Boolean model of anchorage dependence and contact inhibition points to coordinated inhibition but semi-independent induction of proliferation and migration

Guberman, Eric; Sherief, Hikmet; Regan, Erzsébet Ravasz

doi:10.1016/j.csbj.2020.07.016

Cited by 16 publications

(53 citation statements)

References 141 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We have used a similar weakening/strengthening approach on the level of nodes in one of our recent papers [29] and its follow-up study [30], where we imposed external perturbations to specific nodes, especially input nodes, setting them to a certain Boolean value with probability in every time-step. This helps to establish a continuous "concentration"-like parameter, which can influence the downstream dynamics to a great degree.…”

Section: Murphy Et Al Have Shownmentioning

confidence: 99%

Probabilistic Edge Weights fine-tune Boolean network dynamics

Deritei

Kunsic

Csermely

2022

Preprint

View full text Add to dashboard Cite

Biological systems are noisy by nature. This aspect is reflected in our experimental measurements and should be reflected in the models we build to better understand these systems. Noise can be especially consequential when trying to interpret specific regulatory interactions, i.e. regulatory network edges. In this paper, we propose a method to explicitly encode edge-noise in Boolean dynamical systems by probabilistic edge-weight (PEW) operators. PEW operators have two important features: first, they introduce a form of edge-weight into Boolean models through the noise, second, the noise is dependent on the dynamical state of the system, which enables more biologically meaningful modeling choices. Moreover, we offer a simple-to-use implementation in the already well-established BooleanNet framework. In two application cases, we show how the introduction of just a few PEW operators in Boolean models can fine-tune the emergent dynamics and increase the accuracy of qualitative predictions. This includes fine-tuning interactions which cause non-biological behaviors when switching between asynchronous and synchronous update schemes in dynamical simulations. Moreover, PEW operators also open the way to encode more exotic cellular dynamics, such as cellular learning, and to implementing edge-weights for regulatory networks inferred from omics data.

show abstract

Section: Murphy Et Al Have Shownmentioning

confidence: 99%

Probabilistic Edge Weights fine-tune Boolean network dynamics

Deritei

Kunsic

Csermely

2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Meyer et al presented a mechanistic model predicting switch-like activation of YAP upon mechanical stimulation during regeneration (Meyer et al, 2020 ). Guberman et al ( 2020 ) developed a Boolean regulatory network model in epithelial cells that synthesizes mechanosensitive signaling linking anchorage and matrix stiffness to proliferation and migration, including key signaling players such as YAP, PI3K, AKT, and MAPK.…”

Section: Data Integration and Computational Modelingmentioning

confidence: 99%

Hepatectomy-Induced Alterations in Hepatic Perfusion and Function - Toward Multi-Scale Computational Modeling for a Better Prediction of Post-hepatectomy Liver Function

et al. 2021

View full text Add to dashboard Cite

Liver resection causes marked perfusion alterations in the liver remnant both on the organ scale (vascular anatomy) and on the microscale (sinusoidal blood flow on tissue level). These changes in perfusion affect hepatic functions via direct alterations in blood supply and drainage, followed by indirect changes of biomechanical tissue properties and cellular function. Changes in blood flow impose compression, tension and shear forces on the liver tissue. These forces are perceived by mechanosensors on parenchymal and non-parenchymal cells of the liver and regulate cell-cell and cell-matrix interactions as well as cellular signaling and metabolism. These interactions are key players in tissue growth and remodeling, a prerequisite to restore tissue function after PHx. Their dysregulation is associated with metabolic impairment of the liver eventually leading to liver failure, a serious post-hepatectomy complication with high morbidity and mortality. Though certain links are known, the overall functional change after liver surgery is not understood due to complex feedback loops, non-linearities, spatial heterogeneities and different time-scales of events. Computational modeling is a unique approach to gain a better understanding of complex biomedical systems. This approach allows (i) integration of heterogeneous data and knowledge on multiple scales into a consistent view of how perfusion is related to hepatic function; (ii) testing and generating hypotheses based on predictive models, which must be validated experimentally and clinically. In the long term, computational modeling will (iii) support surgical planning by predicting surgery-induced perfusion perturbations and their functional (metabolic) consequences; and thereby (iv) allow minimizing surgical risks for the individual patient. Here, we review the alterations of hepatic perfusion, biomechanical properties and function associated with hepatectomy. Specifically, we provide an overview over the clinical problem, preoperative diagnostics, functional imaging approaches, experimental approaches in animal models, mechanoperception in the liver and impact on cellular metabolism, omics approaches with a focus on transcriptomics, data integration and uncertainty analysis, and computational modeling on multiple scales. Finally, we provide a perspective on how multi-scale computational models, which couple perfusion changes to hepatic function, could become part of clinical workflows to predict and optimize patient outcome after complex liver surgery.

show abstract

“…The majority of these models pertains to mammalian systems and a much smaller fraction pertains to plant systems. The mammalian models include networks for signaling pathways [24,64,65], differentiation [66,67] and various cancers [68][69][70]. Among the plant models, this compilation includes cases from flower organ specification [67], root stem cells [71], and guard cell signalling [72].…”

Section: Nested Canalyzing Functionsmentioning

confidence: 99%

“…Extensive studies of these real networks has revealed that their structure is very different from being random [5,6,9]. Moreover, Boolean dynamical models for several biological systems [19][20][21][22][23][24][25] have been con-structed in the last two decades. It will be important to characterize the properties associated with the logical update rules or BFs in these real models that distinguish them from randomly chosen functions.…”

Section: Introductionmentioning

confidence: 99%

Minimum complexity drives regulatory logic in Boolean models of living systems

Subbaroyan

Martin

Samal

2021

Preprint

View full text Add to dashboard Cite

The properties of random Boolean networks as models of gene regulation have been investigated extensively by the statistical physics community. In the past two decades, there has been a dramatic increase in the reconstruction and analysis of Boolean models of biological networks. In such models, neither network topology nor Boolean functions (or logical update rules) should be expected to be random. In this contribution, we focus on biologically meaningful types of Boolean functions, and perform a systematic study of their preponderance in gene regulatory networks. By applying the k[P] classification based on number of inputs k and bias P of functions, we find that most Boolean functions astonishingly have odd bias in a reference biological dataset of 2687 functions compiled from published models. Subsequently, we are able to explain this observation along with the enrichment of read-once functions (RoFs) and its subset, nested canalyzing functions (NCFs), in the reference dataset in terms of two complexity measures: Boolean complexity based on string lengths in formal logic which is yet unexplored in the biological context, and the average sensitivity. Minimizing the Boolean complexity naturally sifts out a subset of odd-biased Boolean functions which happen to be the RoFs. Finally, we provide an analytical proof that NCFs minimize not only the Boolean complexity, but also the average sensitivity in their k[P] set.

show abstract

Boolean model of anchorage dependence and contact inhibition points to coordinated inhibition but semi-independent induction of proliferation and migration

Abstract: Graphical abstract

Cited by 16 publications

References 141 publications

Probabilistic Edge Weights fine-tune Boolean network dynamics

Probabilistic Edge Weights fine-tune Boolean network dynamics

Hepatectomy-Induced Alterations in Hepatic Perfusion and Function - Toward Multi-Scale Computational Modeling for a Better Prediction of Post-hepatectomy Liver Function

Minimum complexity drives regulatory logic in Boolean models of living systems

Contact Info

Product

Resources

About