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