The regulatory network that controls the differentiation of T lymphocytes

Martínez‐Sosa, Pablo; Mendoza, Luis

doi:10.1016/j.biosystems.2013.05.007

Cited by 34 publications

(24 citation statements)

References 68 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…According to what just said, it can be classified as a multiscale agent-based model. It consists in an agent-based formulation of the cell-cell/molecules interaction pertaining to hypersensitive responses to a generic allergen in which a detailed gene regulation dynamics is modeled by means of a Boolean network [55] (other approaches, as the use of a system of ordinary differential equations, would work as well [56]). The two levels (the intra- and the inter-cellular) are integrated in a quite intuitive way.…”

Section: Multiscale Methodsmentioning

confidence: 99%

Modeling Biology Spanning Different Scales: An Open Challenge

Castiglione

Pappalardo

Bianca

et al. 2014

BioMed Research International

View full text Add to dashboard Cite

It is coming nowadays more clear that in order to obtain a unified description of the different mechanisms governing the behavior and causality relations among the various parts of a living system, the development of comprehensive computational and mathematical models at different space and time scales is required. This is one of the most formidable challenges of modern biology characterized by the availability of huge amount of high throughput measurements. In this paper we draw attention to the importance of multiscale modeling in the framework of studies of biological systems in general and of the immune system in particular.

show abstract

Section: Multiscale Methodsmentioning

confidence: 99%

Modeling Biology Spanning Different Scales: An Open Challenge

Castiglione

Pappalardo

Bianca

et al. 2014

BioMed Research International

View full text Add to dashboard Cite

show abstract

“…In addition, a few studies have extended pre-existing networks to investigate specific interests. The main goals for developing Boolean networks have been to identify potential therapeutic strategies [106,17,18,107,25,26,28–32,34], characterize cellular differentiation [14,11,13,36], understand differential responses to cancer therapies due to mutational differences [27], understand the impact of patient heterogeneity on the response to drug treatments [21,35], and as an initial framework prior to the development of quantitative models [23]. The networks provided in this table could be potentially extended or repurposed for investigating additional features of interest, as opposed to starting from the ground up.…”

Section: An Overview Of Boolean Network Applicationsmentioning

confidence: 99%

“…The state of each node is governed by the previous states of its regulating nodes through a set of logical functions. Boolean networks have been applied to model signal transduction, gene regulation, and cellular differentiation for several types of physiological and pathophysiological systems, such as the immune system and related diseases [11–23], breast cancer [24–29], gastrointestinal cancers [30–32], hepatic cancer [33,34], lung cancer [35], and several others [36–40]. In oncology, Boolean network modeling can provide a framework for studying system trajectories under pathophysiological conditions and in response to drug treatment.…”

Section: Introductionmentioning

confidence: 99%

Boolean network modeling in systems pharmacology

Bloomingdale

Nguyen

Niu

et al. 2018

J Pharmacokinet Pharmacodyn

View full text Add to dashboard Cite

Quantitative systems pharmacology (QSP) is an emerging discipline that aims to discover how drugs modulate the dynamics of biological components in molecular and cellular networks and the impact of those perturbations on human pathophysiology. The integration of systems-based experimental and computational approaches is required to facilitate the advancement of this field. QSP models typically consist of a series of ordinary differential equations (ODE). However, this mathematical framework requires extensive knowledge of parameters pertaining to biological processes, which is often unavailable. An alternative framework that does not require knowledge of system-specific parameters, such as Boolean network modeling, could serve as an initial foundation prior to the development of an ODE-based model. Boolean network models have been shown to efficiently describe, in a qualitative manner, the complex behavior of signal transduction and gene/protein regulatory processes. In addition to providing a starting point prior to quantitative modeling, Boolean network models can also be utilized to discover novel therapeutic targets and combinatorial treatment strategies. Identifying drug targets using a network-based approach could supplement current drug discovery methodologies and help to fill the innovation gap across the pharmaceutical industry. In this review, we discuss the process of developing Boolean network models and the various analyses that can be performed to identify novel drug targets and combinatorial approaches. An example for each of these analyses is provided using a previously developed Boolean network of signaling pathways in multiple myeloma. Selected examples of Boolean network models of human (patho-)physiological systems are also reviewed in brief.

show abstract

“…10,11 The CD 41 T-lymphocytes could differentiate into several subsets such as Th1, Th2, and Treg etc., and they are involved in various immune responses. 12,13 Th1 is induced by IL-12 and IFN-g, secretes IFN-g and is involved in cell-mediated immune response; Th2 is induced by IL-4, secretes IL-4, IL-5, and IL-13 and is involved in the humoral immune response; studies have demonstrated that during immune response, IL-2, IL-4, IL-5, IL-6, etc. can promote T-lymphocytes activation, proliferation, and differentiation.…”

Section: Introductionmentioning

confidence: 99%

Analysis of the roles of dietary protein and calcium in fluoride‐induced changes in T‐lymphocyte subsets in rat

Wang

Zhou

Niu

et al. 2017

Environmental Toxicology

View full text Add to dashboard Cite

The roles of dietary protein (Pr) and calcium (Ca) levels on the changes in T-lymphocyte subsets induced by excessive fluoride (F) intake were assessed using rats that were malnourished for 120 days as a model. The CD and CD T-lymphocytes in the spleen tissue were determined by flow cytometry and immunofluorescence assay. The percentages of CD , CD , and CD T-lymphocytes were reduced in the spleen of rats exposed to excessive F, and malnutrition aggravated these changes in the T-lymphocytes. In addition, the mRNA expression levels of IL-1β, IL-2, IL-6, TNF-α, and IFN-γ in the spleen were downregulated significantly. We also reported herein the increased apoptosis ratio following caspase-9 and caspase-3 upregulation in the spleen of rats exposed to excessive amount of F. Light and transmisison electron microscopy revealed the irregularly arranged lymphocytes, few lymph nodules and the apoptotic characteristic of lymphocytes, which are caused by the increased expression of caspase. In addition, Pr and Ca supplementation reversed the morphologic and T-lymphocytic changes in spleen under malnutrition. Taken together, our results revealed an endogenous caspase-mediated mechanism of regulating the apoptosis of the T-lymphocyte subsets, as well as the immune-related cytokine secretion, which reduces the immune function in F-induced rats. © 2016 Wiley Periodicals, Inc. Environ Toxicol 32: 1587-1595, 2017.

show abstract

The regulatory network that controls the differentiation of T lymphocytes

Cited by 34 publications

References 68 publications

Modeling Biology Spanning Different Scales: An Open Challenge

Modeling Biology Spanning Different Scales: An Open Challenge

Boolean network modeling in systems pharmacology

Analysis of the roles of dietary protein and calcium in fluoride‐induced changes in T‐lymphocyte subsets in rat

Contact Info

Product

Resources

About