Biology subjects

Reinitz, J.

Publications and source records attributed to Reinitz, J..

5 recordsLinked to original sources

bioRxiv2025

Characterizing the regulatory logic of transcriptionalcontrol at the DNA sequence level by ensembles ofthermodynamic models

Understanding how the genome encodes the regulatory logics of transcription is a main challenge of the post-genomic era to be overcome with the aid of customized computational tools. We report an automatic framework for analyzing an ensemble of fittings to data of a thermodynamics-based sequence-level model for transcriptional regulation. The fittings are clustered accordingly with their intrinsic regulatory logics. A multiscale analysis enables visualization of quantitative features resulting from the deconvolution of the regulatory profile provided by multiple transcription factors interacting with the locus of a gene. Quantitative experimental data on reporters driven by the whole locus of the even-skipped gene in blastoderm of Drosophila embryos was used for validating our approach. A few clusters of highly active DNA binding sites within the enhancers collectively modulate even-skipped gene transcription. Analysis of variable enhancers length shows the importance of bound protein-protein interactions for transcriptional regulation.

systems biology↗

bioRxiv2019

An in silico analysis of robust but fragile gene regulation links enhancer length to robustness

1Organisms must ensure that expression of genes is directed to the appropriate tissues at the correct times, while simultaneously ensuring that these gene regulatory systems are robust to perturbation. This idea is captured by a mathematical concept called r-robustness, which says that a system is robust to a perturbation in up to r - 1 randomly chosen parameters. In this work we use this idea to investigate the robustness of gene regulation using a sequence level model of the Drosophila melanogaster gene even-skipped. We find that gene regulation can be remarkably sensitive to changes in transcription factor concentrations at the boundaries of expression features, while it is robust to perturbation elsewhere. We also find that the length of sequence used to control an expression feature correlates negatively with the number of nucleotides that are sensitive to mutation in both natural and in silico predicted enhancers. In all cases, the exact degree of robustness obtained is dependent not only on DNA sequence, but also on the local concentration of regulatory factors. By analyzing both natural and synthetic sequences, we provide strong quantitative evidence that increased sequence length makes gene regulatory systems more robust to genetic perturbation.\n\n2 Author SummaryRobustness assures that organisms can survive when faced with unpredictable environments or genetic mutations. In this work, we characterize the robustness of gene regulation using an experimentally validated model of the regulation of the Drosophila gene even-skipped. We use a mathematically precise definition of robustness that allows us to make quantitative comparisons of robustness between different genetic sequences or between different nuclei. From this analysis, we found that genetic sequences that were not previously known to be important for gene regulation reduce sensitivity to genetic perturbation. In contrast, we found that gene regulation can be very sensitive to the levels of regulators. This extreme sensitivity was only observed at the boundaries of expression features, where switch-like behavior is desirable. This highlights the importance of considering context when assessing robustness.

systems biology↗

bioRxiv2019

Fully Interpretable Deep Learning Model of Transcriptional Control

The universal expressibility assumption of Deep Neural Networks (DNNs) is the key motivation behind recent work in the system biology community to employ DNNs to solve important problems in functional genomics and molecular genetics. Because of the black box nature of DNNs, such assumptions, while useful in practice, are unsatisfactory for scientific analysis. In this paper, we give an example of a DNN in which every layer is interpretable. Moreover, this DNN is biologically validated and predictive. We derive our DNN from a systems biology model that was not previously recognized as having a DNN structure. This DNN is concerned with a key unsolved biological problem, which is to understand the DNA regulatory code which controls how genes in multicellular organisms are turned on and off. Although we apply our DNN to data from the early embryo of the fruit fly Drosophila, this system serves as a testbed for analysis of much larger data sets obtained by systems biology studies on a genomic scale.

systems biology↗

bioRxiv2018

The relationship between robustness and evolution

Canalization involves mutational robustness, the lack of phenotypic change as a result of genetic mutations. Given the large divergence in phenotype across species, understanding the relationship between high robustness and evolvability has been of interest to both theorists and experimentalists. Although canalization was originally proposed in the context of multicellular organisms, the effect of multicellularity on evolvability has not been considered by theoreticians. We address this issue using a Boolean population model with explicit representation of an environment in which multicellular individuals with explicit genotype and phenotype evolve. Robustness is described by a single real number between zero and one. We find that high robustness is favored in constant environments, and lower robustness is favored after environmental change. Multicellularity severely constrains robustness: peak evolvability occurs at an absolute level of robustness of about 0.99 compared with values of about 0.5 in a classical neutral network model. Multicellularity results in a sharp peak of evolvability in which the maximum is set by the fact that the fixation of adaptive mutations becomes more improbable as robustness decreases. When robustness is put under genetic control, robustness levels leading to maximum evolvability are selected for, but maximal relative fitness appears to require recombination.

evolutionary biology↗

bioRxiv2017

Bursting on a two states stochastic model for gene transcription in Drosophila embryos

Recent experimental data on the transcription dynamics of eve gene stripe two formation of Drosophila melanogaster embryos occurs in bursts of multiple sizes and durations. That has motivated the proposition of a transcription model having multiple ON states for the promoter of the eve gene each of them characterized by different synthesis rate. To understand the role of multiple ON states on gene transcription we approach the exact solutions for a two state stochastic model for gene transcription in D. melanogaster embryos and derive its bursting limit. Simulations based on the Gillespie algorithm at the bursting limit show the occurrence of bursts of multiple sizes and durations. Based on our theoretical approach, we interpret the aforementioned experimental data as a demonstration of the intrinsic stochasticity of the transcriptional processes in fruit fly embryos. Then, we conceive the experimental arrangement to determine when gene transcription has multiple ON promoter state in a noisy environment.

developmental biology↗