Time | Speaker/ Title/ Abstract/ VOD | Location |

08:30 - 09:00 |
**Breakfast**
| CAMP |

09:00 - 09:40 |
**Ryo Kobayasi / Towards the Construction of Dialogical Control**
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Conventional control theory has developed highly sophisticated framework by separating the system and the environment, in which the interactions between the system and the environment are treated as a “disturbance”. The theory has been made big successes in the areas where it is applicable. Typical examples are machines (including robots) working in the factories where environment is completely known.
However, mobile robots are not the case. They encounter unknown environments as they move into new sites. In such situations, the interaction between the system and the environment can no more be regarded as disturbance. That means the system cannot be closed (like the conventional theory assumed), and we definitely need a new framework of control. The most promising approach seems to learn from the animals, because even the lower animals can make locomotion easily in the complex environment. Thus, collaboration between mathematical biology and robotics can be just the potential way for achieving our goal. I will introduce our challenge to construct a novel control principle for mobile robots.
| CAMP |

09:40 - 10:10 |
**Lei Zhang / Rare event and transition state in complex biological systems**
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The dynamics of complex biological systems is often driven by multiscale, rare but important events. Transition state theory provides a powerful mathematical tool to find the dynamic bottleneck and transition pathway. In this talk, I will apply three biological examples to show how rare event and transition state can help us understand the mechanisms and functions in biology, including stem cell differentiation, boundary sharpening in zebrafish hindbrain, and neuroblast delamination in Drosophila. The joint work with Qing Nie (UC Irvine), Yan Yan (HKUST), Chao Tang (PKU).
| CAMP |

10:10 - 10:40 |
**Coffee Break**
| CAMP |

10:40 - 11:10 |
**Andrew Phillips / Using physiologically-based models to understand human sleep dynamics in the real world**
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From laboratory studies, it is known that the human circadian clock is highly sensitive to environmental light patterns, including electric sources of light. Sleep timing in modern societies is also known to be very different from sleep timing in pre-industrial societies or modern societies that lack access to electric light. Using a mathematical model of human sleep and circadian physiology, we investigated how our modern patterns of light consumption affect the timing of sleep and circadian timing across the population. The model provides a quantitative framework for understanding how individual differences arise, why we experience social jet-lag, and how changes to our social constraints are predicted to affect sleep and circadian timing.
| CAMP |

11:10 - 11:40 |
**Jinzhi Lei / Data driven single-cell based model reveals multiple pathways of inflammation-induced tumorigenesis**
Chronic inflammation is a high-risk factor for tumorigenesis, how- ever the routes from inflammation to cancer are poorly understood. Here, driven by major pathways affected by high frequent mutant genes that are associated with inflammation and cancer, we presented a single-cell based computational model for long-term dynamics from inflammation to tumorigenesis. The model incorporates crosstalk among multi-scale interactions, including DNA damage response, gene mutation, cell behavior, population dynamics, inflammation, and metabolism-immune balance. Model simulation reveals two stages of inflammation-induced tumorigenesis: precancerous lesion and tumorigenesis. Precancerous lesion is mainly caused by mutations in the proliferation pathways; the transition from precancerous lesion to tumorigenesis is induced by mutations in the path- ways of cell apoptosis, differentiation, and metabolism-immune balance. We identified the two sides of inflammation in tumorigenesis: mild inflammation removes DNA damaged cell through DNA damage- induced cell death, while severe inflammation accelerates the accumulation of mutations and hence promote tumorigenesis. These results provide insights to the dynamics of inflammation-induced can- cer, and highlight the combination of inflammation and metabolism- immune imbalance in tumorigenesis. The approach establishes a modeling framework of quantifying the cancer risk and discovering the driver paths of mutations in inflammation-induced tumorigenesis, which are useful in guiding the validation of promising targets for therapeutic intervention.
| CAMP |

11:40 - 12:10 |
**Masakazu Akiyama / A mathematical model of collective cell migration based on cell polarity**
Individual cells migrate toward the direction of the polarity induced by interior or exterior factors. In situations without guides such as chemoattractants, cells migrate randomly. However, it is observed that cell aggregation may lead to systematic collective cell migration. For example, Dictyostelium discoideum and epithelial MDCK cells exhibit typical collective cell migration patterns such as uniformly directional migration and rotational migration. In particular, it has been observed from experimental investigations that rotational migrations are intimately related to the morphogenesis of organized cells and tissues in several species. Thus, it has been suggested that collective cell migrations are controlled by a universal mechanism of cells. In this talk, we review the actual experimental data related to collective cell migrations on dishes and show that our self-propelled particle model based on the cell polarity can accurately represent the observed migration behaviors. Furthermore, we show that the collective cell migration modes observed in our model are robust.
| CAMP |

12:10 - 14:00 |
**Lunch**
| CAMP |

14:00 - 14:30 |
**Ramon Grima / Mean field theory of diffusion in heterogeneous intracellular crowded conditions**
It is now well established that cell interiors are significantly crowded by macromolecules, which impede diffusion and enhance binding rates. However, it is not fully appreciated that levels of crowding are heterogeneous, and can vary substantially between subcellular regions. Starting from a stochastic microscopic model, we derive coupled nonlinear PDEs for the concentrations of two populations of large and small spherical particles with steric volume exclusion. By performing a perturbative expansion in the ratio of the particle sizes, we find that the diffusion of a small particle in the presence of large particles obeys an advection-diffusion equation, with a reduced diffusion coefficient and a velocity directed towards less crowded regions. The interplay between advection and diffusion leads to behaviour that differs significantly from Brownian diffusion. We show that biologically plausible distributions of macromolecules can lead to (i) highly non-Gaussian probability densities for the small particle position, including asymmetrical and multimodal densities, (ii) both sub- and super-diffusion for short times. We confirm all our results using hard-sphere Brownian dynamics simulations.
| CAMP |

14:30 - 15:00 |
**Jaeyoung Sung / Chemical fluctuation theorem for intracellular reaction networks**
Conventional control theory has developed highly sophisticated framework by separating the system and the environment, in which the interactions between the system and the environment are treated as a “disturbance”. The theory has been made big successes in the areas where it is applicable. Typical examples are machines (including robots) working in the factories where environment is completely known.
However, mobile robots are not the case. They encounter unknown environments as they move into new sites. In such situations, the interaction between the system and the environment can no more be regarded as disturbance. That means the system cannot be closed (like the conventional theory assumed), and we definitely need a new framework of control. The most promising approach seems to learn from the animals, because even the lower animals can make locomotion easily in the complex environment. Thus, collaboration between mathematical biology and robotics can be just the potential way for achieving our goal. I will introduce our challenge to construct a novel control principle for mobile robots.
| CAMP |

15:00 - 15:30 |
**Chunhe Li / Landscape and path of gene networks**
Cellular functions in biological systems are regulated by the underlying gene regulatory networks. How to investigate the global properties of gene networks is a challenging problem. In this talk, I will present some approaches we recently developed, i.e. the potential landscape and path framework, to study the stochastic dynamics of gene networks. The basins on the landscape characterize different cell states. The landscape topography in terms of barrier heights between stable states quantifies the global stability of the gene regulatory system. The kinetic paths quantify the transition processes between different cell states. I will also discuss some applications of this approach in the biological systems, including cancer, cell cycle, and stem cell differentiation.
| CAMP |

15:30 - 16:00 |
**Coffee Break**
| CAMP |

16:00 - 16:30 |
**Young (Yang) Cao / Hybrid stochastic model and simulation of the budding yeast cell cycle control mechanism**
The budding yeast cell cycle is regulated by a complex chemical reaction network. Several deterministic models have been proposed to model this control mechanism. However, experimental data exhibit considerable variability from cell to cell during cell growth and division. The observed variability comes from two sources: intrinsic noise coming from fluctuations of molecules and extrinsic noise introduced by variations in the division process. As a result, molecular fluctuations cannot be neglected, and they may significantly affect the behavior of a cell. To accurately model the cell cycle control mechanism, stochastic models are needed. A rigorous solution to this problem is to convert a deterministic model to its stochastic equivalent and apply Gillespie's stochastic simulation algorithm (SSA). But the conversion process is not straightforward and often results in a much larger system. Moreover, the high computational cost of Gillespie's algorithm make it difficult to simulate a practical budding yeast cell cycle model. Here we present a hybrid (ODE/SSA) stochastic model for the budding yeast cell cycle. Based on error analysis for multiscale systems and the observation that fluctuations of mRNAs are the primary sources of noise, in this hybrid model the dynamics of mRNAs are simulated by Gillespie's algorithm, while the cell cycle mechanism at the protein level is modeled by ordinary differential equations (ODEs). Numerical experiments, implemented by Haseltine and Rawlings' hybrid simulation algorithm, demonstrate that the hybrid model matches very well with biological experimental data for both wild-type cells and mutant cells in different nutrient media.
| CAMP |

16:30 - 17:00 |
**Yong Jung Kim / Lotka-Volterra equations with finite time extinction**
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Most of population models, if not all of them, do not have a finite time extinction property. The Lotka-Volterra ordinary differential equations are such cases and solutions never become zero if they are not initially zero. Their PDE extensions also have the same property and solutions are always strictly positive everywhere. However, the population becomes extinct locally in space always and the survival of a species is a global phenomenon, but not a local one. The spatial pattern of biological organisms reflects its history of extinction and growth. In this paper Lotka-Volterra equations equipped with extinction dynamics are introduced. We will see beautiful patterns of life generated by the newly added extinction dynamics.
| CAMP |

17:00 - 17:30 |
**Jae Kyoung Kim / When can we use the Michales-Menten function for the stochastic simulations?**
The quasi steady-state approximation (QSSA) is frequently used to reduce deterministic models of biochemical networks. The resulting equations provide a simplified description of the network in terms of non-elementary reaction functions (e.g. Hill functions). Such deterministic reductions are frequently a basis for heuristic stochastic models in which non-elementary reaction functions are used as propensities of Gillespie algorithm. Despite the popularity of this heuristic stochastic simualtions, it remains unclear when such stochastic reductions are valid. In this talk, I will present conditions under which the stochastic models with the non-elementary propensity functions accurately approximate the full stochastic models. If the validity condition is satisfied, we can perform accurate and computationally inexpensive stochastic simulation without converting the non-elementary functions to the elementary functions (e.g. mass action kinetics).
| CAMP |