Biology subjects

Rozum, J. C.

Publications and source records attributed to Rozum, J. C..

9 recordsLinked to original sources

bioRxiv2026

Cell State Transitions Drive Transcriptome-Proteome Alignment in a Minimal Photoautotroph

The cyanobacterium Prochlorococcus marinus MED4 is a minimal photoautotroph that maintains circadian regulation despite its reduced circadian regulatory toolkit relative to other cyanobacteria. Moreover, MED4, like other small prokaryotes, has low transcript counts per cell and poor transcriptome-proteome alignment. A systems-level understanding of how MED4 uses its minimal regulatory machinery to achieve a well-coordinated circadian cycle is currently lacking. We address this knowledge gap by analyzing global proteomics, transcriptomics, metabolomics, and lipidomics collected across a circadian day. Integrating these data with statistical analyses, enzyme-constrained metabolic modeling, and stochastic mathematical modeling allows us to reveal and characterize six major population-level cell states. Here we present evidence that high-light response regulator RpaB plays an important night-time regulatory role in MED4 by inhibiting RpaA, the master transcriptional activator of dusk genes and HimA, a regulatory target of RpaA previously shown to influence DNA conformation, is highly correlated with global transcriptome-proteome alignment (R2 = 0.67, p < 0.001). We construct stochastic differential equations consistent with genome-wide promotion of transcription by HimA as a plausible mechanism. Our HimA-driven model recapitulates alignment dynamics (R2 = 0.59, p < 0.01) as one plausible explanation. Collectively, our results elucidate how a minimal circadian regulatory program achieves robust coordination across transcriptional, translational, and metabolic levels

systems biology↗

bioRxiv2025

ProteoMeter: A pipeline for integrating multi-PTM and limited proteolysis data to reveal modification-structure coupling at the residue level

Systemic perturbations trigger extensive changes across the proteome-altering protein abundance, post-translational modifications (PTMs), conformational states, and complex assembly. Interpreting these effects demands computational pipelines capable of integrating diverse proteomics modalities, such as multi-PTM profiling, limited proteolysis mass spectrometry (LiP-MS), and cross-linking mass spectrometry (XL-MS), within a unified and interoperable framework. Because instrument data are quantified at the peptide level, mapping these measurements to individual residue or modification site is essential for biologically meaningful interpretation. We introduce ProteoMeter, an open-source Python library designed to integrate multi-modal proteomics datasets and map them to single-residue resolution using a standardized coordinate framework. We showcase its capabilities in a combined multi-PTM and LiP-MS analysis profiling the proteomic response to human coronavirus 229e (HCoV-229E) infection.ProteoMeteris actively maintained and is freely available-including all source code and figure-generation scripts-at the following repository: https://github.com/PNNL-Predictive-Phenomics/ProteoMeter.

systems biology↗

bioRxiv2024

Synergy and antagonism in a genome-scale model of metabolic hijacking by bacteriophage

Bacteriophage auxiliary metabolic genes (AMGs) alter host metabolism by hijacking reactions. Previous studies used functional annotations to infer AMG impacts but neglected propagation effects on global metabolism and phage production. We demonstrate the first integration of AMGs and phage assembly into a genome-scale metabolic model, using a general method applied to the infection of Prochloroccocus marinus MED4 by P-HM2. We experimentally validate our approach to predicting AMG impact on growth using cp12 mutations in Syne-chococcus elongatus. We predict that 17 directly hijacked reactions substantially impact over 30% of the metabolism, including carbon fixation, photosynthesis, and nucleotide synthesis. We find that indirect impacts are synergistically and antagonistically coupled and are either phage-aligned--shifting feasible reaction velocities in accordance with maximal phage production--or phage-antialigned. Pareto optimization reveals that phage-aligned reactions limit host growth, while phage-antialigned reactions do not. We provide systems-level insight into AMG perturbations, highlighting how nontrivial cascading effects shape microbial functions.

systems biology↗

bioRxiv2024

Mapping the attractor landscape of Boolean networks

Boolean networks are popular dynamical models of cellular processes in systems biology. Their attractors model phenotypes that arise from the interplay of key regulatory subcircuits. A succession diagram describes this interplay in a discrete analog of Waddingtons epigenetic attractor landscape that allows for fast identification of attractors and attractor control strategies. We present a new approach to succession diagram construction for asynchronously updated Boolean networks, implemented in the biologists Boolean attractor landscape mapper, biobalm, a Python 3 library. We compare the performance of biobalm to similar tools and find a substantial performance increase in succession diagram construction, attractor identification, and attractor control. To illustrate the utility of biobalm, we perform the most comprehensive comparative analysis to date of the succession diagram structure in experimentally-validated Boolean network models of cell processes and random ensembles. We find that random models (including critical Kauffman networks) have relatively small succession diagrams, indicating simple decision structures. In contrast, non-random models from the literature are enriched in extremely large succession diagrams, indicating an abundance of decision points in their dynamics and suggesting the presence of complex Waddington landscapes in nature.

systems biology↗

bioRxiv2023

From Years to Hours: Accelerating Model Refinement

Network-based dynamic modeling is useful for studying the responses of complex biomolecular systems to environmental changes and internal perturbations. In modeling signal transduction and other regulatory networks, it is common to integrate evidence from perturbation (e.g. gene knockout) - observation pairs, where the perturbed and observed variables may be distant in the network. For a model to capture these non-local effects, its construction, validation, and refinement necessarily involve trial and error, constrained by domain knowledge. We propose and implement a genetic algorithm-based workflow to streamline model refinement. This workflow applies to any biological system for which an interaction network and enough perturbation experiments exist. We implement our workflow for Boolean networks, which are a popular and successful tool for modeling biological systems. The algorithm we introduce adjusts the functions of the model to enhance agreement with a corpus of curated experimental results and leverages existing mechanistic knowledge to automatically limit the search space to biologically plausible models. To account for the interdependence of experimental results, we develop a hierarchical scoring technique for assessing model performance. Our implementation is available as the open-source Python library boolmore. We demonstrate boolmores effectiveness in a published plant signaling model that exemplifies the challenges of manual model construction and refinement. This model describes how plant stomata close in response to the drought hormone abscisic acid. After several hours of automatic refinement on a personal computer, the fittest models recapture and surpass the accuracy gain achieved over two years of manual revision. The refined models yield new, testable predictions, such as explanations for the role of reactive oxygen species in drought response. By automating the laborious task of model validation and refinement, this workflow is a step towards fast, fully automated, and reliable model construction.

systems biology↗

bioRxiv2023

Robustness of biomolecular networks suggests functional modules far from the edge of chaos

A common feature of complex systems is their ability to balance the flexibility needed to adapt to their environment with the rigidity required for robust function. It has been conjectured that living systems accomplish this by existing at the "edge of chaos", i.e., the critical boundary between ordered and disordered dynamics. Simple toy models of gene regulatory networks lend support to this idea, and mathematical tools developed for these toy models yield similar results when applied to experimentally-supported models of specific cellular regulatory mechanisms (functional modules). Here, however, we demonstrate that a deeper inspection of 72 experimentally-supported discrete dynamical models of functional modules reveals previously unobserved order in these systems on long time scales, suggesting greater rigidity in these systems than was previously conjectured. Our analysis relies on new measures that quantify the tendency of perturbations to spread through a discrete dynamical system. A benefit of our new approach is that it accounts for how system trajectories are mapped to phenotypes in practice. Because these measures are computationally expensive to estimate, existing tools were insufficient for the ensemble of models considered here. To simulate the tens of millions of trajectories required for convergence, we developed a multipurpose CUDA-based simulation tool, which we have made available as the open-source Python library cubewalkers. We find that in experimentally-supported models of biomolecular functional modules, perturbation propagation is more transitory than previously thought, and that even in cases where large perturbation cascades persist, their phenotypic effects are often minimal. Moreover, by examining the impact of update scheme on experimentally-supported models, we find evidence that stochasticity and desynchronization can lead to increased recovery from regulatory perturbation cascades in functional modules and uncover previously unreported population-level robustness to even timing perturbations in these systems. We identify specific biological mechanisms underlying these dynamical behaviors and highlight them in experimentally-supported regulatory networks from the systems biology literature. Based on novel measures and simulations, our results suggest that-contrary to current theory-functional modules of biological systems are ordered and far from the edge of chaos.

systems biology↗

bioRxiv2023

Inferring gene regulatory networks using transcriptional profiles as dynamical attractors

Genetic regulatory networks (GRNs) regulate the flow of genetic information from the genome to expressed messenger RNAs (mRNAs) and thus are critical to controlling the phenotypic characteristics of cells. Numerous methods exist for profiling mRNA transcript levels and identifying protein-DNA binding interactions at the genome-wide scale. These enable researchers to determine the structure and output of transcriptional regulatory networks, but uncovering the complete structure and regulatory logic of GRNs remains a challenge. The field of GRN inference aims to meet this challenge using computational modeling to derive the structure and logic of GRNs from experimental data and to encode this knowledge in Boolean networks, Bayesian networks, ordinary differential equation (ODE) models, or other modeling frameworks. However, most existing models do not incorporate dynamic transcriptional data since it has historically been less widely available in comparison to "static" transcriptional data. We report the development of an evolutionary algorithm-based ODE modeling approach that integrates kinetic transcription data and the theory of attractor matching to infer GRN architecture and regulatory logic. Our method outperformed six leading GRN inference methods, none of which incorporate kinetic transcriptional data, in predicting regulatory connections among TFs when applied to a small-scale engineered synthetic GRN in Saccharomyces cerevisiae. Moreover, we demonstrate the potential of our method to predict unknown transcriptional profiles that would be produced upon genetic perturbation of the GRN governing a two-state cellular phenotypic switch in Candida albicans. We established an iterative refinement strategy to facilitate candidate selection for experimentation; the experimental results in turn provide validation or improvement for the model. In this way, our GRN inference approach can expedite the development of a sophisticated mathematical model that can accurately describe the structure and dynamics of the in vivo GRN. Author SummaryThe establishment of distinct transcriptional programs, where specific sets of genes are activated or repressed, is fundamental to all forms of life. Sequence-specific DNA-binding proteins, often referred to as regulatory transcription factors, form interconnected gene regulatory networks (GRNs) which underlie the establishment and maintenance of specific transcriptional programs. Since their discovery, many modeling approaches have sought to understand the structure and regulatory behaviors of these GRNs. The field of GRN inference uses experimental measurements of transcript abundance to predict how regulatory transcription factors interact with their downstream target genes to establish specific transcriptional programs. However, most prior approaches have been limited by the exclusive use of "static" or steady-state measurements. We have developed a unique approach which incorporates dynamic transcriptional data into a sophisticated ordinary differential equation model to infer GRN structures that give rise to distinct transcriptional programs. Our model not only outperforms six other leading models, it also is capable of accurately predicting how changes in GRN structure will impact the resulting transcriptional programs. These unique attributes of our model, combined with "real world" experimental validation of our model predictions, represent a significant advance in the field of gene regulatory network inference.

systems biology↗

bioRxiv2019

A feedback loop of conditionally stable circuits drives the cell cycle from checkpoint to checkpoint

We perform logic-based network analysis on a model of the mammalian cell cycle. This model is composed of a Restriction Switch driving cell cycle commitment and a Phase Switch driving mitotic entry and exit. By generalizing the concept of stable motif, i.e., a self-sustaining positive feedback loop that maintains an associated state, we introduce the concept of conditionally stable motif, the stability of which is contingent on external conditions. We show that the stable motifs of the Phase Switch are contingent on the state of three nodes through which it receives input from the rest of the network. Biologically, these conditions correspond to cell cycle checkpoints. Holding these nodes locked (akin to a checkpoint-free cell) transforms the Phase Switch into an autonomous oscillator that robustly toggles through the cell cycle phases G1, G2 and mitosis. The conditionally stable motifs of the Phase Switch Oscillator are organized into an ordered sequence, such that they serially stabilize each other but also cause their own destabilization. Along the way they channel the dynamics of the module onto a narrow path in state space, lending robustness to the oscillation. Self-destabilizing conditionally stable motifs suggest a general negative feedback mechanism leading to sustained oscillations.

systems biology↗

bioRxiv2017

Identifying (un)controllable dynamical behavior with applications to biomolecular networks

We present a technique applicable in any dynamical framework to identify control-robust subsets of an interacting system. These robust subsystems, which we call stable modules, are characterized by constraints on the variables that make up the subsystem. They are robust in the sense that if the defining constraints are satisfied at a given time, they remain satisfied for all later times, regardless of what happens in the rest of the system, and can only be broken if the constrained variables are externally manipulated. We identify stable modules as graph structures in an expanded network, which represents causal links between variable constraints. A stable module represents a system \"decision point\", or trap subspace. Using the expanded network, small stable modules can be composed sequentially to form larger stable modules that describe dynamics on the system level. Collections of large, mutually exclusive stable modules describe the systems repertoire of long-term behaviors. We implement this technique in a broad class of dynamical systems and illustrate its practical utility via examples and algorithmic analysis of two published biological network models. In the segment polarity gene network of Drosophila melanogaster, we obtain a state-space visualization that reproduces by novel means the four possible cell fates and predicts the outcome of cell transplant experiments. In the T-cell signaling network, we identify six signaling elements that determine the high-signal response and show that control of an element connected to them cannot disrupt this response.\n\nAuthor summaryWe show how to uncover the causal relationships between qualitative statements about the values of variables in ODE systems. We then show how these relationships can be used to identify subsystem behaviors that are robust to outside interventions. This informs potential system control strategies (e.g., in identifying drug targets). Typical analytical properties of biomolecular systems render them particularly amenable to our techniques. Furthermore, due to their often high dimension and large uncertainties, our results are particularly useful in biomolecular systems. We apply our methods to two quantitative biological models: the segment polarity gene network of Drosophila melanogaster and the T-cell signal transduction network.

systems biology↗