Biology subjects

Lopez, C. F.

Publications and source records attributed to Lopez, C. F..

At least 19 recordsLinked to original sources

bioRxiv2026

Folding-Driven Control of the Functional PP1 Complex via Multiscale Modeling

Understanding how conformational dynamics regulate protein complex assembly remains a central challenge in molecular systems biology. Here, we develop a multiscale modeling framework that integrates all-atom molecular dynamics (MD) simulations with rule-based kinetic modeling to investigate the formation of the PP1-GADD34-eIF2 complex, a critical regulator of the integrated stress response. Using umbrella sampling, we compute the potential of mean force (PMF) for key interactions, revealing strong thermodynamic driving forces for eIF2 binding ({Delta}G = -89.5 kJ/mol, KD {approx} 0.07 fM) and moderate affinity for PP1-GADD34 association ({Delta}G = -40.7 kJ/mol, KD {approx} 0.138 M). These energetics inform a PySB-based model that incorporates GADD34's folding state as a continuous variable ({varphi}), linking conformational transitions to holoenzyme assembly and activity. Simulations show that catalytic efficiency is maximized when GADD34 folding free energy is 20 kJ/mol and {varphi} = 0.9, reflecting a highly ordered state stabilized by actin and PP1 binding. Comparative analysis of energy-constrained and optimized parameter regimes reveals that folding-dependent assembly enhances both eIF2 dephosphorylation and information transmission, with channel capacities increasing from <0.002 bits to >5.5 bits. These results demonstrate how conformational regulation encodes system-level function and underscore the utility of multiscale models in bridging molecular energetics with dynamic biochemical control.

biophysics↗

bioRxiv2026

Deep learning of dynamic signatures resolves mechanistic ambiguity in complex signaling networks

Sparse experimental data often yields vast mechanistic hypothesis spaces with numerous equally probable models. Traditional model selection metrics like the Akaike Information Criterion fall short because they reduce models nonlinear dynamics to a scalar score that masks crucial mechanistic details. Here, we introduce an AI-driven framework treating model dynamics as learnable signatures. Using deep learning autoencoders, we embed the dynamic signatures of thousands of competing models into a low-dimensional latent space. Iterative clustering and physiological constraints systematically refine this space to a manageable number of testable hypotheses. Applying this to the Integrated Stress Response, a fundamental cellular homeostasis mechanism, we converged on 12 compelling mechanistic hypotheses from over 12,000 candidates. Notably, this framework confidently rejected structure-derived kinetic assumptions regarding higher-order PKR activation that traditional metrics could not resolve. Ultimately, this method provides a rigorous, full-state alternative to scalar metrics, accelerating the discovery of driving principles in complex biological systems.

systems biology↗

bioRxiv2026

A Physics-based Model Reveals Mechanisms of Epigenetic Memory

In multi-cellular eukaryotic organisms, cell type and specific functional identity are defined by the epigenetic patterning of chemical modifications to DNA and chromatin that modulate the expression and silencing of specific genes. When a cell divides, histones containing important epigenetic marks are distributed between the two daughter strands leading to a temporary dilution of epigenetic information and cell identity. In this work we introduce a physics-based model of epigenetic memory that explains how cells restore and maintain H3K9me3 and H3K27me3 histone methylation patterning after cell division. We demonstrate that emergence and maintenance of the epigenetic program is driven by an evolved mechanism that makes use of the biophysics of polymers, phase condensates and enzymatic activity. We validate our model via genome-wide epigenetic time-course simulation and comparison to experimental epigenetic data from multiple donors, multiple cell types, and for multiple epigenetic marks. Finally, we use our model as a conceptual framework to understand cellular reprogramming by hypothesizing that these processes first contend with and later utilize somatic epigenetic maintenance programs.

biophysics↗

bioRxiv2025

Intrinsic Molecular Timers and a Biphasic Amplitude Limit Regulate the Integrated Stress Response

The Integrated Stress Response (ISR) is an evolutionarily conserved signaling network that remodels the translatome and transcriptome in response to multiple stresses, including nutrient deprivation, mitochondrial dysfunction, viral infection, and loss of protein homeostasis. Here, we present a comprehensive theoretical model of the ISR, calibrated to time-resolved proteomics data that captures how cells encode the magnitude and duration of stress signals to generate a homeostatic output. Our simulations and data converge on an ISR activation threshold defined by phosphorylated eIF2 levels, and sequential cascading delays in the accumulation of the ISR components ATF4, GADD34, CHOP, and DR5, suggesting hardwired molecular timers regulate ISR behaviors. Our combined experimental and computational analyses reveal limiting ATF4 levels, which can be suppressed when TC levels drop below a threshold that would allow its translation. While our model accurately predicts this initial saturation limit, its divergence from the data at high stress levels, correlated with minimal TC levels, identified a "translational cliff" that defines a finite ATF4-dependent ISR operational range. This work establishes a quantitative platform to probe ISR dynamics and generate novel, testable hypotheses.

systems biology↗

bioRxiv2023

Data-driven structural analysis of Small Cell Lung Cancer transcription factor networksuggests potential subtype regulators and transition pathways

Small Cell Lung Cancer (SCLC) is an aggressive disease and challenging to treat due to its mixture of transcriptional subtypes and subtype transitions. Transcription factor (TF) networks have been the focus of studies to identify SCLC subtype regulators via systems approaches. Yet, their structures, which can provide clues on subtype drivers and transitions, are barely investigated. Here, we analyze the structure of an SCLC TF network by using graph theory concepts and identify its structurally important components responsible for complex signal processing, called hubs. We show that the hubs of the network are regulators of different SCLC subtypes by analyzing first the unbiased network structure and then integrating RNA-seq data as weights assigned to each interaction. Data-driven analysis emphasizes MYC as a hub, consistent with recent reports. Furthermore, we hypothesize that the pathways connecting functionally distinct hubs may control subtype transitions and test this hypothesis via network simulations on a candidate pathway and observe subtype transition. Overall, structural analyses of complex networks can identify their functionally important components and pathways driving the network dynamics. Such analyses can be an initial step for generating hypotheses and can guide the discovery of target pathways whose perturbation may change the network dynamics phenotypically.

systems biology↗

bioRxiv2022

Automatic mechanistic inference from large families of Boolean models generated by Monte Carlo Tree Search

1MotivationMany important processes in biology, such as signaling and gene regulation, can be described using logic models. These logic models are typically built to behaviorally emulate experimentally observed phenotypes, which are assumed to be steady states of a biological system. Most models are built by hand and therefore researchers are only able to consider one or perhaps a few potential mechanisms. We present a method to automatically synthesize Boolean logic models with a specified set of steady states. Our method, called MC-Boomer, is based on Monte Carlo Tree Search (MCTS), an efficient, parallel search method using reinforcement learning. Our approach enables users to constrain the model search space using prior knowledge or biochemical interaction databases, thus leading to generation of biologically plausible mechanistic hypotheses. Our approach can generate very large numbers of data-consistent models. To help develop mechanistic insight from these models, we developed analytical tools for multi-model inference and model selection. These tools reveal the key sets of interactions that govern the behavior of the models. ResultsWe demonstrate that MC-Boomer works well at reconstructing randomly generated models. Then, using single time point measurements and reasonable biological constraints, our method generates hundreds of thousands of candidate models that match experimentally validated in-vivo behaviors of the Drosophila segment polarity network. Finally we outline how our multimodel analysis procedures elucidate potentially novel biological mechanisms and provide opportunities for model-driven experimental validation. AvailabilityCode is available at: www.github.com/bglazer/mcboomer

systems biology↗

bioRxiv2022

Unified Tumor Growth Mechanisms from Multimodel Inference and Dataset Integration

Mechanistic models of biological processes can help explain observed phenomena and predict response to a perturbation. A mathematical model is typically constructed using expert knowledge and informal reasoning to generate a mechanistic explanation for a given observation. Although this approach works well for simple systems with abundant data and well-established principles, quantitative biology is often faced with a dearth of both data and knowledge about a process, thus making it challenging to identify and validate all possible mechanistic hypothesis underlying a system behavior. To overcome these limitations, we introduce a Bayesian multimodel inference (Bayes-MMI) methodology, which quantifies how mechanistic hypotheses can explain a given experimental datasets, and concurrently, how each dataset informs a given model hypothesis, thus enabling hypothesis space exploration in the context of available data. We demonstrate this approach to probe standing questions about heterogeneity, lineage plasticity, and cell-cell interactions in tumor growth mechanisms of small cell lung cancer (SCLC). We integrate three datasets that each formulated different explanations for tumor growth mechanisms in SCLC, apply Bayes-MMI and find that the data supports model predictions for tumor evolution promoted by high lineage plasticity, rather than through expanding rare stem-like populations. In addition, the models predict that in the presence of SCLC-N or SCLC-A2 cells, the transition from SCLC-A to SCLC-Y through an intermediate is decelerated. Together, these predictions provide a testable hypothesis for observed juxtaposed results in SCLC growth and a mechanistic interpretation for tumor recalcitrance. AUTHOR SUMMARYTo make a mathematical model, an investigator needs to know and incorporate biological relationships present in the system of interest. However, if we dont know the exact relationships, how can we build a model? Building a single model may include spurious relationships or exclude important ones, so model selection enables us to build multiple, incorporating various combinations of biological features and the relationships between them. Each biological feature represents a distinct hypothesis, which can be investigated via model fitting to experimental data. We aim to improve upon the information theoretic framework of model selection by incorporating Bayesian elements. We apply our approach to small cell lung cancer (SCLC), using multiple datasets, to address hypotheses about cell-cell interactions, phenotypic transitions, and tumor makeup across experimental model systems. Incorporating Bayesian inference, we can add into model selection an assessment of whether these hypotheses are likely or unlikely, or even whether the data enables assessment of a hypothesis at all. Our analysis finds that SCLC is likely highly plastic, with cells able to transition phenotypic identities easily. These predictions could help explain why SCLC is such a difficult disease to treat, and provide the basis for further experiments.

systems biology↗

bioRxiv2022

A dynamical low-rank approach to solve the chemical master equation for biological reaction networks

Solving the chemical master equation is an indispensable tool in understanding the behavior of biological and chemical systems. In particular, it is increasingly recognized that commonly used ODE models are not able to capture the stochastic nature of many cellular processes. Solving the chemical master equation directly, however, suffers from the curse of dimensionality. That is, both memory and computational effort scale exponentially in the number of species. In this paper we propose a dynamical low-rank approach that enables the simulation of large biological networks. The approach is guided by partitioning the network into biological relevant subsets and thus avoids the use of single species basis functions that are known to give inaccurate results for biological systems. We use the proposed method to gain insight into the nature of asynchronous vs. synchronous updating in Boolean models and successfully simulate a 41 species apoptosis model on a standard desktop workstation.

bioinformatics↗

bioRxiv2022

Distinct execution modes of a biochemical necroptosis model explain cell type-specific responses and variability to cell-death cues

Necroptosis is a form of regulated cell death that has been associated with degenerative disorders, autoimmune processes, inflammatory diseases, and cancer. To better understand the biochemical mechanisms of necroptosis cell death regulation, we constructed a detailed biochemical model of tumor necrosis factor (TNF)-induced necroptosis based on known molecular interactions. Intracellular protein levels, used as model inputs, were quantified using label-free mass spectrometry, and the model was calibrated using Bayesian parameter inference to experimental protein time course data from a well-established necroptosis-executing cell line. The calibrated model accurately reproduced the dynamics of phosphorylated mixed lineage kinase domain-like protein (pMLKL), an established necroptosis reporter. A dynamical systems analysis identified four distinct modes of necroptosis signal execution, which can be distinguished based on rate constant values and the roles of the deubiquitinating enzymes A20 and CYLD in the regulation of RIP1 ubiquitination. In one case, A20 and CYLD both contribute to RIP1 deubiquitination, in another RIP1 deubiquitination is driven exclusively by CYLD, and in two modes either A20 or CYLD acts as the driver with the other enzyme, counterintuitively, inhibiting necroptosis. We also performed sensitivity analyses of initial protein concentrations and rate constants and identified potential targets for modulating necroptosis sensitivity among the biochemical events involved in RIP1 ubiquitination regulation and the decision between complex II degradation and necrosome formation. We conclude by associating numerous contrasting and, in some cases, counterintuitive experimental results reported in the literature with one or more of the model-predicted modes of necroptosis execution. Overall, we demonstrate that a consensus pathway model of TNF-induced necroptosis can provide insights into unresolved controversies regarding the molecular mechanisms driving necroptosis execution for various cell types and experimental conditions.

systems biology↗

bioRxiv2021

Microbench: Automated metadata management for systems biology benchmarking and reproducibility in Python

MotivationComputational systems biology analyses typically make use of multiple software and their dependencies, which often run across heterogeneous compute environments. This can introduce differences in performance and reproducibility. Capturing metadata (e.g. package versions, GPU model) currently requires repetitious code and is difficult to store centrally for analysis. Even where virtual environments and containers are used, updates over time mean that versioning metadata should still be captured within analysis pipelines to guarantee reproducibility. ResultsMicrobench is a simple and extensible Python package to automate metadata capture to a file or Redis database. Captured metadata can include execution time, software package versions, environment variables, hardware information, Python version, and more, with plugins. We present three case studies demonstrating Microbench usage to benchmark code execution and examine environment metadata for reproducibility purposes. AvailabilityInstall from the Python Package Index using pip install microbench. Source code is available from https://github.com/alubbock/microbench. Contactc.lopez@vanderbilt.edu

systems biology↗

bioRxiv2021

The misleading certainty of uncertain data in biological network processes

Mathematical models are often used to explore network-driven cellular processes from a systems perspective. However, a dearth of quantitative data suitable for model calibration leads to models with parameter unidentifiability and questionable predictive power. Here we introduce a Bayesian and Machine-Learning based Measurement Model approach to explore how quantitative and non-quantitative data constrain models of apoptosis execution within a missing data context. We find two orders of magnitude more ordinal (e.g. immunoblot) data are necessary to achieve accuracy comparable to quantitative (e.g. fluorescence) data. Notably, ordinal and nominal (e.g. immunostain) non-quantitative data synergize to reduce model uncertainty and improve accuracy. Further, model prediction accuracy and certainty strongly depend on rigorous data-driven formulations of the measurement, and the size and make-up of the datasets. Finally, we demonstrate the potential of a data-driven Measurement Model approach to identify model features that could lead to informative experimental measurements and improve model predictive power.

systems biology↗

bioRxiv2021

Programmatic modeling for biological systems

Computational modeling has become an established technique to encode mathematical representations of cellular processes and gain mechanistic insights that drive testable predictions. These models are often constructed using graphical user interfaces or domain-specific languages, with SBML used for interchange. Models are typically simulated, calibrated, and analyzed either within a single application, or using import and export from various tools. Here, we describe a programmatic modeling paradigm, in which modeling is augmented with best practices from software engineering. We focus on Python - a popular, user-friendly programming language with a large scientific package ecosystem. Models themselves can be encoded as programs, adding benefits such as modularity, testing, and automated documentation generators while still being exportable to SBML. Automated version control and testing ensures models and their modules have expected properties and behavior. Programmatic modeling is a key technology to enable collaborative model development and enhance dissemination, transparency, and reproducibility. HighlightsO_LIProgrammatic modeling combines computational modeling with software engineering best practices. C_LIO_LIAn executable model enables users to leverage all available resources from the language. C_LIO_LICommunity benefits include improved collaboration, reusability, and reproducibility. C_LIO_LIPython has multiple modeling frameworks with a broad, active scientific ecosystem. C_LI

systems biology↗

bioRxiv2021

Probability-based mechanisms in biological networks with parameter uncertainty

Mathematical models of biomolecular networks are commonly used to study cellular processes; however, their usefulness to explain and predict dynamic behaviors is often questioned due to the unclear relationship between parameter uncertainty and network dynamics. In this work, we introduce PyDyNo (Python Dynamic analysis of biochemical NetwOrks), a non-equilibrium reaction-flux based analysis to identify dominant reaction paths within a biochemical reaction network calibrated to experimental data. We first show, in a simplified apoptosis execution model, that Bayesian parameter optimization can yield thousands of parameter vectors with equally good fits to experimental data. Our analysis however enables us to identify the dynamic differences between these parameter sets and identify three dominant execution modes. We further demonstrate that parameter vectors from each execution mode exhibit varying sensitivity to perturbations. We then apply our methodology to JAK2/STAT5 network in colony-forming unit-erythroid (CFU-E) cells to identify its signal execution modes. Our analysis identifies a previously unrecognized mechanistic explanation for the survival responses of the CFU-E cell population that would have been impossible to deduce with traditional protein-concentration based analyses. Impact StatementGiven the mechanistic models of network-driven cellular processes and the associated parameter uncertainty, we present a framework that can identify dominant reaction paths that could in turn lead to unique signal execution modes (i.e., dominant paths of flux propagation), providing a novel statistical and mechanistic insights to explain and predict signal processing and execution.

systems biology↗

bioRxiv2020

Unsupervised logic-based mechanism inference for network-driven biological processes

Modern analytical techniques enable researchers to collect data about cellular states, before and after perturbations. These states can be characterized using analytical techniques, but the inference of regulatory interactions that explain and predict changes in these states remains a challenge. Here we present a generalizable unsupervised approach to generate parameter-free, logic-based mechanistic hypotheses of cellular processes, described by multiple discrete states. Our algorithm employs a Hamming-distance based approach to formulate, test, and identify, the best mechanism that links two states. Our approach comprises two steps. First, a model with no prior knowledge except for the mapping between initial and attractor states is built. Second, we employ biological constraints to improve model fidelity. Our algorithm automatically recovers the relevant dynamics for the explored models and recapitulates all aspects of the original models biochemical species concentration dynamics. We then conclude by placing our results in the context of ongoing work in the field and discuss how our approach could be used to infer mechanisms of signaling, gene-regulatory, and any other input-output processes describable by logic-based mechanisms.

systems biology↗

bioRxiv2020

ACDC: Automated Cell Detection and Counting for Time-lapse Fluorescence Microscopy

Advances in microscopy imaging technologies have enabled the visualization of live-cell dynamic processes using time-lapse microscopy imaging. However, modern methods exhibit several limitations related to the training phases and to time constraints, hindering their application in the laboratory practice. In this work, we present a novel method, named Automated Cell Detection and Counting (ACDC), designed for activity detection of fluorescent labeled cell nuclei in time-lapse microscopy. ACDC overcomes the limitations of the literature methods, by first applying bilateral filtering on the original image to smooth the input cell images while preserving edge sharpness, and then by exploiting the watershed transform and morphological filtering. Moreover, ACDC represents a feasible solution for the laboratory practice, as it can leverage multi-core architectures in computer clusters to efficiently handle large-scale imaging datasets. Indeed, our Parent-Workers implementation of ACDC allows to obtain up to a 3.7x speed-up compared to the sequential counterpart. ACDC was tested on two distinct cell imaging datasets to assess its accuracy and effectiveness on images with different characteristics. We achieved an accurate cell-count and nuclei segmentation without relying on large-scale annotated datasets, a result confirmed by the average Dice Similarity Coefficients of 76.84 and 88.64 and the Pearson coefficients of 0.99 and 0.96, calculated against the manual cell counting, on the two tested datasets.

bioinformatics↗

bioRxiv2020

A computational framework to explore cellular response mechanisms from multi-omics datasets

Recent technological advances have made it feasible to collect multi-condition transcriptome and proteome time-courses of cellular response to perturbation. The increasing size and complexity of these datasets impedes mechanism of action discovery due to challenges in data management, analysis, visualization, and interpretation. Here, we introduce MAGINE, a software framework to explore complex time-course multi-omics datasets and build mechanistic hypotheses of dynamic cellular response. MAGINE combines data management, enrichment, and network analysis and visualization within an interactive, Jupyter notebook-based environment to enable human-in-the-loop inquiry of complex datasets. We demonstrate how measurements from HL-60 cellular response to bendamustine treatment can be used to build a mechanistic, multi-resolution description of cellular commitment to fate. We present a systems-level description of signal execution from cellular DNA-damage response, to cell cycle arrest, and eventual commitment to apoptosis, mediated by over 2000 biochemical species. We further show that MAGINE can reveal unexpected, non-canonical effects of bendamustine treatment, including disruption of cellular pathways relevant to HIV infection response. MAGINE is available from https://github.com/lolab-vu/magine.

systems biology↗

bioRxiv2020

Accelerated Simulations of Chemical Reaction Systems using the Stochastic Simulation Algorithm on GPUs

1Stochasticity due to fluctuations in chemical reactions can play important roles in cellular network-driven processes. Although the Stochastic Simulation Algorithm (SSA, aka Gillespie Algorithm) has long been accepted as a suitable method to solve the time-dependent chemical master equation, its computational cost is prohibitive for large scale complex networks such as those found in cellular processes. Here we present GPU-SSA, an implementation of the SSA formalism utilizing Graphics Processing Units for use in Python using the PySB modeling framework. We show that the GPU implementation of SSA can achieve significant speedup compared to parallel CPU or single-core CPU implementations. We further include supplementary didactic material to demonstrate how to incorporate GPU-SSA workflows for interested readers.

systems biology↗

bioRxiv2019

Identification of execution modes in the extrinsic apoptosis signaling network via Bayesian inference methods

Mathematical models of biochemical reaction networks are central to the study of dynamic cellular processes and hypothesis generation that informs experimentation and validation. Unfortunately, model parameters are often not available and sparse experimental data leads to challenges in model calibration and parameter estimation. This can in turn lead to unreliable mechanistic interpretations of experimental data and the generation of poorly conceived hypotheses for experimental validation. To address this challenge, we evaluate whether a Bayesian-inspired probability-based approach, that incorporates available information regarding reaction network topology and parameters, can be used to qualitatively explore hypothetical biochemical network execution mechanisms in the context of limited available data. We test our approach on a model of extrinsic apoptosis execution to identify preferred signal execution modes across varying conditions. Apoptosis signal processing can take place either through a mitochondria independent (Type I) mode or a mitochondria dependent (Type II) mode. We first show that in silico knockouts, represented by model subnetworks, successfully identify the most likely execution mode for specific concentrations of key molecular regulators. We then show that changes in molecular regulator concentrations alter the overall reaction flux through the network by shifting the primary route of signal flow between the direct caspase and mitochondrial pathways. Our work thus demonstrates that probabilistic approaches can be used to explore the qualitative dynamic behavior of model biochemical systems even with missing or sparse data.

systems biology↗