A stochastic model of epigenetic dynamics in somatic cell reprogramming

Epigenetic OCT4 regulatory network: stochastic analysis of cellular reprogramming

Experimental studies have shown that chromatin modifiers have a critical effect on cellular reprogramming, i.e., the conversion of differentiated cells to pluripotent stem cells. Here, we develop a model of the OCT4 gene regulatory network that includes genes expressing chromatin modifiers TET1 and JMJD2, and the chromatin modification circuit on which these modifiers act. We employ this model to compare three reprogramming approaches that have been considered in the literature with respect to reprogramming efficiency and latency variability. These approaches are overexpression of OCT4 alone, overexpression of OCT4 with TET1, and overexpression of OCT4 with JMJD2. Our results show more efficient and less variable reprogramming when also JMJD2 and TET1 are overexpressed, consistent with previous experimental data. Nevertheless, TET1 overexpression can lead to more efficient reprogramming compared to JMJD2 overexpression. This is the case when the recruitment of DNA methylation by H3K9me3 is weak and the methyl-CpG-binding domain (MBD) proteins are sufficiently scarce such that they do not hamper TET1 binding to methylated DNA. The model that we developed provides a mechanistic understanding of existing experimental results and is also a tool for designing optimized reprogramming approaches that combine overexpression of cell-fate specific transcription factors (TFs) with targeted recruitment of epigenetic modifiers.

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.

The epigenetic Oct4 gene regulatory network: stochastic analysis of different cellular reprogramming approaches

In the last decade, several experimental studies have shown how chromatin modifications (histone modifications and DNA methylation) and their effect on DNA compaction have a critical effect on cellular reprogramming, i.e., the conversion of differentiated cells to a pluripotent state. In this paper, we compare three reprogramming approaches that have been considered in the literature: (a) prefixed overexpression of transcription factors (TFs) alone (Oct4), (b) prefixed overexpression of Oct4 and DNA methylation "eraser" TET, and (c) prefixed overexpression of Oct4 and H3K9me3 eraser JMJD2. To this end, we develop a model of the pluritpotency gene regulatory network, that includes, for each gene, a circuit recently published encapsulating the main interactions among chromatin modifications and their effect on gene expression. We then conduct a computational study to evaluate, for each reprogramming approach, latency and variability. Our results show a faster and less stochastic reprogramming process when also eraser enzymes are overexpressed, consistent with previous experimental data. However, TET overexpression leads to a faster and more efficient reprogramming compared to JMJD2 overexpression when the recruitment of DNA methylation by H3K9me3 is weak and the MBD protein level is sufficiently low such that it does not hamper TET binding to methylated DNA. The model developed here provides a mechanistic understanding of the outcomes of former experimental studies and is also a tool for the development of optimized reprogramming approaches that combine TF overexpression with modifiers of chromatin state.

The dynamics of chromatin states mediated by epigenetic modifications during somatic cell reprogramming

Somatic cell reprogramming (SCR) is the conversion of differentiated somatic cells into totipotent or pluripotent cells through a variety of methods. Somatic cell reprogramming also provides a platform to investigate the role of chromatin-based factors in establishing and maintaining totipotency or pluripotency, since high expression of totipotency- or pluripotency-related genes usually require an active chromatin state. Several studies in plants or mammals have recently shed light on the molecular mechanisms by which epigenetic modifications regulate the expression of totipotency or pluripotency genes by altering their chromatin states. In this review, we present a comprehensive overview of the dynamic changes in epigenetic modifications and chromatin states during reprogramming from somatic cells to totipotent or pluripotent cells. In addition, we illustrate the potential role of DNA methylation, histone modifications, histone variants, and chromatin remodeling during somatic cell reprogramming, which will pave the way to developing reliable strategies for efficient cellular reprogramming.

Epigenetic cell memory: The gene's inner chromatin modification circuit

Epigenetic cell memory allows distinct gene expression patterns to persist in different cell types despite a common genotype. Although different patterns can be maintained by the concerted action of transcription factors (TFs), it was proposed that long-term persistence hinges on chromatin state. Here, we study how the dynamics of chromatin state affect memory, and focus on a biologically motivated circuit motif, among histones and DNA modifications, that mediates the action of TFs on gene expression. Memory arises from time-scale separation among three circuit's constituent processes: basal erasure, auto and cross-catalysis, and recruited erasure of modifications. When the two latter processes are sufficiently faster than the former, the circuit exhibits bistability and hysteresis, allowing active and repressed gene states to coexist and persist after TF stimulus removal. The duration of memory is stochastic with a mean value that increases as time-scale separation increases, but more so for the repressed state. This asymmetry stems from the cross-catalysis between repressive histone modifications and DNA methylation and is enhanced by the relatively slower decay rate of the latter. Nevertheless, TF-mediated positive autoregulation can rebalance this asymmetry and even confers robustness of active states to repressive stimuli. More generally, by wiring positively autoregulated chromatin modification circuits under time scale separation, long-term distinct gene expression patterns arise, which are also robust to failure in the regulatory links.

Promoter methylation in a mixed feedback loop circadian clock model

We investigate how epigenetic modifications to clock gene promoters affect transcriptomic activity in the circadian clock. Motivated by experimental observations that link DNA methylation with the behavior of the clock, we introduce and analyze an extension of the mixed feedback loop (MFL) model of François and Hakim. We extend the original model to include an additional methylated promoter state and allow for reversible protein sequestration, an important feature for circadian applications. First, working with the general form of the MFL model, we find that the qualitative behavior of the model is dictated by the promoter state with the highest transcription rate. We then build on the work of Kim and Forger, who analyzed the stability of the mammalian circadian clock by using a reduced form of the MFL model. We present a rigorous procedure for translating between the MFL model and the reduction of Kim and Forger. We then propose a model reduction more appropriate for the study of oscillatory promoter states, making use of a fully coupled quasi-steady-state approximation rather than the standard partially uncoupled quasi-steady-state approach. Working with the novel reduced form of the model, we find substantial differences in the transcription function and show that, although methylation contributes to period control, excessive methylation can abolish rhythmicity. Altogether our results show that even in a minimal clock model, DNA methylation has a nontrivial influence on the system's ability to oscillate.

Finite-Time Set Reachability of Probabilistic Boolean Multiplex Control Networks

This study focuses on the finite-time set reachability of probabilistic Boolean multiplex control networks (PBMCNs). Firstly, based on the state transfer graph (STG) reconstruction technique, the PBMCNs are extended to random logic dynamical systems. Then, a necessary and sufficient condition for the finite-time set reachability of PBMCNs is obtained. Finally, the obtained results are effectively illustrated by an example.

A mathematical model exhibiting the effect of DNA methylation on the stability boundary in cell-fate networks

Cell-fate networks are traditionally studied within the framework of gene regulatory networks. This paradigm considers only interactions of genes through expressed transcription factors and does not incorporate chromatin modification processes. This paper introduces a mathematical model that seamlessly combines gene regulatory networks and DNA methylation (DNAm), with the goal of quantitatively characterizing the contribution of epigenetic regulation to gene silencing. The 'Basin of Attraction percentage' is introduced as a metric to quantify gene silencing abilities. As a case study, a computational and theoretical analysis is carried out for a model of the pluripotent stem cell circuit as well as a simplified self-activating gene model. The results confirm that the methodology quantitatively captures the key role that DNAm plays in enhancing the stability of the silenced gene state.

Transcompp: understanding phenotypic plasticity by estimating Markov transition rates for cell state transitions

MOTIVATION

Gradual population-level changes in tissues can be driven by stochastic plasticity, meaning rare stochastic transitions of single-cell phenotype. Quantifying the rates of these stochastic transitions requires time-intensive experiments, and analysis is generally confounded by simultaneous bidirectional transitions and asymmetric proliferation kinetics. To quantify cellular plasticity, we developed Transcompp (Transition Rate ANalysis of Single Cells to Observe and Measure Phenotypic Plasticity), a Markov modeling algorithm that uses optimization and resampling to compute best-fit rates and statistical intervals for stochastic cell-state transitions.

RESULTS

We applied Transcompp to time-series datasets in which purified subpopulations of stem-like or non-stem cancer cells were exposed to various cell culture environments, and allowed to re-equilibrate spontaneously over time. Results revealed that commonly used cell culture reagents hydrocortisone and cholera toxin shifted the cell population equilibrium toward stem-like or non-stem states, respectively, in the basal-like breast cancer cell line MCF10CA1a. In addition, applying Transcompp to patient-derived cells showed that transition rates computed from short-term experiments could predict long-term trajectories and equilibrium convergence of the cultured cell population.

AVAILABILITY AND IMPLEMENTATION

Freely available for download at http://github.com/nsuhasj/Transcompp.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Computational models to explore the complexity of the epithelial to mesenchymal transition in cancer

Epithelial to mesenchymal transition (EMT) is a complex biological process that plays a key role in cancer progression and metastasis formation. Its activation results in epithelial cells losing adhesion and polarity and becoming capable of migrating from their site of origin. At this step the disease is generally considered incurable. As EMT execution involves several individual molecular components, connected by nontrivial relations, in vitro techniques are often inadequate to capture its complexity. Computational models can be used to complement experiments and provide additional knowledge difficult to build up in a wetlab. Indeed in silico analysis gives the user total control on the system, allowing to identify the contribution of each independent element. In the following, two kinds of approaches to the computational study of EMT will be presented. The first relies on signal transduction networks description and details how changes in gene expression could influence this process, both focusing on specific aspects of the EMT and providing a general frame for this phenomenon easily comparable with experimental data. The second integrates single cell and population level descriptions in a multiscale model that can be considered a more accurate representation of the EMT. The advantages and disadvantages of each approach will be highlighted, together with the importance of coupling computational and experimental results. Finally, the main challenges that need to be addressed to improve our knowledge of the role of EMT in the neoplastic disease and the scientific and translational value of computational models in this respect will be presented. This article is categorized under: Analytical and Computational Methods > Computational Methods.

Identification via Numerical Computation of Transcriptional Determinants of a Cell Phenotype Decision Making

Complex cellular processes, such as phenotype decision making, are exceedingly difficult to analyze experimentally, due to the multiple-layer regulation of gene expression and the intercellular variability referred to as biological noise. Moreover, the heterogeneous experimental approaches used to investigate distinct macromolecular species, and their intrinsic differential time-scale dynamics, add further intricacy to the general picture of the physiological phenomenon. In this respect, a computational representation of the cellular functions of interest can be used to extract relevant information, being able to highlight meaningful active markers within the plethora of actors forming an active molecular network. The multiscale power of such an approach can also provide meaningful descriptions for both population and single-cell level events. To validate this paradigm a Boolean and a Markov model were combined to identify, in an objective and user-independent manner, a signature of genes recapitulating epithelial to mesenchymal transition in-vitro. The predictions of the model are in agreement with experimental data and revealed how the expression of specific molecular markers is related to distinct cell behaviors. The presented method strengthens the evidence of a role for computational representation of active molecular networks to gain insight into cellular physiology and as a general approach for integrating in-silico/in-vitro study of complex cell population dynamics to identify their most relevant drivers.

Computational modelling folate metabolism and DNA methylation: implications for understanding health and ageing

Dietary folates have a key role to play in health, as deficiencies in the intake of these B vitamins have been implicated in a wide variety of clinical conditions. The reason for this is folates function as single carbon donors in the synthesis of methionine and nucleotides. Moreover, folates have a vital role to play in the epigenetics of mammalian cells by supplying methyl groups for DNA methylation reactions. Intriguingly, a growing body of experimental evidence suggests that DNA methylation status could be a central modulator of the ageing process. This has important health implications because the methylation status of the human genome could be used to infer age-related disease risk. Thus, it is imperative we further our understanding of the processes which underpin DNA methylation and how these intersect with folate metabolism and ageing. The biochemical and molecular mechanisms, which underpin these processes, are complex. However, computational modelling offers an ideal framework for handling this complexity. A number of computational models have been assembled over the years, but to date, no model has represented the full scope of the interaction between the folate cycle and the reactions, which governs the DNA methylation cycle. In this review, we will discuss several of the models, which have been developed to represent these systems. In addition, we will present a rationale for developing a combined model of folate metabolism and the DNA methylation cycle.

NetLand: quantitative modeling and visualization of Waddington's epigenetic landscape using probabilistic potential

Summary

Waddington’s epigenetic landscape is a powerful metaphor for cellular dynamics driven by gene regulatory networks (GRNs). Its quantitative modeling and visualization, however, remains a challenge, especially when there are more than two genes in the network. A software tool for Waddington’s landscape has not been available in the literature. We present NetLand, an open-source software tool for modeling and simulating the kinetic dynamics of GRNs, and visualizing the corresponding Waddington’s epigenetic landscape in three dimensions without restriction on the number of genes in a GRN. With an interactive and graphical user interface, NetLand can facilitate the knowledge discovery and experimental design in the study of cell fate regulation (e.g. stem cell differentiation and reprogramming).

Availability and Implementation

NetLand can run under operating systems including Windows, Linux and OS X. The executive files and source code of NetLand as well as a user manual, example models etc. can be downloaded from http://netland-ntu.github.io/NetLand/.

Supplementary information

Supplementary data are available at Bioinformatics online.

Exact Identification of the Structure of a Probabilistic Boolean Network from Samples

We study the number of samples required to uniquely determine the structure of a probabilistic Boolean network (PBN), where PBNs are probabilistic extensions of Boolean networks. We show via theoretical analysis and computational analysis that the structure of a PBN can be exactly identified with high probability from a relatively small number of samples for interesting classes of PBNs of bounded indegree. On the other hand, we also show that there exist classes of PBNs for which it is impossible to uniquely determine the structure of a PBN from samples.

Mathematical Models of Pluripotent Stem Cells: At the Dawn of Predictive Regenerative Medicine

Regenerative medicine, ranging from stem cell therapy to organ regeneration, is promising to revolutionize treatments of diseases and aging. These approaches require a perfect understanding of cell reprogramming and differentiation. Predictive modeling of cellular systems has the potential to provide insights about the dynamics of cellular processes, and guide their control. Moreover in many cases, it provides alternative to experimental tests, difficult to perform for practical or ethical reasons. The variety and accuracy of biological processes represented in mathematical models grew in-line with the discovery of underlying molecular mechanisms. High-throughput data generation led to the development of models based on data analysis, as an alternative to more established modeling based on prior mechanistic knowledge. In this chapter, we give an overview of existing mathematical models of pluripotency and cell fate, to illustrate the variety of methods and questions. We conclude that current approaches are yet to overcome a number of limitations: Most of the computational models have so far focused solely on understanding the regulation of pluripotency, and the differentiation of selected cell lineages. In addition, models generally interrogate only a few biological processes. However, a better understanding of the reprogramming process leading to ESCs and iPSCs is required to improve stem-cell therapies. One also needs to understand the links between signaling, metabolism, regulation of gene expression, and the epigenetics machinery.

Relative stability of network states in Boolean network models of gene regulation in development

Progress in cell type reprogramming has revived the interest in Waddington's concept of the epigenetic landscape. Recently researchers developed the quasi-potential theory to represent the Waddington's landscape. The Quasi-potential U(x), derived from interactions in the gene regulatory network (GRN) of a cell, quantifies the relative stability of network states, which determine the effort required for state transitions in a multi-stable dynamical system. However, quasi-potential landscapes, originally developed for continuous systems, are not suitable for discrete-valued networks which are important tools to study complex systems. In this paper, we provide a framework to quantify the landscape for discrete Boolean networks (BNs). We apply our framework to study pancreas cell differentiation where an ensemble of BN models is considered based on the structure of a minimal GRN for pancreas development. We impose biologically motivated structural constraints (corresponding to specific type of Boolean functions) and dynamical constraints (corresponding to stable attractor states) to limit the space of BN models for pancreas development. In addition, we enforce a novel functional constraint corresponding to the relative ordering of attractor states in BN models to restrict the space of BN models to the biological relevant class. We find that BNs with canalyzing/sign-compatible Boolean functions best capture the dynamics of pancreas cell differentiation. This framework can also determine the genes' influence on cell state transitions, and thus can facilitate the rational design of cell reprogramming protocols.

A predictive computational framework for direct reprogramming between human cell types

Transdifferentiation, the process of converting from one cell type to another without going through a pluripotent state, has great promise for regenerative medicine. The identification of key transcription factors for reprogramming is currently limited by the cost of exhaustive experimental testing of plausible sets of factors, an approach that is inefficient and unscalable. Here we present a predictive system (Mogrify) that combines gene expression data with regulatory network information to predict the reprogramming factors necessary to induce cell conversion. We have applied Mogrify to 173 human cell types and 134 tissues, defining an atlas of cellular reprogramming. Mogrify correctly predicts the transcription factors used in known transdifferentiations. Furthermore, we validated two new transdifferentiations predicted by Mogrify. We provide a practical and efficient mechanism for systematically implementing novel cell conversions, facilitating the generalization of reprogramming of human cells. Predictions are made available to help rapidly further the field of cell conversion.

Efficient experimental design for uncertainty reduction in gene regulatory networks

Background

An accurate understanding of interactions among genes plays a major role in developing therapeutic intervention methods. Gene regulatory networks often contain a significant amount of uncertainty. The process of prioritizing biological experiments to reduce the uncertainty of gene regulatory networks is called experimental design. Under such a strategy, the experiments with high priority are suggested to be conducted first.

Results

The authors have already proposed an optimal experimental design method based upon the objective for modeling gene regulatory networks, such as deriving therapeutic interventions. The experimental design method utilizes the concept of mean objective cost of uncertainty (MOCU). MOCU quantifies the expected increase of cost resulting from uncertainty. The optimal experiment to be conducted first is the one which leads to the minimum expected remaining MOCU subsequent to the experiment. In the process, one must find the optimal intervention for every gene regulatory network compatible with the prior knowledge, which can be prohibitively expensive when the size of the network is large. In this paper, we propose a computationally efficient experimental design method. This method incorporates a network reduction scheme by introducing a novel cost function that takes into account the disruption in the ranking of potential experiments. We then estimate the approximate expected remaining MOCU at a lower computational cost using the reduced networks.

Conclusions

Simulation results based on synthetic and real gene regulatory networks show that the proposed approximate method has close performance to that of the optimal method but at lower computational cost. The proposed approximate method also outperforms the random selection policy significantly. A MATLAB software implementing the proposed experimental design method is available at http://gsp.tamu.edu/Publications/supplementary/roozbeh15a/.

Modeling the epigenetic attractors landscape: toward a post-genomic mechanistic understanding of development

Robust temporal and spatial patterns of cell types emerge in the course of normal development in multicellular organisms. The onset of degenerative diseases may result from altered cell fate decisions that give rise to pathological phenotypes. Complex networks of genetic and non-genetic components underlie such normal and altered morphogenetic patterns. Here we focus on the networks of regulatory interactions involved in cell-fate decisions. Such networks modeled as dynamical non-linear systems attain particular stable configurations on gene activity that have been interpreted as cell-fate states. The network structure also restricts the most probable transition patterns among such states. The so-called Epigenetic Landscape (EL), originally proposed by C. H. Waddington, was an early attempt to conceptually explain the emergence of developmental choices as the result of intrinsic constraints (regulatory interactions) shaped during evolution. Thanks to the wealth of molecular genetic and genomic studies, we are now able to postulate gene regulatory networks (GRN) grounded on experimental data, and to derive EL models for specific cases. This, in turn, has motivated several mathematical and computational modeling approaches inspired by the EL concept, that may be useful tools to understand and predict cell-fate decisions and emerging patterns. In order to distinguish between the classical metaphorical EL proposal of Waddington, we refer to the Epigenetic Attractors Landscape (EAL), a proposal that is formally framed in the context of GRNs and dynamical systems theory. In this review we discuss recent EAL modeling strategies, their conceptual basis and their application in studying the emergence of both normal and pathological developmental processes. In addition, we discuss how model predictions can shed light into rational strategies for cell fate regulation, and we point to challenges ahead.

optPBN: an optimisation toolbox for probabilistic Boolean networks

BACKGROUND

There exist several computational tools which allow for the optimisation and inference of biological networks using a Boolean formalism. Nevertheless, the results from such tools yield only limited quantitative insights into the complexity of biological systems because of the inherited qualitative nature of Boolean networks.

RESULTS

We introduce optPBN, a Matlab-based toolbox for the optimisation of probabilistic Boolean networks (PBN) which operates under the framework of the BN/PBN toolbox. optPBN offers an easy generation of probabilistic Boolean networks from rule-based Boolean model specification and it allows for flexible measurement data integration from multiple experiments. Subsequently, optPBN generates integrated optimisation problems which can be solved by various optimisers. In term of functionalities, optPBN allows for the construction of a probabilistic Boolean network from a given set of potential constitutive Boolean networks by optimising the selection probabilities for these networks so that the resulting PBN fits experimental data. Furthermore, the optPBN pipeline can also be operated on large-scale computational platforms to solve complex optimisation problems. Apart from exemplary case studies which we correctly inferred the original network, we also successfully applied optPBN to study a large-scale Boolean model of apoptosis where it allows identifying the inverse correlation between UVB irradiation, NFκB and Caspase 3 activations, and apoptosis in primary hepatocytes quantitatively. Also, the results from optPBN help elucidating the relevancy of crosstalk interactions in the apoptotic network.

SUMMARY

The optPBN toolbox provides a simple yet comprehensive pipeline for integrated optimisation problem generation in the PBN formalism that can readily be solved by various optimisers on local or grid-based computational platforms. optPBN can be further applied to various biological studies such as the inference of gene regulatory networks or the identification of the interaction's relevancy in signal transduction networks.

A general strategy for cellular reprogramming: the importance of transcription factor cross-repression

Transcription factor cross-repression is an important concept in cellular differentiation. A bistable toggle switch constitutes a molecular mechanism that determines cellular commitment and provides stability to transcriptional programs of binary cell fate choices. Experiments support that perturbations of these toggle switches can interconvert these binary cell fate choices, suggesting potential reprogramming strategies. However, more complex types of cellular transitions could involve perturbations of combinations of different types of multistable motifs. Here, we introduce a method that generalizes the concept of transcription factor cross-repression to systematically predict sets of genes, whose perturbations induce cellular transitions between any given pair of cell types. Furthermore, to our knowledge, this is the first method that systematically makes these predictions without prior knowledge of potential candidate genes and pathways involved, providing guidance on systems where little is known. Given the increasing interest of cellular reprogramming in medicine and basic research, our method represents a useful computational methodology to assist researchers in the field in designing experimental strategies.

Single cell analysis reveals the stochastic phase of reprogramming to pluripotency is an ordered probabilistic process

Despite years of research, the reprogramming of human somatic cells to pluripotency remains a slow, inefficient process, and a detailed mechanistic understanding of reprogramming remains elusive. Current models suggest reprogramming to pluripotency occurs in two-phases: a prolonged stochastic phase followed by a rapid deterministic phase. In this paradigm, the early stochastic phase is marked by the random and gradual expression of pluripotency genes and is thought to be a major rate-limiting step in the successful generation of induced Pluripotent Stem Cells (iPSCs). Recent evidence suggests that the epigenetic landscape of the somatic cell is gradually reset during a period known as the stochastic phase, but it is known neither how this occurs nor what rate-limiting steps control progress through the stochastic phase. A precise understanding of gene expression dynamics in the stochastic phase is required in order to answer these questions. Moreover, a precise model of this complex process will enable the measurement and mechanistic dissection of treatments that enhance the rate or efficiency of reprogramming to pluripotency. Here we use single-cell transcript profiling, FACS and mathematical modeling to show that the stochastic phase is an ordered probabilistic process with independent gene-specific dynamics. We also show that partially reprogrammed cells infected with OSKM follow two trajectories: a productive trajectory toward increasingly ESC-like expression profiles or an alternative trajectory leading away from both the fibroblast and ESC state. These two pathways are distinguished by the coordinated expression of a small group of chromatin modifiers in the productive trajectory, supporting the notion that chromatin remodeling is essential for successful reprogramming. These are the first results to show that the stochastic phase of reprogramming in human fibroblasts is an ordered, probabilistic process with gene-specific dynamics and to provide a precise mathematical framework describing the dynamics of pluripotency gene expression during reprogramming by OSKM.

End of inevitability: programming and reprogramming

Stem cell commitment and differentiation leading to functional cell types and organs has generally been considered unidirectional and deterministic. Starting first with a landmark study 50 years ago, and now with more recent observations, this paradigm has been challenged, necessitating a rethink of what constitutes both programming and reprogramming processes, and how we can use this new understanding for new approaches to drug discovery and regenerative medicine.

Generation of functional neurons from feeder-free, keratinocyte-derived equine induced pluripotent stem cells

Pluripotent stem cells (PSCs) offer unprecedented biomedical potential not only in relation to humans but also companion animals, particularly the horse. Despite this, attempts to generate bona fide equine embryonic stem cells have been unsuccessful. A very limited number of induced PSC lines have so far been generated from equine fibroblasts but their potential for directed differentiation into clinically relevant tissues has not been explored. In this study, we used retroviral vectors to generate induced pluripotent stem cells (iPSCs) with comparatively high efficiency from equine keratinocytes. Expression of endogenous PSC markers (OCT4, SOX2, LIN28, NANOG, DNMT3B, and REX1) was effectively restored in these cells, which could also form in vivo several tissue derivatives of the three germ layers, including functional neurons, keratinized epithelium, cartilage, bone, muscle, and respiratory and gastric epithelia. Comparative analysis of different reprogrammed cell lines revealed an association between the ability of iPSCs to form well-differentiated teratomas and the distinct endogenous expression of OCT4 and REX1 and reduced expression of viral transgenes. Importantly, unlike in previous studies, equine iPSCs were successfully expanded using simplified feeder-free culture conditions, constituting significant progress toward future biomedical applications. Further, under appropriate conditions equine iPSCs generated cells with features of cholinergic motor neurons including the ability to generate action potentials, providing the first report of functional cells derived from equine iPSCs. The ability to derive electrically active neurons in vitro from a large animal reveals highly conserved pathways of differentiation across species and opens the way for new and exciting applications in veterinary regenerative medicine.

Mathematical models of mitochondrial aging and dynamics

Research on the role of mitochondria in aging and disease is rapidly growing. Furthermore, in recent years, it also became clear that mitochondria are dynamic structures undergoing constant and rapid cycles of fusion and fission. The involvement of mitochondria in multiple complex processes makes them a prime target for mathematical and computational modeling. This review consists of two parts. In the first (Section 2), we provide a detailed introduction to the underlying concepts of mathematical modeling to help the reader who is not so familiar with these techniques to judge the requirements and results that can be obtained through modeling. In the second part (Section 3), we review existing mathematical and computational models that investigate mitochondrial dynamics and the role of mitochondria for the aging process.

Qualitative modeling identifies IL-11 as a novel regulator in maintaining self-renewal in human pluripotent stem cells

Pluripotency in human embryonic stem cells (hESCs) and induced pluripotent stem cells (iPSCs) is regulated by three transcription factors—OCT3/4, SOX2, and NANOG. To fully exploit the therapeutic potential of these cells it is essential to have a good mechanistic understanding of the maintenance of self-renewal and pluripotency. In this study, we demonstrate a powerful systems biology approach in which we first expand literature-based network encompassing the core regulators of pluripotency by assessing the behavior of genes targeted by perturbation experiments. We focused our attention on highly regulated genes encoding cell surface and secreted proteins as these can be more easily manipulated by the use of inhibitors or recombinant proteins. Qualitative modeling based on combining boolean networks and in silico perturbation experiments were employed to identify novel pluripotency-regulating genes. We validated Interleukin-11 (IL-11) and demonstrate that this cytokine is a novel pluripotency-associated factor capable of supporting self-renewal in the absence of exogenously added bFGF in culture. To date, the various protocols for hESCs maintenance require supplementation with bFGF to activate the Activin/Nodal branch of the TGFβ signaling pathway. Additional evidence supporting our findings is that IL-11 belongs to the same protein family as LIF, which is known to be necessary for maintaining pluripotency in mouse but not in human ESCs. These cytokines operate through the same gp130 receptor which interacts with Janus kinases. Our finding might explain why mESCs are in a more naïve cell state compared to hESCs and how to convert primed hESCs back to the naïve state. Taken together, our integrative modeling approach has identified novel genes as putative candidates to be incorporated into the expansion of the current gene regulatory network responsible for inducing and maintaining pluripotency.

Reaction-contingency based bipartite Boolean modelling

Background

Intracellular signalling systems are highly complex, rendering mathematical modelling of large signalling networks infeasible or impractical. Boolean modelling provides one feasible approach to whole-network modelling, but at the cost of dequantification and decontextualisation of activation. That is, these models cannot distinguish between different downstream roles played by the same component activated in different contexts.

Results

Here, we address this with a bipartite Boolean modelling approach. Briefly, we use a state oriented approach with separate update rules based on reactions and contingencies. This approach retains contextual activation information and distinguishes distinct signals passing through a single component. Furthermore, we integrate this approach in the rxncon framework to support automatic model generation and iterative model definition and validation. We benchmark this method with the previously mapped MAP kinase network in yeast, showing that minor adjustments suffice to produce a functional network description.

Conclusions

Taken together, we (i) present a bipartite Boolean modelling approach that retains contextual activation information, (ii) provide software support for automatic model generation, visualisation and simulation, and (iii) demonstrate its use for iterative model generation and validation.

Recent development and biomedical applications of probabilistic Boolean networks

Probabilistic Boolean network (PBN) modelling is a semi-quantitative approach widely used for the study of the topology and dynamic aspects of biological systems. The combined use of rule-based representation and probability makes PBN appealing for large-scale modelling of biological networks where degrees of uncertainty need to be considered.A considerable expansion of our knowledge in the field of theoretical research on PBN can be observed over the past few years, with a focus on network inference, network intervention and control. With respect to areas of applications, PBN is mainly used for the study of gene regulatory networks though with an increasing emergence in signal transduction, metabolic, and also physiological networks. At the same time, a number of computational tools, facilitating the modelling and analysis of PBNs, are continuously developed.A concise yet comprehensive review of the state-of-the-art on PBN modelling is offered in this article, including a comparative discussion on PBN versus similar models with respect to concepts and biomedical applications. Due to their many advantages, we consider PBN to stand as a suitable modelling framework for the description and analysis of complex biological systems, ranging from molecular to physiological levels.

Modeling to optimize terminal stem cell differentiation

Embryonic stem cell (ESC), iPCs, and adult stem cells (ASCs) all are among the most promising potential treatments for heart failure, spinal cord injury, neurodegenerative diseases, and diabetes. However, considerable uncertainty in the production of ESC-derived terminally differentiated cell types has limited the efficiency of their development. To address this uncertainty, we and other investigators have begun to employ a comprehensive statistical model of ESC differentiation for determining the role of intracellular pathways (e.g., STAT3) in ESC differentiation and determination of germ layer fate. The approach discussed here applies the Baysian statistical model to cell/developmental biology combining traditional flow cytometry methodology and specific morphological observations with advanced statistical and probabilistic modeling and experimental design. The final result of this study is a unique tool and model that enhances the understanding of how and when specific cell fates are determined during differentiation. This model provides a guideline for increasing the production efficiency of therapeutically viable ESCs/iPSCs/ASC derived neurons or any other cell type and will eventually lead to advances in stem cell therapy.