实验室将于2019年4月12日-14日在华中科技大学召开生物医学的建模与计算学术研讨会。

报告摘要

Mathematical Modeling and Quantitative Analysis for the Tumor- Immune interactions

邹秀芬

武汉大学

With the use of cancer immunotherapy has obtained a more prominent application in the clinic, better understanding of complex relationships between the tumor and immunity microenvironments is critical to design successful therapeutic protocol. In this talk, I will introduce the recent progress and our work in mathematical modeling of tumor-immune ecosystem. Qualitative and quantitative results will help to guide the identification of targets for combinatorial therapy to overcome mechanisms of resistance to immune therapy.

A general mathematical framework for understanding the behavior of heterogeneous stem cell regeneration

雷锦誌

清华大学

Stem cell heterogeneity is essential for the homeostasis in tissue development. This paper established a general formulation for understanding the dynamics of stem cell regeneration with cell heterogeneity and random transitions of epigenetic states. The model generalizes the classical G0 cell cycle model, and incorporates the epigenetic states of stem cells that are represented by a continuous multidimensional variable and the kinetic rates of cell behaviors, including proliferation, differentiation, and apoptosis, that are dependent on their epigenetic states. Moreover, the random transition of epigenetic states is represented by an inheritance probability that can be described as a conditional beta distribution. This model can be extended to investigate gene mutation-induced tumor development. The proposed formula is a generalized formula that helps us to understand various dynamic processes of stem cell regeneration, including tissue development, degeneration, and abnormal growth.

基于动力学的组合药物研究

王瑞琦

上海大学

该报告主要从非线性动力学的角度讨论如何研究组合药物，包括药物靶点的筛选以及组合药物的协作型的判定等。该研究为组合药物的研究提供了新方法与思路。

Landscape of cellular networks

李春贺

复旦大学

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 based on the minimum action principles 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 epithelial-mesenchymal transitions (EMT).

蛋白质聚集的定量调控

洪柳

清华大学

蛋白质聚集与许多著名神经退行性型疾病相关，如老年痴呆症、帕金森症、渐冻症等。基于对蛋白质聚集过程的定量数学刻画及抑制剂分子作用机制的研究，我们对若干种典型的蛋白质聚集体系开发了相应的小分子或纳米型抑制剂，并通过生化实验加以验证。我们的研究为未来神经退行性型疾病相关药物的设计和研发提供了前提保障。

Markovian approaches to modeling non-Markovian reaction processes

周天寿

中山大学

反应系统的研究长期依赖于马氏假设（即反应物的随机运动只与当前状态有关，而与历史无关）。然而，由于反应系统的非完全混合或由于一个宏观分子的产生需要经过若干小的反应步，因此反应通常是以非马氏的方式发生。我将介绍研究一般（即具有任意等待时间分布）反应网络的建模与分析方法，特别是导出静态广义化学主方程，它为理论分析带来了极大方便，能够帮助我们发现新的生物学知识。

The impact of radiation on the development of lung cancer

张兴安

华中师范大学

Environment factors such as radiation play an important role in the incidence of lung cancer. In spite of substantial efforts in experimental study and mathematical modeling, it is still a significant challenge to estimate lung cancer risk from radiation. To address this issue, we propose a stochastic model to investigate the impact of radiation on the development of lung cancer. The proposed three-stage model with clonal expansion is used to match the data of the male and female patients in the Osaka Cancer Registry (OCR) and Life Span Study (LSS) cohort of atomic bomb survivors in Hiroshima and Nagasaki. Our results indicate that the major effect of radiation on the development of lung cancer is to induce gene mutations for both male and female patients. In particular, for male patients, radiation affects the mutation in normal cells and the transformation from premalignant cells to malignant ones. However, radiation for female patients increases the mutation rates of the first two mutations in the stochastic model. The established relationship between parameters and radiation will provide insightful prediction for the lung cancer incidence in the radiation exposure.

Model of motor protein kinesin based on the mechanics of neck linkers

张云新

复旦大学

Kinesin is one of the most important motor proteins in cells, which can move processively along microtubule to the plus end. With the development of experimental techniques (especially optical tweezers), many experimental data, which are related to varies biophysical properties of kinesin, have been obtained. Meanwhile, in recent decades, based on biophysical and biochemical principles, varies kinds of model have also been designed to try to obtain the motion mechanism of kinesin. In which,

most of them assume that the hand-over-hand motion of kinesin is tightly coupled with the cyclic hydrolysis of ATP molecules, and then use the chemical master equation to describe the corresponding chemical reaction cycle. Where the external load dependence of transition rates is usually given by Bell approximation. In this study, the mechanochemical cyclic of kinesin will be described by a different way, where the rebinding of the dissociated head of kinesin to microtubule binding site is assumed to be a stochastic search process, but this search process is tethered by neck linkers. By regarding the two neck linkers as a wormlike chain, the mean search time can be obtained explicitly, and consequently the mean velocity of kinesin can be derived. By fitting to experimental data, several biophysical quantities can be obtained, including the counter/persistence length of neck linkers, the dwell times of kinesin in two-head binding state and one-head binding state. This study also finds that, for kinesin purified from squid, there exists two substeps in each 8.2 nm step, which around 2-3 nm and 5-6 nm respectively. While for kinesin from bovine brain or Drosophila, the substeps are not evident.

An Adaptation Model for Slime Mold Physarum Polycephalum

胡丹

上海交通大学

A Physarum Polycephalum is a single-celled animal that appears to be able to form intelligent network structures. Such a network is used for transportation of mass and energy in its body. There have been a few models discussing the formation dynamics of the networks structure. Nevertheless, very few has been discussed about the biological stimuli that drive such adaptation models. In this talk, we present a mathematical model to show that by an adaptation dynamics in response to local shear stress on the cell wall, the Physarum Polycephalum is able to minimize the total energy cost in fluid delivery. Furthermore, using an asymptotic analysis, we reduce the three-dimensional fluid flow to a two-dimensional flow and obtain an adaptation model of the thickness of Physarum Polycephalum. This model appears to be very similar to our previous model on the initiation of biological transport networks, thus can lead to the formation of networks structure with optimized energy cost.

A stochastic model of axonal organelle accumulation

induced by reduction of molecular motors

赖秀兰

中国人民大学

Nerve cells are critically dependent on the transport of intracellular cargoes, which are moved by motor proteins along microtubule tracks. Impairments in this movement are thought to explain the focal accumulations of axonal cargoes and axonal swellings observed in many neurodegenerative diseases. In some cases, these diseases are caused by mutations that impair motor protein function, and genetic depletion of functional molecular motors has been shown to lead to cargo accumulations in axons. The evolution of these accumulations has been compared to the formation of traffic jams on a highway, but this idea remains largely untested. In this paper, we investigated the underlying mechanism of local axonal cargo accumulation induced by a global reduction of functional molecular motors in axons. We hypothesized that (i) a reduction in motor number leads to a reduction in the number of active motors on each cargo which in turn leads to less persistent movement, more frequent stops and thus shorter runs; (ii) as cargoes stop more frequently, they impede the passage of other cargoes, leading to local ‘traffic jams’; and (iii) collisions between moving and stopping cargoes can push stopping cargoes further away from their microtubule tracks, preventing them from reattaching and leading to the evolution of local cargo accumulations. We used a lattice-based stochastic model to test whether this mechanism can lead to the cargo accumulation patterns observed in experiments. Simulation results of the model support the hypothesis and identify key questions that must be tested experimentally.

Rules behind Complex Aspects of Cancer:

stochastic dynamics as a revealing approach

敖平

上海大学

A coarse-graining framework for spiking neuronal networks: from local, low-order moments to large-scale spatiotemporal activities

张继伟

武汉大学

In this talk we provide a general methodology for systematically reducing the dynamics of a class of integrate-and-fire networks down to an augmented 4-dimensional system of ordinary-differential-equations. The class of integrate-and-fire networks we focus on are homogeneously-structured, strongly coupled, and fluctuation-driven. Our reduction succeeds where most current firing-rate and population-dynamics models fail because we account for the emergence of ‘multiple-firing- events’ involving the semi-synchronous firing of many neurons. These multiple-firing-events are largely responsible for the fluctuations generated by the network and, as a result, our reduction faithfully describes many dynamic regimes ranging from homogeneous to synchronous. Our reduction is based on first principles, and provides an analyzable link between the integrate-and-fire network parameters and the relatively low-dimensional dynamics underlying the 4-dimensional augmented ODE.

Network connectivity reconstruction from dynamics in neuronal systems

周栋焯

上海交通大学

How neurons are connected in the brain to perform computation is a key issue in neuroscience. Recently, the development of calcium imaging and multi-electrode array techniques have greatly enhanced our ability to measure the firing activities of neuronal populations at single cell level. Meanwhile, the intracellular recording technique is able to measure subthreshold voltage dynamics of a neuron. Our work addresses the issue of how to combine these measurements to reveal the underlying network structure. We propose the spike-triggered regression (STR) method, which employs both the voltage trace and firing activity of the neuronal population to reconstruct the underlying synaptic connectivity. Our numerical study of the conductance-based integrate-and-fire neuronal network shows that only short data of 20 ∼ 100 s is required for an accurate recovery of network topology as well as the corresponding coupling strength. Our method can yield an accurate reconstruction of a large neuronal network even in the case of dense connectivity and nearly synchronous dynamics, which many other network reconstruction methods cannot successfully handle. In addition, we point out that, for sparse networks, the STR method can infer coupling strength between each pair of neurons with high accuracy in the absence of the global information of all other neurons.

Vulnerability and cosusceptibility on Brain cascading networks

纪鹏

复旦大学

In networked systems, a local perturbation can propagate by following paths along the network of interactions between the system's units. Such behaviour can lead to a large-scale cascade of interaction failures. We adapt a classical load-redistribution model and based on brain networks that have been investigated with diffusion MRI, conduct an analysis of the vulnerability and cosusceptibility of the corresponding brain networks. We find a group of nodes that can, potentially, fail simultaneously. The cascade model advances our understanding of linked failures in brain networks, and our results provide new insights in understanding disease progression in, e.g., Dementia.

Spatial dynamics of interacting contagions

陈理

陕西西师范大学

The spread of infectious diseases, rumors, fashions, innovations are complex dynamical processes, embedded both in network and spatial contexts. While most of previous work are based on single infection modeling, the reality is that hundreds or even thousands of different infectious strains simultaneously spread around the world. In this talk, I will show that when two infections are allowed to circulate around in the population and the presence of possible interaction, the contagion dynamics could exhibit quite different behaviors, including the abrupt outbreak transition, an avalanche scenario that much difficult to contain; In the spatial context, unexpected propagation modes are revealed such as receding waves or the standing wave; persistent spatial patterns are also possible, which make the eradication quite difficult. The uncovered dynamics are in sharp contrast with the case of single infection. These observations of "more is different" imply that the wisdom obtained from previous work based on single infection may not sufficient to capture the contagion complexities in the real world.

Predicting future dynamics and quantifying critical states in complex systems

陈洛南

中科院上海生命科学研究院

This talk includes two parts, (1) randomly distributed embedding for future dynamics prediction on steady states, and (2) dynamic network marker for criticality quantification near critical states (or tipping points).

(1) Future state prediction for nonlinear dynamical systems is a challenging task, particularly when only a few time series samples for high-dimensional variables are available from real-world systems. In this work, we propose a model-free framework, named randomly distributed embedding (RDE), to achieve accurate future state prediction based on short-term high-dimensional data. Specifically, from the observed data of high-dimensional variables, the RDE framework randomly generates a sufficient number of low-dimensional “nondelay embeddings” and maps each of them to a “delay embedding,” which is constructed from the data of a to be predicted target variable. Any of these mappings can perform as a low-dimensional weak predictor for future state prediction, and all of such mappings generate a distribution of predicted future states. This distribution actually patches all pieces of association information from various embeddings unbiasedly or biasedly into the whole dynamics of the target variable, which after operated by appropriate estimation strategies, creates a stronger predictor for achieving prediction in a more reliable and robust form. Through applying the RDE framework to data from both representative models and real-world systems, we reveal that a high-dimension feature is no longer an obstacle but a source of information crucial to accurate prediction for short-term data, even under noise deterioration.

(2) Considerable evidence suggests that during the progression of complex diseases, the deteriorations are not necessarily smooth but are abrupt, and may cause a critical transition from one state to another at a tipping point. Here, we develop a model-free method to detect early-warning signals of such critical transitions, even with only a small number of samples. Specifically, we theoretically derive an index based on a dynamic network biomarker (DNB) or dynamic network marker (DNM) that serves as a general early-warning signal indicating an imminent bifurcation or sudden deterioration before the critical transition occurs. Based on theoretical analyses, we show that predicting a sudden transition from small samples is achievable provided that there are a large number of measurements for each sample, e.g., high-throughput data. We employ microarray data of three diseases to demonstrate the effectiveness of our method. The relevance of DNBs with the diseases was also validated by related experimental data and functional analysis.

Identification of pre-microRNAs by characterizing their sequence order evolution information and secondary structure graphs

喻祖国

湘潭大学

Distinction between pre-microRNAs (precursor microRNAs) and length-similar pseudo pre-microRNAs can reveal more about the regulatory mechanism of RNA biological processes. Machine learning techniques have been widely applied to deal with this challenging problem. We use new features for the machine learning algorithms to improve the classification performance by characterizing both sequence order evolution information and secondary structure graphs. We developed three steps to extract these features of pre-microRNAs. We first extract features from PSI-BLAST profiles and Hilbert-Huang transforms, which contain rich sequence evolution information and sequence-order information respectively. We then obtain properties of small molecular networks of pre-microRNAs, which contain refined secondary structure information. Then support vector machine (SVM) is applied as our classifier. The constructed classification model is named MicroRNA-NHPred. The performance of MicroRNA-NHPred is high and stable, which is better than that of those state-of-the-art methods, achieving an accuracy of up to 94.83% on same benchmark datasets.

Robust Sparse Additive Models

陈洪

华中农业大学

Sparse additive models have been successfully applied to high dimensional data analysis due to their representation flexibility and interpretability. However, existing methods are often formulated with the least squares loss under the mean square error (MSE) criterion, which may result in performance degradation for data with skewed noise, heavy-tailed noise, and outliers. In this talk, we introduce a new sparse method, called sparse modal additive model (SpMAM), by integrating the mode-induced loss and the coefficient-based \ell_q-regularizer into additive models. In contrast to existing methods that aim to learning the mean, the proposed modal regression approximates the intrinsic mode and is robust to the complex noise. The performance of SpMAM is evaluated by theoretical characterizations on generalization bound and variable selection consistency, and by experimental analysis on simulated and benchmark datasets.

一个基于基因调控的肿瘤细胞多表型和表型之间转换的数学模型

贾亚

华中师范大学

人们相信细胞的phenotype是由其genotype所决定的。针对个体细胞中基因调节机制的定量研究是理解相同细胞群体的各种宏观生理现象的关键一步。 基于单个乳腺肿瘤细胞中基因zeb1和cdh1之间的调控机制，我们提出了一个单个乳腺肿瘤细胞的基因调控数学模型，该模型揭示出： (i) 单个乳腺癌细胞具有三种表型； (ii) 肿瘤细胞表型之间的相互转换可以通过噪声诱导，表型转换特性可用平均第一次通过时间来量化； (iii) 在一定条件下，单个肿瘤细胞出现在三种表型的概率与乳腺癌SUM159细胞系中的宏观表型比例的实验观测一致；(iv) 该模型还可以定性解释乳腺癌细胞涉及TGF-β信号的几种宏观生理现象，如：肿瘤治疗中的‘TGF-β悖论’、乳腺癌细胞的五种临床亚型、瞬时TGF-β信号对乳腺癌转移的效应。

拟南芥根细胞发育的调控模块及动力学

易鸣

华中农业大学

本报告中，我们将围绕拟南芥根细胞应激响应调控动力学以及拟南芥侧根发育前馈调控动力学相关的数学建模和随机动力学介绍我们取得的初步成果。