Networks of motifs from sequences of symbols

反应性核酸系统中序列基序动力学的贝叶斯速率推断

Bayesian Rate Inference for Sequence Motif Dynamics in Systems of Reactive Nucleic Acids

提出贝叶斯推断框架，从链反应器模拟的连接计数数据中推断基序速率方程参数，为匹配简化模型与复杂模拟提供方法，并迈向从实验数据直接推断反应速率常数。

MoRSAIK：基于 Python 的 RNA 反应器模拟、分析与推断工具包

MoRSAIK: Sequence Motif Reactor Simulation, Analysis and Inference Kit in Python

MoRSAIK 是一个基于 Python 的工具包，用于模拟、分析和推断 RNA 反应器中的序列 motif 动态，通过平均场近似和贝叶斯方法进行参数推断和结果分析。

Local graph alignment and motif search in biological networks

Interaction networks are of central importance in post-genomic molecular biology, with increasing amounts of data becoming available by high-throughput methods. Examples are gene regulatory networks or protein interaction maps. The main challenge in the analysis of these data is to read off biological functions from the topology of the network. Topological motifs, i.e., patterns occurring repeatedly at different positions in the network have recently been identified as basic modules of molecular information processing. In this paper, we discuss motifs derived from families of mutually similar …

Edge usage, motifs and regulatory logic for cell cycling genetic networks

The cell cycle is a tightly controlled process, yet its underlying genetic network shows marked differences across species. Which of the associated structural features follow solely from the ability to impose the appropriate gene expression patterns? We tackle this question in silico by examining the ensemble of all regulatory networks which satisfy the constraint of producing a given sequence of gene expressions. We focus on three cell cycle profiles coming from baker's yeast, fission yeast and mammals. First, we show that the networks in each of the ensembles use just a few interactions that…

Subgraph Ensembles and Motif Discovery Using a New Heuristic for Graph Isomorphism

A new heuristic based on vertex invariants is developed to rapidly distinguish non-isomorphic graphs to a desired level of accuracy. The method is applied to sample subgraphs from an E.coli protein interaction network, and as a probe for discovery of extended motifs. The network's structure is described using statistical properties of its $N$-node subgraphs for $N\leq 14$. The Zipf plots for subgraph occurrences are robust power laws that do not change when rewiring the network while fixing the degree sequence -- although the specific subgraphs may exchange ranks. However the exponent depends …