Design and Implementation of a Tool to Assess Students' Understanding of Metabolic Pathways Dynamics and Regulation.
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ABSTRACT:
SUBMITTER: Villafane SM
PROVIDER: S-EPMC8715806 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundMore and more completely sequenced fungal genomes are becoming available and many more sequencing projects are in progress. This deluge of data should improve our knowledge of the various primary and secondary metabolisms of Fungi, including their synthesis of useful compounds such as antibiotics or toxic molecules such as mycotoxins. Functional annotation of many fungal genomes is imperfect, especially of genes encoding enzymes, so we need dedicated tools to analyze their metabolic pathways in depth.DescriptionFUNGIpath is a new tool built using a two-stage approach. Groups of orthologous proteins predicted using complementary methods of detection were collected in a relational database. Each group was further mapped on to steps in the metabolic pathways published in the public databases KEGG and MetaCyc. As a result, FUNGIpath allows the primary and secondary metabolisms of the different fungal species represented in the database to be compared easily, making it possible to assess the level of specificity of various pathways at different taxonomic distances. It is freely accessible at http://www.fungipath.u-psud.fr.ConclusionsAs more and more fungal genomes are expected to be sequenced during the coming years, FUNGIpath should help progressively to reconstruct the ancestral primary and secondary metabolisms of the main branches of the fungal tree of life and to elucidate the evolution of these ancestral fungal metabolisms to various specific derived metabolisms.
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Project description:One of the primary aims of synthetic biology is to (re)design metabolic pathways towards the production of desired chemicals. The fast pace of developments in molecular biology increasingly makes it possible to experimentally redesign existing pathways and implement de novo ones in microbes or using in vitro platforms. For such experimental studies, the bottleneck is shifting from implementation of pathways towards their initial design. Here, we present an online tool called 'Metabolic Tinker', which aims to guide the design of synthetic metabolic pathways between any two desired compounds. Given two user-defined 'target' and 'source' compounds, Metabolic Tinker searches for thermodynamically feasible paths in the entire known metabolic universe using a tailored heuristic search strategy. Compared with similar graph-based search tools, Metabolic Tinker returns a larger number of possible paths owing to its broad search base and fast heuristic, and provides for the first time thermodynamic feasibility information for the discovered paths. Metabolic Tinker is available as a web service at http://osslab.ex.ac.uk/tinker.aspx. The same website also provides the source code for Metabolic Tinker, allowing it to be developed further or run on personal machines for specific applications.
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Project description:The dynamic features of a genetic network's response to environmental fluctuations represent essential functional specifications and thus may constrain the possible choices of network architecture and kinetic parameters. To explore the connection between dynamics and network design, we have analyzed a general regulatory architecture that is commonly found in many metabolic pathways. Such architecture is characterized by a dual control mechanism, with end product feedback inhibition and transcriptional regulation mediated by an intermediate metabolite. As a case study, we measured with high temporal resolution the induction profiles of the enzymes in the leucine biosynthetic pathway in response to leucine depletion, using an automated system for monitoring protein expression levels in single cells. All the genes in the pathway are known to be coregulated by the same transcription factors, but we observed drastically different dynamic responses for enzymes upstream and immediately downstream of the key control point-the intermediate metabolite alpha-isopropylmalate (alphaIPM), which couples metabolic activity to transcriptional regulation. Analysis based on genetic perturbations suggests that the observed dynamics are due to differential regulation by the leucine branch-specific transcription factor Leu3, and that the downstream enzymes are strictly controlled and highly expressed only when alphaIPM is available. These observations allow us to build a simplified mathematical model that accounts for the observed dynamics and can correctly predict the pathway's response to new perturbations. Our model also suggests that transient dynamics and steady state can be separately tuned and that the high induction levels of the downstream enzymes are necessary for fast leucine recovery. It is likely that principles emerging from this work can reveal how gene regulation has evolved to optimize performance in other metabolic pathways with similar architecture.
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Project description:SummaryArcadia translates text-based descriptions of biological networks (SBML files) into standardized diagrams (SBGN PD maps). Users can view the same model from different perspectives and easily alter the layout to emulate traditional textbook representations.Availability and implementationArcadia is written in C++. The source code is available (along with Mac OS and Windows binaries) under the GPL from http://arcadiapathways.sourceforge.net/.
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Project description:Designing experiments and applying the process of science are core competencies for many introductory courses and course-based undergraduate research experiences (CUREs). However, experimental design is a complex process that challenges many introductory students. We describe the development of a tool to assess interrelated experimental design (TIED) in an introductory biology lab course. We describe the interrater reliability of the tool, its effectiveness in detecting variability and growth in experimental-design skills, and its adaptability for use in various contexts. The final tool contained five components, each with multiple criteria in the form of a checklist such that a high-quality response-in which students align the different components of their experimental design-satisfies all criteria. The tool showed excellent interrater reliability and captured the full range of introductory-student skill levels, with few students hitting the assessment ceiling or floor. The scoring tool detected growth in student skills from the beginning to the end of the semester, with significant differences between pre- and post-assessment scores for the Total Score and for the Data Collection and Observations component scores. This authentic assessment task and scoring tool provide meaningful feedback to instructors about the strengths, gaps, and growth in introductory students' experimental-design skills and can be scored reliably by multiple instructors. The TIED can also be adapted to a number of experimental-design prompts and learning objectives, and therefore can be useful for a variety of introductory courses and CUREs.
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Project description:Miscommunication is a source of clinical errors. Tools to decrease the risk of miscommunication (ie, patient handoff tools) are routinely used in clinical specialties that see patients but not routinely used in pathology residency programs. Our primary goal was to develop a structured handoff tool for pathology residents useful for both patient-specific communication and information about general laboratory operation with a secondary goal to increase resident confidence in on-call situations. The CATCH tool was developed and implemented in a pathology residency program with a pre- and postimplementation survey given to residents. The structured handoff tool for pathology residents provided consistent and timely communication between residents and attending physicians. Resident confidence with pathology on-call issues was more likely related to progression through the residency training program rather than implementation of a structured handoff tool.
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Project description:Soil organic carbon is one of the largest surface pools of carbon that humans can manage in order to partially mitigate annual anthropogenic CO2 emissions. A significant element to assess soil sequestration potential is the carbon age, which is evaluated by modelling or experimentally using carbon isotopes. Results, however, are not consistent. The 14C derived approach seems to overestimate by a factor of 6-10 the average carbon age in soils estimated by modeling and 13C approaches and thus the sequestration potential. A fully independent method is needed. The cosmogenic chlorine nuclide, 36Cl, is a potential alternative. 36Cl is a naturally occurring cosmogenic radionuclide with a production that increased by three orders of magnitude during nuclear bomb tests. Part of this production is retained by soil organic matter in organochloride form and hence acts as a tracer of the fate of soil organic carbon. We here quantify the fraction and the duration of 36Cl retained in the soil and we show that retention time increases with depth from 20 to 322 years, in agreement with both modelling and 13C-derived estimates. This work demonstrates that 36Cl retention duration can be a proxy for the age of soil organic carbon.
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Project description:BackgroundBy studying genome-wide expression patterns in healthy and diseased tissues across a wide range of pathophysiological conditions, DNA microarrays have revealed unique insights into complex diseases. However, the high-dimensionality of microarray data makes interpretation of heterogeneous gene expression studies inherently difficult.ResultsUsing a large-scale analysis of more than 40 microarray studies encompassing ~2400 mammalian tissue samples, we identified a common theme across heterogeneous microarray studies evident by a robust genome-wide inverse regulation of metabolic and cell signaling pathways: We found that upregulation of cell signaling pathways was invariably accompanied by downregulation of cell metabolic transcriptional activity (and vice versa). Several findings suggest that this characteristic gene expression pattern represents a new principle of mammalian transcriptional regulation. First, this coordinated transcriptional pattern occurred in a wide variety of physiological and pathophysiological conditions and was identified across all 20 human and animal tissue types examined. Second, the differences in metabolic gene expression predicted the magnitude of differences for signaling and all other pathways, i.e. tissue samples with similar expression levels of metabolic transcripts did not show any differences in gene expression for all other pathways. Third, this transcriptional pattern predicted a profound effect on the proteome, evident by differences in structure, stability and post-translational modifications of proteins belonging to signaling and metabolic pathways, respectively.ConclusionsOur data suggest that in a wide range of physiological and pathophysiological conditions, gene expression changes exhibit a recurring pattern along a transcriptional axis, characterized by an inverse regulation of major metabolic and cell signaling pathways. Given its widespread occurrence and its predicted effects on protein structure, protein stability and post-translational modifications, we propose a new principle for transcriptional regulation in mammalian biology.
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Project description:Abstract The ability of current kinetic models to simulate the phenotypic behaviour of cells is limited since cell metabolism is regulated at different levels including enzyme regulation. The small molecule regulation network (SMRN) enables cells to respond rapidly to environmental fluctuations by controlling the activity of enzymes in metabolic pathways. However, SMRN is not as well studied relative to metabolic networks. The main contributor to the lack of knowledge on this regulatory system is the sparsity of experimental data and the absence of a standard framework for representing available information. In this paper, we introduce the KinMod database that encompasses more than 2 million data points on the metabolism and metabolic regulation network of 9814 organisms KinMod database employs a hierarchical data structure to: (i) signify relationships between kinetic information obtained through in-vitro experiments and proteins, with an emphasis on SMRN, (ii) provide a thorough insight into available kinetic parameters and missing experimental measurements of this regulatory network and (iii) facilitate machine learning approaches for parameter estimation and accurate kinetic model construction by providing a homogeneous list of linked omics data. The hierarchical ontology of the KinMod database allows flexible exploration of data attributes and investigation of metabolic relationships within- and cross-species. Identifying missing experimental values suggests additional experiments required for kinetic parameter estimation. Linking multi-omics data and providing data on SMRN encourages the development of novel machine learning techniques for predicting missing kinetic parameters and promotes accurate kinetic model construction of cells metabolism by providing a comprehensive list of available kinetic measurements. To illustrate the value of KinMod data, we develop six analyses to visualize associations between data classes belonging to separate sections of the metabolism. Through these analyses, we demonstrate that the KinMod database provides a unique framework for biologists and engineers to retrieve, evaluate and compare the functional metabolism of species, including the regulatory network, and discover the extent of available and missing experimental values of the metabolic regulation. Database URL: https://lmse.utoronto.ca/kinmod/KINMOD.sql.gz
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Project description:An Understanding Checkpoint (UC) presents students with previously-unseen figures from published scientific studies accompanied by questions about the study methods, results, and implications. The UCs incorporate content, concepts, and techniques previously discussed in class, although the figures and study from which they are taken are new to students. They are in-class, open-note, time-limited assessments that simultaneously assess course learning goals related to: neurobiology principles and content; process of scientific investigation, including neurobiological research tools and data interpretation; and reading and analyzing primary research literature. After students submit their work, they are provided the full publication and are asked to grade their own work, providing rationale for their evaluation. The self-evaluative portion of the assignment incentivizes students to identify and remediate ongoing weaknesses. It also provides spaced retrieval practice to enhance learning. The final grade for the UC incorporates the student's original answers and the accuracy of the self-assessment rationale. Student and instructor feedback indicates that the self - evaluative requirement develops a deeper understanding of the course material and enhances metacognitive effort, in addition to providing an opportunity to improve the UC grade. This strategy was originally presented as a teaching demonstration at the 2017 Faculty for Undergraduate Neuroscience Workshop.
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