Biological Network Reconstruction and Equation Inference with Hidden Nodes

隐藏节点的生物网络重建与方程推理

• 批准号: 2748008
• 金额: --
• 依托单位: Imperial College London
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2748008
• 关键词: Biological; Network; Reconstruction; Equation; Inference

Biological systems are highly complex and usually involve components which cannot be directly measured. These systems can be modelled using networks, with nodes representing variables and edges their interactions. This project will start from methodology developed by the Isambert group at Institut Curie, Paris called temporal multivariate information-based inductive causation (tMIIC) which takes time series data and constructs a causal network. A key feature of tMIIC is its ability to identify latent causal factors, what we will call 'hidden nodes'. This project can be split into three main subsections. Firstly, tMIIC will be benchmarked on its ability to reconstruct hidden nodes. Here, toy models with known network structures are used to generate trajectories. These are given to tMIIC which should be able to reconstruct the network. A trajectory corresponding to a variable will then be omitted, and tMIIC will be expected to identify the presence of the hidden node. Its performance will be assessed and can be used as a benchmark.Once tMIIC has been used to infer a network to uncover the interactions of the system, we plan to use a generative modelling approach to infer the equations which describe them. This involves the use of two machine learning models: a generator and a discriminator. The objective of the generator is to generate trajectories of the model which are indistinguishable from the true data. The discriminator is a classifier and is trained simultaneously, with the objective of distinguishing true trajectories from those produced by the generator. Initially, the dynamic form of the equations will be assumed as Langevin type stochastic equations which couple the observed and hidden dynamics. A starting point would be to use linear equations where the interaction matrix is determined by the adjacency matrix of the learned network structure. Depending on the system being modelled, different functional forms for terms in the equations (such as mass-action and Michaelis-Menten type kinetics) can be adapted and used. An extension to this part of the project is to use neural ODEs to learn appropriate functions for the equations. Neural ODEs are models which use neural networks trained on observational data to specify the dynamics of the system of interest.Once this methodology has been developed, we plan to apply the techniques to a dataset of live cell imaging microscopy from an ex-vivo tumour ecosystem. This is a technology which allows cancer cells to grow in the presence of components found where they normally grow (what is known as the tumour microenvironment). Specifically, the data that we will be looking at considers the effect of immune cells and cancer associated fibroblasts (CAFs). This data comes from experimental collaborators in the Parrini group at Institut Curie, Paris. A preliminary literature search found a gap in published models for this system, so novel model design will be required here.This project falls within the EPSRC biological informatics and mathematical biology research themes.

生物系统非常复杂，通常涉及无法直接测量的成分。这些系统可以用网络来建模，节点代表变量，边代表变量的相互作用。该项目将从巴黎居里研究所的Isambert小组开发的称为时间多变量基于信息的归纳因果关系（tMIIC）的方法开始，该方法采用时间序列数据并构建因果网络。tMIIC的一个关键特征是它能够识别潜在的因果因素，我们称之为“隐藏节点”。该项目可分为三个主要部分。首先，tMIIC将以其重建隐藏节点的能力为基准。这里，具有已知网络结构的玩具模型用于生成轨迹。这些都是给tMIIC，它应该能够重建网络。对应于变量的轨迹将被省略，并且tMIIC将被期望识别隐藏节点的存在。它的性能将被评估，并可以作为一个基准。一旦tMIIC已被用来推断一个网络，以揭示系统的相互作用，我们计划使用生成建模方法来推断描述它们的方程。这涉及到两个机器学习模型的使用：生成器和学习器。生成器的目标是生成与真实数据无法区分的模型轨迹。该分类器是一个分类器，同时进行训练，其目的是将真实轨迹与生成器产生的轨迹区分开来。首先，将方程的动力学形式假设为耦合观测和隐藏动力学的Langevin型随机方程。一个起点是使用线性方程，其中交互矩阵由学习的网络结构的邻接矩阵确定。取决于被建模的系统，方程中的项的不同函数形式（例如质量作用和Michaelis-Menten型动力学）可以被调整和使用。该项目这一部分的扩展是使用神经ODE来学习方程的适当函数。神经ODE是使用在观测数据上训练的神经网络来指定感兴趣的系统的动态的模型。一旦这种方法被开发出来，我们计划将该技术应用于来自体外肿瘤生态系统的活细胞成像显微镜数据集。这是一种允许癌细胞在正常生长的成分（即所谓的肿瘤微环境）存在下生长的技术。具体来说，我们将研究的数据考虑了免疫细胞和癌症相关成纤维细胞（CAF）的影响。这些数据来自巴黎居里研究所帕里尼小组的实验合作者。初步的文献检索发现，该系统的已发表的模型的差距，所以新的模型设计将需要在这里。这个项目属于EPSRC生物信息学和数学生物学的研究主题福尔斯。