Finding Missing Interactions of the Arabidopsis thaliana Root Stem Cell Niche Gene Regulatory Network

A Lifelike guided journey through the pathophysiology of pulmonary hypertension-from measured metabolites to the mechanism of action of drugs

Introduction

Pulmonary hypertension (PH) is a pathological condition that affects approximately 1% of the population. The prognosis for many patients is poor, even after treatment. Our knowledge about the pathophysiological mechanisms that cause or are involved in the progression of PH is incomplete. Additionally, the mechanism of action of many drugs used to treat pulmonary hypertension, including sotatercept, requires elucidation.

Methods

Using our graph-powered knowledge mining software Lifelike in combination with a very small patient metabolite data set, we demonstrate how we derive detailed mechanistic hypotheses on the mechanisms of PH pathophysiology and clinical drugs.

Results

In PH patients, the concentration of hypoxanthine, 12(S)-HETE, glutamic acid, and sphingosine 1 phosphate is significantly higher, while the concentration of L-arginine and L-histidine is lower than in healthy controls. Using the graph-based data analysis, gene ontology, and semantic association capabilities of Lifelike, led us to connect the differentially expressed metabolites with G-protein signaling and SRC. Then, we associated SRC with IL6 signaling. Subsequently, we found associations that connect SRC, and IL6 to activin and BMP signaling. Lastly, we analyzed the mechanisms of action of several existing and novel pharmacological treatments for PH. Lifelike elucidated the interplay between G-protein, IL6, activin, and BMP signaling. Those pathways regulate hallmark pathophysiological processes of PH, including vasoconstriction, endothelial barrier function, cell proliferation, and apoptosis.

Discussion

The results highlight the importance of SRC, ERK1, AKT, and MLC activity in PH. The molecular pathways affected by existing and novel treatments for PH also converge on these molecules. Importantly, sotatercept affects SRC, ERK1, AKT, and MLC simultaneously. The present study shows the power of mining knowledge graphs using Lifelike's diverse set of data analytics functionalities for developing knowledge-driven hypotheses on PH pathophysiological and drug mechanisms and their interactions. We believe that Lifelike and our presented approach will be valuable for future mechanistic studies of PH, other diseases, and drugs.

Leveraging developmental landscapes for model selection in Boolean gene regulatory networks

Boolean models are a well-established framework to model developmental gene regulatory networks (DGRNs) for acquisition of cellular identities. During the reconstruction of Boolean DGRNs, even if the network structure is given, there is generally a large number of combinations of Boolean functions that will reproduce the different cell fates (biological attractors). Here we leverage the developmental landscape to enable model selection on such ensembles using the relative stability of the attractors. First we show that previously proposed measures of relative stability are strongly correlated and we stress the usefulness of the one that captures best the cell state transitions via the mean first passage time (MFPT) as it also allows the construction of a cellular lineage tree. A property of great computational importance is the insensitivity of the different stability measures to changes in noise intensities. That allows us to use stochastic approaches to estimate the MFPT and thereby scale up the computations to large networks. Given this methodology, we revisit different Boolean models of Arabidopsis thaliana root development, showing that a most recent one does not respect the biologically expected hierarchy of cell states based on relative stabilities. We therefore developed an iterative greedy algorithm that searches for models which satisfy the expected hierarchy of cell states and found that its application to the root development model yields many models that meet this expectation. Our methodology thus provides new tools that can enable reconstruction of more realistic and accurate Boolean models of DGRNs.

CANTATA - prediction of missing links in Boolean networks using genetic programming

Motivation

Biological processes are complex systems with distinct behaviour. Despite the growing amount of available data, knowledge is sparse and often insufficient to investigate the complex regulatory behaviour of these systems. Moreover, different cellular phenotypes are possible under varying conditions. Mathematical models attempt to unravel these mechanisms by investigating the dynamics of regulatory networks. Therefore, a major challenge is to combine regulations and phenotypical information as well as the underlying mechanisms. To predict regulatory links in these models, we established an approach called CANTATA to support the integration of information into regulatory networks and retrieve potential underlying regulations. This is achieved by optimizing both static and dynamic properties of these networks.

Results

Initial results show that the algorithm predicts missing interactions by recapitulating the known phenotypes while preserving the original topology and optimizing the robustness of the model. The resulting models allow for hypothesizing about the biological impact of certain regulatory dependencies.

Availability and implementation

Source code of the application, example files and results are available at https://github.com/sysbio-bioinf/Cantata.

Supplementary information

Supplementary data are available at Bioinformatics online.

Inference of a Boolean Network From Causal Logic Implications

Biological systems contain a large number of molecules that have diverse interactions. A fruitful path to understanding these systems is to represent them with interaction networks, and then describe flow processes in the network with a dynamic model. Boolean modeling, the simplest discrete dynamic modeling framework for biological networks, has proven its value in recapitulating experimental results and making predictions. A first step and major roadblock to the widespread use of Boolean networks in biology is the laborious network inference and construction process. Here we present a streamlined network inference method that combines the discovery of a parsimonious network structure and the identification of Boolean functions that determine the dynamics of the system. This inference method is based on a causal logic analysis method that associates a logic type (sufficient or necessary) to node-pair relationships (whether promoting or inhibitory). We use the causal logic framework to assimilate indirect information obtained from perturbation experiments and infer relationships that have not yet been documented experimentally. We apply this inference method to a well-studied process of hormone signaling in plants, the signaling underlying abscisic acid (ABA)—induced stomatal closure. Applying the causal logic inference method significantly reduces the manual work typically required for network and Boolean model construction. The inferred model agrees with the manually curated model. We also test this method by re-inferring a network representing epithelial to mesenchymal transition based on a subset of the information that was initially used to construct the model. We find that the inference method performs well for various likely scenarios of inference input information. We conclude that our method is an effective approach toward inference of biological networks and can become an efficient step in the iterative process between experiments and computations.

Identification of dynamic driver sets controlling phenotypical landscapes

Controlling phenotypical landscapes is of vital interest to modern biology. This task becomes highly demanding because cellular decisions involve complex networks engaging in crosstalk interactions. Previous work on control theory indicates that small sets of compounds can control single phenotypes. However, a dynamic approach is missing to determine the drivers of the whole network dynamics. By analyzing 35 biologically motivated Boolean networks, we developed a method to identify small sets of compounds sufficient to decide on the entire phenotypical landscape. These compounds do not strictly prefer highly related compounds and show a smaller impact on the stability of the attractor landscape. The dynamic driver sets include many intervention targets and cellular reprogramming drivers in human networks. Finally, by using a new comprehensive model of colorectal cancer, we provide a complete workflow on how to implement our approach to shift from in silico to in vitro guided experiments.

Boolean modelling in plant biology

Signalling and genetic networks underlie most biological processes and are often complex, containing many highly connected components. Modelling these networks can provide insight into mechanisms but is challenging given that rate parameters are often not well defined. Boolean modelling, in which components can only take on a binary value with connections encoded by logic equations, is able to circumvent some of these challenges, and has emerged as a viable tool to probe these complex networks. In this review, we will give an overview of Boolean modelling, with a specific emphasis on its use in plant biology. We review how Boolean modelling can be used to describe biological networks and then discuss examples of its applications in plant genetics and plant signalling.

Multi-level gene regulatory network models to understand complex mechanisms underlying plant development

Patterning in plant development is the emergent outcome of the feedback-based interplay between tissue-coupled intracellular regulatory networks and physicochemical fields. This interplay gives rise to dynamics that evolve on a wide spectrum of spatiotemporal scales. This imposes important challenges for computational approaches to model the dynamics of plant development. These challenges are being tackled in recent times by computational and mathematical advances that have made progress in the modelling of regulatory networks, as well as in approaches to couple the latter to physicochemical fields. Efforts in this direction are fundamental to identify the dynamical constraints that emerge from non-cellular autonomous activity in cell-fate decisions and patterning, and requires an understanding of how multi-level and multi-scale processes are coupled. Here, we discuss the use of multi-level modeling and simulation tools for the study of multicellular systems, with emphasis on plants. As illustrative examples, we discuss recent works elucidating the mechanisms that underlie patterning in the root meristem of Arabidopsis thaliana, and in plant responses to environmental conditions.

Reconstruction of a gene regulatory network of the induced systemic resistance defense response in Arabidopsis using boolean networks

Background

An important process for plant survival is the immune system. The induced systemic resistance (ISR) triggered by beneficial microbes is an important cost-effective defense mechanism by which plants are primed to an eventual pathogen attack. Defense mechanisms such as ISR depend on an accurate and context-specific regulation of gene expression. Interactions between genes and their products give rise to complex circuits known as gene regulatory networks (GRNs). Here, we explore the regulatory mechanism of the ISR defense response triggered by the beneficial bacterium Paraburkholderia phytofirmans PsJN in Arabidopsis thaliana plants infected with Pseudomonas syringae DC3000. To achieve this, a GRN underlying the ISR response was inferred using gene expression time-series data of certain defense-related genes, differential evolution, and threshold Boolean networks.

Results

One thousand threshold Boolean networks were inferred that met the restriction of the desired dynamics. From these networks, a consensus network was obtained that helped to find plausible interactions between the genes. A representative network was selected from the consensus network and biological restrictions were applied to it. The dynamics of the selected network showed that the largest attractor, a limit cycle of length 3, represents the final stage of the defense response (12, 18, and 24 h). Also, the structural robustness of the GRN was studied through the networks’ attractors.

Conclusions

A computational intelligence approach was designed to reconstruct a GRN underlying the ISR defense response in plants using gene expression time-series data of A. thaliana colonized by P. phytofirmans PsJN and subsequently infected with P. syringae DC3000. Using differential evolution, 1000 GRNs from time-series data were successfully inferred. Through the study of the network dynamics of the selected GRN, it can be concluded that it is structurally robust since three mutations were necessary to completely disarm the Boolean trajectory that represents the biological data. The proposed method to reconstruct GRNs is general and can be used to infer other biologically relevant networks to formulate new biological hypotheses.

Regulatory networks controlling the development of the root system and the formation of lateral roots: a comparative analysis of the roles of pericycle and vascular cambium

Background

The production of a new lateral root from parental root primary tissues has been investigated extensively, and the most important regulatory mechanisms are now well known. A first regulatory mechanism is based on the synthesis of small peptides which interact ectopically with membrane receptors to elicit a modulation of transcription factor target genes. A second mechanism involves a complex cross-talk between plant hormones. It is known that lateral roots are formed even in parental root portions characterized by the presence of secondary tissues, but there is not yet agreement about the putative tissue source providing the cells competent to become founder cells of a new root primordium.

Scope

We suggest models of possible regulatory mechanisms for inducing specific root vascular cambium (VC) stem cells to abandon their activity in the production of xylem and phloem elements and to start instead the construction of a new lateral root primordium. Considering the ontogenic nature of the VC, the models which we suggest are the result of a comparative review of mechanisms known to control the activity of stem cells in the root apical meristem, procambium and VC. Stem cells in the root meristems can inherit various competences to play different roles, and their fate could be decided in response to cross-talk between endogenous and exogenous signals.

Conclusions

We have found a high degree of relatedness among the regulatory mechanisms controlling the various root meristems. This fact suggests that competence to form new lateral roots can be inherited by some stem cells of the VC lineage. This kind of competence could be represented by a sensitivity of specific stem cells to factors such as those presented in our models.

Medical Systems Biology

The aim of this volume is to encourage the use of systems-level methodologies to contribute to the improvement of human-health . We intend to motivate biomedical researchers to complement their current theoretical and empirical practice with up-to-date systems biology conceptual approaches. Our perspective is based on the deep understanding of the key biomolecular regulatory mechanisms that underlie health, as well as the emergence and progression of human-disease . We strongly believe that the contemporary systems biology perspective opens the door to the effective development of novel methodologies to the improvement of prevention . This requires a deeper and integrative understanding of the involved underlying systems-level mechanisms. In order to explain our proposal in a simple way, in this chapter we privilege the conceptual exposition of our chosen framework over formal considerations. The formal exposition of our proposal will be expanded and discussed later in the next chapters.

Transcriptomics insights into the genetic regulation of root apical meristem exhaustion and determinate primary root growth in Pachycereus pringlei (Cactaceae)

Many Cactaceae species exhibit determinate growth of the primary root as a consequence of root apical meristem (RAM) exhaustion. The genetic regulation of this growth pattern is unknown. Here, we de novo assembled and annotated the root apex transcriptome of the Pachycereus pringlei primary root at three developmental stages, with active or exhausted RAM. The assembled transcriptome is robust and comprehensive, and was used to infer a transcriptional regulatory network of the primary root apex. Putative orthologues of Arabidopsis regulators of RAM maintenance, as well as putative lineage-specific transcripts were identified. The transcriptome revealed putative orthologues of most proteins involved in housekeeping processes, hormone signalling, and metabolic pathways. Our results suggest that specific transcriptional programs operate in the root apex at specific developmental time points. Moreover, the transcriptional state of the P. pringlei root apex as the RAM becomes exhausted is comparable to the transcriptional state of cells from the meristematic, elongation, and differentiation zones of Arabidopsis roots along the root axis. We suggest that the transcriptional program underlying the drought stress response is induced during Cactaceae root development, and that lineage-specific transcripts could contribute to RAM exhaustion in Cactaceae.

On the role of sparseness in the evolution of modularity in gene regulatory networks

Modularity is a widespread property in biological systems. It implies that interactions occur mainly within groups of system elements. A modular arrangement facilitates adjustment of one module without perturbing the rest of the system. Therefore, modularity of developmental mechanisms is a major factor for evolvability, the potential to produce beneficial variation from random genetic change. Understanding how modularity evolves in gene regulatory networks, that create the distinct gene activity patterns that characterize different parts of an organism, is key to developmental and evolutionary biology. One hypothesis for the evolution of modules suggests that interactions between some sets of genes become maladaptive when selection favours additional gene activity patterns. The removal of such interactions by selection would result in the formation of modules. A second hypothesis suggests that modularity evolves in response to sparseness, the scarcity of interactions within a system. Here I simulate the evolution of gene regulatory networks and analyse diverse experimentally sustained networks to study the relationship between sparseness and modularity. My results suggest that sparseness alone is neither sufficient nor necessary to explain modularity in gene regulatory networks. However, sparseness amplifies the effects of forms of selection that, like selection for additional gene activity patterns, already produce an increase in modularity. That evolution of new gene activity patterns is frequent across evolution also supports that it is a major factor in the evolution of modularity. That sparseness is widespread across gene regulatory networks indicates that it may have facilitated the evolution of modules in a wide variety of cases.

Modular systems have performance and design advantages over non-modular systems. Thus, modularity is very important for the development of a wide range of new technological or clinical applications. Moreover, modularity is paramount to evolutionary biology since it allows adjusting one organismal function without disturbing other previously evolved functions. But how does modularity itself evolve? Here I analyse the structure of regulatory networks and follow simulations of network evolution to study two hypotheses for the origin of modules in gene regulatory networks. The first hypothesis considers that sparseness, a low number of interactions among the network genes, could be responsible for the evolution of modular networks. The second, that modules evolve when selection favours the production of additional gene activity patterns. I found that sparseness alone is neither sufficient nor necessary to explain modularity in gene regulatory networks. However, it enhances the effects of selection for multiple gene activity patterns. While selection for multiple patterns may be decisive in the evolution of modularity, that sparseness is widespread across gene regulatory networks suggests that its contributions should not be neglected.

Griffin: A Tool for Symbolic Inference of Synchronous Boolean Molecular Networks

Boolean networks are important models of biochemical systems, located at the high end of the abstraction spectrum. A number of Boolean gene networks have been inferred following essentially the same method. Such a method first considers experimental data for a typically underdetermined “regulation” graph. Next, Boolean networks are inferred by using biological constraints to narrow the search space, such as a desired set of (fixed-point or cyclic) attractors. We describe Griffin, a computer tool enhancing this method. Griffin incorporates a number of well-established algorithms, such as Dubrova and Teslenko's algorithm for finding attractors in synchronous Boolean networks. In addition, a formal definition of regulation allows Griffin to employ “symbolic” techniques, able to represent both large sets of network states and Boolean constraints. We observe that when the set of attractors is required to be an exact set, prohibiting additional attractors, a naive Boolean coding of this constraint may be unfeasible. Such cases may be intractable even with symbolic methods, as the number of Boolean constraints may be astronomically large. To overcome this problem, we employ an Artificial Intelligence technique known as “clause learning” considerably increasing Griffin's scalability. Without clause learning only toy examples prohibiting additional attractors are solvable: only one out of seven queries reported here is answered. With clause learning, by contrast, all seven queries are answered. We illustrate Griffin with three case studies drawn from the Arabidopsis thaliana literature. Griffin is available at: http://turing.iimas.unam.mx/griffin.

A dynamic genetic-hormonal regulatory network model explains multiple cellular behaviors of the root apical meristem of Arabidopsis thaliana

The study of the concerted action of hormones and transcription factors is fundamental to understand cell differentiation and pattern formation during organ development. The root apical meristem of Arabidopsis thaliana is a useful model to address this. It has a stem cell niche near its tip conformed of a quiescent organizer and stem or initial cells around it, then a proliferation domain followed by a transition domain, where cells diminish division rate before transiting to the elongation zone; here, cells grow anisotropically prior to their final differentiation towards the plant base. A minimal model of the gene regulatory network that underlies cell-fate specification and patterning at the root stem cell niche was proposed before. In this study, we update and couple such network with both the auxin and cytokinin hormone signaling pathways to address how they collectively give rise to attractors that correspond to the genetic and hormonal activity profiles that are characteristic of different cell types along A. thaliana root apical meristem. We used a Boolean model of the genetic-hormonal regulatory network to integrate known and predicted regulatory interactions into alternative models. Our analyses show that, after adding some putative missing interactions, the model includes the necessary and sufficient components and regulatory interactions to recover attractors characteristic of the root cell types, including the auxin and cytokinin activity profiles that correlate with different cellular behaviors along the root apical meristem. Furthermore, the model predicts the existence of activity configurations that could correspond to the transition domain. The model also provides a possible explanation for apparently paradoxical cellular behaviors in the root meristem. For example, how auxin may induce and at the same time inhibit WOX5 expression. According to the model proposed here the hormonal regulation of WOX5 might depend on the cell type. Our results illustrate how non-linear multi-stable qualitative network models can aid at understanding how transcriptional regulators and hormonal signaling pathways are dynamically coupled and may underlie both the acquisition of cell fate and the emergence of hormonal activity profiles that arise during complex organ development.

The combination of the functionalities of feedback circuits is determinant for the attractors' number and size in pathway-like Boolean networks

Molecular regulation was initially assumed to follow both a unidirectional and a hierarchical organization forming pathways. Regulatory processes, however, form highly interlinked networks with non-hierarchical and non-unidirectional structures that contain statistically overrepresented circuits or motifs. Here, we analyze the behavior of pathways containing non-unidirectional (i.e. bidirectional) and non-hierarchical interactions that create motifs. In comparison with unidirectional and hierarchical pathways, our pathways have a high diversity of behaviors, characterized by the size and number of attractors. Motifs have been studied individually showing that feedback circuit motifs regulate the number and size of attractors. It is less clear what happens in molecular networks that usually contain multiple feedbacks. Here, we find that the way feedback circuits couple to each other (i.e., the combination of the functionalities of feedback circuits) regulate both the number and size of the attractors. We show that the different expected results of epistasis analysis (a method to infer regulatory interactions) are produced by many non-hierarchical and non-unidirectional structures. Thus, these structures cannot be correctly inferred by epistasis analysis. Finally, we show that the combinations of functionalities, combined with other network properties, allow for a better characterization of regulatory structures.

Boolean Dynamic Modeling Approaches to Study Plant Gene Regulatory Networks: Integration, Validation, and Prediction

Mathematical models based on dynamical systems theory are well-suited tools for the integration of available molecular experimental data into coherent frameworks in order to propose hypotheses about the cooperative regulatory mechanisms driving developmental processes. Computational analysis of the proposed models using well-established methods enables testing the hypotheses by contrasting predictions with observations. Within such framework, Boolean gene regulatory network dynamical models have been extensively used in modeling plant development. Boolean models are simple and intuitively appealing, ideal tools for collaborative efforts between theorists and experimentalists. In this chapter we present protocols used in our group for the study of diverse plant developmental processes. We focus on conceptual clarity and practical implementation, providing directions to the corresponding technical literature.

The MADS-box XAANTAL1 increases proliferation at the Arabidopsis root stem-cell niche and participates in transition to differentiation by regulating cell-cycle components

Background Morphogenesis depends on the concerted modulation of cell proliferation and differentiation. Such modulation is dynamically adjusted in response to various external and internal signals via complex transcriptional regulatory networks that mediate between such signals and regulation of cell-cycle and cellular responses (proliferation, growth, differentiation). In plants, which are sessile, the proliferation/differentiation balance is plastically adjusted during their life cycle and transcriptional networks are important in this process. MADS-box genes are key developmental regulators in eukaryotes, but their role in cell proliferation and differentiation modulation in plants remains poorly studied. Methods We characterize the XAL1 loss-of-function xal1-2 allele and overexpression lines using quantitative cellular and cytometry analyses to explore its role in cell cycle, proliferation, stem-cell patterning and transition to differentiation. We used quantitative PCR and cellular markers to explore if XAL1 regulates cell-cycle components and PLETHORA1 (PLT1) gene expression, as well as confocal microscopy to analyse stem-cell niche organization. Key Results We previously showed that XAANTAL1 (XAL1/AGL12) is necessary for Arabidopsis root development as a promoter of cell proliferation in the root apical meristem. Here, we demonstrate that XAL1 positively regulates the expression of PLT1 and important components of the cell cycle: CYCD3;1, CYCA2;3, CYCB1;1, CDKB1;1 and CDT1a. In addition, we show that xal1-2 mutant plants have a premature transition to differentiation with root hairs appearing closer to the root tip, while endoreplication in these plants is partially compromised. Coincidently, the final size of cortex cells in the mutant is shorter than wild-type cells. Finally, XAL1 overexpression-lines corroborate that this transcription factor is able to promote cell proliferation at the stem-cell niche. Conclusion XAL1 seems to be an important component of the networks that modulate cell proliferation/differentiation transition and stem-cell proliferation during Arabidopsis root development; it also regulates several cell-cycle components.

The effect of spaceflight on the gravity-sensing auxin gradient of roots: GFP reporter gene microscopy on orbit

Our primary aim was to determine whether gravity has a direct role in establishing the auxin-mediated gravity-sensing system in primary roots. Major plant architectures have long been thought to be guided by gravity, including the directional growth of the primary root via auxin gradients that are then disturbed when roots deviate from the vertical as a gravity sensor. However, experiments on the International Space Station (ISS) now allow physical clarity with regard to any assumptions regarding the role of gravity in establishing fundamental root auxin distributions. We examined the spaceflight green fluorescent protein (GFP)-reporter gene expression in roots of transgenic lines of Arabidopsis thaliana: pDR5r::GFP, pTAA1::TAA1–GFP, pSCR::SCR–GFP to monitor auxin and pARR5::GFP to monitor cytokinin. Plants on the ISS were imaged live with the Light Microscopy Module (LMM), and compared with control plants imaged on the ground. Preserved spaceflight and ground control plants were examined post flight with confocal microscopy. Plants on orbit, growing in the absence of any physical reference to the terrestrial gravity vector, displayed typically “vertical” distribution of auxin in the primary root. This confirms that the establishment of the auxin-gradient system, the primary guide for gravity signaling in the root, is gravity independent. The cytokinin distribution in the root tip differs between spaceflight and the ground controls, suggesting spaceflight-induced features of root growth may be cytokinin related. The distribution of auxin in the gravity-sensing portion of the root is not dependent on gravity. Spaceflight appears benign to auxin and its role in the development of the primary root tip, whereas spaceflight may influence cytokinin-associated processes.

PP2A-3 interacts with ACR4 and regulates formative cell division in the Arabidopsis root

In plants, the generation of new cell types and tissues depends on coordinated and oriented formative cell divisions. The plasma membrane-localized receptor kinase ARABIDOPSIS CRINKLY 4 (ACR4) is part of a mechanism controlling formative cell divisions in the Arabidopsis root. Despite its important role in plant development, very little is known about the molecular mechanism with which ACR4 is affiliated and its network of interactions. Here, we used various complementary proteomic approaches to identify ACR4-interacting protein candidates that are likely regulators of formative cell divisions and that could pave the way to unraveling the molecular basis behind ACR4-mediated signaling. We identified PROTEIN PHOSPHATASE 2A-3 (PP2A-3), a catalytic subunit of PP2A holoenzymes, as a previously unidentified regulator of formative cell divisions and as one of the first described substrates of ACR4. Our in vitro data argue for the existence of a tight posttranslational regulation in the associated biochemical network through reciprocal regulation between ACR4 and PP2A-3 at the phosphorylation level.

Convergence behaviour and Control in Non-Linear Biological Networks

Control of genetic regulatory networks is challenging to define and quantify. Previous control centrality metrics, which aim to capture the ability of individual nodes to control the system, have been found to suffer from plausibility and applicability problems. Here we present a new approach to control centrality based on network convergence behaviour, implemented as an extension of our genetic regulatory network simulation framework Jimena ( http://stefan-karl.de/jimena). We distinguish three types of network control, and show how these mathematical concepts correspond to experimentally verified node functions and signalling pathways in immunity and cell differentiation: Total control centrality quantifies the impact of node mutations and identifies potential pharmacological targets such as genes involved in oncogenesis (e.g. zinc finger protein GLI2 or bone morphogenetic proteins in chondrocytes). Dynamic control centrality describes relaying functions as observed in signalling cascades (e.g. src kinase or Jak/Stat pathways). Value control centrality measures the direct influence of the value of the node on the network (e.g. Indian hedgehog as an essential regulator of proliferation in chondrocytes). Surveying random scale-free networks and biological networks, we find that control of the network resides in few high degree driver nodes and networks can be controlled best if they are sparsely connected.

Role of transcriptional regulation in the evolution of plant phenotype: A dynamic systems approach

A growing body of evidence suggests that alterations in transcriptional regulation of genes involved in modulating development are an important part of phenotypic evolution, and this can be documented among species and within populations. While the effects of differential transcriptional regulation in organismal development have been preferentially studied in animal systems, this phenomenon has also been addressed in plants. In this review, we summarize evidence for cis-regulatory mutations, trans-regulatory changes and epigenetic modifications as molecular events underlying important phenotypic alterations, and thus shaping the evolution of plant development. We postulate that a mechanistic understanding of why such molecular alterations have a key role in development, morphology and evolution will have to rely on dynamic models of complex regulatory networks that consider the concerted action of genetic and nongenetic components, and that also incorporate the restrictions underlying the genotype to phenotype mapping process. Developmental Dynamics 244:1074-1095, 2015. © 2015 Wiley Periodicals, Inc.

Descriptive vs. mechanistic network models in plant development in the post-genomic era

Network modeling is now a widespread practice in systems biology, as well as in integrative genomics, and it constitutes a rich and diverse scientific research field. A conceptually clear understanding of the reasoning behind the main existing modeling approaches, and their associated technical terminologies, is required to avoid confusions and accelerate the transition towards an undeniable necessary more quantitative, multidisciplinary approach to biology. Herein, we focus on two main network-based modeling approaches that are commonly used depending on the information available and the intended goals: inference-based methods and system dynamics approaches. As far as data-based network inference methods are concerned, they enable the discovery of potential functional influences among molecular components. On the other hand, experimentally grounded network dynamical models have been shown to be perfectly suited for the mechanistic study of developmental processes. How do these two perspectives relate to each other? In this chapter, we describe and compare both approaches and then apply them to a given specific developmental module. Along with the step-by-step practical implementation of each approach, we also focus on discussing their respective goals, utility, assumptions, and associated limitations. We use the gene regulatory network (GRN) involved in Arabidopsis thaliana Root Stem Cell Niche patterning as our illustrative example. We show that descriptive models based on functional genomics data can provide important background information consistent with experimentally supported functional relationships integrated in mechanistic GRN models. The rationale of analysis and modeling can be applied to any other well-characterized functional developmental module in multicellular organisms, like plants and animals.

Plant and animal stem cells: similar yet different

The astonishingly long lives of plants and their regeneration capacity depend on the activity of plant stem cells. As in animals, stem cells reside in stem cell niches, which produce signals that regulate the balance between self-renewal and the generation of daughter cells that differentiate into new tissues. Plant stem cell niches are located within the meristems, which are organized structures that are responsible for most post-embryonic development. The continuous organ production that is characteristic of plant growth requires a robust regulatory network to keep the balance between pluripotent stem cells and differentiating progeny. Components of this network have now been elucidated and provide a unique opportunity for comparing strategies that were developed in the animal and plant kingdoms, which underlie the logic of stem cell behaviour.

Postembryonic control of root meristem growth and development

Organ development in multicellular organisms is dependent on the proper balance between cell proliferation and differentiation. In the Arabidopsis root apical meristem, meristem growth is the result of cell divisions in the proximal meristem and cell differentiation in the elongation and differentiation zones. Hormones, transcription factors and small peptides underpin the molecular mechanisms governing these processes. Computer modeling has aided our understanding of the dynamic interactions involved in stem cell maintenance and meristem activity. Here we review recent advances in our understanding of postembryonic root stem cell maintenance and control of meristem size.