Boolean approach to signalling pathway modelling in HGF-induced keratinocyte migration

Normalizing Input-Output Relationships of Cancer Networks for Reversion Therapy

Accumulated genetic alterations in cancer cells distort cellular stimulus-response (or input-output) relationships, resulting in uncontrolled proliferation. However, the complex molecular interaction network within a cell implicates a possibility of restoring such distorted input-output relationships by rewiring the signal flow through controlling hidden molecular switches. Here, a system framework of analyzing cellular input-output relationships in consideration of various genetic alterations and identifying possible molecular switches that can normalize the distorted relationships based on Boolean network modeling and dynamics analysis is presented. Such reversion is demonstrated by the analysis of a number of cancer molecular networks together with a focused case study on bladder cancer with in vitro experiments and patient survival data analysis. The origin of reversibility from an evolutionary point of view based on the redundancy and robustness intrinsically embedded in complex molecular regulatory networks is further discussed.

Target Control of Asynchronous Boolean Networks

We study the target control of asynchronous Boolean networks, to identify interventions that can drive the dynamics of a given Boolean network from any initial state to the desired target attractor. Based on the application time, the control can be realised with three types of perturbations, including instantaneous, temporary and permanent perturbations. We develop efficient methods to compute the target control for a given target attractor with these three types of perturbations. We compare our methods with the stable motif-based control method on a variety of real-life biological networks to evaluate their performance. We show that our methods scale well for large Boolean networks and they are able to identify a rich set of solutions with a small number of perturbations.

A mechanistic protrusive-based model for 3D cell migration

Cell migration is essential for a variety of biological processes, such as embryogenesis, wound healing, and the immune response. After more than a century of research-mainly on flat surfaces-, there are still many unknowns about cell motility. In particular, regarding how cells migrate within 3D matrices, which more accurately replicate in vivo conditions. We present a novel in silico model of 3D mesenchymal cell migration regulated by the chemical and mechanical profile of the surrounding environment. This in silico model considers cell's adhesive and nuclear phenotypes, the effects of the steric hindrance of the matrix, and cells ability to degradate the ECM. These factors are crucial when investigating the increasing difficulty that migrating cells find to squeeze their nuclei through dense matrices, which may act as physical barriers. Our results agree with previous in vitro observations where fibroblasts cultured in collagen-based hydrogels did not durotax toward regions with higher collagen concentrations. Instead, they exhibited an adurotactic behavior, following a more random trajectory. Overall, cell's migratory response in 3D domains depends on its phenotype, and the properties of the surrounding environment, that is, 3D cell motion is strongly dependent on the context.

Finding the fixed points of a Boolean network from a positive feedback vertex set

MOTIVATION

In the modeling of biological systems by Boolean networks, a key problem is finding the set of fixed points of a given network. Some constructed algorithms consider certain structural properties of the regulatory graph like those proposed by Akutsu et al. and Zhang et al., which consider a feedback vertex set of the graph. However, these methods do not take into account the type of action (activation and inhibition) between its components.

RESULTS

In this article, we propose a new algorithm for finding the set of fixed points of a Boolean network, based on a positive feedback vertex set P of its regulatory graph and which works, by applying a sequential update schedule, in time O(2|P|·n2+k), where n is the number of components and the regulatory functions of the network can be evaluated in time O(nk), k≥0. The theoretical foundation of this algorithm is due a nice characterization, that we give, of the dynamical behavior of the Boolean networks without positive cycles and with a fixed point.

AVAILABILITY AND IMPLEMENTATION

An executable file of FixedPoint algorithm made in Java and some examples of input files are available at: www.inf.udec.cl/˜lilian/FPCollector/.

SUPPLEMENTARY INFORMATION

Supplementary material is available at Bioinformatics online.

Combinatorial mathematical modelling approaches to interrogate rear retraction dynamics in 3D cell migration

Cell migration in 3D microenvironments is a complex process which depends on the coordinated activity of leading edge protrusive force and rear retraction in a push-pull mechanism. While the potentiation of protrusions has been widely studied, the precise signalling and mechanical events that lead to retraction of the cell rear are much less well understood, particularly in physiological 3D extra-cellular matrix (ECM). We previously discovered that rear retraction in fast moving cells is a highly dynamic process involving the precise spatiotemporal interplay of mechanosensing by caveolae and signalling through RhoA. To further interrogate the dynamics of rear retraction, we have adopted three distinct mathematical modelling approaches here based on (i) Boolean logic, (ii) deterministic kinetic ordinary differential equations (ODEs) and (iii) stochastic simulations. The aims of this multi-faceted approach are twofold: firstly to derive new biological insight into cell rear dynamics via generation of testable hypotheses and predictions; and secondly to compare and contrast the distinct modelling approaches when used to describe the same, relatively under-studied system. Overall, our modelling approaches complement each other, suggesting that such a multi-faceted approach is more informative than methods based on a single modelling technique to interrogate biological systems. Whilst Boolean logic was not able to fully recapitulate the complexity of rear retraction signalling, an ODE model could make plausible population level predictions. Stochastic simulations added a further level of complexity by accurately mimicking previous experimental findings and acting as a single cell simulator. Our approach highlighted the unanticipated role for CDK1 in rear retraction, a prediction we confirmed experimentally. Moreover, our models led to a novel prediction regarding the potential existence of a 'set point' in local stiffness gradients that promotes polarisation and rapid rear retraction.

An Efficient Approach Towards the Source-Target Control of Boolean Networks

We study the problem of computing a minimal subset of nodes of a given asynchronous Boolean network that need to be perturbed in a single-step to drive its dynamics from an initial state to a target steady state (or attractor), which we call the source-target control of Boolean networks. Due to the phenomenon of state-space explosion, a simple global approach that performs computations on the entire network may not scale well for large networks. We believe that efficient algorithms for such networks must exploit the structure of the networks together with their dynamics. Taking this view, we derive a decomposition-based solution to the minimal source-target control problem which can be significantly faster than the existing approaches on large networks. We then show that the solution can be further optimized if we take into account appropriate information about the source state. We apply our solutions to both real-life biological networks and randomly generated networks, demonstrating the efficiency and efficacy of our approach.

Logic-based analysis of gene expression data predicts association between TNF, TGFB1 and EGF pathways in basal-like breast cancer

For breast cancer, clinically important subtypes are well characterized at the molecular level in terms of gene expression profiles. In addition, signaling pathways in breast cancer have been extensively studied as therapeutic targets due to their roles in tumor growth and metastasis. However, it is challenging to put signaling pathways and gene expression profiles together to characterize biological mechanisms of breast cancer subtypes since many signaling events result from post-translational modifications, rather than gene expression differences. We designed a logic-based computational framework to explain the differences in gene expression profiles among breast cancer subtypes using Pathway Logic and transcriptional network information. Pathway Logic is a rewriting-logic-based formal system for modeling biological pathways including post-translational modifications. Our method demonstrated its utility by constructing subtype-specific path from key receptors (TNFR, TGFBR1 and EGFR) to key transcription factor (TF) regulators (RELA, ATF2, SMAD3 and ELK1) and identifying potential association between pathways via TFs in basal-specific paths, which could provide a novel insight on aggressive breast cancer subtypes. Codes and results are available at http://epigenomics.snu.ac.kr/PL/.

A Near-Optimal Control Method for Stochastic Boolean Networks

One of the ultimate goals in systems biology is to develop control strategies to find efficient medical treatments. One step towards this goal is to develop methods for changing the state of a cell into a desirable state. We propose an efficient method that determines combinations of network perturbations to direct the system towards a predefined state. The method requires a set of control actions such as the silencing of a gene or the disruption of the interaction between two genes. An optimal control policy defined as the best intervention at each state of the system can be obtained using existing methods. However, these algorithms are computationally prohibitive for models with tens of nodes. Our method generates control actions that approximates the optimal control policy with high probability with a computational efficiency that does not depend on the size of the state space. Our C++ code is available at https://github.com/boaguilar/SDDScontrol.

Controlling large Boolean networks with single-step perturbations

Motivation

The control of Boolean networks has traditionally focussed on strategies where the perturbations are applied to the nodes of the network for an extended period of time. In this work, we study if and how a Boolean network can be controlled by perturbing a minimal set of nodes for a single-step and letting the system evolve afterwards according to its original dynamics. More precisely, given a Boolean network (BN), we compute a minimal subset Cmin of the nodes such that BN can be driven from any initial state in an attractor to another ‘desired’ attractor by perturbing some or all of the nodes of Cmin for a single-step. Such kind of control is attractive for biological systems because they are less time consuming than the traditional strategies for control while also being financially more viable. However, due to the phenomenon of state-space explosion, computing such a minimal subset is computationally inefficient and an approach that deals with the entire network in one-go, does not scale well for large networks.

Results

We develop a ‘divide-and-conquer’ approach by decomposing the network into smaller partitions, computing the minimal control on the projection of the attractors to these partitions and then composing the results to obtain Cmin for the whole network. We implement our method and test it on various real-life biological networks to demonstrate its applicability and efficiency.

Supplementary information

Supplementary data are available at Bioinformatics online.

Robustness of Nutrient Signaling Is Maintained by Interconnectivity Between Signal Transduction Pathways

Systems biology approaches provide means to study the interplay between biological processes leading to the mechanistic understanding of the properties of complex biological systems. Here, we developed a vector format rule-based Boolean logic model of the yeast S. cerevisiae cAMP-PKA, Snf1, and the Snf3-Rgt2 pathway to better understand the role of crosstalk on network robustness and function. We identified that phosphatases are the common unknown components of the network and that crosstalk from the cAMP-PKA pathway to other pathways plays a critical role in nutrient sensing events. The model was simulated with known crosstalk combinations and subsequent analysis led to the identification of characteristics and impact of pathway interconnections. Our results revealed that the interconnections between the Snf1 and Snf3-Rgt2 pathway led to increased robustness in these signaling pathways. Overall, our approach contributes to the understanding of the function and importance of crosstalk in nutrient signaling.

P_UNSAT approach of attractor calculation for Boolean gene regulatory networks

Boolean network models provide an efficient way for studying gene regulatory networks. The main dynamics of a Boolean network is determined by its attractors. Attractor calculation plays a key role for analyzing Boolean gene regulatory networks. An approach of attractor calculation was proposed in this study, which combined the predecessor approach and the logic unsatisfiability approach to accelerate attractor calculation. The proposed algorithm is effective to calculate all attractors for large-scale Boolean gene regulatory networks even the networks with a relatively large average degree.

The Impact of the Epithelial-Mesenchymal Transition Regulator Hepatocyte Growth Factor Receptor/Met on Skin Immunity by Modulating Langerhans Cell Migration

Langerhans cells (LCs), the epidermal dendritic cell (DC) subset, express the transmembrane tyrosine kinase receptor Met also known as hepatocyte growth factor (HGF) receptor. HGF is the exclusive ligand of Met and upon binding executes mitogenic, morphogenic, and motogenic activities to various cells. HGF exerts anti-inflammatory activities via Met signaling and was found to regulate various functions of immune cells, including differentiation and maturation, cytokine production, cellular migration and adhesion, and T cell effector function. It has only recently become evident that a number of HGF-regulated functions in inflammatory processes and immune responses are imparted via DCs. However, the mechanisms by which Met signaling in DCs conveys its immunoregulatory effects have not yet been fully understood. In this review, we focus on the current knowledge of Met signaling in DCs with particular attention on the morphogenic and motogenic activities. Met signaling was shown to promote DC mobility by regulating matrix metalloproteinase activities and adhesion. This is a striking resemblance to the role of Met in regulating a cell fate program during embryonic development, wound healing, and in tumor invasion known as epithelial–mesenchymal transition (EMT). Hence, we propose the concept that an EMT program is executed by Met signaling in LCs.

BIOPYDB: A Dynamic Human Cell Specific Biochemical Pathway Database with Advanced Computational Analyses Platform

BIOPYDB: BIOchemical PathwaY DataBase is developed as a manually curated, readily updatable, dynamic resource of human cell specific pathway information along with integrated computational platform to perform various pathway analyses. Presently, it comprises of 46 pathways, 3189 molecules, 5742 reactions and 6897 different types of diseases linked with pathway proteins, which are referred by 520 literatures and 17 other pathway databases. With its repertoire of biochemical pathway data, and computational tools for performing Topological, Logical and Dynamic analyses, BIOPYDB offers both the experimental and computational biologists to acquire a comprehensive understanding of signaling cascades in the cells. Automated pathway image reconstruction, cross referencing of pathway molecules and interactions with other databases and literature sources, complex search operations to extract information from other similar resources, integrated platform for pathway data sharing and computation, etc. are the novel and useful features included in this database to make it more acceptable and attractive to the users of pathway research communities. The RESTful API service is also made available to the advanced users and developers for accessing this database more conveniently through their own computer programmes.

Boolean network modeling in systems pharmacology

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

Modeling Rho GTPase Dynamics Using Boolean Logic

Rho GTPases such as the canonical Rac1 and RhoA are embedded within complex networks requiring the precise spatiotemporal balance of GEFs, GAPs, upstream regulators, growth factors, and downstream effectors. A modeling approach based on Boolean logical networks is becoming an increasingly relied-upon tool to harness this complexity and elucidate further details regarding Rho GTPase signaling. In this methods chapter we describe how to initially create appropriately sized networks based on literature evidence; formalize these networks with reactions based on Boolean logical operators; implement the network into appropriate simulation software (CellNetAnalyzer); and finally perform simulations and make novel, testable predictions via in silico knockouts. Given this predictive power, the Boolean approach may ultimately help to highlight potential future avenues of experimental research.

Specific role of RhoC in tumor invasion and metastasis

Rho GTPases are regulators of many cellular functions and are often dysregulated in cancer. However, the precise role of Rho proteins for tumor development is not well understood. In breast cancer, overexpression of RhoC is linked with poor prognosis. Here, we aim to compare the function of RhoC and its homolog family member RhoA in breast cancer progression. We established stable breast epithelial cell lines with inducible expression of RhoA and RhoC, respectively. Moreover, we made use of Rho-activating bacterial toxins (Cytotoxic Necrotizing Factors) to stimulate the endogenous pool of Rho GTPases in benign breast epithelial cells and simultaneously knocked down specific Rho proteins. Whereas activation of Rho GTPases was sufficient to induce an invasive phenotype in three-dimensional culture systems, overexpression of RhoA or RhoC were not. However, RhoC but not RhoA was required for invasion, whereas RhoA and RhoC equally regulated proliferation. We further identified downstream target genes of RhoC involved in invasion and identified PTGS2 (COX-2) being preferentially upregulated by RhoC. Consistently, the COX-2 inhibitor Celecoxib blocked the invasive phenotype induced by the Rho-activating toxins.

Understanding the mTOR signaling pathway via mathematical modeling

The mechanistic target of rapamycin (mTOR) is a central regulatory pathway that integrates a variety of environmental cues to control cellular growth and homeostasis by intricate molecular feedbacks. In spite of extensive knowledge about its components, the molecular understanding of how these function together in space and time remains poor and there is a need for Systems Biology approaches to perform systematic analyses. In this work, we review the recent progress how the combined efforts of mathematical models and quantitative experiments shed new light on our understanding of the mTOR signaling pathway. In particular, we discuss the modeling concepts applied in mTOR signaling, the role of multiple feedbacks and the crosstalk mechanisms of mTOR with other signaling pathways. We also discuss the contribution of principles from information and network theory that have been successfully applied in dissecting design principles of the mTOR signaling network. We finally propose to classify the mTOR models in terms of the time scale and network complexity, and outline the importance of the classification toward the development of highly comprehensive and predictive models. WIREs Syst Biol Med 2017, 9:e1379. doi: 10.1002/wsbm.1379 For further resources related to this article, please visit the WIREs website.

An efficient approach of attractor calculation for large-scale Boolean gene regulatory networks

Boolean network models provide an efficient way for studying gene regulatory networks. The main dynamics of a Boolean network is determined by its attractors. Attractor calculation plays a key role for analyzing Boolean gene regulatory networks. An approach of attractor calculation was proposed in this study, which improved the predecessor-based approach. Furthermore, the proposed approach combined with the identification of constant nodes and simplified Boolean networks to accelerate attractor calculation. The proposed algorithm is effective to calculate all attractors for large-scale Boolean gene regulatory networks. If the average degree of the network is not too large, the algorithm can get all attractors of a Boolean network with dozens or even hundreds of nodes.

Multimodal Quantitative Phase Imaging with Digital Holographic Microscopy Accurately Assesses Intestinal Inflammation and Epithelial Wound Healing

The incidence of inflammatory bowel disease, i.e., Crohn's disease and Ulcerative colitis, has significantly increased over the last decade. The etiology of IBD remains unknown and current therapeutic strategies are based on the unspecific suppression of the immune system. The development of treatments that specifically target intestinal inflammation and epithelial wound healing could significantly improve management of IBD, however this requires accurate detection of inflammatory changes. Currently, potential drug candidates are usually evaluated using animal models in vivo or with cell culture based techniques in vitro. Histological examination usually requires the cells or tissues of interest to be stained, which may alter the sample characteristics and furthermore, the interpretation of findings can vary by investigator expertise. Digital holographic microscopy (DHM), based on the detection of optical path length delay, allows stain-free quantitative phase contrast imaging. This allows the results to be directly correlated with absolute biophysical parameters. We demonstrate how measurement of changes in tissue density with DHM, based on refractive index measurement, can quantify inflammatory alterations, without staining, in different layers of colonic tissue specimens from mice and humans with colitis. Additionally, we demonstrate continuous multimodal label-free monitoring of epithelial wound healing in vitro, possible using DHM through the simple automated determination of the wounded area and simultaneous determination of morphological parameters such as dry mass and layer thickness of migrating cells. In conclusion, DHM represents a valuable, novel and quantitative tool for the assessment of intestinal inflammation with absolute values for parameters possible, simplified quantification of epithelial wound healing in vitro and therefore has high potential for translational diagnostic use.

A MAPK-Driven Feedback Loop Suppresses Rac Activity to Promote RhoA-Driven Cancer Cell Invasion

Cell migration in 3D microenvironments is fundamental to development, homeostasis and the pathobiology of diseases such as cancer. Rab-coupling protein (RCP) dependent co-trafficking of α5β1 and EGFR1 promotes cancer cell invasion into fibronectin (FN) containing extracellular matrix (ECM), by potentiating EGFR1 signalling at the front of invasive cells. This promotes a switch in RhoGTPase signalling to inhibit Rac1 and activate a RhoA-ROCK-Formin homology domain-containing 3 (FHOD3) pathway and generate filopodial actin-spike protrusions which drive invasion. To further understand the signalling network that drives RCP-driven invasive migration, we generated a Boolean logical model based on existing network pathways/models, where each node can be interrogated by computational simulation. The model predicted an unanticipated feedback loop, whereby Raf/MEK/ERK signalling maintains suppression of Rac1 by inhibiting the Rac-activating Sos1-Eps8-Abi1 complex, allowing RhoA activity to predominate in invasive protrusions. MEK inhibition was sufficient to promote lamellipodia formation and oppose filopodial actin-spike formation, and led to activation of Rac and inactivation of RhoA at the leading edge of cells moving in 3D matrix. Furthermore, MEK inhibition abrogated RCP/α5β1/EGFR1-driven invasive migration. However, upon knockdown of Eps8 (to suppress the Sos1-Abi1-Eps8 complex), MEK inhibition had no effect on RhoGTPase activity and did not oppose invasive migration, suggesting that MEK-ERK signalling suppresses the Rac-activating Sos1-Abi1-Eps8 complex to maintain RhoA activity and promote filopodial actin-spike formation and invasive migration. Our study highlights the predictive potential of mathematical modelling approaches, and demonstrates that a simple intervention (MEK-inhibition) could be of therapeutic benefit in preventing invasive migration and metastasis.

The majority of cancer-related fatalities are caused by the movement of cancer cells away from the primary site to form metastases, making understanding the signalling mechanisms which underpin cell migration and invasion through their local environment of paramount importance. Much has been discovered about key events leading to invasive cell migration. Here, we have taken this prior knowledge to build a powerful predictive model based on simple ON/OFF relations and logic to determine potential intervention targets to reduce harmful invasive migration. Interrogating our model, we have identified a negative feedback loop important to the signalling that determines invasive migration, the breaking of which reverts cells to a slower, less invasive phenotype. We have supported this feedback loop prediction using an array of in vitro experiments performed in cells within 2-D and physiologically relevant 3-D environments. Our findings demonstrate the predictive power of such modelling techniques, and could form the basis for clinical intervention to prevent metastasis in certain cancers.

Boolean Modeling Reveals the Necessity of Transcriptional Regulation for Bistability in PC12 Cell Differentiation

The nerve growth factor NGF has been shown to cause cell fate decisions toward either differentiation or proliferation depending on the relative activity of downstream pERK, pAKT, or pJNK signaling. However, how these protein signals are translated into and fed back from transcriptional activity to complete cellular differentiation over a time span of hours to days is still an open question. Comparing the time-resolved transcriptome response of NGF- or EGF-stimulated PC12 cells over 24 h in combination with protein and phenotype data we inferred a dynamic Boolean model capturing the temporal sequence of protein signaling, transcriptional response and subsequent autocrine feedback. Network topology was optimized by fitting the model to time-resolved transcriptome data under MEK, PI3K, or JNK inhibition. The integrated model confirmed the parallel use of MAPK/ERK, PI3K/AKT, and JNK/JUN for PC12 cell differentiation. Redundancy of cell signaling is demonstrated from the inhibition of the different MAPK pathways. As suggested in silico and confirmed in vitro, differentiation was substantially suppressed under JNK inhibition, yet delayed only under MEK/ERK inhibition. Most importantly, we found that positive transcriptional feedback induces bistability in the cell fate switch. De novo gene expression was necessary to activate autocrine feedback that caused Urokinase-Type Plasminogen Activator (uPA) Receptor signaling to perpetuate the MAPK activity, finally resulting in the expression of late, differentiation related genes. Thus, the cellular decision toward differentiation depends on the establishment of a transcriptome-induced positive feedback between protein signaling and gene expression thereby constituting a robust control between proliferation and differentiation.

Identifying robust communities and multi-community nodes by combining top-down and bottom-up approaches to clustering

Biological functions are carried out by groups of interacting molecules, cells or tissues, known as communities. Membership in these communities may overlap when biological components are involved in multiple functions. However, traditional clustering methods detect non-overlapping communities. These detected communities may also be unstable and difficult to replicate, because traditional methods are sensitive to noise and parameter settings. These aspects of traditional clustering methods limit our ability to detect biological communities, and therefore our ability to understand biological functions. To address these limitations and detect robust overlapping biological communities, we propose an unorthodox clustering method called SpeakEasy which identifies communities using top-down and bottom-up approaches simultaneously. Specifically, nodes join communities based on their local connections, as well as global information about the network structure. This method can quantify the stability of each community, automatically identify the number of communities, and quickly cluster networks with hundreds of thousands of nodes. SpeakEasy shows top performance on synthetic clustering benchmarks and accurately identifies meaningful biological communities in a range of datasets, including: gene microarrays, protein interactions, sorted cell populations, electrophysiology and fMRI brain imaging.

Network modelling reveals the mechanism underlying colitis-associated colon cancer and identifies novel combinatorial anti-cancer targets

The connection between inflammation and tumourigenesis has been well established. However, the detailed molecular mechanism underlying inflammation-associated tumourigenesis remains unknown because this process involves a complex interplay between immune microenvironments and epithelial cells. To obtain a more systematic understanding of inflammation-associated tumourigenesis as well as to identify novel therapeutic approaches, we constructed a knowledge-based network describing the development of colitis-associated colon cancer (CAC) by integrating the extracellular microenvironment and intracellular signalling pathways. Dynamic simulations of the CAC network revealed a core network module, including P53, MDM2, and AKT, that may govern the malignant transformation of colon epithelial cells in a pro-tumor inflammatory microenvironment. Furthermore, in silico mutation studies and experimental validations led to a novel finding that concurrently targeting ceramide and PI3K/AKT pathway by chemical probes or marketed drugs achieves synergistic anti-cancer effects. Overall, our network model can guide further mechanistic studies on CAC and provide new insights into the design of combinatorial cancer therapies in a rational manner.

Discovery of Drug Synergies in Gastric Cancer Cells Predicted by Logical Modeling

Discovery of efficient anti-cancer drug combinations is a major challenge, since experimental testing of all possible combinations is clearly impossible. Recent efforts to computationally predict drug combination responses retain this experimental search space, as model definitions typically rely on extensive drug perturbation data. We developed a dynamical model representing a cell fate decision network in the AGS gastric cancer cell line, relying on background knowledge extracted from literature and databases. We defined a set of logical equations recapitulating AGS data observed in cells in their baseline proliferative state. Using the modeling software GINsim, model reduction and simulation compression techniques were applied to cope with the vast state space of large logical models and enable simulations of pairwise applications of specific signaling inhibitory chemical substances. Our simulations predicted synergistic growth inhibitory action of five combinations from a total of 21 possible pairs. Four of the predicted synergies were confirmed in AGS cell growth real-time assays, including known effects of combined MEK-AKT or MEK-PI3K inhibitions, along with novel synergistic effects of combined TAK1-AKT or TAK1-PI3K inhibitions. Our strategy reduces the dependence on a priori drug perturbation experimentation for well-characterized signaling networks, by demonstrating that a model predictive of combinatorial drug effects can be inferred from background knowledge on unperturbed and proliferating cancer cells. Our modeling approach can thus contribute to preclinical discovery of efficient anticancer drug combinations, and thereby to development of strategies to tailor treatment to individual cancer patients.

HGF/Met-Signaling Contributes to Immune Regulation by Modulating Tolerogenic and Motogenic Properties of Dendritic Cells

Hepatocyte growth factor (HGF)-signaling via Met can induce mitogenic, morphogenic, and motogenic activity in various cell types. Met expression in the immune system is limited to cells with antigen-presenting capacities, including dendritic cells (DCs). Thus, it appears highly conceivable that Met-signaling impacts on adaptive immune responses. However, the mechanisms by which HGF imparts its effects on immunological responses are not yet fully understood. DCs possess unique functionalities that are critically involved in controlling both tolerance and immunity. HGF conveys immunoregulatory functions, which strongly correlate with that of DCs orchestrating the apt immune response in inflammation. Therefore, this review focuses on the current knowledge of Met-signaling in DCs with specific emphasis on the morphogenic and motogenic activities. HGF has been identified to play a role in peripheral immune tolerance by directing DC differentiation towards a tolerogenic phenotype. In skin immunity, Met-signaling was shown to drive mobilization of DCs by regulating matrix metalloproteinase activities. This is strikingly reminiscent of the role of Met for regulating a cell fate program during embryonic development, wound healing, and in tumor invasion known as epithelial-mesenchymal transition (EMT). Thus, the concept emerges that an EMT program is executed by Met-signaling in DCs, which will be also discussed.

Quantitative stain-free and continuous multimodal monitoring of wound healing in vitro with digital holographic microscopy

Impaired epithelial wound healing has significant pathophysiological implications in several conditions including gastrointestinal ulcers, anastomotic leakage and venous or diabetic skin ulcers. Promising drug candidates for accelerating wound closure are commonly evaluated in in vitro wound assays. However, staining procedures and discontinuous monitoring are major drawbacks hampering accurate assessment of wound assays. We therefore investigated digital holographic microscopy (DHM) to appropriately monitor wound healing in vitro and secondly, to provide multimodal quantitative information on morphological and functional cell alterations as well as on motility changes upon cytokine stimulation. Wound closure as reflected by proliferation and migration of Caco-2 cells in wound healing assays was studied and assessed in time-lapse series for 40 h in the presence of stimulating epidermal growth factor (EGF) and inhibiting mitomycin c. Therefore, digital holograms were recorded continuously every thirty minutes. Morphological changes including cell thickness, dry mass and tissue density were analyzed by data from quantitative digital holographic phase microscopy. Stimulation of Caco-2 cells with EGF or mitomycin c resulted in significant morphological changes during wound healing compared to control cells. In conclusion, DHM allows accurate, stain-free and continuous multimodal quantitative monitoring of wound healing in vitro and could be a promising new technique for assessment of wound healing.

Robustness and evolvability of the human signaling network

Biological systems are known to be both robust and evolvable to internal and external perturbations, but what causes these apparently contradictory properties? We used Boolean network modeling and attractor landscape analysis to investigate the evolvability and robustness of the human signaling network. Our results show that the human signaling network can be divided into an evolvable core where perturbations change the attractor landscape in state space, and a robust neighbor where perturbations have no effect on the attractor landscape. Using chemical inhibition and overexpression of nodes, we validated that perturbations affect the evolvable core more strongly than the robust neighbor. We also found that the evolvable core has a distinct network structure, which is enriched in feedback loops, and features a higher degree of scale-freeness and longer path lengths connecting the nodes. In addition, the genes with high evolvability scores are associated with evolvability-related properties such as rapid evolvability, low species broadness, and immunity whereas the genes with high robustness scores are associated with robustness-related properties such as slow evolvability, high species broadness, and oncogenes. Intriguingly, US Food and Drug Administration-approved drug targets have high evolvability scores whereas experimental drug targets have high robustness scores.

Biological systems are known to be robust and evolvable to internal mutations and external environmental changes. What causes these apparently contradictory properties? This study shows that the human signaling network can be decomposed into two structurally distinct subgroups of links that provide both evolvability to environmental changes and robustness against internal mutations. The decomposition of the human signaling network reveals an evolutionary design principle of the network, and also facilitates the identification of potential drug targets.

Steady state analysis of Boolean molecular network models via model reduction and computational algebra

Background

A key problem in the analysis of mathematical models of molecular networks is the determination of their steady states. The present paper addresses this problem for Boolean network models, an increasingly popular modeling paradigm for networks lacking detailed kinetic information. For small models, the problem can be solved by exhaustive enumeration of all state transitions. But for larger models this is not feasible, since the size of the phase space grows exponentially with the dimension of the network. The dimension of published models is growing to over 100, so that efficient methods for steady state determination are essential. Several methods have been proposed for large networks, some of them heuristic. While these methods represent a substantial improvement in scalability over exhaustive enumeration, the problem for large networks is still unsolved in general.

Results

This paper presents an algorithm that consists of two main parts. The first is a graph theoretic reduction of the wiring diagram of the network, while preserving all information about steady states. The second part formulates the determination of all steady states of a Boolean network as a problem of finding all solutions to a system of polynomial equations over the finite number system with two elements. This problem can be solved with existing computer algebra software. This algorithm compares favorably with several existing algorithms for steady state determination. One advantage is that it is not heuristic or reliant on sampling, but rather determines algorithmically and exactly all steady states of a Boolean network. The code for the algorithm, as well as the test suite of benchmark networks, is available upon request from the corresponding author.

Conclusions

The algorithm presented in this paper reliably determines all steady states of sparse Boolean networks with up to 1000 nodes. The algorithm is effective at analyzing virtually all published models even those of moderate connectivity. The problem for large Boolean networks with high average connectivity remains an open problem.

From a traditional medicinal plant to a rational drug: understanding the clinically proven wound healing efficacy of birch bark extract

BACKGROUND

Birch bark has a long lasting history as a traditional medicinal remedy to accelerate wound healing. Recently, the efficacy of birch bark preparations has also been proven clinically. As active principle pentacyclic triterpenes are generally accepted. Here, we report a comprehensive study on the underlying molecular mechanisms of the wound healing properties of a well-defined birch bark preparation named as TE (triterpene extract) as well as the isolated single triterpenes in human primary keratinocytes and porcine ex-vivo wound healing models.

METHODOLOGY/PRINCIPAL FINDINGS

We show positive wound healing effects of TE and betulin in scratch assay experiments with primary human keratinocytes and in a porcine ex-vivo wound healing model (WHM). Mechanistical studies elucidate that TE and betulin transiently upregulate pro-inflammatory cytokines, chemokines and cyclooxygenase-2 on gene and protein level. For COX-2 and IL-6 this increase of mRNA is due to an mRNA stabilizing effect of TE and betulin, a process in which p38 MAPK and HuR are involved. TE promotes keratinocyte migration, putatively by increasing the formation of actin filopodia, lamellipodia and stress fibers. Detailed analyses show that the TE components betulin, lupeol and erythrodiol exert this effect even in nanomolar concentrations. Targeting the actin cytoskeleton is dependent on the activation of Rho GTPases.

CONCLUSION/SIGNIFICANCE

Our results provide insights to understand the molecular mechanism of the clinically proven wound healing effect of birch bark. TE and betulin address the inflammatory phase of wound healing by transient up-regulation of several pro-inflammatory mediators. Further, they enhance migration of keratinocytes, which is essential in the second phase of wound healing. Our results, together with the clinically proven efficacy, identify birch bark as the first medical plant with a high potential to improve wound healing, a field which urgently needs effective remedies.

Heart failure gene therapy: the path to clinical practice

Gene therapy, aimed at the correction of key pathologies being out of reach for conventional drugs, bears the potential to alter the treatment of cardiovascular diseases radically and thereby of heart failure. Heart failure gene therapy refers to a therapeutic system of targeted drug delivery to the heart that uses formulations of DNA and RNA, whose products determine the therapeutic classification through their biological actions. Among resident cardiac cells, cardiomyocytes have been the therapeutic target of numerous attempts to regenerate systolic and diastolic performance, to reverse remodeling and restore electric stability and metabolism. Although the concept to intervene directly within the genetic and molecular foundation of cardiac cells is simple and elegant, the path to clinical reality has been arduous because of the challenge on delivery technologies and vectors, expression regulation, and complex mechanisms of action of therapeutic gene products. Nonetheless, since the first demonstration of in vivo gene transfer into myocardium, there have been a series of advancements that have driven the evolution of heart failure gene therapy from an experimental tool to the threshold of becoming a viable clinical option. The objective of this review is to discuss the current state of the art in the field and point out inevitable innovations on which the future evolution of heart failure gene therapy into an effective and safe clinical treatment relies.

Consistency of the proteome in primary human keratinocytes with respect to gender, age, and skin localization

Keratinocytes account for 95% of all cells of the epidermis, the stratified squamous epithelium forming the outer layer of the skin, in which a significant number of skin diseases takes root. Immortalized keratinocyte cell lines are often used as research model systems providing standardized, reproducible, and homogenous biological material. Apart from that, primary human keratinocytes are frequently used for medical studies because the skin provides an important route for drug administration and is readily accessible for biopsies. However, comparability of these cell systems is not known. Cell lines may undergo phenotypic shifts and may differ from the in vivo situation in important aspects. Primary cells, on the other hand, may vary in biological functions depending on gender and age of the donor and localization of the biopsy specimen. Here we employed metabolic labeling in combination with quantitative mass spectrometry-based proteomics to assess A431 and HaCaT cell lines for their suitability as model systems. Compared with cell lines, comprehensive profiling of the primary human keratinocyte proteome with respect to gender, age, and skin localization identified an unexpected high proteomic consistency. The data were analyzed by an improved ontology enrichment analysis workflow designed for the study of global proteomics experiments. It enables a quick, comprehensive and unbiased overview of altered biological phenomena and links experimental data to literature. We guide through our workflow, point out its advantages compared with other methods and apply it to visualize differences of cell lines compared with primary human keratinocytes.

Hybrid modeling of the crosstalk between signaling and transcriptional networks using ordinary differential equations and multi-valued logic

A decade of successful results indicates that systems biology is the appropriate approach to investigate the regulation of complex biochemical networks involving transcriptional and post-transcriptional regulations. It becomes mandatory when dealing with highly interconnected biochemical networks, composed of hundreds of compounds, or when networks are enriched in non-linear motifs like feedback and feedforward loops. An emerging dilemma is to conciliate models of massive networks and the adequate description of non-linear dynamics in a suitable modeling framework. Boolean networks are an ideal representation of massive networks that are humble in terms of computational complexity and data demand. However, they are inappropriate when dealing with nested feedback/feedforward loops, structural motifs common in biochemical networks. On the other hand, models of ordinary differential equations (ODEs) cope well with these loops, but they require enormous amounts of quantitative data for a full characterization of the model. Here we propose hybrid models, composed of ODE and logical sub-modules, as a strategy to handle large scale, non-linear biochemical networks that include transcriptional and post-transcriptional regulations. We illustrate the construction of this kind of models using as example a regulatory network centered on E2F1, a transcription factor involved in cancer. The hybrid modeling approach proposed is a good compromise between quantitative/qualitative accuracy and scalability when considering large biochemical networks with a small highly interconnected core, and module of transcriptionally regulated genes that are not part of critical regulatory loops. This article is part of a Special Issue entitled: Computational Proteomics, Systems Biology & Clinical Implications. Guest Editor: Yudong Cai.