Network models of differentiation landscapes for angiogenesis and hematopoiesis

我的长期研究目标是开发数据驱动的数学模型来理解和控制 细胞分化我的实验室的方法将单细胞转录组学数据整合到一个数学模型中， Hopfield模型，用于描述基因网络中的信号动力学，并模拟扰动对 一组靶基因。Hopfield模型最初是作为大脑的数学模型开发的，它允许一个 将关联记忆模式直接映射到网络中的动态吸引子状态， 可以利用部分信息恢复大量记忆我们代表细胞表型状态的基本原理 当一个细胞"决定"通过表达一种新的基因模式来分化时， 在网络中，细胞依赖于一组由进化形成的内置关联记忆模式。与许多 深度学习和其他机器学习方法，使用关联表示细胞决策过程 存储器提供可整合预先存在的生物学知识的可解释信息（例如，途径 信息），以帮助阐明基本的生物学规则。此外，该方法超越了描述性的 单细胞数据分析。它为计算机模拟实验铺平了道路，这些实验可以识别新的药物靶点， 为临床前和临床研究产生新的假设。 在接下来的五年里，我们计划应用我们的吸引子模型来帮助理解和控制两个相互作用的系统。 参与许多疾病的生物过程：血管生成和造血。血管生成是 新血管的发育，并且是胚胎发育、成人血管稳态所必需的， 组织修复我们在血管生成方面的目标是识别器官特异性信号，这将提供新的机会 为了设计新的治疗剂以刺激或抑制患病器官中的血管生成（例如，患者视网膜 患有年龄相关性黄斑变性），而不会影响健康器官。在造血中，我们将使用 我们的计算方法，以确定新的计划，以产生淋巴细胞从诱导多能干细胞， 细胞（iPSC）。iPSC衍生的淋巴细胞的生物制造对于许多应用是重要的，例如 有效生产用于CAR-T疗法的T细胞和开发癌症疫苗。 通过我们的计算机模拟实验确定的新靶标和靶标组合可能导致新的 治疗许多疾病，包括癌症、失明、关节炎、牛皮癣和许多其它缺血性， 炎性、感染性和免疫病症。同样重要的是，这个MIRA项目将使我们能够继续 与我们的合作者网络合作，不断与更广泛的生物医学领域分享创新的软件工具， 社区，并进一步促进跨学科健康科学劳动力的培训， 计算和数学技能。

My long-term research goal is to develop data-driven mathematical models to understand and control cell differentiation. My lab’s approach integrates single-cell transcriptomics data in a mathematical model, the Hopfield model, to describe the signaling dynamics in gene networks and simulate the effect of perturbations on sets of target genes. Originally developed as a mathematical model of the brain, the Hopfield model allows for a direct mapping of associative memory patterns into dynamical attractor states in a network, so that the system can recover a host of memories using partial information. Our rationale for representing phenotypic cell states as associative memories is that when a cell “decides” to differentiate by expressing a new pattern of genes in a network, the cell relies on a set of built-in associative memory patterns shaped by evolution. In contrast to many deep learning and other machine learning methods, representing cellular decision processes using associative memories provides interpretable information that can integrate pre-existing biological knowledge (e.g., pathway information) to help elucidate fundamental biological rules. Moreover, the approach goes beyond a descriptive analysis of single-cell data. It paves the way for in-silico experiments that could identify new drug targets and generate new hypotheses for pre-clinical and clinical investigation. In the next five years, we plan on applying our attractor models to help understand and control two inter- playing biological processes involved in many diseases: angiogenesis and hematopoiesis. Angiogenesis is the development of new blood vessels and is required for embryonic development, adult vascular homeostasis, and tissue repair. Our goal in angiogenesis is to identify organ-specific signals, which will provide new opportunities to design new therapeutics to stimulate or inhibit angiogenesis in diseased organs (e.g., retinas of patients suffering from age-related macular degeneration) without affecting healthy organs. In hematopoiesis, we will use our computational approaches to identify new schemes to generate lymphocytes from induced pluripotent stem cells (iPSC). The bio-manufacturing of iPSC-derived lymphocytes is important for many applications, such as effective production of T cells for CAR-T therapies and development of cancer vaccines. The new targets and target combinations identified by our in-silico experiments could lead to novel therapeutics for many diseases, including cancer, blindness, arthritis, psoriasis, and many other ischemic, inflammatory, infectious, and immune disorders. Just as important, this MIRA project will allow us to continue working with our network of collaborators, keep sharing innovative software tools with the broader biomedical community, and further contribute to the training of an interdisciplinary health sciences workforce with strong computational and mathematical skills.