CAREER: Signal Crosstalk Within Complex Microbial Ecosystems

自然界中的微生物生态系统通常由成百上千个微生物物种组成，在空间和时间上分布不均。需要新的实验和理论工具来发展对控制这些复杂蜂窝网络输出的调控过程的预测性理解。目前的方法侧重于系统各组成部分之间的相关性(如遗传组成、表达水平、代谢物浓度)，然而，仅有这种相关性通常不足以制定出强有力地控制不同微生物群落的状态和功能的策略。在这个项目中，PI将使用多尺度方法，将理论预测与精确的实验测试相结合，以定量地了解个体和细胞群体如何做出决定，以协调微生物生态系统中的全球行为。应对与健康、农业和环境相关的现代社会挑战，将受益于对网络的多个组件如何协同工作的预测性了解，特别是对于涉及许多密集连接组件的复杂网络。为了激励下一代科学家研究系统物理和复杂性问题，PI与当地一家非营利性科学组织合作，设计和实施与信号交换生物物理相关的动手学习活动。学生将直观地了解支配蜂窝网络的物理原理，包括洞察群体中个人的行为如何产生网络属性。这项研究的主题也将出现在本科通识教育课程中，重点是培训来自不同背景的本科生，包括非理科专业的本科生，掌握物理科学固有的基本数量技能。PI将研究不同微生物网络中不同群体感应信号的交换，以了解个体菌株之间的信号相互作用如何导致网络的新特性。人工神经网络模型预测小区类型之间的串扰对系统级信令状态的影响。这些信号驱动的调控状态的稳定性将在模型和实验中进行检查，以衡量通过物种组成或信号交换路径的扰动来指导多物种网络输出的潜力。这种理论和实验的结合阐明了系统级别的行为，如健壮性，是如何从多种类型的细胞共同工作的集体行动中出现的。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Microbial ecosystems in nature are typically composed of hundreds or thousands of microbial species, heterogeneously distributed in space and time. New experimental and theoretical tools are needed to develop a predictive understanding of the regulatory processes that control the outputs of these complex cellular networks. Current approaches focus on correlations between the components of the system (such as genetic composition, expression levels, metabolite concentrations), however such correlations alone are usually insufficient to develop strategies for robust control of the state and function of diverse microbial communities. In this project the PI will use multiscale approach that combines theoretical predictions with precision experimental tests to quantitatively understand how individual and groups of cells reach decisions to coordinate global behavior within microbial ecosystems. Tackling modern societal challenges related to health, agriculture, and the environment will benefit from a predictive understanding of how multiple components of a network work together, particularly for complex networks involving many densely connected components. To inspire the next generation of scientists to take up problems on the physics of systems and complexity, the PI has partnered with a local science non-profit to design and implement hands-on learning activities related to the biophysics of signal exchange. Students will gain an intuitive understanding of the physical principles that govern cellular networks, including insight into how network properties emerge from the behavior of individuals within a population. Themes of this research will also appear in an undergraduate general education course focused on training undergraduates from diverse background, including non-science majors, in essential quantitative skills intrinsic to the physical sciences.The PI will examine the exchange of variant quorum sensing signals within diverse microbial networks to understand how signaling interactions between individual strains give rise to emergent properties of the network. An artificial neural network model predicts the impact of crosstalk between cell types on system-level signaling states. The stability of these signal-driven regulatory states will be examined in both models and experiments to gauge the potential to direct the outputs of multispecies networks through perturbations of species composition or signal exchange pathways. This combination of theory and experiment illuminates how systems-level behaviors such as robustness emerge from the collective action of multiple cell types working together.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.