS as a prophylactic and therapeutic intervention against a murine fibrosarcoma. In this study, we constructed a recombinant Salmonella strain translocating the immunogenic H-2D(b)-specific CD8 T-cell epitope VILTNPISM (KDR2) from the murine vascular endothelial growth factor receptor 2 (VEGFR2). VEGFR2 is a member of the tyrosine protein kinase family and is upregulated on proliferating endothelial cells of the tumor vasculature. After single orogastric vaccination, we detected significant numbers of KDR2-tetramer-positive CD8 T cells in the spleens of immunized mice. The efficacy of these cytotoxic T cells was evaluated in a prophylactic setting to protect mice from challenges with B16F10 melanoma cells in a flank tumor model, and to reduce dissemination of spontaneous pulmonary melanoma metastases. Vaccinated mice revealed a reduction of angiogenesis by 62% in the solid tumor and consequently a significant decrease of tumor growth as compared to non-immunized mice. Moreover, in the lung metastasis model, immunization with recombinant Salmonella resulted in a reduction of the metastatic melanoma burden by approximately 60%

Computational structure‐based drug design: Predicting target flexibility

The role of molecular modeling in drug design has experienced a significant revamp in the last decade. The increase in computational resources and molecular models, along with software developments, is finally introducing a competitive advantage in early phases of drug discovery. Medium and small companies with strong focus on computational chemistry are being created, some of them having introduced important leads in drug design pipelines. An important source for this success is the extraordinary development of faster and more efficient techniques for describing flexibility in three‐dimensional structural molecular modeling. At different levels, from docking techniques to atomistic molecular dynamics, conformational sampling between receptor and drug results in improved predictions, such as screening enrichment, discovery of transient cavities, etc. In this review article we perform an extensive analysis of these modeling techniques, dividing them into high and low throughput, and emphasizing in their application to drug design studies. We finalize the review with a section describing our Monte Carlo method, PELE, recently highlighted as an outstanding advance in an international blind competition and industrial benchmarks.We acknowledge the BSC-CRG-IRB Joint Research Program in Computational Biology. This work was supported by a grant from the Spanish Government CTQ2016-79138-R.J.I. acknowledges support from SVP-2014-068797, awarded by the Spanish Government.Peer ReviewedPostprint (author's final draft