CAREER: Emergence of Functional Organization in the Adaptive Immune System

人类需要几十年才能繁殖，但我们的病原体可以在不到一天的时间内繁殖。我们如何与进化速度比我们快一万倍的病原体共存？答案在于我们的适应性免疫系统，这是一个高度多样化的免疫细胞的自组织系统，在生物体的一生中发展。适应性免疫系统包含了生命的各个方面，从分子信号到细胞进化。其结果是一个具有许多相互作用的成分的信息处理分子组织，它可以可靠地感知和适应性地响应不同和不断变化的病原体。个体之间免疫系统的巨大差异表明，对于统计学上相似的致病环境，存在许多分子解决方案。该项目的目标是使用机器学习从受体库序列的多样性和高维空间导出与免疫识别生物物理学相关的免疫功能的低维空间的映射。这种有效的免疫库的功能表示将允许构建对病原体的免疫应答的预测模型，并将揭示免疫库的功能组织。为了弥合物理学和生物学之间的差距，PI将向大学预科生和本科生介绍生物物理学研究，特别强调妇女和代表性不足的少数民族。PI将创建新的教学模块，向物理学和生命科学本科生和研究生介绍生命系统物理学的当前进展。此外，PI将在暑期和学年期间投入大量资源指导本科生和高中生进行生物物理学研究。本系将邀请生物物理学研究人员参加本系的“物理学前沿公开讲座系列”，以促进社会各界对这一领域令人兴奋的发展的赞赏和支持。适应性免疫系统在生物体的一生中发展，由高度多样化的B细胞和T细胞组成，其独特的表面受体通过基因组重排，突变和选择产生。这种多样的受体库可以对多种不断进化的病原体产生特异性反应，并为未来的遭遇保持对过去感染的记忆。作为回报，病原体进化以逃避免疫挑战，在生物体的一生中形成快速的共同进化军备竞赛。在过去的十年中，高通量免疫库测序已经在表征免疫受体序列的多样性方面发挥了作用。然而，我们仍然缺乏对受体序列多样性如何转化为免疫功能的理解。在这个项目中，PI将联合收割机理论建模与分子数据推理相结合，以揭示适应性免疫系统中致病信息的功能组织和编码的生物物理和进化基础。免疫细胞的生命周期由导致其功能特化的连续选择阶段定义。PI将开发生物物理推断技术，以利用不同免疫细胞类型的受体库数据，并使用机器学习来推导免疫功能的序列决定因素。此外，PI将开发预测适应度模型，以表征免疫库的短期和长期动态与对病原体的功能反应的关系。通过建立在机器学习的最新进展基础上，PI将推断出潜在的表示（即，形状空间），其反映免疫识别和功能的相关生物物理学。推断的免疫形状空间将允许询问关于抗原相互作用的生物物理决定簇和自我/非自我辨别的基本问题。最后，一个理论框架，研究如何共同进化的历史，主机和病原体形成免疫战略，无论是在个人和人口的水平将developed.This奖项反映了NSF的法定使命，并已被认为是值得通过评估使用基金会的智力价值和更广泛的影响审查标准的支持。

It takes decades for humans to reproduce, but our pathogens can reproduce in less than a day. How can we coexist with pathogens that can evolve more than 10,000 times faster than us? The answer lies in our adaptive immune system, which is a self-organized system of highly diverse immune cells that develop during the lifetime of an organism. The adaptive immune system incorporates all aspects of life, from molecular signaling to cellular evolution. The result is an information processing molecular organization with many interacting components, which can reliably sense and adaptively respond to diverse and evolving pathogens. The vast differences in immune repertoires between individuals suggest the existence of many molecular solutions to statistically similar pathogenic environments. The goal of this project is to use machine learning to derive a map from the diverse and high-dimensional space of receptor repertoire sequences to a lower dimensional space of immune functions that relates to biophysics of immune recognition. This effective functional representation of immune repertoires would allow construction of predictive models for immune responses to pathogens, and will shed light on functional organization of immune repertoires. To bridge the gap between physics and biology, the PI will introduce pre-college and undergraduate students to biophysics research, with particular emphasis on women and underrepresented minorities. The PI will create new teaching modules to introduce both the physics and the life-science undergraduate and graduate students to current progress in physics of living systems. In addition the PI will commit significant resources to mentor undergraduate and high school students during summers and the academic year to pursue biophysics research. The PI will invite biophysics researchers to the department’s “Frontiers of Physics Public Lecture Series”, to foster an appreciation and support among the community for the exciting developments in this field.The adaptive immune system develops during the lifetime of an organism and consists of highly diverse B-and T-cells, whose unique surface receptors are generated through genomic rearrangement, mutation, and selection. This diverse repertoire of receptors can mount specific responses against a multitude of evolving pathogens and keep a memory of past infections for future encounters. Pathogens in return, evolve to escape the immune challenge, forming a rapid co-evolutionary arms race during the life-time of an organism. Over the past decade, high throughput immune repertoire sequencing has been instrumental in characterizing the diversity of immune receptor sequences. However, we still lack an understating of how receptor sequence diversity translates to immune function. In this project, the PI will combine theoretical modeling with inference from molecular data to uncover the biophysical and evolutionary basis of the functional organization and encoding of pathogenic information in the adaptive immune system. The life-cycle of immune cells is defined by a continuum of selection stages leading to their functional specialization. The PI will develop biophysical inference techniques to leverage receptor repertoire data of distinct immune cell-types and use machine learning to derive sequence-determinants of immune function. In addition the PI will develop predictive fitness models to characterize how the short-and long-term dynamics of immune repertoires relate to functional responses to pathogens. By building upon recent advances in machine learning, the PI will infer a latent representation (i.e., a shape space) for immune receptors that reflects the relevant biophysics of immune recognition and function. The inferred immune shape space will allow to ask fundamental questions regarding the biophysical determinants of antigenic interactions, and self/non-self discrimination. Lastly, a theoretical framework to study how the co-evolutionary history of hosts and pathogens has shaped immune strategies, both at the individual-and the population-level will be developed.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.