Discovery Science - 26th International Conference, DS 2023, Porto, Portugal, October 9-11, 2023, Proceedings
Discovery Science - 26th International Conference, DS 2023, Porto, Portugal, October 9-11, 2023, Proceedings
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发现科学 - 第 26 届国际会议，DS 2023，葡萄牙波尔图，2023 年 10 月 9-11 日，会议记录
DOI：
10.1007/978-3-031-45275-8_42
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发表时间：
2023
期刊：
影响因子：
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通讯作者：
Gower A
中科院分区：
文献类型：
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作者：
Gower A
由于所涉及的系统的复杂性和获得高质量实验数据的费用，生物学中的科学发现是困难的。自动化技术是一种很有前途的方法，可以以模拟大型生物系统所需的规模和速度进行科学发现。建立真核细胞的计算模型是21世纪世纪生物学的一个关键问题。酵母是最好的真核生物，基因组规模的代谢模型（GEM）是丰富的背景知识来源，我们可以使用它作为自动推理和调查的基础。我们提出了LGEM+，一个自动溯因改进的GEM系统，包括：一个划分的一阶逻辑框架，用于描述生物化学途径（使用策划的GEM作为专家知识源）;和两个阶段的假设溯因程序。我们证明了使用LGEM+创建的逻辑理论的演绎推理，使用自动定理证明器iProver，可以预测S的增长/不增长。最低限度的媒体。LGEM+提出了2094个独特的候选假设用于模型改进。我们使用两个标准来评估所产生的假设的价值：（a）全基因组单基因必要性预测，和（B）通量平衡分析（FBA）模拟的约束。对于（B），我们开发了一种算法来集成FBA与逻辑模型。我们使用这些评估对假设进行排名和过滤。我们打算使用机器人科学家Genesis来测试这些假设，Genesis基于恒化器培养和高通量代谢组学。
Scientific discovery in biology is difficult due to the complexity of the systems involved and the expense of obtaining high quality experimental data. Automated techniques are a promising way to make scientific discoveries at the scale and pace required to model large biological systems. A key problem for 21st century biology is to build a computational model of the eukaryotic cell. The yeastSaccharomyces cerevisiaeis the best understood eukaryote, and genome-scale metabolic models (GEMs) are rich sources of background knowledge that we can use as a basis for automated inference and investigation.We present LGEM+, a system for automated abductive improvement of GEMs consisting of: a compartmentalised first-order logic framework for describing biochemical pathways (using curated GEMs as the expert knowledge source); and a two-stage hypothesis abduction procedure.We demonstrate that deductive inference on logical theories created using LGEM+, using the automated theorem prover iProver, can predict growth/no-growth ofS. cerevisiaestrains in minimal media. LGEM+proposed 2094 unique candidate hypotheses for model improvement. We assess the value of the generated hypotheses using two criteria: (a) genome-wide single-gene essentiality prediction, and (b) constraint of flux-balance analysis (FBA) simulations. For (b) we developed an algorithm to integrate FBA with the logic model. We rank and filter the hypotheses using these assessments. We intend to test these hypotheses using the robot scientist Genesis, which is based around chemostat cultivation and high-throughput metabolomics.