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Article: Pancreatic cancer mutationscape: revealing the link between modular restructuring and intervention efficacy amidst common mutations.

Plaugher, Daniel / Murrugarra, David

bioRxiv : the preprint server for biology

2024

Abstract: Across cancer types, the prognosis for pancreatic cancer (PC) is among the worst and options for treatment are limited. There is increasing evidence that biological systems, including PC, are modular in both structure and function. Complex biological ... ...

Abstract	Across cancer types, the prognosis for pancreatic cancer (PC) is among the worst and options for treatment are limited. There is increasing evidence that biological systems, including PC, are modular in both structure and function. Complex biological signaling networks such as gene regulatory networks (GRNs) are proving to be composed of subcategories that are interconnected and hierarchically ranked. These networks contain highly dynamic processes that ultimately dictate cellular function over time. In this work, we use an established Boolean multicellular signaling network of PC to show that the variance in topological rankings of the most phenotypically influential modules implies a strong relationship between structure and function. We further show that induction of mutations alters the modular structure, which analogously influences the aggression and controllability of the disease
Language	English
Publishing date	2024-01-30
Publishing country	United States
Document type	Preprint
DOI	10.1101/2024.01.27.577546
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Article: Phenotype control techniques for Boolean gene regulatory networks.

Plaugher, Daniel / Murrugarra, David

bioRxiv : the preprint server for biology

2023

Abstract: Modeling cell signal transduction pathways via Boolean networks (BNs) has become an established method for analyzing intracellular communications over the last few decades. Whatâ€™s more, BNs provide a course-grained approach, not only to understanding ... ...

Abstract	Modeling cell signal transduction pathways via Boolean networks (BNs) has become an established method for analyzing intracellular communications over the last few decades. Whatâ€™s more, BNs provide a course-grained approach, not only to understanding molecular communications, but also for targeting pathway components that alter the long-term outcomes of the system. This has come to be known as
Language	English
Publishing date	2023-04-18
Publishing country	United States
Document type	Preprint
DOI	10.1101/2023.04.17.537158
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Article ; Online: Phenotype Control techniques for Boolean gene regulatory networks.

Plaugher, Daniel / Murrugarra, David

Bulletin of mathematical biology

2023 Volume 85, Issue 10, Page(s) 89

Abstract: Modeling cell signal transduction pathways via Boolean networks (BNs) has become an established method for analyzing intracellular communications over the last few decades. What's more, BNs provide a course-grained approach, not only to understanding ... ...

Abstract	Modeling cell signal transduction pathways via Boolean networks (BNs) has become an established method for analyzing intracellular communications over the last few decades. What's more, BNs provide a course-grained approach, not only to understanding molecular communications, but also for targeting pathway components that alter the long-term outcomes of the system. This has come to be known as phenotype control theory. In this review we study the interplay of various approaches for controlling gene regulatory networks such as: algebraic methods, control kernel, feedback vertex set, and stable motifs. The study will also include comparative discussion between the methods, using an established cancer model of T-Cell Large Granular Lymphocyte Leukemia. Further, we explore possible options for making the control search more efficient using reduction and modularity. Finally, we will include challenges presented such as the complexity and the availability of software for implementing each of these control techniques.
MeSH term(s)	Gene Regulatory Networks ; Mathematical Concepts ; Models, Biological ; Phenotype ; Software
Language	English
Publishing date	2023-08-30
Publishing country	United States
Document type	Journal Article ; Review ; Research Support, Non-U.S. Gov't ; Research Support, N.I.H., Extramural
ZDB-ID	184905-0
ISSN	1522-9602 ; 0007-4985 ; 0092-8240
ISSN (online)	1522-9602
ISSN	0007-4985 ; 0092-8240
DOI	10.1007/s11538-023-01197-6
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Article ; Online: An information theoretic approach for the inference of Boolean networks and functions from data: BoCSE.

Murrugarra, David / Veliz-Cuba, Alan

Patterns (New York, N.Y.)

2022 Volume 3, Issue 11, Page(s) 100617

Abstract: Building predictive models from data is an important and challenging task in many fields including biology, medicine, engineering, and economy. In this issue, Sun et al. ...

Abstract	Building predictive models from data is an important and challenging task in many fields including biology, medicine, engineering, and economy. In this issue, Sun et al.
Language	English
Publishing date	2022-11-11
Publishing country	United States
Document type	News
ISSN	2666-3899
ISSN (online)	2666-3899
DOI	10.1016/j.patter.2022.100617
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Article ; Online: The nonlinearity of regulation in biological networks.

Manicka, Santosh / Johnson, Kathleen / Levin, Michael / Murrugarra, David

NPJ systems biology and applications

2023 Volume 9, Issue 1, Page(s) 10

Abstract: The extent to which the components of a biological system are (non)linearly regulated determines how amenable they are to therapy and control. To better understand this property termed "regulatory nonlinearity", we analyzed a suite of 137 published ... ...

Abstract	The extent to which the components of a biological system are (non)linearly regulated determines how amenable they are to therapy and control. To better understand this property termed "regulatory nonlinearity", we analyzed a suite of 137 published Boolean network models, containing a variety of complex nonlinear regulatory interactions, using a probabilistic generalization of Boolean logic that George Boole himself had proposed. Leveraging the continuous-nature of this formulation, we used Taylor decomposition to approximate the models with various levels of regulatory nonlinearity. A comparison of the resulting series of approximations of the biological models with appropriate random ensembles revealed that biological regulation tends to be less nonlinear than expected, meaning that higher-order interactions among the regulatory inputs tend to be less pronounced. A further categorical analysis of the biological models revealed that the regulatory nonlinearity of cancer and disease networks could not only be sometimes higher than expected but also be relatively more variable. We show that this variation is caused by differences in the apportioning of information among the various orders of regulatory nonlinearity. Our results suggest that there may have been a weak but discernible selection pressure for biological systems to evolve linear regulation on average, but for certain systems such as cancer, on the other hand, to simultaneously evolve more nonlinear rules.
MeSH term(s)	Models, Biological ; Nonlinear Dynamics
Language	English
Publishing date	2023-04-04
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ISSN	2056-7189
ISSN (online)	2056-7189
DOI	10.1038/s41540-023-00273-w
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Article ; Online: Modeling the Pancreatic Cancer Microenvironment in Search of Control Targets.

Plaugher, Daniel / Murrugarra, David

Bulletin of mathematical biology

2021 Volume 83, Issue 11, Page(s) 115

Abstract: Pancreatic ductal adenocarcinoma is among the leading causes of cancer-related deaths globally due to its extreme difficulty to detect and treat. Recently, research focus has shifted to analyzing the microenvironment of pancreatic cancer to better ... ...

Abstract	Pancreatic ductal adenocarcinoma is among the leading causes of cancer-related deaths globally due to its extreme difficulty to detect and treat. Recently, research focus has shifted to analyzing the microenvironment of pancreatic cancer to better understand its key molecular mechanisms. This microenvironment can be represented with a multi-scale model consisting of pancreatic cancer cells (PCCs) and pancreatic stellate cells (PSCs), as well as cytokines and growth factors which are responsible for intercellular communication between the PCCs and PSCs. We have built a stochastic Boolean network (BN) model, validated by literature and clinical data, in which we probed for intervention strategies that force this gene regulatory network (GRN) from a diseased state to a healthy state. To do so, we implemented methods from phenotype control theory to determine a procedure for regulating specific genes within the microenvironment. We identified target genes and molecules, such that the application of their control drives the GRN to the desired state by suppression (or expression) and disruption of specific signaling pathways that may eventually lead to the eradication of the cancer cells. After applying well-studied control methods such as stable motifs, feedback vertex sets, and computational algebra, we discovered that each produces a different set of control targets that are not necessarily minimal nor unique. Yet, we were able to gain more insight about the performance of each process and the overlap of targets discovered. Nearly every control set contains cytokines, KRas, and HER2/neu, which suggests they are key players in the system's dynamics. To that end, this model can be used to produce further insight into the complex biological system of pancreatic cancer with hopes of finding new potential targets.
MeSH term(s)	Carcinoma, Pancreatic Ductal/genetics ; Gene Expression Regulation, Neoplastic ; Humans ; Mathematical Concepts ; Pancreatic Neoplasms/genetics ; Tumor Microenvironment
Language	English
Publishing date	2021-10-11
Publishing country	United States
Document type	Journal Article
ZDB-ID	184905-0
ISSN	1522-9602 ; 0007-4985 ; 0092-8240
ISSN (online)	1522-9602
ISSN	0007-4985 ; 0092-8240
DOI	10.1007/s11538-021-00937-w
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Article ; Online: Modular Control of Biological Networks.

Murrugarra, David / Veliz-Cuba, Alan / Dimitrova, Elena / Kadelka, Claus / Wheeler, Matthew / Laubenbacher, Reinhard

ArXiv

2024

Abstract: The concept of control is central to understanding and applications of biological network models. Some of their key structural features relate to control functions, through gene regulation, signaling, or metabolic mechanisms, and computational models ... ...

Abstract	The concept of control is central to understanding and applications of biological network models. Some of their key structural features relate to control functions, through gene regulation, signaling, or metabolic mechanisms, and computational models need to encode these. Applications of models often focus on model-based control, such as in biomedicine or metabolic engineering. This paper presents an approach to model-based control that exploits two common features of biological networks, namely their modular structure and canalizing features of their regulatory mechanisms. The paper focuses on intracellular regulatory networks, represented by Boolean network models. A main result of this paper is that control strategies can be identified by focusing on one module at a time. This paper also presents a criterion based on canalizing features of the regulatory rules to identify modules that do not contribute to network control and can be excluded. For even moderately sized networks, finding global control inputs is computationally very challenging. The modular approach presented here leads to a highly efficient approach to solving this problem. This approach is applied to a published Boolean network model of blood cancer large granular lymphocyte (T-LGL) leukemia to identify a minimal control set that achieves a desired control objective.
Language	English
Publishing date	2024-01-23
Publishing country	United States
Document type	Preprint
ISSN	2331-8422
ISSN (online)	2331-8422
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Article ; Online: Uncovering potential interventions for pancreatic cancer patients via mathematical modeling.

Plaugher, Daniel / Aguilar, Boris / Murrugarra, David

Journal of theoretical biology

2022 Volume 548, Page(s) 111197

Abstract: Pancreatic Ductal Adenocarcinoma (PDAC) is widely known for its poor prognosis because it is often diagnosed when the cancer is in a later stage. We built a Boolean model to analyze the microenvironment of pancreatic cancer in order to better understand ... ...

Abstract	Pancreatic Ductal Adenocarcinoma (PDAC) is widely known for its poor prognosis because it is often diagnosed when the cancer is in a later stage. We built a Boolean model to analyze the microenvironment of pancreatic cancer in order to better understand the interplay between pancreatic cancer, stellate cells, and their signaling cytokines. Specifically, we have used our model to study the impact of inducing four common mutations: KRAS, TP53, SMAD4, and CDKN2A. After implementing the various mutation combinations, we used our stochastic simulator to derive aggressiveness scores based on simulated attractor probabilities and long-term trajectory approximations. These aggression scores were then corroborated with clinical data. Moreover, we found sets of control targets that are effective among common mutations. These control sets contain nodes within both the pancreatic cancer cell and the pancreatic stellate cell, including PIP3, RAF, PIK3 and BAX in pancreatic cancer cell as well as ERK and PIK3 in the pancreatic stellate cell. Many of these nodes were found to be differentially expressed among pancreatic cancer patients in the TCGA database. Furthermore, literature suggests that many of these nodes can be targeted by drugs currently in circulation. The results herein help provide a proof of concept in the path towards personalized medicine through a means of mathematical systems biology. All data and code used for running simulations, statistical analysis, and plotting is available on a GitHub repository athttps://github.com/drplaugher/PCC_Mutations.
MeSH term(s)	Carcinoma, Pancreatic Ductal/genetics ; Carcinoma, Pancreatic Ductal/pathology ; Humans ; Mutation ; Pancreatic Neoplasms/genetics ; Pancreatic Neoplasms/pathology ; Tumor Microenvironment/genetics ; Pancreatic Neoplasms
Language	English
Publishing date	2022-06-22
Publishing country	England
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
ZDB-ID	2972-5
ISSN	1095-8541 ; 0022-5193
ISSN (online)	1095-8541
ISSN	0022-5193
DOI	10.1016/j.jtbi.2022.111197
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Article ; Online: The nonlinearity of regulation in biological networks

Santosh Manicka / Kathleen Johnson / Michael Levin / David Murrugarra

npj Systems Biology and Applications, Vol 9, Iss 1, Pp 1-

2023 Volume 9

Abstract: Abstract The extent to which the components of a biological system are (non)linearly regulated determines how amenable they are to therapy and control. To better understand this property termed “regulatory nonlinearity”, we analyzed a suite of 137 ... ...

Abstract	Abstract The extent to which the components of a biological system are (non)linearly regulated determines how amenable they are to therapy and control. To better understand this property termed “regulatory nonlinearity”, we analyzed a suite of 137 published Boolean network models, containing a variety of complex nonlinear regulatory interactions, using a probabilistic generalization of Boolean logic that George Boole himself had proposed. Leveraging the continuous-nature of this formulation, we used Taylor decomposition to approximate the models with various levels of regulatory nonlinearity. A comparison of the resulting series of approximations of the biological models with appropriate random ensembles revealed that biological regulation tends to be less nonlinear than expected, meaning that higher-order interactions among the regulatory inputs tend to be less pronounced. A further categorical analysis of the biological models revealed that the regulatory nonlinearity of cancer and disease networks could not only be sometimes higher than expected but also be relatively more variable. We show that this variation is caused by differences in the apportioning of information among the various orders of regulatory nonlinearity. Our results suggest that there may have been a weak but discernible selection pressure for biological systems to evolve linear regulation on average, but for certain systems such as cancer, on the other hand, to simultaneously evolve more nonlinear rules.
Keywords	Biology (General) ; QH301-705.5
Subject code	612
Language	English
Publishing date	2023-04-01T00:00:00Z
Publisher	Nature Portfolio
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Modularity of biological systems: a link between structure and function.

Kadelka, Claus / Wheeler, Matthew / Veliz-Cuba, Alan / Murrugarra, David / Laubenbacher, Reinhard

Journal of the Royal Society, Interface

2023 Volume 20, Issue 207, Page(s) 20230505

Abstract: This paper addresses two topics in systems biology, the hypothesis that biological systems are modular and the problem of relating structure and function of biological systems. The focus here is on gene regulatory networks, represented by Boolean network ...

Abstract	This paper addresses two topics in systems biology, the hypothesis that biological systems are modular and the problem of relating structure and function of biological systems. The focus here is on gene regulatory networks, represented by Boolean network models, a commonly used tool. Most of the research on gene regulatory network modularity has focused on network structure, typically represented through either directed or undirected graphs. But since gene regulation is a highly dynamic process as it determines the function of cells over time, it is natural to consider functional modularity as well. One of the main results is that the structural decomposition of a network into modules induces an analogous decomposition of the dynamic structure, exhibiting a strong relationship between network structure and function. An extensive simulation study provides evidence for the hypothesis that modularity might have evolved to increase phenotypic complexity while maintaining maximal dynamic robustness to external perturbations.
MeSH term(s)	Computer Simulation ; Systems Biology ; Gene Regulatory Networks ; Models, Biological
Language	English
Publishing date	2023-10-25
Publishing country	England
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't ; Research Support, U.S. Gov't, Non-P.H.S.
ZDB-ID	2156283-0
ISSN	1742-5662 ; 1742-5689
ISSN (online)	1742-5662
ISSN	1742-5689
DOI	10.1098/rsif.2023.0505
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