scholarly journals Probability-based mechanisms in biological networks with parameter uncertainty

2021 ◽  
Author(s):  
Oscar O. Ortega ◽  
Blake A. Wilson ◽  
James C. Pino ◽  
Michael W. Irvin ◽  
Geena V. Ildefonso ◽  
...  
Keyword(s):  
Biological Networks ◽  
Parameter Uncertainty ◽  
Mechanistic Model ◽  
Dynamic Network ◽  
Parameter Inference ◽  
Signal Flow ◽  
Cellular Processes ◽  
Biomolecular Networks ◽  
Extrinsic Apoptosis ◽  
Modulate Signal

AbstractMathematical models of biomolecular networks are commonly used to study mechanisms of cellular processes, but their usefulness is often questioned due to parameter uncertainty. Here, we employ Bayesian parameter inference and dynamic network analysis to study dominant reaction fluxes in models of extrinsic apoptosis. Although a simplified model yields thousands of parameter vectors with equally good fits to data, execution modes based on reaction fluxes clusters to three dominant execution modes. A larger model with increased parameter uncertainty shows that signal flow is constrained to eleven execution modes that use 53 out of 2067 possible signal subnetworks. Each execution mode exhibits different behaviors to in silico perturbations, due to different signal execution mechanisms. Machine learning identifies informative parameters to guide experimental validation. Our work introduces a probability-based paradigm of signaling mechanisms, highlights systems-level interactions that modulate signal flow, and provides a methodology to understand mechanistic model predictions with uncertain parameters.

scholarly journals Interactive Multiresolution Visualization of Cellular Network Processes

2019 ◽  
Author(s):  
Oscar O. Ortega ◽  
Carlos F. Lopez
Keyword(s):  
Computational Models ◽  
Dynamic Network ◽  
Cellular Systems ◽  
Biochemical Network ◽  
Mitochondrial Outer Membrane ◽  
Cellular Processes ◽  
Detection Algorithms ◽  
Mitochondrial Outer Membrane Permeabilization ◽  
Extrinsic Apoptosis ◽  
Dynamic Execution

AbstractComputational models of network-driven processes have become a standard to explain cellular systems-level behavior and predict cellular responses to perturbations. Modern models can span a broad range of biochemical reactions and species that, in principle, comprise the complexity of dynamic cellular processes. Visualization plays a central role in the analysis of biochemical network processes to identify patterns that arise from model dynamics and perform model exploratory analysis. However, most existing visualization tools are limited in their capabilities to facilitate mechanism exploration of large, dynamic, and complex models. Here, we present PyViPR, a visualization tool that provides researchers static and dynamic representations of biochemical network processes within a Python-based Literate Programming environment. PyViPR embeds network visualizations on Jupyter notebooks, thus facilitating integration with Python modeling, simulation, and analysis workflows. To present the capabilities of PyViPR, we explore execution mechanisms of extrinsic apoptosis in HeLa cells. We show how community-detection algorithms can identify groups of molecular species that represent key biological regulatory functions and simplify the apoptosis network by placing those groups into interactively collapsible nodes. We then show how dynamic execution of a signal, under different kinetic parameter sets that fit the experimental data equally well, exhibit significantly different signal-execution modes in mitochondrial outer-membrane permeabilization – the point of no return in extrinsic apoptosis execution. Therefore, PyViPR aids the conceptual understanding of dynamic network processes and accelerates hypothesis generation for further testing and validation.

scholarly journals Layered Feedback Control Overcomes Performance Trade-off in Synthetic Biomolecular Networks

2021 ◽  
Author(s):  
Chelsea Hu ◽  
Richard Murray
Keyword(s):  
Feedback Control ◽  
Control Theory ◽  
Biological Networks ◽  
Performance Optimization ◽  
System Analysis ◽  
Mechanistic Model ◽  
Optimization Strategy ◽  
Trade Off ◽  
Environmental Perturbations ◽  
Biomolecular Networks

Abstract Layered feedback is an optimization strategy in feedback control designs widely used in electrical and mechanical engineering. Layered control theory suggests that the performance of controllers is bound by the universal robustness-efficiency trade-off limit, which could be overcome by layering two or more feedbacks together. In natural biological networks, genes are often regulated with redundancy and layering to adapt to environmental perturbations. Control theory hypothesizes that this layering architecture is also adopted by nature to overcome this performance trade-off. In this work, we validated this property of layered control with a synthetic network in living E. coli cells. We performed system analysis on a node-based design to confirm the trade-off properties before proceeding to simulations with an effective mechanistic model, which guided us to the best performing design to engineer in cells. Finally, we interrogated its system dynamics experimentally with eight sets of perturbations on chemical signals, nutrient abundance, and growth temperature. For all cases, we consistently observed that the layered control overcomes the robustness-efficiency trade-off limit. This work experimentally confirmed that layered control could be adopted in synthetic biomolecular networks as a performance optimization strategy. It also provided insights in understanding genetic feedback control architectures in nature.

scholarly journals Layered Feedback Control Overcomes Performance Trade-off in Synthetic Biomolecular Networks

2021 ◽  
Author(s):  
Chelsea Y. Hu ◽  
Richard M Murray
Keyword(s):  
Feedback Control ◽  
Control Theory ◽  
Biological Networks ◽  
Performance Optimization ◽  
System Analysis ◽  
Mechanistic Model ◽  
Optimization Strategy ◽  
Trade Off ◽  
Environmental Perturbations ◽  
Biomolecular Networks

Layered feedback is an optimization strategy in feedback control designs widely used in electrical and mechanical engineering. Layered control theory suggests that the performance of controllers is bound by the universal robustness-efficiency trade-off limit, which could be overcome by layering two or more feedbacks together. In natural biological networks, genes are often regulated with redundancy and layering to adapt to environmental perturbations. Control theory hypothesizes that this layering architecture is also adopted by nature to overcome this performance trade-off. In this work, we validated this property of layered control with a synthetic network in living E. coli cells. We performed system analysis on a node-based design to confirm the trade-off properties before proceeding to simulations with an effective mechanistic model, which guided us to the best performing design to engineer in cells. Finally, we interrogated its system dynamics experimentally with eight sets of perturbations on chemical signals, nutrient abundance, and growth temperature. For all cases, we consistently observed that the layered control overcomes the robustness-efficiency trade-off limit. This work experimentally confirmed that layered control could be adopted in synthetic biomolecular networks as a performance optimization strategy. It also provided insights in understanding genetic feedback control architectures in nature.

scholarly journals A control engineering approach to understanding the TGF-β paradox in cancer

2011 ◽  
Vol 9 (71) ◽  
pp. 1389-1397 ◽  
Author(s):  
Seung-Wook Chung ◽  
Carlton R. Cooper ◽  
Mary C. Farach-Carson ◽  
Babatunde A. Ogunnaike
Keyword(s):  
Cancer Cells ◽  
Mechanistic Model ◽  
Tumour Suppressor ◽  
Cancer Tissue ◽  
Control Engineering ◽  
Cellular Processes ◽  
Proliferation And Apoptosis ◽  
High Level ◽  
Quantitative Explanation

TGF-β, a key cytokine that regulates diverse cellular processes, including proliferation and apoptosis, appears to function paradoxically as a tumour suppressor in normal cells, and as a tumour promoter in cancer cells, but the mechanisms underlying such contradictory roles remain unknown. In particular, given that this cytokine is primarily a tumour suppressor, the conundrum of the unusually high level of TGF-β observed in the primary cancer tissue and blood samples of cancer patients with the worst prognosis, remains unresolved. To provide a quantitative explanation of these paradoxical observations, we present, from a control theory perspective, a mechanistic model of TGF-β-driven regulation of cell homeostasis. Analysis of the overall system model yields quantitative insight into how cell population is regulated, enabling us to propose a plausible explanation for the paradox: with the tumour suppressor role of TGF-β unchanged from normal to cancer cells, we demonstrate that the observed increased level of TGF-β is an effect of cancer cell phenotypic progression (specifically, acquired TGF-β resistance), not the cause . We are thus able to explain precisely why the clinically observed correlation between elevated TGF-β levels and poor prognosis is in fact consistent with TGF-β's original (and unchanged) role as a tumour suppressor.

CAN COMPLEX CELLULAR PROCESSES BE GOVERNED BY SIMPLE LINEAR RULES?

2009 ◽  
Vol 07 (01) ◽  
pp. 243-268 ◽  
Author(s):  
KUMAR SELVARAJOO ◽  
MASARU TOMITA ◽  
MASA TSUCHIYA
Keyword(s):  
Biological Networks ◽  
Biological Network ◽  
Living Systems ◽  
Regulatory Motifs ◽  
Cellular Processes ◽  
Self Organized ◽  
Wide Range ◽  
Activation Response ◽  
Physical Reasons

Complex living systems have shown remarkably well-orchestrated, self-organized, robust, and stable behavior under a wide range of perturbations. However, despite the recent generation of high-throughput experimental datasets, basic cellular processes such as division, differentiation, and apoptosis still remain elusive. One of the key reasons is the lack of understanding of the governing principles of complex living systems. Here, we have reviewed the success of perturbation–response approaches, where without the requirement of detailed in vivo physiological parameters, the analysis of temporal concentration or activation response unravels biological network features such as causal relationships of reactant species, regulatory motifs, etc. Our review shows that simple linear rules govern the response behavior of biological networks in an ensemble of cells. It is daunting to know why such simplicity could hold in a complex heterogeneous environment. Provided physical reasons can be explained for these phenomena, major advancement in the understanding of basic cellular processes could be achieved.

scholarly journals Dissecting molecular network structures using a network subgraph approach

PeerJ ◽  
2020 ◽  
Vol 8 ◽  
pp. e9556
Author(s):  
Chien-Hung Huang ◽  
Efendi Zaenudin ◽  
Jeffrey J.P. Tsai ◽  
Nilubon Kurubanjerdjit ◽  
Eskezeia Y. Dessie ◽  
...  
Keyword(s):  
Biological Networks ◽  
Spectral Graph Theory ◽  
Molecular Networks ◽  
Driver Genes ◽  
Complexity Measures ◽  
Cellular Processes ◽  
Graph Energy ◽  
Energy Cutoff ◽  
Cancer Networks ◽  
Irreducible Graphs

Biological processes are based on molecular networks, which exhibit biological functions through interactions of genetic elements or proteins. This study presents a graph-based method to characterize molecular networks by decomposing the networks into directed multigraphs: network subgraphs. Spectral graph theory, reciprocity and complexity measures were used to quantify the network subgraphs. Graph energy, reciprocity and cyclomatic complexity can optimally specify network subgraphs with some degree of degeneracy. Seventy-one molecular networks were analyzed from three network types: cancer networks, signal transduction networks, and cellular processes. Molecular networks are built from a finite number of subgraph patterns and subgraphs with large graph energies are not present, which implies a graph energy cutoff. In addition, certain subgraph patterns are absent from the three network types. Thus, the Shannon entropy of the subgraph frequency distribution is not maximal. Furthermore, frequently-observed subgraphs are irreducible graphs. These novel findings warrant further investigation and may lead to important applications. Finally, we observed that cancer-related cellular processes are enriched with subgraph-associated driver genes. Our study provides a systematic approach for dissecting biological networks and supports the conclusion that there are organizational principles underlying molecular networks.

scholarly journals Intermediate structural hierarchy in biological networks modulates the fractal dimension and force distribution of percolating clusters

2021 ◽  
Author(s):  
Benjamin S Hanson ◽  
Lorna Dougan
Keyword(s):  
Biological Networks ◽  
Building Block ◽  
Rational Design ◽  
Network Formation ◽  
Dynamic Network ◽  
Force Transmission ◽  
Structural Hierarchy ◽  
Network Properties ◽  
Protein Hydrogels ◽  
Dynamic Network Formation

Globular protein hydrogels are an emerging class of materials with the potential for rational design, and a generalised understanding of how their network properties emerge from the structure and dynamics of the building block is a key challenge. Here we computationally investigate the effect of intermediate (polymeric) nanoscale structure on the formation of protein hydrogels. We show that changes in both the cross-link topology and flexibility of the polymeric building block lead to changes in the force transmission around the system, and provide insight into the dynamic network formation processes.

An engineered protein-phosphorylation toggle network with implications for endogenous network discovery

Science ◽  
2021 ◽  
Vol 373 (6550) ◽  
pp. eaav0780
Author(s):  
Deepak Mishra ◽  
Tristan Bepler ◽  
Brian Teague ◽  
Bonnie Berger ◽  
Jim Broach ◽  
...  
Keyword(s):  
Protein Phosphorylation ◽  
Biological Networks ◽  
Toggle Switch ◽  
Computational Framework ◽  
Cellular Processes ◽  
Network Discovery ◽  
Endogenous Protein ◽  
Synthetic Protein ◽  
Endogenous Networks ◽  
Cellular Behaviors

Synthetic biological networks comprising fast, reversible reactions could enable engineering of new cellular behaviors that are not possible with slower regulation. Here, we created a bistable toggle switch in Saccharomyces cerevisiae using a cross-repression topology comprising 11 protein-protein phosphorylation elements. The toggle is ultrasensitive, can be induced to switch states in seconds, and exhibits long-term bistability. Motivated by our toggle’s architecture and size, we developed a computational framework to search endogenous protein pathways for other large and similar bistable networks. Our framework helped us to identify and experimentally verify five formerly unreported endogenous networks that exhibit bistability. Building synthetic protein-protein networks will enable bioengineers to design fast sensing and processing systems, allow sophisticated regulation of cellular processes, and aid discovery of endogenous networks with particular functions.

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