Olariu Victor, Manesso Erica, Peterson Carsten
Computational Biology and Biological Physics, Department of Astronomy and Theoretical Physics, Lund University, Lund 22362, Sweden.
Center for Models of Life, Niels Bohr Institute, University of Copenhagen, Copenhagen 2100, Denmark.
R Soc Open Sci. 2017 Jun 7;4(6):160765. doi: 10.1098/rsos.160765. eCollection 2017 Jun.
Depicting developmental processes as movements in free energy genetic landscapes is an illustrative tool. However, exploring such landscapes to obtain quantitative or even qualitative predictions is hampered by the lack of free energy functions corresponding to the biochemical Michaelis-Menten or Hill rate equations for the dynamics. Being armed with energy landscapes defined by a network and its interactions would open up the possibility of swiftly identifying cell states and computing optimal paths, including those of cell reprogramming, thereby avoiding exhaustive trial-and-error simulations with rate equations for different parameter sets. It turns out that sigmoidal rate equations do have approximate free energy associations. With this replacement of rate equations, we develop a deterministic method for estimating the free energy surfaces of systems of interacting genes at different noise levels or temperatures. Once such free energy landscape estimates have been established, we adapt a shortest path algorithm to determine optimal routes in the landscapes. We explore the method on three circuits for haematopoiesis and embryonic stem cell development for commitment and reprogramming scenarios and illustrate how the method can be used to determine sequential steps for onsets of external factors, essential for efficient reprogramming.
将发育过程描绘为自由能遗传景观中的运动是一种具有启发性的工具。然而，由于缺乏与动力学的生化米氏或希尔速率方程相对应的自由能函数，探索此类景观以获得定量甚至定性预测受到了阻碍。拥有由网络及其相互作用定义的能量景观将开启迅速识别细胞状态并计算最优路径的可能性，包括细胞重编程的路径，从而避免针对不同参数集使用速率方程进行详尽的试错模拟。事实证明，S形速率方程确实具有近似的自由能关联。通过这种速率方程的替代，我们开发了一种确定性方法，用于估计不同噪声水平或温度下相互作用基因系统的自由能表面。一旦建立了此类自由能景观估计，我们就采用最短路径算法来确定景观中的最优路线。我们在造血和胚胎干细胞发育的三个回路中探索该方法，用于分化和重编程场景，并说明该方法如何用于确定外部因素起始的顺序步骤，这对于高效重编程至关重要。
