Knowledge Representation, Reasoning, and Problem Solving in a Cellular Domain

提出的研究的目的是开发新的和适应现有的知识表示和推理方案，以表示和执行各种推理和解决问题的任务，如预测，解释，诊断和规划细胞内的活动。该项目旨在关注信号转导，蛋白质-蛋白质相互作用和其他生化反应等事件和机制。该项目的初步结果表明，人工智能的知识表示，推理和解决问题的方法非常适合这项任务。在细胞中，当配体与适当的受体结合时，它会激活受体，进而引发一系列事件。在行动的规划和推理的术语中，配体与受体的结合可以被认为是一个行动;配体与受体结合的条件，作为规划域中行动的可执行性条件;类似地，配体与受体结合的直接影响可以使用规划术语来表达。规划只是在细胞域中推理行为的背景下的问题解决和推理任务之一。其他包括预测（假设推理），解释和诊断。在细胞的上下文中，使用动作表示细胞行为并确定药物（表现为配体）的效果和副作用将对应于预测。类似地，找出哪些干预措施会以特定的方式改变细胞行为，这与计划相对应;确定一些关于细胞的意外观察背后的原因，这与诊断相对应。由于对细胞解码的研究正在进行中，该项目将只有关于细胞内发生的部分信息，其推理方案将需要能够处理不完整的信息。通过这种方式，该项目与人工智能研究中关于知识表示和推理的重要目标联系在一起。虽然现有的知识表示和推理研究为细胞行为的表示和推理提供了良好的基础，但在这一领域中有几个新的特征，提出了新的研究挑战。解决这些问题是这个项目的主要目标。要解决的挑战包括建模触发活动，受体敏化的概念，和蛋白质“招募”蛋白质。

The aim of the proposed research is to develop new and adapt existing knowledge representation and reasoning schemes to represent and perform various kinds of reasoning and problem solving tasks, such as prediction, explanation, diagnosis and planning about the activities inside a cell. This project aims to focus on happenings and mechanisms such as signal transduction, protein-protein interactions and other biochemical reactions. The project's preliminary results suggest that the knowledge representation, reasoning, and problem solving approaches of Artificial Intelligence are well suited for this task. In a cell when a ligand binds to an appropriate receptor it activates the receptor which in turn triggers a series of events. In the terminology of planning and reasoning about actions, the binding of a ligand to the receptor can be thought of as an action; the conditions under which a ligand can bind to a receptor, as the executability conditions of an action in a planning domain; Similarly, the direct effects of the ligand binding to a receptor can be expressed using planning terminologies. Planning is just one of the problem solving and reasoning tasks in the context of reasoning about actions in ther cellular domain. Others include prediction (hypothetical reasoning), explanation, and diagnosis. In the context of a cell, representing the cell behavior using actions and determining the effects and side effects of a drug (that manifests as a ligand) would correspond to prediction. Similarly, figuring out what interventions will change the cell behavior in a particular way corresponds to planning; determining the cause behind some unexpected observations about the cell corresponds to diagnosis. Since research on cell decoding is ongoing this project will have only partial information about the happenings inside a cell, and its reasoning schemes will need to be able to deal with incomplete information. In this way, this project ties into important goals in AI research on knowledge representation and reasoning. Although the existing research in knowledge representation and reasoning provides a good foundation for representing and reasoning about cell behavior, there are several new features in this domain that pose new research challenges. Tackling them is the main goal of this project. Challenges to be addressed include modeling triggering activity, notions of sensitization of receptors, and protein 'recruiting' proteins.