生物分子网络弹性研究进展

Advances in Biomolecular Network Resilience

弹性是生物分子网络重要且基础的属性之一,一方面弹性赋予生物分子网络抵抗内部噪声与环境干扰并维持其自身基本功能的能力,另一方面,弹性为网络状态的恢复制造了阻力。生物分子网络弹性研究试图回答如下3个问题:a.生物分子网络弹性的产生机理是什么?b.弹性影响下生物分子网络的状态如何发生转移?c.如何预测生物网络状态转换临界点,以防止系统向不理想的状态演化?因此,研究生物分子网络弹性有助于理解生物系统内部运作机理,同时对诸如疾病发生临界点预测、生物系统状态逆转等临床应用具有重要的指导意义。鉴于此,本文主要针对以上生物分子网络弹性领域的3个热点研究问题,在研究方法和生物学应用上进行了系统地综述,并对未来生物分子网络弹性的研究方向进行了展望。

Resilience,which is defined as the ability of a system to adjust its activity for retaining the basic functionality when errors,failures and environmental changes occur,is an essential and fundamental property for biomolecular networks.The studies of biomolecular network resilience attempt to answer the following 3questions.(1)What is the potential mechanism of the resilience of biomolecular networks?(2)How the state of biomolecular network migrates from one stable steady state to another under the effect of resilience?(3)How to predict the tipping points of state transitions to prevent the system from evolving into undesirable states(such as disease states)?In view of the importance of resilience for biomolecular networks and its clinical application value,we systematically review the research progress focusing on 3 questions above in the past 20 years.As one of the important steady-state characteristics of resilience systems,bi-stability(or multi-stability)can help us to uncover the underlying mechanism of the resilience.Biomolecular networks consist of numerous repeated network motifs,and the steady-state response of almost all network motifs which contain feedback loops(e.g.positive autoregulation motif,mutual inhibition motif)is bi-stability(or multi-stability).Based on our numeric simulation,the network motifs with feedback loops have different steady-state response characteristics although they all bi-stability(or multi-stability),which result in the different biological functions they can describe.Many studies also indicated that stochastic noise from internal or external could affect the number of stable-steady states and hysteresis of the network motifs with bi-stability(or multi-stability).Furthermore,the bi-stable network motifs have been used to model many biological processes,such as cell cycle and embryonic development,to reveal their mechanism.The network motifs are too simple to model complex biological processes,which generally involved in interactions between lots of biomolecules.Potential function,as a powerful tool in the field of dynamical system,is widely used to uncover the state transition properties of high-dimensional biomolecular networks.Many methods have been proposed to reconstruct the potential function of equilibrium systems and nonequilibrium systems based on network dynamics.Using these methods,a vast number of studies revealed the state transition mechanism of various biological processes,such as cell differentiation,cancer initiation,etc.The state of biomolecular network generally migrates from one stable steady state to another abruptly and drastically under the effect of resilience.However,detecting tipping points of state transitions in system level is impossible based on network dynamic,due to the complexity and nonlinearity of biomolecular networks.To fill this gap,many indicators have been proposed to predict the upcoming tipping points from biological data under network perspective,such as dynamic network biomarker(DNB).And these indicators were also used to detect the critical transitions in the development of various diseases(e.g.,diabetes,influenza,cancer).So far,the study of biomolecular network resilience has been helped us to understand the mechanism of state transitions in many biological processes.However,there are still some important issues that have not been resolved.(1)Studying the resilience of large-scale biomolecular networks which consist of thousands of biomolecules;(2)reconstructing potential function of large-scale biomolecular networks based on biological data;(3)control biomolecular network resilience.