Root Systems Biology: Integrative Modeling across Scales, from Gene Regulatory Networks to the Rhizosphere

AbstractHigh-throughput technologies, such as chromatin immunoprecipitation (ChIP) with massively parallel sequencing (ChIP-seq) have enabled cost and time efficient generation of immense amount of genome data. The advent of advanced sequencing techniques allowed biologists and bioinformaticians to investigate biological aspects of cell function and understand or reveal unexplored disease etiologies. Systems biology attempts to formulate the molecular mechanisms in mathematical models and one of the most important areas is the gene regulatory networks (GRNs), a collection of DNA segments that somehow interact with each other. GRNs incorporate valuable information about molecular targets that can be corellated to specific phenotype.In our study we highlight the need to develop new explorative tools and approaches for the integration of different types of -omics data such as ChIP-seq and GRNs using pathway analysis methodologies. We present an integrative approach for ChIP-seq and gene expression data on GRNs. Using public microarray expression samples for lung cancer and healthy subjects along with the KEGG human gene regulatory networks, we identified ways to disrupt functional sub-pathways on lung cancer with the aid of CTCF ChIP-seq data, as a proof of concept.We expect that such a systems biology pipeline could assist researchers to identify corellations and causality of transcription factors over functional or disrupted biological sub-pathways.

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Causal Concepts Guiding Model Specification in Systems Biology

Disputatio ◽

10.1515/disp-2017-0016 ◽

2017 ◽

Vol 9 (47) ◽

pp. 499-527

Author(s):

Dana Matthiessen

Keyword(s):

Systems Biology ◽

High Throughput ◽

Gene Regulatory Networks ◽

Regulatory Networks ◽

Causal Structure ◽

Model Specification ◽

Biology I ◽

Adequate Representation ◽

Gene Regulatory ◽

High Throughput Experiments

Abstract In this paper I analyze the process by which modelers in systems biology arrive at an adequate representation of the biological structures thought to underlie data gathered from high-throughput experiments. Contrary to views that causal claims and explanations are rare in systems biology, I argue that in many studies of gene regulatory networks modelers aim at a representation of causal structure. In addressing modeling challenges, they draw on assumptions informed by theory and pragmatic considerations in a manner that is guided by an interventionist conception of causal structure. While doubts have been raised about the applicability of this notion of causality to complex biological systems, it is here seen to be an adequate guide to inquiry.

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COMPLEX NETWORKS EVOLUTIONARY DYNAMICS USING GENETIC ALGORITHMS

International Journal of Bifurcation and Chaos ◽

10.1142/s0218127412501568 ◽

2012 ◽

Vol 22 (07) ◽

pp. 1250156 ◽

Cited By ~ 5

Author(s):

DANIEL AGUILAR-HIDALGO ◽

ANTONIO CÓRDOBA ZURITA ◽

Ma CARMEN LEMOS FERNÁNDEZ

Keyword(s):

Genetic Algorithm ◽

Systems Biology ◽

Complex Networks ◽

Gene Regulatory Networks ◽

Regulatory Networks ◽

Evolutionary Dynamics ◽

Network Dynamics ◽

Activity Level ◽

Transformation Rules ◽

Gene Regulatory

Gene regulatory networks set a second order approximation to genetics understanding, where the first order is the knowledge at the single gene activity level. With the increasing number of sequenced genomes, including humans, the time has come to investigate the interactions among myriads of genes that result in complex behaviors. These characteristics are included in the novel discipline of Systems Biology. The composition and unfolding of interactions among genes determine the activity of cells and, when is considered during development, the organogenesis. Hence the interest of building representative networks of gene expression and their time evolution, i.e. the structure as the network dynamics, for certain development processes. The complexity of this kind of problems makes imperative to analyze the problem in the field of network theory and the evolutionary dynamics of complex systems.All this has led us to investigate, in a first step, the evolutionary dynamics in generic networks. Thus, the results can be used in experimental researches in the field of Systems Biology. This research aims to decode the transformation rules governing the evolutionary dynamics in a network. To do this, a genetic algorithm has been implemented in which, starting from initial and ending network states, it is possible to determine the transformation dynamics between these states by using simple acting rules. The network description is the following: (a) The network node values in the initial and ending states can be active or inactive; (b) The network links can act as activators or repressors; (c) A set of rules is established in order to transform the initial state into the ending one; (d) Due to the low connectivity, frequently observed, in gene regulatory networks, each node will hold a maximum of three inputs with no restriction on outputs. The "chromosomes" of the genetic algorithm include two parts, one related to the node links and another related to the transformation rules.The implemented rules are based on certain genetic interactions behavior. The rules and their combinations are compound by logic conditions and set the bases to the network motifs formation, which are the building blocks of the network dynamics.The implemented algorithm is able to find appropriate dynamics in complex networks evolution among different states for several cases.

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MOLECULAR BIOCIRCUITS

Modern Physics Letters B ◽

10.1142/s021798490901903x ◽

2009 ◽

Vol 23 (06) ◽

pp. 773-789 ◽

Cited By ~ 1

Author(s):

OVIDIU LIPAN

Keyword(s):

Systems Biology ◽

Master Equation ◽

Gene Regulatory Networks ◽

Regulatory Networks ◽

Stochastic Approach ◽

Genetic Networks ◽

Equation Approach ◽

Master Equation Approach ◽

Gene Regulatory

Systems biology aims to describe gene regulatory networks at both experimental and theoretical levels. Mathematical formalisms used at present to describe the behavior of genetic networks range from stochastic to deterministic. The stochastic approach is further subdivided and moves from Langevin to the Master equation. This review presents the Master equation approach.

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Exploration of leukemia gene regulatory networks using a systems biology approach

2014 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) ◽

10.1109/bibm.2014.6999250 ◽

2014 ◽

Cited By ~ 1

Author(s):

Dong Ling Tong ◽

Graham R Ball

Keyword(s):

Systems Biology ◽

Gene Regulatory Networks ◽

Regulatory Networks ◽

Gene Regulatory

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CoryneRegNet 3.0—An interactive systems biology platform for the analysis of gene regulatory networks in corynebacteria and Escherichia coli

Journal of Biotechnology ◽

10.1016/j.jbiotec.2006.12.012 ◽

2007 ◽

Vol 129 (2) ◽

pp. 279-289 ◽

Cited By ~ 20

Author(s):

Jan Baumbach ◽

Tobias Wittkop ◽

Katrin Rademacher ◽

Sven Rahmann ◽

Karina Brinkrolf ◽

...

Keyword(s):

Escherichia Coli ◽

Systems Biology ◽

Gene Regulatory Networks ◽

Regulatory Networks ◽

Interactive Systems ◽

Gene Regulatory

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Efficiency, Robustness, and Stochasticity of Gene Regulatory Networks in Systems Biology: λ Switch as a Working Example

Introduction to Systems Biology ◽

10.1007/978-1-59745-531-2_18 ◽

2008 ◽

pp. 336-371 ◽

Cited By ~ 7

Author(s):

Xiaomei Zhu ◽

Lan Yin ◽

Leroy Hood ◽

David Galas ◽

Ping Ao

Keyword(s):

Systems Biology ◽

Gene Regulatory Networks ◽

Regulatory Networks ◽

Gene Regulatory

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Time-Based Systems Biology Approaches to Capture and Model Dynamic Gene Regulatory Networks

Annual Review of Plant Biology ◽

10.1146/annurev-arplant-081320-090914 ◽

2021 ◽

Vol 72 (1) ◽

Author(s):

Jose M. Alvarez ◽

Matthew D. Brooks ◽

Joseph Swift ◽

Gloria M. Coruzzi

Keyword(s):

Systems Biology ◽

Gene Regulatory Networks ◽

Regulatory Networks ◽

Environmental Changes ◽

Annual Review ◽

Publication Date ◽

Learning Approaches ◽

Dynamic Events ◽

Genome Wide ◽

Gene Regulatory

All aspects of transcription and its regulation involve dynamic events. However, capturing these dynamic events in gene regulatory networks (GRNs) offers both a promise and a challenge. The promise is that capturing and modeling the dynamic changes in GRNs will allow us to understand how organisms adapt to a changing environment. The ability to mount a rapid transcriptional response to environmental changes is especially important in nonmotile organisms such as plants. The challenge is to capture these dynamic, genome-wide events and model them in GRNs. In this review, we cover recent progress in capturing dynamic interactions of transcription factors with their targets—at both the local and genome-wide levels—and using them to learn how GRNs operate as a function of time. We also discuss recent advances that employ time-based machine learning approaches to forecast gene expression at future time points, a key goal of systems biology. Expected final online publication date for the Annual Review of Plant Biology, Volume 72 is May 2021. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

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