Stochastic inference and control of complex biological networks

复杂生物网络的随机推理和控制

• 批准号: 1711548
• 负责人: Abhyudai Singh
• 金额: $ 25万
• 依托单位: University of Delaware
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-08-01 至 2023-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1711548&HistoricalAwards=false
• 关键词: Stochastic; inference; control; complex; biological

Living cells encode complex and dynamic networks of interconnected biomolecular components that orchestrate diverse life processes. Unlike electrical circuits, these networks consist of biochemical species (genes, proteins, RNAs, etc.) that interact and regulate each other via chemical reactions. A holistic understanding of how biomolecular networks process information for cellular decision-making will have tremendous long-term impact on human health. For example, a systems-level deciphering of biomolecular networks will fundamentally transform our knowledge of aberrant regulation driving diseased states, and will lead to novel ways of finding biomarkers and drug targets that take the network dynamics into account. Moreover, designing and rewiring of networks using systems approaches will open doors for different applications, such as, production of biofuels and therapeutics. To facilitate these transformations, this project aims to build scalable mathematical tools for modeling, analysis, inference and control of biological networks.Measurements inside individual cells reveal biological networks with rich stochastic dynamics, owing to the inherent probabilistic nature of biochemical processes. The intellectual merit lies in modeling of biomolecular networks via a Stochastic Hybrid Systems (SHS) framework. Combining tools from control theory, dynamical systems and random processes, the project will develop computationally tractable methods for analyzing deterministic and stochastic dynamics of complex biomolecular systems. These methods will improve computational efficiency of predicting network dynamics by orders of magnitude as compared to traditionally used Monte Carlo simulation techniques. Analysis tools will be used to explore designs of feedback and feedforward loops in biomolecular systems that allow for systematic manipulation of network activity, for example, controlling fluctuations in the level of a specific protein embedded in a larger network. Accurate methods for predicting stochastic dynamics also motivate an intriguing inference problem of learning about the underlying network architecture from measured joint fluctuations in the network components. Advances in single-cell technologies enable precise quantitative measurements of protein copy numbers inside individual cells over time. Motivated by increasing availability of such measurements, the project will build inference methods for improved characterization and reverse engineering of network interactions from time-series measurements of protein levels. In terms of broader impact, the project tools will be applied to study diverse biological pathways in close collaborations with experimental researchers in the life sciences. These include regulatory networks underlying several medically important systems, such as, cell-fate regulation in the human immunodeficiency virus (HIV), and nongenetic drug resistance in cancer cells. Finally, courses applying systems and control concepts to biological networks will be developed for both students and professionals from local industry.

活细胞编码相互连接的生物分子组件的复杂和动态网络，这些组件协调不同的生命过程。与电路不同，这些网络由生物化学物质（基因、蛋白质、RNA等）组成。它们通过化学反应相互作用和调节。全面了解生物分子网络如何处理细胞决策信息将对人类健康产生巨大的长期影响。例如，生物分子网络的系统级破译将从根本上改变我们对异常调节驱动疾病状态的认识，并将导致寻找生物标志物和药物靶点的新方法，这些方法将网络动态考虑在内。此外，使用系统方法设计和重新连接网络将为不同的应用打开大门，例如生物燃料和治疗剂的生产。为了促进这些转变，该项目旨在建立可扩展的数学工具，用于建模，分析，推理和控制生物网络。单个细胞内的测量揭示了生物网络具有丰富的随机动力学，这是由于生物化学过程固有的概率性质。 智能的优点在于通过随机混合系统（SHS）框架的生物分子网络建模。结合控制理论，动力系统和随机过程的工具，该项目将开发用于分析复杂生物分子系统的确定性和随机动力学的计算方法。这些方法将提高预测网络动态的计算效率的数量级相比，传统上使用的蒙特卡罗模拟技术。分析工具将用于探索生物分子系统中反馈和前馈回路的设计，这些系统允许系统地操纵网络活动，例如，控制嵌入较大网络中的特定蛋白质水平的波动。预测随机动态的准确方法也激发了一个有趣的推理问题，即从网络组件的测量联合波动中了解底层网络架构。单细胞技术的进步使得能够随着时间的推移精确定量测量单个细胞内的蛋白质拷贝数。由于这些测量的可用性越来越高，该项目将建立推理方法，以改进蛋白质水平时间序列测量的网络相互作用的表征和逆向工程。就更广泛的影响而言，该项目工具将与生命科学实验研究人员密切合作，用于研究各种生物途径。这些包括几个医学上重要的系统，如人类免疫缺陷病毒（HIV）中的细胞命运调控和癌细胞中的非遗传性耐药性的调控网络。最后，课程应用系统和控制概念的生物网络将开发为学生和专业人士从当地产业。

项目成果

Stochastic analysis of feedback control by molecular sequestration

分子隔离反馈控制的随机分析

• DOI: 10.23919/acc.2019.8814973
• 发表时间: 2019
• 期刊: 2019 American Control Conference (ACC
• 作者: Dey, Supravat;Singh, Abhyudai
• 通讯作者: Singh, Abhyudai

Analysis and Control of Stochastic Systems Using Semidefinite Programming Over Moments

使用矩上半定规划的随机系统分析与控制

• DOI: 10.1109/tac.2018.2872274
• 发表时间: 2019
• 期刊: IEEE Transactions on Automatic Control
• 影响因子: 6.8
• 作者: Lamperski, Andrew;Ghusinga, Khem Raj;Singh, Abhyudai
• 通讯作者: Singh, Abhyudai

Proportional and derivative controllers for buffering noisy gene expression

• DOI: 10.1109/cdc40024.2019.9030175
• 发表时间: 2019-12
• 期刊: 2019 IEEE 58th Conference on Decision and Control (CDC)
• 作者: Saurabh Modi;Supravat Dey;Abhyudai Singh

MicroRNA Based Feedforward Control of Intrinsic Gene Expression Noise

• DOI: 10.1109/tcbb.2019.2938502
• 发表时间: 2021-01-01
• 期刊: IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS
• 影响因子: 4.5
• 作者: Bokes, Pavol;Hojcka, Michal;Singh, Abhyudai
• 通讯作者: Singh, Abhyudai

Driving an Ornstein-Uhlenbeck Process to Desired First-Passage Time Statistics

将 Ornstein-Uhlenbeck 过程驱动到所需的首次通过时间统计

• DOI: 10.23919/ecc.2019.8795862
• 发表时间: 2019
• 期刊: 2019 18th European Control Conference (ECC
• 作者: Ghusinga, Khem Raj;Srivastava, Vaibhav;Singh, Abhyudai
• 通讯作者: Singh, Abhyudai