Network models of differentiation landscapes for angiogenesis and hematopoiesis

血管生成和造血分化景观的网络模型

• 批准号: 10622797
• 负责人: Carlo Piermarocchi
• 金额: $ 37.67万
• 依托单位: MICHIGAN STATE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-01 至 2028-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10622797
• 关键词: Adult; Affect; Age related macular degeneration; Angiogenesis Inhibition; Arthritis; Biological; Biological Process; Biomanufacturing; Blindness; Blood Vessels; Brain; CAR T cell therapy; Cancer Vaccine Related Development; Cell Differentiation process; Cells; Cellular biology; Communicable Diseases; Communities; Data; Development; Disease; Embryonic Development; Evolution; Genes; Goals; Health Sciences; Hematopoiesis; Homeostasis; Immune System Diseases; Inflammatory; Investigation; Ischemia; Knowledge; Lymphocyte; Malignant Neoplasms; Maps; Mathematics; Memory; Modeling; Organ; Pathway interactions; Patients; Pattern; Phenotype; Play; Process; Production; Psoriasis; Research; Retina; Scheme; Shapes; Signal Transduction; Software Tools; System; Training; angiogenesis; chimeric antigen receptor T cells; clinical investigation; deep learning; design; experimental study; gene network; in silico; induced pluripotent stem cell; innovation; machine learning method; mathematical model; network models; new therapeutic target; novel therapeutics; pre-clinical; single cell analysis; single-cell RNA sequencing; skills; tissue repair; transcriptomics

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.

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 我实验室的方法将单细胞转录组数据整合到一个数学模型中，即 Hopfield 模型，描述基因网络中的信号动态并模拟扰动对基因网络的影响 霍普菲尔德模型最初是作为大脑的数学模型开发的，它允许 将联想记忆模式直接映射到网络中的动态吸引子状态，以便系统 可以使用部分信息恢复大量记忆。 联想记忆是指当一个细胞“决定”通过表达一种新的基因模式来进行分化时 与许多细胞网络不同，细胞依赖于一组由进化形成的内置联想记忆模式。 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 记忆提供可解释的信息，可以整合预先存在的生物知识（例如，通路 信息）来帮助阐明基本的生物学规则，此外，该方法超越了描述性。 它为可以识别新药物靶标的计算机实验铺平了道路。 为临床前和临床研究产生新的假设。 在接下来的五年中，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 许多疾病涉及的生物学过程是：血管生成和造血。 新血管的发育，是胚胎发育、成人血管稳态和 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。 设计新的疗法来刺激或抑制患病器官（例如患者的视网膜）的血管生成 患有年龄相关性黄斑变性）而不影响健康器官的造血功能。 我们的计算方法确定从诱导多能干细胞生成淋巴细胞的新方案 iPSC 衍生淋巴细胞的生物制造对于许多应用都很重要，例如 有效生产用于 CAR-T 疗法的 T 细胞和开发癌症疫苗。 我们的计算机实验确定的新目标和目标组合可能会产生新的结果 许多疾病的治疗方法，包括癌症、失明、关节炎、牛皮癣和许多其他缺血性疾病， 同样重要的是，这个 MIRA 项目将使我们能够继续下去。 与我们的合作者网络合作，不断与更广泛的生物医学领域共享创新的软件工具 社区，并进一步为培养具有强大能力的跨学科健康科学劳动力做出贡献 计算和数学技能。