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A Method to Identify and Analyze Biological Programs through Automated Reasoning.

ABSTRACT: Predictive biology is elusive because rigorous, data-constrained, mechanistic models of complex biological systems are difficult to derive and validate. Current approaches tend to construct and examine static interaction network models, which are descriptively rich but often lack explanatory and predictive power, or dynamic models that can be simulated to reproduce known behavior. However, in such approaches implicit assumptions are introduced as typically only one mechanism is considered, and exhaustively investigating all scenarios is impractical using simulation. To address these limitations, we present a methodology based on automated formal reasoning, which permits the synthesis and analysis of the complete set of logical models consistent with experimental observations. We test hypotheses against all candidate models, and remove the need for simulation by characterizing and simultaneously analyzing all mechanistic explanations of observed behavior. Our methodology transforms knowledge of complex biological processes from sets of possible interactions and experimental observations to precise, predictive biological programs governing cell function.

SUBMITTER: Yordanov B

PROVIDER: S-EPMC5034891 | biostudies-literature | 2016 Jul

REPOSITORIES: biostudies-literature

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A Method to Identify and Analyze Biological Programs through Automated Reasoning.

Yordanov Boyan B Dunn Sara-Jane SJ Kugler Hillel H Smith Austin A Martello Graziano G Emmott Stephen S

NPJ systems biology and applications 20160701

Predictive biology is elusive because rigorous, data-constrained, mechanistic models of complex biological systems are difficult to derive and validate. Current approaches tend to construct and examine static interaction network models, which are descriptively rich but often lack explanatory and predictive power, or dynamic models that can be simulated to reproduce known behavior. However, in such approaches implicit assumptions are introduced as typically only one mechanism is considered, and e ...[more]

PMID: 27668090

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Project description:ObjectiveIt is crucial that teaching faculties determine and remain informed of medical school learners' clinical reasoning competence. We created an innovative assessment method for fourth-year medical students to identify deficiencies in various components of their clinical reasoning ability.MethodsThis was a cross-sectional observational study of fourth-year medical students' reasoning assessments from 2019 to 2022. Teams of four-five trainees questioned standardized patients in clinical scenarios, including fever, abdominal pain, and weight loss. They then individually documented key information to reflect comprehension of patient problems. Trainees were tasked with differentiating diagnoses and associated statuses and reaching the most likely diagnosis along with two tentative diagnoses. The correlations observed between 2020 and 2022 for abdominal pain were analyzed using student t-tests.ResultsA total of 177 students participated in this study. Across the scenarios, there was no significant difference in key information representation scores (56%-58%). Reasoning ability scores were 49% for fever, 57% for abdominal pain, and 61% for weight loss. A comparison between 2020 and 2022 revealed a significant improvement in the objective structured clinical examination scores and differential diagnoses (P < .01). Shortcomings included brief chief complaint duration, lack of detailed presentation, and insufficient description of negative information. Differential diagnosis and diagnostic justification were inadequate for acute and chronic conditions, and disease location clarity within the organ system was lacking. On average, students presented two correct diagnoses.ConclusionsFourth-year medical students exhibited inadequate reasoning abilities, particularly in fever and abdominal pain scenarios, with deficiencies in hypothesis generation and differential diagnosis. Group history-taking with individual reasoning assessment identified students' shortcomings and provided faculty feedback to improve their teaching strategies.

Dataset Information

A Method to Identify and Analyze Biological Programs through Automated Reasoning.

Publications

A Method to Identify and Analyze Biological Programs through Automated Reasoning.

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