Recent experimental evidence strongly supports that three-dimensional (3D) long-range enhancer-promoter (E-P) interactions have important influences on gene-expression dynamics, but it is unclear how the interaction information is translated into gene expression over time (4D). To address this question, we developed a general theoretical framework (named as a 4D nucleome equation), which integrates E-P interactions on chromatin and biochemical reactions of gene transcription. With this equation, we first present the distribution of mRNA counts as a function of the E-P genomic distance and then reveal a power-law scaling of the expression level in this distance. Interestingly, we find that long-range E-P interactions can induce bimodal and trimodal mRNA distributions. The 4D nucleome equation also allows for model selection and parameter inference. When this equation is applied to the mouse embryonic stem cell smRNA-FISH data and the E-P genomic-distance data, the predicted E-P contact probability and mRNA distribution are in good agreement with experimental results. Further statistical inference indicates that the E-P interactions prefer to modulate the mRNA level by controlling promoter activation and transcription initiation rates. Our model and results provide quantitative insights into both spatiotemporal gene-expression determinants (i.e., long-range E-P interactions) and cellular fates during development.

Occurrence and development of cancers are governed by complex networks of interacting intercellular and intracellular signals. The technology of single-cell RNA sequencing (scRNA-seq) provides an unprecedented opportunity for dissecting the interplay between the cancer cells and the associated microenvironment. Here we combined scRNA-seq data with clinical bulk gene expression data to develop a computational pipeline for identifying the prognostic and predictive signature that connects cancer cells and microenvironmental cells. The pipeline was applied to glioma scRNA-seq data and revealed a tumor-associated microglia/macrophage-mediated EGFR/ERBB2 feedback-crosstalk signaling module, which was defined as a multilayer network biomarker (MNB) to predict survival outcome and therapeutic response of glioma patients. We used publicly available clinical data sets from large cohorts of glioma patients to examine the prognostic significance and predictive accuracy of the MNB, which outperformed conventional gene biomarkers and other methods. Additionally, the MNB was found to be predictive of the sensitivity or resistance of glioma patients to molecularly targeted therapeutics. Moreover, the MNB was an independent and the strongest prognostic factor when adjusted for clinicopathologic risk factors and other existing gene signatures. The robustness of the MNB was further tested on additional data sets. Our study presents a promising scRNA-seq transcriptome-based multilayer network approach to elucidate the interactions between tumor cell and tumor-associated microenvironment and to identify prognostic and predictive signatures of cancer patients. The proposed MNB method may facilitate the design of more effective biomarkers for predicting prognosis and therapeutic resistance of cancer patients.

According to recent experimental evidence, the interaction between chromatin loops, which can be characterized by three factors-connection pattern, distance between regulatory elements, and communication form, play an important role in determining the level of cell-to-cell variability in gene expression. These quantitative experiments call for a corresponding modeling effect that addresses the question of how changes in these factors affect variability at the expression level in a systematic rather than case-by-case fashion. Here we make such an effort, based on a mechanic model that maps three fundamental patterns for two interacting DNA loops into a 4-state model of stochastic transcription. We first show that in contrast to side-by-side loops, nested loops enhance mRNA expression and reduce expression noise whereas alternating loops have just opposite effects. Then, we compare effects of facilitated tracking and direct looping on gene expression. We find that the former performs better than the latter in controlling mean expression and in tuning expression noise, but this control or tuning is distance-dependent, remarkable for moderate loop lengths, and there is a limit loop length such that the difference in effect between two communication forms almost disappears. Our analysis and results justify the facilitated chromatin-looping hypothesis.

MOTIVATION: All cell components exhibit intracellular noise on account of random births and deaths of individual molecules, and extracellular noise because of environment perturbations. Gene regulation in particular, is an inherently noisy process with transcriptional control, alternative splicing, translation, diffusion and chemical modification reactions, all of which involve stochastic fluctuations. Such stochastic noises may not only affect the dynamics of the entire system but may also be exploited by living organisms to actively facilitate certain functions, such as cooperative behavior and communication. RESULTS: We have provided a general model and an analytic tool to examine the cooperative behavior of a multicell system with both intracellular and extracellular stochastic fluctuations. A multicell system with a synthetic gene network is adopted to demonstrate the effects of noises and coupling on collective dynamics. These results establish not only a theoretical foundation but also a quantitative basis for understanding essential roles of noises on cooperative dynamics, such as synchronization and communication among cells.

MOTIVATION: Protein structure alignment is one of the most important computational problems in molecular biology and plays a key role in protein structure prediction, fold family classification, motif finding, phylogenetic tree reconstruction and so on. From the viewpoint of computational complexity, a pairwise structure alignment is also a NP-hard problem, in contrast to the polynomial time algorithm for a pairwise sequence alignment. RESULTS: We propose a method for solving the structure alignment problem in an accurate manner at the amino acid level, based on a mean field annealing technique. We define the structure alignment as a mixed integer-programming (MIP) problem. By avoiding complicated combinatorial computation and exploiting the special structure of the continuous partial problem, we transform the MIP into a reduced non-linear continuous optimization problem (NCOP) with a much simpler form. To optimize the reduced NCOP, a mean field annealing procedure is adopted with a modified Potts model, whose solution is generally identical to that of the MIP. There is no 'soft constraint' in our mean field model and all constraints are automatically satisfied throughout the annealing process, thereby not only making the optimization more efficient but also eliminating many unnecessary parameters that depend on problems and usually require careful tuning. A number of benchmark examples are tested by the proposed method with comparisons to several existing approaches.