Biology subjects

Albert, R.

Publications and source records attributed to Albert, R..

11 recordsLinked to original sources

bioRxiv2025

Succession-diagram-based Markov chains reveal the attractor landscape of asynchronous Boolean networks

Comprehensive analysis of the dynamics of Boolean models of biological systems is hampered by the exponentially large state space. Here we introduce the succession-diagram-based Markov chain (SD Markov chain), a coarse-grained representation that uses trap spaces (unescapable state subspaces) of the Boolean model as the states of a Markov chain. These trap spaces and their succession diagram can be efficiently identified, and constitute a dramatic reduction compared to the full state space. The SD Markov chain preserves the decisions that trap the systems dynamics while making the state space computationally tractable. Using an ensemble of random Boolean networks with known state transition matrices, we show that the SD Markov chain accurately reproduces attractors, basins of attraction, convergence probabilities, decision transitions, and sequences of events. By combining the interpretability of the succession diagram with the probabilistic rigor of Markov analysis, the SD Markov chain offers a compact quantitative description of the attractor landscape and provides a new avenue for studying control and stability in complex biological systems.

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

Dynamic model of carbon dioxide-induced stomatal closure reveals a feedback core for cellular decision-making

Stomata are pores on plant aerial surfaces, each bordered by a pair of guard cells. They control gas exchange vital for plant survival. Understanding how guard cells respond to environmental signals such as atmospheric carbon dioxide (CO2) levels is not only insightful to fundamental biology but also relevant to real-world issues of crop productivity under global climate change. In the past decade, multiple important signaling elements for stomatal closure induced by elevated CO2 have been identified. Yet, there is no comprehensive understanding of high CO2 induced stomatal closure. In this work we assemble a cellular signaling network underlying high CO2-induced stomatal closure by integrating evidence from a comprehensive literature analysis. We further construct a Boolean dynamic model of the network, which allows in silico simulation of the stomatal closure response to high CO2 in wild-type Arabidopsis thaliana plants and in cases of pharmacological or genetic manipulation of network nodes. Our model has a 91% accuracy in capturing known experimental observations. We perform network-based logical analysis and reveal a feedback core of the network, which dictates cellular decisions in closure response to high CO2. Based on these analyses, we predict and experimentally confirm that applying nitric oxide (NO) induces stomatal closure in ambient CO2 and causes hypersensitivity to elevated CO2. Moreover, we predict a negative regulatory relationship between NO and the protein phosphatase ABI2 and find experimentally that NO inhibits ABI2 phosphatase activity. The experimental validation of these model predictions demonstrates the effectiveness of network-based modeling and highlights the decision-making role of the feedback core of the network in signal transduction. We further explore the models potential in predicting targets of signaling elements not yet connected to the CO2 network. Our combination of network science, in silico model simulation, and experimental assays demonstrates an effective interdisciplinary approach to understanding system-level biology.

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↗

bioRxiv2021

Mathematical modeling of the Candida albicans yeast to hyphal transition reveals novel control strategies

Candida albicans, an opportunistic fungal pathogen, is a significant cause of human infections, particularly in immunocompromised individuals. Phenotypic plasticity between two morphological phenotypes, yeast and hyphae, is a key mechanism by which C. albicans can thrive in many microenvironments and cause disease in the host. Understanding the decision points and key driver genes controlling this important transition, and how these genes respond to different environmental signals is critical to understanding how C. albicans causes infections in the host. Here we build and analyze a Boolean dynamical model of the C. albicans yeast to hyphal transition, integrating multiple environmental factors and regulatory mechanisms. We validate the model by a systematic comparison to prior experiments, which led to agreement in 18 out of 22 cases. The discrepancies motivate alternative hypotheses that are testable by follow-up experiments. Analysis of this model revealed two time-constrained windows of opportunity that must be met for the complete transition from the yeast to hyphal phenotype, as well as control strategies that can robustly prevent this transition. We experimentally validate two of these control predictions in C. albicans strains lacking the transcription factor UME6 and the histone deacetylase HDA1, respectively. This model will serve as a strong base from which to develop a systems biology understanding of C. albicans morphogenesis.

systems biology↗

bioRxiv2020

Cell-line-specific network models of ER+ breast cancer identify PI3Kα inhibitor sensitivity factors and drug combinations

Durable control of invasive solid tumors necessitates identifying therapeutic resistance mechanisms and effective drug combinations. A promising approach to tackle the cancer drug resistance problem is to build mechanistic mathematical models of the signaling network of cancer cells, and explicitly model the dynamics of information flow through this network under distinct genetic conditions and in response to perturbations. In this work, we used a network-based mathematical model to identify sensitivity factors and drug combinations for the PI3K inhibitor alpelisib, which was recently approved for ER+ PIK3CA mutant breast cancer. We experimentally validated the model-predicted efficacious combination of alpelisib and BH3 mimetics (e.g. MCL1 inhibitors) in ER+ breast cancer cell lines. We also experimentally validated the reduced sensitivity to alpelisib caused by FOXO3 knockdown, which is a novel potential resistance mechanism. Our experimental results showed cell line-specific sensitivity to the combination of alpelisib and BH3 mimetics, which was driven by the choice of BH3 mimetics. We find that cell lines were sensitive to the addition of either MCL1 inhibitor s63845 alone or in combination with BCL-XL/BCL-2 inhibitor navitoclax, and that the need for the combination of both BH3 mimetics was predicted by the expression of BCL-XL. Based on these results, we developed cell line-specific network models that are able to recapitulate the observed differential response to alpelisib and BH3 mimetics, and also incorporate the most recent knowledge on resistance and response to PI3K inhibitors. Overall, we present an approach for the development, experimental testing, and refining of mathematical models, which we apply to the context of PI3K inhibitor drug resistance in breast cancer. Our approach predicted and validated PI3K inhibitor sensitivity factors (FOXO3 knockdown) and drug combinations (BH3 mimetics), and illustrates that network-based mathematical models can contribute to overcoming the challenge of cancer drug resistance.

cancer 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↗

bioRxiv2018

Target Control in Logical Models Using the Domain of Influence of Nodes

Dynamical models of biomolecular networks are successfully used to understand the mechanisms underlying complex diseases and to design therapeutic strategies. Network control, and its special case of target control, is a promising avenue toward developing disease therapies. In target control it is assumed that a small subset of nodes is most relevant to the systems state and the goal is to drive the target nodes into their desired states. An example of target control would be driving a cell to commit to apoptosis (programmed cell death). From the experimental perspective, gene knockout, pharmacological inhibition of proteins and providing sustained external signals are among practical intervention techniques. We identify methodologies to use the stabilizing effect of sustained interventions for target control in logical models of biomolecular networks. Specifically, we define the domain of influence of a node (in a certain state) to be the nodes (and their corresponding states) that will be ultimately stabilized by the sustained state of this node regardless of the initial state of the system. We also define the related concept of the logical domain of influence of a node, and develop an algorithm for its identification using an auxiliary network that incorporates the regulatory logic. This way a solution to the target control problem is a set of nodes whose domain of influence can cover the desired target node states. We perform greedy randomized adaptive search in state space to find such solutions. We apply our strategy to several biological networks to demonstrate its effectiveness.

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↗

bioRxiv2017

A network modeling approach to elucidate drug resistance mechanisms and predict combinatorial drug treatments in breast cancer

BackgroundMechanistic models of within-cell signal transduction networks can explain how these networks integrate internal and external inputs to give rise to the appropriate cellular response. These models can be fruitfully used in cancer cells, whose aberrant decision-making regarding their survival or death, proliferation or quiescence can be connected to errors in the state of nodes or edges of the signal transduction network.\n\nResultsHere we present a comprehensive network, and discrete dynamic model, of signal transduction in breast cancer based on the literature of ER+, HER2+, and PIK3CA-mutant breast cancers. The network model recapitulates known resistance mechanisms to PI3K inhibitors and suggests other possibilities for resistance. The model also reveals known and novel combinatorial interventions that are more effective than PI3K inhibition alone.\n\nConclusionsThe use of a logic-based, discrete dynamic model enables the identification of results that are mainly due to the organization of the signaling network, and those that also depend on the kinetics of individual events. Network-based models such as this will play an increasing role in the rational design of high-order therapeutic combinations.

cancer biology↗