Modeling Evolution of Crosstalk in Noisy Signal Transduction Networks

Signal transduction networks can form highly interconnected systems within cells due to network crosstalk, the sharing of input signals between multiple downstream responses. To better understand the evolutionary design principles underlying such networks, we study the evolution of crosstalk and the emergence of specificity for two parallel signaling pathways that arise via gene duplication and are subsequently allowed to diverge. We focus on a sequence based evolutionary algorithm and evolve the network based on two physically motivated fitness functions related to information transmission. Surprisingly, we find that the two fitness functions lead to very different evolutionary outcomes, one with a high degree of crosstalk and the other without.Comment: 18 Pages, 16 Figure

Biophysical models of cis-regulation as interpretable neural networks

Abstract The adoption of deep learning techniques in genomics has been hindered by the difficulty of mechanistically interpreting the models that these techniques produce. In recent years, a variety of post-hoc attribution methods have been proposed for addressing this neural network interpretability problem in the context of gene regulation. Here we describe a complementary way of approaching this problem. Our strategy is based on the observation that two large classes of biophysical models of cis-regulatory mechanisms can be expressed as deep neural networks in which nodes and weights have explicit physiochemical interpretations. We also demonstrate how such biophysical networks can be rapidly inferred, using modern deep learning frameworks, from the data produced by certain types of massively parallel reporter assays (MPRAs). These results suggest a scalable strategy for using MPRAs to systematically characterize the biophysical basis of gene regulation in a wide range of biological contexts. They also highlight gene regulation as a promising venue for the development of scientifically interpretable approaches to deep learning

MAVE-NN: learning genotype-phenotype maps from multiplex assays of variant effect

Multiplex assays of variant effect (MAVEs), which include massively parallel reporter assays (MPRAs) and deep mutational scanning (DMS) experiments, are being rapidly adopted in many areas of biology. However, inferring quantitative models of genotype-phenotype (G-P) maps from MAVE data remains challenging, and different inference approaches have been advocated in different MAVE contexts. Here we introduce a conceptually unified approach to the problem of learning G-P maps from MAVE data. Our strategy is grounded in concepts from information theory, and is based on the view of G-P maps as a form of information compression. We also introduce MAVE-NN, a Python package that implements this approach using a neural network backend. The capabilities and advantages of MAVE-NN are then demonstrated on three diverse DMS and MPRA datasets. MAVE-NN thus fills a major need in the computational analysis of MAVE data. Installation instructions, tutorials, and documentation are provided at https://mavenn.readthedocs.io

MAVE-NN: learning genotype-phenotype maps from multiplex assays of variant effect

Multiplex assays of variant effect (MAVEs) are a family of methods that includes deep mutational scanning experiments on proteins and massively parallel reporter assays on gene regulatory sequences. Despite their increasing popularity, a general strategy for inferring quantitative models of genotype-phenotype maps from MAVE data is lacking. Here we introduce MAVE-NN, a neural-network-based Python package that implements a broadly applicable information-theoretic framework for learning genotype-phenotype maps-including biophysically interpretable models-from MAVE datasets. We demonstrate MAVE-NN in multiple biological contexts, and highlight the ability of our approach to deconvolve mutational effects from otherwise confounding experimental nonlinearities and noise

Structural and mechanistic basis of σ-dependent transcriptional pausing

In σ-dependent transcriptional pausing, the transcription initiation factor σ, translocating with RNA polymerase (RNAP), makes sequence-specific protein-DNA interactions with a promoter-like sequence element in the transcribed region, inducing pausing. It has been proposed that, in σ-dependent pausing, the RNAP active center can access off-pathway “backtracked” states that are substrates for the transcript-cleavage factors of the Gre family, and on-pathway “scrunched” states that mediate pause escape. Here, using site-specific protein-DNA photocrosslinking to define positions of the RNAP trailing and leading edges and of σ relative to DNA at the λPR’ promoter, we show directly that σ-dependent pausing in the absence of GreB in vitro predominantly involves a state backtracked by 2-4 bp, and that σ-dependent pausing in the presence of GreB in vitro and in vivo predominantly involves a state scrunched by 2-3 bp. Analogous experiments with a library of 47 (∼16,000) transcribed-region sequences show that the state scrunched by 2-3 bp--and only that state--is associated with the consensus sequence, T-3N-2Y-1G+1, (where -1 corresponds to the position of the RNA 3’ end), which is identical to the consensus for pausing in initial transcription, and which is related to the consensus for pausing in transcription elongation. Experiments with heteroduplex templates show that sequence information at position T-3 resides in the DNA nontemplate strand. A cryo-EM structure of a complex engaged in σ-dependent pausing reveals positions of DNA scrunching on the DNA nontemplate and template strands and suggests that position T-3 of the consensus sequence exerts its effects by facilitating scrunching

Asymmetry between Activators and Deactivators in Functional Protein Networks.

Are "turn-on" and "turn-off" functions in protein-protein interaction networks exact opposites of each other? To answer this question, we implement a minimal model for the evolution of functional protein-interaction networks using a sequence-based mutational algorithm, and apply the model to study neutral drift in networks that yield oscillatory dynamics. We study the roles of activators and deactivators, two core components of oscillatory protein interaction networks, and find a striking asymmetry in the roles of activating and deactivating proteins, where activating proteins tend to be synergistic and deactivating proteins tend to be competitive