사람이 어떻게 만들어지고 각 기관이 어떻게 발달하는지에 대한 질문은 아주 오래전부터 있었습니다. 체외수정(IVF)의 고유의 장점으로 인해 과학자들이 수정란을 외부에서 관찰할 수 있게 되었습니다. 하지만, 1979년도에 제정된 14일 규정(the 14-day rule)으로 인해, 수정 후 최대 14일까지의 배아 만의 연구가 가능합니다. 따라서, 이 14일 규정은 발생 생물학자들이 사람 발생학 연구에 있어서 수정 후 2주 이상(신경계 발달, 기관 형성 등)에 나타나는 현상을 연구하고자 할 경우 다른 방향을 모색할 수밖에 없게 되었습니다. 본 연구는 이 지점에서부터 시작합니다. 연구진들은 세포 분열 때 우연히 발생하는 생리학적 체세포 변이(Post-zygotic Variants)를 추적하여 각 세포들의 운명을 재구성하였습니다. 특히 사망 후 기증된 시신에서 단일 세포를 배양하고, 최근 개발된 차세대 염기서열 분석 기술을 사용하여 인간 발생 연구의 후향적 혈통 추적(Retrospective Lineage Tracing)을 수행하는 과정을 발표하고자 합니다. 이번 발표를 통해서 이런 방법론이 어떻게 가능했는지에 대한 생물학적 및 과학적 배경과 인간 발생학의 미래에서 해결해야 할 과제와 가설을 강조할 예정입니다. 추가로, 이 과정에서 필요한 수학적인 해석이 필요한 질문들에 대해서도 논의할 예정입니다. 여러분들의 참신한 시각과 질문을 크게 환영합니다. 1) Park, S., Mali, N.M., Kim, R. et al. Clonal dynamics in early human embryogenesis inferred from somatic mutation. Nature 597, 393–397 (2021). https://doi.org/10.1038/s41586-021-03786-8 2) Kwon, S.G., Bae, G.H., Choi, J.H. et al. Asymmetric Contribution of Blastomere Lineages of First Division of the Zygote to Entire Human Body Using Post-Zygotic Variants. Tissue Eng Regen Med 19, 809–821 (2022). https://doi.org/10.1007/s13770-022-00443-7

Absolute Concentration Robustness (ACR) was introduced by Shinar and Feinberg (Science 327:1389-1391, 2010) as robustness of equilibrium species concentration in a mass action dynamical system. Their aim was to devise a mathematical condition that will ensure robustness in the function of the biological system being modeled. The robustness of function rests on what we refer to as empirical robustness — the concentration of a species remains unvarying, when measured in the long run, across arbitrary initial conditions. Even simple examples show that the ACR notion introduced in Shinar and Feinberg (here referred to as static ACR) is neither necessary nor sufficient for empirical robustness. To make a stronger connection with empirical robustness, we define dynamic ACR, a property related to long-term, global dynamics, rather than only to equilibrium behavior. We discuss general dynamical systems with dynamic ACR properties as well as parametrized families of dynamical systems related to reaction networks. In particular, we find necessary and sufficient conditions for dynamic ACR in complex balanced reaction networks, a class of networks that is central to the theory of reaction networks.This is joint work with Badal Joshi (CSUSM)

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ZOOM ID: 997 8258 4700 (Biomedical Mathematics Online Colloquium), (pw: 1234)

The ability to reliably engineer the mammalian cell will impact a variety of applications in a disruptive way, including cell fate control and reprogramming, targeted drug delivery, and regenerative medicine. However, our current ability to engineer mammalian genetic circuits that behave as predicted remains limited. These circuits depend on the intra and extra cellular environment in ways that are difficult to anticipate, and this fact often hampers genetic circuit performance. This lack of robustness to poorly known and often variable cellular environment is the subject of this talk. Specifically, I will describe control engineering approaches that make the performance of genetic devices robust to context. I will show a feedforward controller that makes gene expression robust to variability in cellular resources and, more generally, to changes in intra-cellular context linked to differences in cell type. I will then show a feedback controller that uses bacterial two component signaling systems to create a quasi-integral controller that makes the input/output response of a genetic device robust to a variety of perturbations that affect gene expression. These solutions support rational and modular design of sophisticated genetic circuits and can serve for engineering biological circuits that are more robust and predictable across changing contexts.

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ZOOM ID: 997 8258 4700 (Biomedical Mathematics Online Colloquium), (pw: 1234)

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ZOOM ID: 997 8258 4700 (Biomedical Mathematics Online Colloquium), (pw: 1234)

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ZOOM ID: 997 8258 4700 (Biomedical Mathematics Online Colloquium), (pw: 1234)

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ZOOM ID: 997 8258 4700 (Biomedical Mathematics Online Colloquium), (pw: 1234)

Shift workers experience profound circadian disruption due to the nature of their work, which often has them working at times when their internal clock is sending a strong signal for sleep. Mathematical models can be used to generate recommendations for shift workers that shift their body’s clock to better align with their work schedules, to help them sleep, feel, and perform better. In this talk, I will discuss our recent mobile app, Shift, which pulls wearable data from user’s devices and generates personalized recommendations to help them manage shift work schedules. I will also discuss how this product was designed, how it can interface with Internet of Things devices, and how its insights can be useful for other groups beyond shift workers.