本文選題：基因調(diào)控網(wǎng)絡(luò) + 優(yōu)化控制　； 參考：《南京航空航天大學(xué)》2017年碩士論文

【摘要】：基因調(diào)控網(wǎng)絡(luò)是一類基本且重要的生物網(wǎng)絡(luò),通過(guò)對(duì)其控制可以實(shí)現(xiàn)生物系統(tǒng)功能的調(diào)節(jié)。在系統(tǒng)生物學(xué)中,通過(guò)外部的干預(yù)控制構(gòu)造關(guān)于基因調(diào)控網(wǎng)絡(luò)的控制理論成為一個(gè)非常熱門(mén)的主題。該控制理論有助于發(fā)展未來(lái)的基因治療技術(shù)以及解決一些生命科學(xué)問(wèn)題。目前,布爾網(wǎng)絡(luò)和概率布爾網(wǎng)絡(luò)作為重要的網(wǎng)絡(luò)模型已經(jīng)被廣泛地用于基因調(diào)控網(wǎng)絡(luò)優(yōu)化控制問(wèn)題的研究中�；蛘{(diào)控網(wǎng)絡(luò)的優(yōu)化控制問(wèn)題是指尋找有效的控制策略使得網(wǎng)絡(luò)按照期望的方式演變,同時(shí)最小化控制成本。由于采取不同的控制時(shí)長(zhǎng),優(yōu)化控制問(wèn)題可以分為有限范圍優(yōu)化控制問(wèn)題和無(wú)限范圍優(yōu)化控制問(wèn)題。根據(jù)兩類優(yōu)化控制問(wèn)題的定義,本文提出了以概率模型檢測(cè)技術(shù)為基礎(chǔ)的解決方法。針對(duì)有限范圍的優(yōu)化控制問(wèn)題,本文提出了一種以概率模型檢測(cè)為基礎(chǔ)求解最優(yōu)期望總成本以及相應(yīng)優(yōu)化控制策略的方法。首先,該方法使用概率模型檢測(cè)器PRISM提供的建模語(yǔ)言描述在具有控制輸入情況下帶有干擾且上下文相關(guān)的概率布爾網(wǎng)絡(luò)。然后,歸約優(yōu)化控制中定義的最優(yōu)期望總成本為馬爾科決策過(guò)程上的最小可達(dá)性回報(bào)。最后,在PRISM中,reward結(jié)構(gòu)用于描述優(yōu)化控制中的定量信息,最小可達(dá)性回報(bào)屬性使用概率計(jì)算樹(shù)邏輯公式表達(dá)并且通過(guò)模型檢測(cè)算法實(shí)現(xiàn)自動(dòng)求解。獲取到的有限范圍優(yōu)化控制策略能夠有效地改變網(wǎng)絡(luò)的短期運(yùn)行行為。提出的方法被應(yīng)用到細(xì)胞凋亡網(wǎng)絡(luò)和WNT5A網(wǎng)絡(luò)中。實(shí)驗(yàn)結(jié)果證明了方法的正確性和有效性。進(jìn)一步,本文還考慮了使用該方法求解帶有硬性約束條件的有限范圍優(yōu)化控制問(wèn)題,并在實(shí)驗(yàn)中證明了方法的靈活性。針對(duì)無(wú)限范圍的優(yōu)化控制問(wèn)題,本文提出了一種以遺傳算法和概率模型檢測(cè)器PRISM相結(jié)合的方式求解最優(yōu)期望總成本以及相應(yīng)優(yōu)化控制策略的方法。無(wú)限范圍控制策略是一種固定控制策略,即不依賴控制時(shí)間,確定在每個(gè)網(wǎng)絡(luò)狀態(tài)上的控制決策。首先,該方法歸約無(wú)限范圍控制中定義的期望總成本為離散時(shí)間馬爾科夫鏈上的平穩(wěn)狀態(tài)回報(bào)。其次,構(gòu)建包含固定控制策略的帶有干擾且上下文相關(guān)概率布爾網(wǎng)絡(luò)的PRISM模型。然后,為了求解無(wú)限范圍優(yōu)化控制問(wèn)題,固定控制策略被編碼為遺傳算法解空間中的一個(gè)個(gè)體。個(gè)體的適應(yīng)度值在PRISM中被計(jì)算。最后,遺傳算法在解空間上迭代地執(zhí)行遺傳操作從而獲取最優(yōu)解。獲取到的無(wú)限范圍優(yōu)化控制策略能夠有效地改變網(wǎng)絡(luò)長(zhǎng)期運(yùn)行行為。提出的方法被應(yīng)用到WNT5A網(wǎng)絡(luò)中。實(shí)驗(yàn)結(jié)果證明了方法的正確性和有效性。
[Abstract]:Gene regulation network is a kind of basic and important biological network, which can regulate the function of biological system by controlling it. In systems biology, it is a hot topic to construct the control theory of gene regulation network through external intervention control. The control theory can help to develop future gene therapy technology and solve some life science problems. At present, Boolean networks and probabilistic Boolean networks, as important network models, have been widely used in the study of genetic regulation network optimization control problem. The optimal control problem of genetic control network is to find an effective control strategy to make the network evolve in the desired way while minimizing the control cost. Because of the different control time, the optimal control problem can be divided into finite optimal control problem and infinite optimal control problem. According to the definition of two kinds of optimal control problems, this paper presents a method based on probabilistic model detection technology. For the problem of finite range optimal control, this paper presents a method to solve the optimal expected total cost and corresponding optimal control strategy based on probabilistic model detection. Firstly, the probabilistic Boolean network with interference and context is described by using the modeling language provided by the probabilistic model detector (PRISM). Then, the optimal expected total cost defined in reductive optimization control is the minimum reachability return in the Markov decision process. Finally, in PRISM, the reward structure is used to describe the quantitative information in the optimal control. The minimum reachability return attribute is expressed by the probabilistic computational tree logic formula and solved automatically by the model checking algorithm. The obtained finite range optimal control strategy can effectively change the short-term operation behavior of the network. The proposed method has been applied to apoptotic networks and WNT5A networks. The experimental results show that the method is correct and effective. Furthermore, this paper also considers the use of this method to solve the finite range optimal control problem with rigid constraints, and proves the flexibility of the method in experiments. To solve the problem of infinite optimal control, this paper presents a method to solve the optimal expected total cost and corresponding optimal control strategy by combining genetic algorithm and probabilistic model detector (PRISM). Infinite range control strategy is a kind of fixed control strategy, which is independent of control time and determines the control decision in each network state. Firstly, the expected total cost defined in the reduced infinite range control is the stationary state return on the discrete time Markov chain. Secondly, a PRISM model with interference and context-dependent probabilistic Boolean networks with fixed control strategies is constructed. Then, in order to solve the infinite optimal control problem, the fixed control strategy is encoded as an individual in the solution space of genetic algorithm. The fitness of individuals is calculated in PRISM. Finally, the genetic algorithm iteratively performs genetic operations on the solution space to obtain the optimal solution. The obtained infinite range optimal control strategy can effectively change the long-term operation behavior of the network. The proposed method is applied to WNT5A network. The experimental results show that the method is correct and effective.
【學(xué)位授予單位】：南京航空航天大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：Q811.4

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本文編號(hào)：2067283