A bottom-up approach to construct or deconstruct a fluid instability.
Ontology highlight
ABSTRACT:
SUBMITTER: Escala DM
PROVIDER: S-EPMC8692339 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:One-bead-one-compound peptide libraries, developed following the top-down experimental approach, have attracted great interest in the identification of potential ligands or active peptides. By exploiting a reverse experimental design approach based on the bottom-up strategy, we aimed to develop simplified, maximally diverse peptide libraries that resulted in the successful characterization of mixture components. We show that libraries of 32 and 48 components can be successfully detected in a single run using chromatography coupled to mass spectrometry (UPLC-MS). The proposed libraries were further theoretically evaluated in terms of their composition and physico-chemical properties. By combining the knowledge obtained on single libraries we can cover larger sequence spaces and provide a controlled exploration of the peptide chemical space both theoretically and experimentally. Designing libraries by using the bottom-up approach opens up the possibility of rationally fine-tuning the library complexity based on the available analytical methods.
| S-EPMC7435479 | biostudies-literature
Project description:Genetic coessentiality analysis, a computational approach which identifies genes sharing a common effect on cell fitness across large-scale screening datasets, has emerged as a powerful tool to identify functional relationships between human genes. However, widespread implementation of coessentiality to study individual genes and pathways is limited by systematic biases in existing coessentiality approaches and accessibility barriers for investigators without computational expertise. We created FIREWORKS, a method and interactive tool for the construction and statistical analysis of coessentiality networks centered around gene(s) provided by the user. FIREWORKS incorporates a novel bias reduction approach to reduce false discoveries, enables restriction of coessentiality analyses to custom subsets of cell lines, and integrates multiomic and drug-gene interaction datasets to investigate and target contextual gene essentiality. We demonstrate the broad utility of FIREWORKS through case vignettes investigating gene function and specialization, indirect therapeutic targeting of "undruggable" proteins, and context-specific rewiring of genetic networks.
| S-EPMC7756899 | biostudies-literature
Project description:Quorum sensing (QS) enables bacterial multicellularity and selective advantage for communicating populations. While genetic "switching" phenomena are a common feature, their mechanistic underpinnings have remained elusive. The interplay between circuit components and their regulation are intertwined and embedded. Observable phenotypes are complex and context dependent. We employed a combination of experimental work and mathematical models to decipher network connectivity and signal transduction in the autoinducer-2 (AI-2) quorum sensing system of E. coli. Negative and positive feedback mechanisms were examined by separating the network architecture into sub-networks. A new unreported negative feedback interaction was hypothesized and tested via a simple mathematical model. Also, the importance of the LsrR regulator and its determinant role in the E. coli QS "switch", normally masked by interfering regulatory loops, were revealed. Our simple model allowed mechanistic understanding of the interplay among regulatory sub-structures and their contributions to the overall native functioning network. This "bottom up" approach in understanding gene regulation will serve to unravel complex QS network architectures and lead to the directed coordination of emergent behaviors.
| S-EPMC3182856 | biostudies-literature
Project description:ObjectivesThe study sought to learn if it were possible to develop an ontology that would allow the Food and Drug Administration approved indications to be expressed in a manner computable and comparable to what is expressed in an electronic health record.Materials and methodsA random sample of 1177 of the 3000+ extant, distinct medical products (identified by unique new drug application numbers) was selected for investigation. Close manual examination of the indication portion of the labels for these drugs led to the development of a formal model of indications.ResultsThe model represents each narrative indication as a disjunct of conjuncts of assertions about an individual. A desirable attribute is that each assertion about an individual should be testable without reference to other contextual information about the situation. The logical primitives are chosen from 2 categories (context and conditions) and are linked to an enumeration of uses, such as prevention. We found that more than 99% of approved label indications for treatment or prevention could be so represented.DiscussionWhile some indications are straightforward to represent, difficulties stem from the need to represent temporal or sequential references. In addition, there is a mismatch of terminologies between what is present in an electronic health record and in the label narrative.ConclusionsA workable model for formalizing drug indications is possible. Remaining challenges include designing workflow to model narrative label indications for all approved drug products and incorporation of standard vocabularies.
| S-EPMC7973454 | biostudies-literature
Project description:BackgroundTraditionally top-down method was used to identify prognostic features in cancer research. That is to say, differentially expressed genes usually in cancer versus normal were identified to see if they possess survival prediction power. The problem is that prognostic features identified from one set of patient samples can rarely be transferred to other datasets. We apply bottom-up approach in this study: survival correlated or clinical stage correlated genes were selected first and prioritized by their network topology additionally, then a small set of features can be used as a prognostic signature.MethodsGene expression profiles of a cohort of 221 hepatocellular carcinoma (HCC) patients were used as a training set, 'bottom-up' approach was applied to discover gene-expression signatures associated with survival in both tumor and adjacent non-tumor tissues, and compared with 'top-down' approach. The results were validated in a second cohort of 82 patients which was used as a testing set.ResultsTwo sets of gene signatures separately identified in tumor and adjacent non-tumor tissues by bottom-up approach were developed in the training cohort. These two signatures were associated with overall survival times of HCC patients and the robustness of each was validated in the testing set, and each predictive performance was better than gene expression signatures reported previously. Moreover, genes in these two prognosis signature gave some indications for drug-repositioning on HCC. Some approved drugs targeting these markers have the alternative indications on hepatocellular carcinoma.ConclusionUsing the bottom-up approach, we have developed two prognostic gene signatures with a limited number of genes that associated with overall survival times of patients with HCC. Furthermore, prognostic markers in these two signatures have the potential to be therapeutic targets.
| S-EPMC4349868 | biostudies-literature
Project description:Aptamer-based point-of-care (POC) diagnostics platforms may be of substantial benefit in forensic analysis as they provide rapid, sensitive, user-friendly, and selective analysis tools for detection. Aptasensors have not yet been adapted commercially. However, the significance of the applications of aptasensors in the literature exceeded their potential. Herein, in this review, a bottom-up approach is followed to describe the aptasensor development and application procedure, starting from the synthesis of the corresponding aptamer sequence for the selected analyte to creating a smart surface for the sensitive detection of the molecule of interest. Optical and electrochemical biosensing platforms, which are designed with aptamers as recognition molecules, detecting abused drugs are critically reviewed, and existing and possible applications of different designs are discussed. Several potential disciplines in which aptamer-based biosensing technology can be of greatest value, including forensic drug analysis and biological evidence, are then highlighted to encourage researchers to focus on developing aptasensors in these specific areas.
| S-EPMC6955935 | biostudies-literature
Project description:Deposition of fibrillar amyloid ? (A?) in senile plaques is a pathological signature of Alzheimer's disease. However, senile plaques also contain many other components, including a range of different proteins. Although the composition of the plaques can be analyzed in post-mortem tissue, knowledge of the molecular details of these multiprotein inclusions and their assembly processes is limited, which impedes the progress in deciphering the biochemical mechanisms associated with A? pathology. We describe here a bottom-up approach to monitor how proteins from human cerebrospinal fluid associate with A? amyloid fibrils to form plaque particles. The method combines flow cytometry and mass spectrometry proteomics and allowed us to identify and quantify 128 components of the captured multiprotein aggregates. The results provide insights into the functional characteristics of the sequestered proteins and reveal distinct interactome responses for the two investigated A? variants, A?(1-40) and A?(1-42). Furthermore, the quantitative data is used to build models of the structural organization of the multiprotein aggregates, which suggests that A? is not the primary binding target for all the proteins; secondary interactions account for the majority of the assembled components. The study elucidates how different proteins are recruited into senile plaques and establishes a new model system for exploring the pathological mechanisms of Alzheimer's disease from a molecular perspective.
| S-EPMC7243255 | biostudies-literature
Project description:A common goal across different fields (e.g. separations, biosensors, biomaterials, pharmaceuticals) is to understand how protein behavior at solid-liquid interfaces is affected by environmental conditions. Temperature, pH, ionic strength, and the chemical and physical properties of the solid surface, among many factors, can control microscopic protein dynamics (e.g. adsorption, desorption, diffusion, aggregation) that contribute to macroscopic properties like time-dependent total protein surface coverage and protein structure. These relationships are typically studied through a top-down approach in which macroscopic observations are explained using analytical models that are based upon reasonable, but not universally true, simplifying assumptions about microscopic protein dynamics. Conclusions connecting microscopic dynamics to environmental factors can be heavily biased by potentially incorrect assumptions. In contrast, more complicated models avoid several of the common assumptions but require many parameters that have overlapping effects on predictions of macroscopic, average protein properties. Consequently, these models are poorly suited for the top-down approach. Because the sophistication incorporated into these models may ultimately prove essential to understanding interfacial protein behavior, this article proposes a bottom-up approach in which direct observations of microscopic protein dynamics specify parameters in complicated models, which then generate macroscopic predictions to compare with experiment. In this framework, single-molecule tracking has proven capable of making direct measurements of microscopic protein dynamics, but must be complemented by modeling to combine and extrapolate many independent microscopic observations to the macro-scale. The bottom-up approach is expected to better connect environmental factors to macroscopic protein behavior, thereby guiding rational choices that promote desirable protein behaviors.
| S-EPMC4028386 | biostudies-literature
Project description:Opportunistic fungal pathogens can cause bloodstream infection and severe sepsis upon entering the blood stream of the host. The early immune response in human blood comprises the elimination of pathogens by antimicrobial peptides and innate immune cells, such as neutrophils or monocytes. Mathematical modeling is a predictive method to examine these complex processes and to quantify the dynamics of pathogen-host interactions. Since model parameters are often not directly accessible from experiment, their estimation is required by calibrating model predictions with experimental data. Depending on the complexity of the mathematical model, parameter estimation can be associated with excessively high computational costs in terms of run time and memory. We apply a strategy for reliable parameter estimation where different modeling approaches with increasing complexity are used that build on one another. This bottom-up modeling approach is applied to an experimental human whole-blood infection assay for Candida albicans. Aiming for the quantification of the relative impact of different routes of the immune response against this human-pathogenic fungus, we start from a non-spatial state-based model (SBM), because this level of model complexity allows estimating a priori unknown transition rates between various system states by the global optimization method simulated annealing. Building on the non-spatial SBM, an agent-based model (ABM) is implemented that incorporates the migration of interacting cells in three-dimensional space. The ABM takes advantage of estimated parameters from the non-spatial SBM, leading to a decreased dimensionality of the parameter space. This space can be scanned using a local optimization approach, i.e., least-squares error estimation based on an adaptive regular grid search, to predict cell migration parameters that are not accessible in experiment. In the future, spatio-temporal simulations of whole-blood samples may enable timely stratification of sepsis patients by distinguishing hyper-inflammatory from paralytic phases in immune dysregulation.
| S-EPMC4473060 | biostudies-literature
Project description:A third of global fish stocks are overexploited and many are economically underperforming, resulting in potential unrealized net economic benefits of USD 51 to 83 billion annually. However, this aggregate view, while useful for global policy discussion, may obscure the view for those actors who engage at a regional level. Therefore, we develop a method to associate large companies with their fishing operations and evaluate the biological sustainability of these operations. We link current fish biomass levels and landings to the revenue streams of the companies under study to compute potentially unrealized fisheries revenues and profits at the level of individual firms. We illustrate our method using two case studies: anchoveta (Engraulis ringens; Engraulidae) in Peru and menhaden in the USA (Brevoortia patronus and B. tyrannus; Clupeidae). We demonstrate that both these fisheries could potentially increase their revenues compared to the current levels of exploitation. We estimate the net but unrealized fishery benefits for the companies under question. This information could be useful to investors and business owners who might want to be aware of the actual fisheries performance options of the companies they invest in.
| S-EPMC6245793 | biostudies-literature