Structure-based approach to identifying small sets of driver nodes in biological networks

Cell reprogramming design by transfer learning of functional transcriptional networks

Recent developments in synthetic biology, next-generation sequencing, and machine learning provide an unprecedented opportunity to rationally design new disease treatments based on measured responses to gene perturbations and drugs to reprogram cells. The main challenges to seizing this opportunity are the incomplete knowledge of the cellular network and the combinatorial explosion of possible interventions, both of which are insurmountable by experiments. To address these challenges, we develop a transfer learning approach to control cell behavior that is pre-trained on transcriptomic data associated with human cell fates, thereby generating a model of the network dynamics that can be transferred to specific reprogramming goals. The approach combines transcriptional responses to gene perturbations to minimize the difference between a given pair of initial and target transcriptional states. We demonstrate our approach's versatility by applying it to a microarray dataset comprising >9,000 microarrays across 54 cell types and 227 unique perturbations, and an RNASeq dataset consisting of >10,000 sequencing runs across 36 cell types and 138 perturbations. Our approach reproduces known reprogramming protocols with an AUROC of 0.91 while innovating over existing methods by pre-training an adaptable model that can be tailored to specific reprogramming transitions. We show that the number of gene perturbations required to steer from one fate to another increases with decreasing developmental relatedness and that fewer genes are needed to progress along developmental paths than to regress. These findings establish a proof-of-concept for our approach to computationally design control strategies and provide insights into how gene regulatory networks govern phenotype.

Dynamic Boolean modeling of molecular and cellular interactions in psoriasis predicts drug target candidates

Psoriasis arises from complex interactions between keratinocytes and immune cells, leading to uncontrolled inflammation, immune hyperactivation, and a perturbed keratinocyte life cycle. Despite the availability of drugs for psoriasis management, the disease remains incurable. Treatment response variability calls for new tools and approaches to comprehend the mechanisms underlying disease development. We present a Boolean multiscale population model that captures the dynamics of cell-specific phenotypes in psoriasis, integrating discrete logical formalism and population dynamics simulations. Through simulations and network analysis, the model predictions suggest that targeting neutrophil activation in conjunction with inhibition of either prostaglandin E2 (PGE2) or STAT3 shows promise comparable to interleukin-17 (IL-17) inhibition, one of the most effective treatment options for moderate and severe cases. Our findings underscore the significance of considering complex intercellular interactions and intracellular signaling in psoriasis and highlight the importance of computational approaches in unraveling complex biological systems for drug target identification.

Statistical control of structural networks with limited interventions to minimize cellular phenotypic diversity represented by point attractors

The underlying genetic networks of cells give rise to diverse behaviors known as phenotypes. Control of this cellular phenotypic diversity (CPD) may reveal key targets that govern differentiation during development or drug resistance in cancer. This work establishes an approach to control CPD that encompasses practical constraints, including model limitations, the number of simultaneous control targets, which targets are viable for control, and the granularity of control. Cellular networks are often limited to the structure of interactions, due to the practical difficulty of modeling interaction dynamics. However, these dynamics are essential to CPD. In response, our statistical control approach infers the CPD directly from the structure of a network, by considering an ensemble average function over all possible Boolean dynamics for each node in the network. These ensemble average functions are combined with an acyclic form of the network to infer the number of point attractors. Our approach is applied to several known biological models and shown to outperform existing approaches. Statistical control of CPD offers a new avenue to contend with systemic processes such as differentiation and cancer, despite practical limitations in the field.

Effective Connectivity and Bias Entropy Improve Prediction of Dynamical Regime in Automata Networks

Biomolecular network dynamics are thought to operate near the critical boundary between ordered and disordered regimes, where large perturbations to a small set of elements neither die out nor spread on average. A biomolecular automaton (e.g., gene, protein) typically has high regulatory redundancy, where small subsets of regulators determine activation via collective canalization. Previous work has shown that effective connectivity, a measure of collective canalization, leads to improved dynamical regime prediction for homogeneous automata networks. We expand this by (i) studying random Boolean networks (RBNs) with heterogeneous in-degree distributions, (ii) considering additional experimentally validated automata network models of biomolecular processes, and (iii) considering new measures of heterogeneity in automata network logic. We found that effective connectivity improves dynamical regime prediction in the models considered; in RBNs, combining effective connectivity with bias entropy further improves the prediction. Our work yields a new understanding of criticality in biomolecular networks that accounts for collective canalization, redundancy, and heterogeneity in the connectivity and logic of their automata models. The strong link we demonstrate between criticality and regulatory redundancy provides a means to modulate the dynamical regime of biochemical networks.

Finding influential edges in multilayer networks: Perspective from multilayer diffusion model

With the popularization of social network analysis, information diffusion models have a wide range of applications, such as viral marketing, publishing predictions, and social recommendations. The emergence of multiplex social networks has greatly enriched our daily life; meanwhile, identifying influential edges remains a significant challenge. The key problem lies that the edges of the same nodes are heterogeneous at different layers of the network. To solve this problem, we first develop a general information diffusion model based on the adjacency tensor for the multiplex network and show that the n-mode singular value can control the level of information diffusion. Then, to explain the suppression of information diffusion through edge deletion, efficient edge eigenvector centrality is proposed to identify the influence of heterogeneous edges. The numerical results from synthetic networks and real-world multiplex networks show that the proposed strategy outperforms some existing edge centrality measures. We devise an experimental strategy to demonstrate that influential heterogeneous edges can be successfully identified by considering the network layer centrality, and the deletion of top edges can significantly reduce the diffusion range of information across multiplex networks.