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1.  Complex regulation of Hsf1-Skn7 activities by the catalytic subunits of PKA in Saccharomyces cerevisiae: experimental and computational evidences 
BMC Systems Biology  2015;9:42.
Background
The cAMP-dependent protein kinase regulatory network (PKA-RN) regulates metabolism, memory, learning, development, and response to stress. Previous models of this network considered the catalytic subunits (CS) as a single entity, overlooking their functional individualities. Furthermore, PKA-RN dynamics are often measured through cAMP levels in nutrient-depleted cells shortly after being fed with glucose, dismissing downstream physiological processes.
Results
Here we show that temperature stress, along with deletion of PKA-RN genes, significantly affected HSE-dependent gene expression and the dynamics of the PKA-RN in cells growing in exponential phase. Our genetic analysis revealed complex regulatory interactions between the CS that influenced the inhibition of Hsf1/Skn7 transcription factors. Accordingly, we found new roles in growth control and stress response for Hsf1/Skn7 when PKA activity was low (cdc25Δ cells). Experimental results were used to propose an interaction scheme for the PKA-RN and to build an extension of a classic synchronous discrete modeling framework. Our computational model reproduced the experimental data and predicted complex interactions between the CS and the existence of a repressor of Hsf1/Skn7 that is activated by the CS. Additional genetic analysis identified Ssa1 and Ssa2 chaperones as such repressors. Further modeling of the new data foresaw a third repressor of Hsf1/Skn7, active only in theabsence of Tpk2. By averaging the network state over all its attractors, a good quantitative agreement between computational and experimental results was obtained, as the averages reflected more accurately the population measurements.
Conclusions
The assumption of PKA being one molecular entity has hindered the study of a wide range of behaviors. Additionally, the dynamics of HSE-dependent gene expression cannot be simulated accurately by considering the activity of single PKA-RN components (i.e., cAMP, individual CS, Bcy1, etc.). We show that the differential roles of the CS are essential to understand the dynamics of the PKA-RN and its targets. Our systems level approach, which combined experimental results with theoretical modeling, unveils the relevance of the interaction scheme for the CS and offers quantitative predictions for several scenarios (WT vs. mutants in PKA-RN genes and growth at optimal temperature vs. heat shock).
Electronic supplementary material
The online version of this article (doi:10.1186/s12918-015-0185-8) contains supplementary material, which is available to authorized users.
doi:10.1186/s12918-015-0185-8
PMCID: PMC4515323  PMID: 26209979
Yeast; Signal transduction; Hsf1 function; Skn7 function; Windowed discrete model
2.  Regulation of ERK-MAPK signaling in human epidermis 
BMC Systems Biology  2015;9:41.
Background
The skin is largely comprised of keratinocytes within the interfollicular epidermis. Over approximately two weeks these cells differentiate and traverse the thickness of the skin. The stage of differentiation is therefore reflected in the positions of cells within the tissue, providing a convenient axis along which to study the signaling events that occur in situ during keratinocyte terminal differentiation, over this extended two-week timescale. The canonical ERK-MAPK signaling cascade (Raf-1, MEK-1/2 and ERK-1/2) has been implicated in controlling diverse cellular behaviors, including proliferation and differentiation. While the molecular interactions involved in signal transduction through this cascade have been well characterized in cell culture experiments, our understanding of how this sequence of events unfolds to determine cell fate within a homeostatic tissue environment has not been fully characterized.
Methods
We measured the abundance of total and phosphorylated ERK-MAPK signaling proteins within interfollicular keratinocytes in transverse cross-sections of human epidermis using immunofluorescence microscopy. To investigate these data we developed a mathematical model of the signaling cascade using a normalized-Hill differential equation formalism.
Results
These data show coordinated variation in the abundance of phosphorylated ERK-MAPK components across the epidermis. Statistical analysis of these data shows that associations between phosphorylated ERK-MAPK components which correspond to canonical molecular interactions are dependent upon spatial position within the epidermis. The model demonstrates that the spatial profile of activation for ERK-MAPK signaling components across the epidermis may be maintained in a cell-autonomous fashion by an underlying spatial gradient in calcium signaling.
Conclusions
Our data demonstrate an extended phospho-protein profile of ERK-MAPK signaling cascade components across the epidermis in situ, and statistical associations in these data indicate canonical ERK-MAPK interactions underlie this spatial profile of ERK-MAPK activation. Using mathematical modelling we have demonstrated that spatially varying calcium signaling components across the epidermis may be sufficient to maintain the spatial profile of ERK-MAPK signaling cascade components in a cell-autonomous manner. These findings may have significant implications for the wide range of cancer drugs which therapeutically target ERK-MAPK signaling components.
Electronic supplementary material
The online version of this article (doi:10.1186/s12918-015-0187-6) contains supplementary material, which is available to authorized users.
doi:10.1186/s12918-015-0187-6
PMCID: PMC4514964  PMID: 26209520
3.  The gastrin and cholecystokinin receptors mediated signaling network: a scaffold for data analysis and new hypotheses on regulatory mechanisms 
BMC Systems Biology  2015;9:40.
Background
The gastrointestinal peptide hormones cholecystokinin and gastrin exert their biological functions via cholecystokinin receptors CCK1R and CCK2R respectively. Gastrin, a central regulator of gastric acid secretion, is involved in growth and differentiation of gastric and colonic mucosa, and there is evidence that it is pro-carcinogenic. Cholecystokinin is implicated in digestion, appetite control and body weight regulation, and may play a role in several digestive disorders.
Results
We performed a detailed analysis of the literature reporting experimental evidence on signaling pathways triggered by CCK1R and CCK2R, in order to create a comprehensive map of gastrin and cholecystokinin-mediated intracellular signaling cascades. The resulting signaling map captures 413 reactions involving 530 molecular species, and incorporates the currently available knowledge into one integrated signaling network. The decomposition of the signaling map into sub-networks revealed 18 modules that represent higher-level structures of the signaling map. These modules allow a more compact mapping of intracellular signaling reactions to known cell behavioral outcomes such as proliferation, migration and apoptosis. The integration of large-scale protein-protein interaction data to this literature-based signaling map in combination with topological analyses allowed us to identify 70 proteins able to increase the compactness of the map. These proteins represent experimentally testable hypotheses for gaining new knowledge on gastrin- and cholecystokinin receptor signaling. The CCKR map is freely available both in a downloadable, machine-readable SBML-compatible format and as a web resource through PAYAO (http://sblab.celldesigner.org:18080/Payao11/bin/).
Conclusion
We have demonstrated how a literature-based CCKR signaling map together with its protein interaction extensions can be analyzed to generate new hypotheses on molecular mechanisms involved in gastrin- and cholecystokinin-mediated regulation of cellular processes.
Electronic supplementary material
The online version of this article (doi:10.1186/s12918-015-0181-z) contains supplementary material, which is available to authorized users.
doi:10.1186/s12918-015-0181-z
PMCID: PMC4513977  PMID: 26205660
Cholecystokinin receptor; Map; Model; Modules; Network; Protein-protein interaction; Signaling pathway; Gastrin
4.  Logical-continuous modelling of post-translationally regulated bistability of curli fiber expression in Escherichia coli 
BMC Systems Biology  2015;9:39.
Background
Bacteria have developed a repertoire of signalling mechanisms that enable adaptive responses to fluctuating environmental conditions. The formation of biofilm, for example, allows persisting in times of external stresses, e.g. induced by antibiotics or a lack of nutrients. Adhesive curli fibers, the major extracellular matrix components in Escherichia coli biofilms, exhibit heterogeneous expression in isogenic cells exposed to identical external conditions. The dynamical mechanisms underlying this heterogeneity remain poorly understood. In this work, we elucidate the potential role of post-translational bistability as a source for this heterogeneity.
Results
We introduce a structured modelling workflow combining logical network topology analysis with time-continuous deterministic and stochastic modelling. The aim is to evaluate the topological structure of the underlying signalling network and to identify and analyse model parameterisations that satisfy observations from a set of genetic knockout experiments. Our work supports the hypothesis that the phenotypic heterogeneity of curli expression in biofilm cells is induced by bistable regulation at the post-translational level. Stochastic modelling suggests diverse noise-induced switching behaviours between the stable states, depending on the expression levels of the c-di-GMP-producing (diguanylate cyclases, DGCs) and -degrading (phosphodiesterases, PDEs) enzymes and reveals the quantitative difference in stable c-di-GMP levels between distinct phenotypes. The most dominant type of behaviour is characterised by a fast switching from curli-off to curli-on with a slow switching in the reverse direction and the second most dominant type is a long-term differentiation into curli-on or curli-off cells. This behaviour may implicate an intrinsic feature of the system allowing for a fast adaptive response (curli-on) versus a slow transition to the curli-off state, in line with experimental observations.
Conclusion
The combination of logical and continuous modelling enables a thorough analysis of different determinants of bistable regulation, i.e. network topology and biochemical kinetics, and allows for an incorporation of experimental data from heterogeneous sources. Our approach yields a mechanistic explanation for the phenotypic heterogeneity of curli fiber expression. Furthermore, the presented work provides a detailed insight into the interactions between the multiple DGC- and PDE-type enzymes and the role of c-di-GMP in dynamical regulation of cellular decisions.
Electronic supplementary material
The online version of this article (doi:10.1186/s12918-015-0183-x) contains supplementary material, which is available to authorized users.
doi:10.1186/s12918-015-0183-x
PMCID: PMC4511525  PMID: 26201334
Logical modelling; Stochastic modelling; Bistability; Phenotypic heterogeneity; Biofilm; C-di-GMP; Escherichia coli
5.  Estimating cell diffusivity and cell proliferation rate by interpreting IncuCyte ZOOM™ assay data using the Fisher-Kolmogorov model 
BMC Systems Biology  2015;9:38.
Background
Standard methods for quantifying IncuCyte ZOOM™ assays involve measurements that quantify how rapidly the initially-vacant area becomes re-colonised with cells as a function of time. Unfortunately, these measurements give no insight into the details of the cellular-level mechanisms acting to close the initially-vacant area. We provide an alternative method enabling us to quantify the role of cell motility and cell proliferation separately. To achieve this we calibrate standard data available from IncuCyte ZOOM™ images to the solution of the Fisher-Kolmogorov model.
Results
The Fisher-Kolmogorov model is a reaction-diffusion equation that has been used to describe collective cell spreading driven by cell migration, characterised by a cell diffusivity, D, and carrying capacity limited proliferation with proliferation rate, λ, and carrying capacity density, K. By analysing temporal changes in cell density in several subregions located well-behind the initial position of the leading edge we estimate λ and K. Given these estimates, we then apply automatic leading edge detection algorithms to the images produced by the IncuCyte ZOOM™ assay and match this data with a numerical solution of the Fisher-Kolmogorov equation to provide an estimate of D. We demonstrate this method by applying it to interpret a suite of IncuCyte ZOOM™ assays using PC-3 prostate cancer cells and obtain estimates of D, λ and K. Comparing estimates of D, λ and K for a control assay with estimates of D, λ and K for assays where epidermal growth factor (EGF) is applied in varying concentrations confirms that EGF enhances the rate of scratch closure and that this stimulation is driven by an increase in D and λ, whereas K is relatively unaffected by EGF.
Conclusions
Our approach for estimating D, λ and K from an IncuCyte ZOOM™ assay provides more detail about cellular-level behaviour than standard methods for analysing these assays. In particular, our approach can be used to quantify the balance of cell migration and cell proliferation and, as we demonstrate, allow us to quantify how the addition of growth factors affects these processes individually.
doi:10.1186/s12918-015-0182-y
PMCID: PMC4506581  PMID: 26188761
Cell motility; Cell proliferation; Scratch assay; Leading edge detection; Cancer; Wound healing
6.  Identification of putative drug targets for human sperm-egg interaction defect using protein network approach 
BMC Systems Biology  2015;9:37.
Background
Sperm-egg interaction defect is a significant cause of in-vitro fertilization failure for infertile cases. Numerous molecular interactions in the form of protein-protein interactions mediate the sperm-egg membrane interaction process. Recent studies have demonstrated that in addition to experimental techniques, computational methods, namely protein interaction network approach, can address protein-protein interactions between human sperm and egg. Up to now, no drugs have been detected to treat sperm-egg interaction disorder, and the initial step in drug discovery research is finding out essential proteins or drug targets for a biological process. The main purpose of this study is to identify putative drug targets for human sperm-egg interaction deficiency and consider if the detected essential proteins are targets for any known drugs using protein-protein interaction network and ingenuity pathway analysis.
Results
We have created human sperm-egg protein interaction networks with high confidence, including 106 nodes and 415 interactions. Through topological analysis of the network with calculation of some metrics, such as connectivity and betweenness centrality, we have identified 13 essential proteins as putative drug targets. The potential drug targets are from integrins, fibronectins, epidermal growth factor receptors, collagens and tetraspanins protein families. We evaluated these targets by ingenuity pathway analysis, and the known drugs for the targets have been detected, and the possible effective role of the drugs on sperm-egg interaction defect has been considered. These results showed that the drugs ocriplasmin (Jetrea©), gefitinib (Iressa©), erlotinib hydrochloride (Tarceva©), clingitide, cetuximab (Erbitux©) and panitumumab (Vectibix©) are possible candidates for efficacy testing for the treatment of sperm-egg interaction deficiency. Further experimental validation can be carried out to confirm these results.
Conclusion
We have identified the first potential list of drug targets for human sperm-egg interaction defect using the protein interaction network approach. The essential proteins or potential drug targets were found using topological analysis of the protein network. These putative targets are promising for further experimental validation. These study results, if validated, may develop drug discovery techniques for sperm-egg interaction defect and also improve assisted reproductive technologies to avoid in-vitro fertilization failure.
Electronic supplementary material
The online version of this article (doi:10.1186/s12918-015-0186-7) contains supplementary material, which is available to authorized users.
doi:10.1186/s12918-015-0186-7
PMCID: PMC4506605  PMID: 26187737
Protein-protein interactions; Sperm-egg interaction; Essential proteins; Potential drug targets
7.  Systems biology surveillance decrypts pathological transcriptome remodeling 
BMC Systems Biology  2015;9:36.
Background
Pathological cardiac development is precipitated by dysregulation of calreticulin, an endoplasmic reticulum (ER)-resident calcium binding chaperone and critical contributor to cardiogenesis and embryonic viability. However, pleiotropic phenotype derangements induced by calreticulin deficiency challenge the identification of specific downstream transcriptome elements that direct proper cardiac formation. Here, differential transcriptome navigation was used to diagnose high priority calreticulin domain-specific gene expression changes and decrypt complex cardiac-specific molecular responses elicited by discrete functional regions of calreticulin.
Methods
Wild type (WT), calreticulin-deficient (CALR−/−), and calreticulin truncation variant (CALR−/−-NP and CALR−/−-PC) pluripotent stem cells were used to investigate molecular remodeling underlying a model of cardiopathology. Bioinformatic deconvolution of isolated transcriptomes was performed to identify predominant expression trends, gene ontology prioritizations, and molecular network features characteristic of discrete cell types.
Results
Stem cell lines with wild type (WT), calreticulin-deficient (CALR−/−) genomes, as well as calreticulin truncation variants exclusively expressing either the chaperoning (CALR−/−-NP) or the calcium binding (CALR−/−-PC) domain exhibited characteristic molecular signatures determined by unsupervised agglomerative clustering. Kohonen mapping of RNA expression changes identified transcriptome dynamics that segregated into 12 discrete gene expression meta-profiles which were enriched for regulation of Eukaryotic Initiation Factor 2 (EIF2) signaling. Focused examination of domain-specific gene ontology remodeling revealed a general enrichment of Cardiovascular Development in the truncation variants, with unique prioritization of “Cardiovascular Disease” exclusive to the cohort of down regulated genes of the PC truncation variant. Molecular cartography of genes that comprised this cardiopathological category revealed uncharacterized and novel gene relationships, with identification of Pitx2 as a critical hub within the topology of a CALR−/− compromised network.
Conclusions
Diagnostic surveillance, through an algorithm that integrates pluripotent stem cell transcriptomes with advanced high throughput assays and computational bioinformatics, revealed collective gene expression network changes that underlie differential phenotype development. Stem cell transcriptomes provide a deep collective molecular index that reflects ad hoc robustness of the pluripotent gene network. Remodeling events such as monogenic lesions provide a background by which high priority candidate disease effectors and regulators can be identified, demonstrated here by a molecular profiling algorithm that decrypts pluripotent wild type versus disrupted genomes.
Electronic supplementary material
The online version of this article (doi:10.1186/s12918-015-0177-8) contains supplementary material, which is available to authorized users.
doi:10.1186/s12918-015-0177-8
PMCID: PMC4504166  PMID: 26179794
Stem cells; Pluripotent; Microarray; Gene ontology; Network; Transcriptome; Bioinformatics; Systems biology
8.  Finding directionality and gene-disease predictions in disease associations 
BMC Systems Biology  2015;9:35.
Background
Understanding the underlying molecular mechanisms in human diseases is important for diagnosis and treatment of complex conditions and has traditionally been done by establishing associations between disorder-genes and their associated diseases. This kind of network analysis usually includes only the interaction of molecular components and shared genes. The present study offers a network and association analysis under a bioinformatics frame involving the integration of HUGO Gene Nomenclature Committee approved gene symbols, KEGG metabolic pathways and ICD-10-CM codes for the analysis of human diseases based on the level of inclusion and hypergeometric enrichment between genes and metabolic pathways shared by the different human disorders.
Methods
The present study offers the integration of HGNC approved gene symbols, KEGG metabolic pathways andICD-10-CM codes for the analysis of associations based on the level of inclusion and hypergeometricenrichment between genes and metabolic pathways shared by different diseases.
Results
880 unique ICD-10-CM codes were mapped to the 4315 OMIM phenotypes and 3083 genes with phenotype-causing mutation. From this, a total of 705 ICD-10-CM codes were linked to 1587 genes with phenotype-causing mutations and 801 KEGG pathways creating a tripartite network composed by 15,455 code-gene-pathway interactions. These associations were further used for an inclusion analysis between diseases along with gene-disease predictions based on a hypergeometric enrichment methodology.
Conclusions
The results demonstrate that even though a large number of genes and metabolic pathways are shared between diseases of the same categories, inclusion levels between these genes and pathways are directional and independent of the disease classification. However, the gene-disease-pathway associations can be used for prediction of new gene-disease interactions that will be useful in drug discovery and therapeutic applications.
Electronic supplementary material
The online version of this article (doi:10.1186/s12918-015-0184-9) contains supplementary material, which is available to authorized users.
doi:10.1186/s12918-015-0184-9
PMCID: PMC4501277  PMID: 26168918
9.  CytoASP: a Cytoscape app for qualitative consistency reasoning, prediction and repair in biological networks 
BMC Systems Biology  2015;9:34.
Background
Qualitative reasoning frameworks, such as the Sign Consistency Model (SCM), enable modelling regulatory networks to check whether observed behaviour can be explained or if unobserved behaviour can be predicted. The BioASP software collection offers ideal tools for such analyses. Additionally, the Cytoscape platform can offer extensive functionality and visualisation capabilities. However, specialist programming knowledge is required to use BioASP and no methods exist to integrate both of these software platforms effectively.
Results
We report the implementation of CytoASP, an app that allows the use of BioASP for influence graph consistency checking, prediction and repair operations through Cytoscape. While offering inherent benefits over traditional approaches using BioASP, it provides additional advantages such as customised visualisation of predictions and repairs, as well as the ability to analyse multiple networks in parallel, exploiting multi-core architecture. We demonstrate its usage in a case study of a yeast genetic network, and highlight its capabilities in reasoning over regulatory networks.
Conclusion
We have presented a user-friendly Cytoscape app for the analysis of regulatory networks using BioASP. It allows easy integration of qualitative modelling, combining the functionality of BioASP with the visualisation and processing capability in Cytoscape, and thereby greatly simplifying qualitative network modelling, promoting its use in relevant projects.
doi:10.1186/s12918-015-0179-6
PMCID: PMC4499222  PMID: 26163265
Biological networks; Regulatory networks; BioASP; Qualitative modelling
10.  Validation and selection of ODE based systems biology models: how to arrive at more reliable decisions 
BMC Systems Biology  2015;9:32.
Background
Most ordinary differential equation (ODE) based modeling studies in systems biology involve a hold-out validation step for model validation. In this framework a pre-determined part of the data is used as validation data and, therefore it is not used for estimating the parameters of the model. The model is assumed to be validated if the model predictions on the validation dataset show good agreement with the data. Model selection between alternative model structures can also be performed in the same setting, based on the predictive power of the model structures on the validation dataset. However, drawbacks associated with this approach are usually under-estimated.
Results
We have carried out simulations by using a recently published High Osmolarity Glycerol (HOG) pathway from S.cerevisiae to demonstrate these drawbacks. We have shown that it is very important how the data is partitioned and which part of the data is used for validation purposes. The hold-out validation strategy leads to biased conclusions, since it can lead to different validation and selection decisions when different partitioning schemes are used. Furthermore, finding sensible partitioning schemes that would lead to reliable decisions are heavily dependent on the biology and unknown model parameters which turns the problem into a paradox. This brings the need for alternative validation approaches that offer flexible partitioning of the data. For this purpose, we have introduced a stratified random cross-validation (SRCV) approach that successfully overcomes these limitations.
Conclusions
SRCV leads to more stable decisions for both validation and selection which are not biased by underlying biological phenomena. Furthermore, it is less dependent on the specific noise realization in the data. Therefore, it proves to be a promising alternative to the standard hold-out validation strategy.
Electronic supplementary material
The online version of this article (doi:10.1186/s12918-015-0180-0) contains supplementary material, which is available to authorized users.
doi:10.1186/s12918-015-0180-0
PMCID: PMC4493957  PMID: 26152206
Kinetic models; ODE; Differential equations; Model validation; Model selection; Cross validation; Hold-out validation
11.  Domain-specific model selection for structural identification of the Rab5-Rab7 dynamics in endocytosis 
BMC Systems Biology  2015;9:31.
Background
Given its recent rapid development and the central role that modeling plays in the discipline, systems biology clearly needs methods for automated modeling of dynamical systems. Process-based modeling focuses on explanatory models of dynamical systems; it constructs such models from measured time-course data and formalized modeling knowledge. In this paper, we apply process-based modeling to the practically relevant task of modeling the Rab5-Rab7 conversion switch in endocytosis. The task is difficult due to the limited observability of the system variables and the noisy measurements, which pose serious challenges to the process of model selection. To address these issues, we propose a domain-specific model selection criteria that take into account knowledge about the necessary properties of the simulated model behavior.
Results
In a series of modeling experiments, we compare the results of process-based modeling obtained with different model selection criteria. The first is the standard maximum likelihood criterion based solely on least-squares model error. The second one is a parsimony-based criterion that also takes into account model complexity. We also introduce three domain-specific criteria based on domain expert expectations about the simulated behavior of an endocytosis model. According to the first criterion, 90 of the candidate models are indistinguishable. Furthermore, taking into account the complexity of the model does not lead to better model selection. However, the use of domain-specific criteria results in a remarkable improvement over the other two model selection criteria.
Conclusions
We demonstrate the applicability of process-based modeling to the task of modeling the Rab5-Rab7 dynamics in endocytosis. Our experiments show that the domain-specific criteria outperform the standard domain-independent criteria for model selection. We also find that some of the model structures discarded as implausible in previous studies lead to the expected Rab5-Rab7 switch behavior.
Electronic supplementary material
The online version of this article (doi:10.1186/s12918-015-0175-x) contains supplementary material, which is available to authorized users.
doi:10.1186/s12918-015-0175-x
PMCID: PMC4482292  PMID: 26112042
Process-based modeling; Dynamical systems; Structural identification; Model selection; Endocytosis
12.  Genome-scale resources for Thermoanaerobacterium saccharolyticum 
BMC Systems Biology  2015;9:30.
Background
Thermoanaerobacterium saccharolyticum is a hemicellulose-degrading thermophilic anaerobe that was previously engineered to produce ethanol at high yield. A major project was undertaken to develop this organism into an industrial biocatalyst, but the lack of genome information and resources were recognized early on as a key limitation.
Results
Here we present a set of genome-scale resources to enable the systems level investigation and development of this potentially important industrial organism. Resources include a complete genome sequence for strain JW/SL-YS485, a genome-scale reconstruction of metabolism, tiled microarray data showing transcription units, mRNA expression data from 71 different growth conditions or timepoints and GC/MS-based metabolite analysis data from 42 different conditions or timepoints. Growth conditions include hemicellulose hydrolysate, the inhibitors HMF, furfural, diamide, and ethanol, as well as high levels of cellulose, xylose, cellobiose or maltodextrin. The genome consists of a 2.7 Mbp chromosome and a 110 Kbp megaplasmid. An active prophage was also detected, and the expression levels of CRISPR genes were observed to increase in association with those of the phage. Hemicellulose hydrolysate elicited a response of carbohydrate transport and catabolism genes, as well as poorly characterized genes suggesting a redox challenge. In some conditions, a time series of combined transcription and metabolite measurements were made to allow careful study of microbial physiology under process conditions. As a demonstration of the potential utility of the metabolic reconstruction, the OptKnock algorithm was used to predict a set of gene knockouts that maximize growth-coupled ethanol production. The predictions validated intuitive strain designs and matched previous experimental results.
Conclusion
These data will be a useful asset for efforts to develop T. saccharolyticum for efficient industrial production of biofuels. The resources presented herein may also be useful on a comparative basis for development of other lignocellulose degrading microbes, such as Clostridium thermocellum.
Electronic supplementary material
The online version of this article (doi:10.1186/s12918-015-0159-x) contains supplementary material, which is available to authorized users.
doi:10.1186/s12918-015-0159-x
PMCID: PMC4518999  PMID: 26111937
13.  Distinguishing the rates of gene activation from phenotypic variations 
BMC Systems Biology  2015;9:29.
Background
Stochastic genetic switching driven by intrinsic noise is an important process in gene expression. When the rates of gene activation/inactivation are relatively slow, fast, or medium compared with the synthesis/degradation rates of mRNAs and proteins, the variability of protein and mRNA levels may exhibit very different dynamical patterns. It is desirable to provide a systematic approach to identify their key dynamical features in different regimes, aiming at distinguishing which regime a considered gene regulatory network is in from their phenotypic variations.
Results
We studied a gene expression model with positive feedbacks when genetic switching rates vary over a wide range. With the goal of providing a method to distinguish the regime of the switching rates, we first focus on understanding the essential dynamics of gene expression system in different cases. In the regime of slow switching rates, we found that the effective dynamics can be reduced to independent evolutions on two separate layers corresponding to gene activation and inactivation states, and the transitions between two layers are rare events, after which the system goes mainly along deterministic ODE trajectories on a particular layer to reach new steady states. The energy landscape in this regime can be well approximated by using Gaussian mixture model. In the regime of intermediate switching rates, we analyzed the mean switching time to investigate the stability of the system in different parameter ranges. We also discussed the case of fast switching rates from the viewpoint of transition state theory. Based on the obtained results, we made a proposal to distinguish these three regimes in a simulation experiment. We identified the intermediate regime from the fact that the strength of cellular memory is lower than the other two cases, and the fast and slow regimes can be distinguished by their different perturbation-response behavior with respect to the switching rates perturbations.
Conclusions
We proposed a simulation experiment to distinguish the slow, intermediate and fast regimes, which is the main point of our paper. In order to achieve this goal, we systematically studied the essential dynamics of gene expression system when the switching rates are in different regimes. Our theoretical understanding provides new insights on the gene expression experiments.
Electronic supplementary material
The online version of this article (doi:10.1186/s12918-015-0172-0) contains supplementary material, which is available to authorized users.
doi:10.1186/s12918-015-0172-0
PMCID: PMC4479085  PMID: 26084378
Gene expression; Positive feedback; Energy landscape; Flow cytometry experiment
14.  From ERα66 to ERα36: a generic method for validating a prognosis marker of breast tumor progression 
BMC Systems Biology  2015;9:28.
Background
Estrogen receptor alpha36 (ERalpha36), a variant of estrogen receptor alpha (ER) is expressed in about half of breast tumors, independently of the [ER+]/[ER-] status. In vitro, ERalpha36 triggers mitogenic non-genomic signaling and migration ability in response to 17beta-estradiol and tamoxifen. In vivo, highly ERalpha36 expressing tumors are of poor outcome especially as [ER+] tumors are submitted to tamoxifen treatment which, in turn, enhances ERalpha36 expression.
Results
Our study aimed to validate ERalpha36 expression as a reliable prognostic factor for cancer progression from an estrogen dependent proliferative tumor toward an estrogen dispensable metastatic disease. In a retrospective study, we tried to decipher underlying mechanisms of cancer progression by using an original modeling of the relationships between ERalpha36, other estrogen and growth factor receptors and metastatic marker expression. Nonlinear correlation analyses and mutual information computations led to characterize a complex network connecting ERalpha36 to either non-genomic estrogen signaling or to metastatic process.
Conclusions
This study identifies ERalpha36 expression level as a relevant classifier which should be taken into account for breast tumors clinical characterization and [ER+] tumor treatment orientation, using a generic approach for the rapid, cheap and relevant evaluation of any candidate gene expression as a predictor of a complex biological process.
Electronic supplementary material
The online version of this article (doi:10.1186/s12918-015-0178-7) contains supplementary material, which is available to authorized users.
doi:10.1186/s12918-015-0178-7
PMCID: PMC4469423  PMID: 26080803
ERalpha36; Breast tumor; Retrospective study; Gene network identification; Metastatic potential; Nonlinear correlation; Distance based tumor classification
15.  Predicting and exploring network components involved in pathogenesis in the malaria parasite via novel subnetwork alignments 
BMC Systems Biology  2015;9(Suppl 4):S1.
Background
Malaria is a major health threat, affecting over 40% of the world's population. The latest report released by the World Health Organization estimated about 207 million cases of malaria infection, and about 627,000 deaths in 2012 alone. During the past decade, new therapeutic targets have been identified and are at various stages of characterization, thanks to the emerging omics-based technologies. However, the mechanism of malaria pathogenesis remains largely unknown. In this paper, we employ a novel neighborhood subnetwork alignment approach to identify network components that are potentially involved in pathogenesis.
Results
Our module-based subnetwork alignment approach identified 24 functional homologs of pathogenesis-related proteins in the malaria parasite P. falciparum, using the protein-protein interaction networks in Escherichia coli as references. Eighteen out of these 24 proteins are associated with 418 other proteins that are related to DNA replication, transcriptional regulation, translation, signaling, metabolism, cell cycle regulation, as well as cytoadherence and entry to the host.
Conclusions
The subnetwork alignments and subsequent protein-protein association network mining predicted a group of malarial proteins that may be involved in parasite development and parasite-host interaction, opening a new systems-level view of parasite pathogenesis and virulence.
doi:10.1186/1752-0509-9-S4-S1
PMCID: PMC4474416  PMID: 26100579
16.  A weighted and integrated drug-target interactome: drug repurposing for schizophrenia as a use case 
BMC Systems Biology  2015;9(Suppl 4):S2.
Background
Computational pharmacology can uniquely address some issues in the process of drug development by providing a macroscopic view and a deeper understanding of drug action. Specifically, network-assisted approach is promising for the inference of drug repurposing. However, the drug-target associations coming from different sources and various assays have much noise, leading to an inflation of the inference errors. To reduce the inference errors, it is necessary and critical to create a comprehensive and weighted data set of drug-target associations.
Results
In this study, we created a weighted and integrated drug-target interactome (WinDTome) to provide a comprehensive resource of drug-target associations for computational pharmacology. We first collected drug-target interactions from six commonly used drug-target centered data sources including DrugBank, KEGG, TTD, MATADOR, PDSP Ki Database, and BindingDB. Then, we employed the record linkage method to normalize drugs and targets to the unique identifiers by utilizing the public data sources including PubChem, Entrez Gene, and UniProt. To assess the reliability of the drug-target associations, we assigned two scores (Score_S and Score_R) to each drug-target association based on their data sources and publication references. Consequently, the WinDTome contains 546,196 drug-target associations among 303,018 compounds and 4,113 genes. To assess the application of the WinDTome, we designed a network-based approach for drug repurposing using mental disorder schizophrenia (SCZ) as a case. Starting from 41 known SCZ drugs and their targets, we inferred a total of 264 potential SCZ drugs through the associations of drug-target with Score_S higher than two in WinDTome and human protein-protein interactions. Among the 264 SCZ-related drugs, 39 drugs have been investigated in clinical trials for SCZ treatment and 74 drugs for the treatment of other mental disorders, respectively. Compared with the results using other Score_S cutoff values, single data source, or the data from STITCH, the inference of 264 SCZ-related drugs had the highest performance.
Conclusions
The WinDTome generated in this study contains comprehensive drug-target associations with confidence scores. Its application to the SCZ drug repurposing demonstrated that the WinDTome is promising to serve as a useful resource for drug repurposing.
doi:10.1186/1752-0509-9-S4-S2
PMCID: PMC4474536  PMID: 26100720
17.  Dynamics of the discovery process of protein-protein interactions from low content studies 
BMC Systems Biology  2015;9:26.
Background
Thousands of biological and biomedical investigators study of the functional role of single genes and their protein products in normal physiology and in disease. The findings from these studies are reported in research articles that stimulate new research. It is now established that a complex regulatory networks's is controlling human cellular fate, and this community of researchers are continually unraveling this network topology. Attempts to integrate results from such accumulated knowledge resulted in literature-based protein-protein interaction networks (PPINs) and pathway databases. These databases are widely used by the community to analyze new data collected from emerging genome-wide studies with the assumption that the data within these literature-based databases is the ground truth and contain no biases. While suspicion for research focus biases is growing, a concrete proof for it is still missing. It is difficult to prove because the real PPINs are mostly unknown.
Results
Here we analyzed the longitudinal discovery process of literature-based mammalian and yeast PPINs to observe that these networks are discovered non-uniformly. The pattern of discovery is related to a theoretical concept proposed by Kauffman called “expanding the adjacent possible”. We introduce a network discovery model which explicitly includes the space of possibilities in the form of a true underlying PPIN.
Conclusions
Our model strongly suggests that research focus biases exist in the observed discovery dynamics of these networks. In summary, more care should be placed when using PPIN databases for analysis of newly acquired data, and when considering prior knowledge when designing new experiments.
Electronic supplementary material
The online version of this article (doi:10.1186/s12918-015-0173-z) contains supplementary material, which is available to authorized users.
doi:10.1186/s12918-015-0173-z
PMCID: PMC4456804  PMID: 26048415
18.  CD8 + T cell response to adenovirusvaccination and subsequent suppression of tumor growth: modeling, simulation and analysis 
BMC Systems Biology  2015;9:27.
Background
Using immune checkpoint modulators in the clinic to increase the number and activity of cytotoxic T lymphocytes that recognize tumor antigens can prolong survival for metastatic melanoma. Yet, only a fraction of the patient population receives clinical benefit. In short, these clinical trials demonstrate proof-of-principle but optimizing the specific therapeutic strategies remains a challenge. In many fields, CAD (computer-aided design) is a tool used to optimize integrated system behavior using a mechanistic model that is based upon knowledge of constitutive elements. The objective of this study was to develop a predictive simulation platform for optimizing anti-tumor immunity using different treatment strategies.
Methods
To better understand the therapeutic role that cytotoxic CD8 + T cells can play in controlling tumor growth, we developed a multi-scale mechanistic model of the biology using impulsive differential equations and calibrated it to a self-consistent data set.
Results
The multi-scale model captures the activation and differentiation of naïve CD8 + T cells into effector cytotoxic T cells in the lymph node following adenovirus-mediated vaccination against a tumor antigen, the trafficking of the resulting cytotoxic T cells into blood and tumor microenvironment, the production of cytokines within the tumor microenvironment, and the interactions between tumor cells, T cells and cytokines that control tumor growth. The calibrated model captures the modest suppression of tumor cell growth observed in the B16F10 model, a transplantable mouse model for metastatic melanoma, and was used to explore the impact of multiple vaccinations on controlling tumor growth.
Conclusions
Using the calibrated mechanistic model, we found that the cytotoxic CD8 + T cell response was prolonged by multiple adenovirus vaccinations. However, the strength of the immune response cannot be improved enough by multiple adenovirus vaccinations to reduce tumor burden if the cytotoxic activity or local proliferation of cytotoxic T cells in response to tumor antigens is not greatly enhanced. Overall, this study illustrates how mechanistic models can be used for in silico screening of the optimal therapeutic dosage and timing in cancer treatment.
doi:10.1186/s12918-015-0168-9
PMCID: PMC4458046  PMID: 26048402
Adenovirus; Vaccination; Modeling; Impulsive ordinary differential equation
19.  Gene network analysis shows immune-signaling and ERK1/2 as novel genetic markers for multiple addiction phenotypes: alcohol, smoking and opioid addiction 
BMC Systems Biology  2015;9:25.
Background
Addictions to alcohol and tobacco, known risk factors for cancer, are complex heritable disorders. Addictive behaviors have a bidirectional relationship with pain. We hypothesize that the associations between alcohol, smoking, and opioid addiction observed in cancer patients have a genetic basis. Therefore, using bioinformatics tools, we explored the underlying genetic basis and identified new candidate genes and common biological pathways for smoking, alcohol, and opioid addiction.
Results
Literature search showed 56 genes associated with alcohol, smoking and opioid addiction. Using Core Analysis function in Ingenuity Pathway Analysis software, we found that ERK1/2 was strongly interconnected across all three addiction networks. Genes involved in immune signaling pathways were shown across all three networks. Connect function from IPA My Pathway toolbox showed that DRD2 is the gene common to both the list of genetic variations associated with all three addiction phenotypes and the components of the brain neuronal signaling network involved in substance addiction. The top canonical pathways associated with the 56 genes were: 1) calcium signaling, 2) GPCR signaling, 3) cAMP-mediated signaling, 4) GABA receptor signaling, and 5) G-alpha i signaling.
Conlusions
Cancer patients are often prescribed opioids for cancer pain thus increasing their risk for opioid abuse and addiction. Our findings provide candidate genes and biological pathways underlying addiction phenotypes, which may be future targets for treatment of addiction. Further study of the variations of the candidate genes could allow physicians to make more informed decisions when treating cancer pain with opioid analgesics.
Electronic supplementary material
The online version of this article (doi:10.1186/s12918-015-0167-x) contains supplementary material, which is available to authorized users.
doi:10.1186/s12918-015-0167-x
PMCID: PMC4456775  PMID: 26044620
Pain; Opioid; Smoking; Alcohol; Addiction; Genes; Inflammation; Cancer
20.  Uncovering distinct protein-network topologies in heterogeneous cell populations 
BMC Systems Biology  2015;9:24.
Background
Cell biology research is fundamentally limited by the number of intracellular components, particularly proteins, that can be co-measured in the same cell. Therefore, cell-to-cell heterogeneity in unmeasured proteins can lead to completely different observed relations between the same measured proteins. Attempts to infer such relations in a heterogeneous cell population can yield uninformative average relations if only one underlying biochemical network is assumed. To address this, we developed a method that recursively couples an iterative unmixing process with a Bayesian analysis of each unmixed subpopulation.
Results
Our approach enables to identify the number of distinct cell subpopulations, unmix their corresponding observations and resolve the network structure of each subpopulation. Using simulations of the MAPK pathway upon EGF and NGF stimulations we assess the performance of the method. We demonstrate that the presented method can identify better than clustering approaches the number of subpopulations within a mixture of observations, thus resolving correctly the statistical relations between the proteins.
Conclusions
Coupling the unmixing of multiplexed observations with the inference of statistical relations between the measured parameters is essential for the success of both of these processes. Here we present a conceptual and algorithmic solution to achieve such coupling and hence to analyze data obtained from a natural mixture of cell populations. As the technologies and necessity for multiplexed measurements are rising in the systems biology era, this work addresses an important current challenge in the analysis of the derived data.
Electronic supplementary material
The online version of this article (doi:10.1186/s12918-015-0170-2) contains supplementary material, which is available to authorized users.
doi:10.1186/s12918-015-0170-2
PMCID: PMC4480582  PMID: 26040458
Bayesian analysis; Cluster analysis; Intercellular variability; Network analysis; Protein networks; Reverse engineering; Unmixing
21.  Increased robustness of early embryogenesis through collective decision-making by key transcription factors 
BMC Systems Biology  2015;9:23.
Background
Understanding the mechanisms by which hundreds of diverse cell types develop from a single mammalian zygote has been a central challenge of developmental biology. Conrad H. Waddington, in his metaphoric “epigenetic landscape” visualized the early embryogenesis as a hierarchy of lineage bifurcations. In each bifurcation, a single progenitor cell type produces two different cell lineages. The tristable dynamical systems are used to model the lineage bifurcations. It is also shown that a genetic circuit consisting of two auto-activating transcription factors (TFs) with cross inhibitions can form a tristable dynamical system.
Results
We used gene expression profiles of pre-implantation mouse embryos at the single cell resolution to visualize the Waddington landscape of the early embryogenesis. For each lineage bifurcation we identified two clusters of TFs – rather than two single TFs as previously proposed – that had opposite expression patterns between the pair of bifurcated cell types. The regulatory circuitry among each pair of TF clusters resembled a genetic circuit of a pair of single TFs; it consisted of positive feedbacks among the TFs of the same cluster, and negative interactions among the members of the opposite clusters. Our analyses indicated that the tristable dynamical system of the two-cluster regulatory circuitry is more robust than the genetic circuit of two single TFs.
Conclusions
We propose that a modular hierarchy of regulatory circuits, each consisting of two mutually inhibiting and auto-activating TF clusters, can form hierarchical lineage bifurcations with improved safeguarding of critical early embryogenesis against biological perturbations. Furthermore, our computationally fast framework for modeling and visualizing the epigenetic landscape can be used to obtain insights from experimental data of development at the single cell resolution.
Electronic supplementary material
The online version of this article (doi:10.1186/s12918-015-0169-8) contains supplementary material, which is available to authorized users.
doi:10.1186/s12918-015-0169-8
PMCID: PMC4450992  PMID: 26033487
Waddington landscape; Early embryogenesis; Differentiation; Developmental bifurcations; Genetic circuit; Single cell analysis
22.  The hierarchical organization of natural protein interaction networks confers self-organization properties on pseudocells 
BMC Systems Biology  2015;9(Suppl 3):S3.
Background
Cell organization is governed and maintained via specific interactions among its constituent macromolecules. Comparison of the experimentally determined protein interaction networks in different model organisms has revealed little conservation of the specific edges linking ortholog proteins. Nevertheless, some topological characteristics of the graphs representing the networks - namely non-random degree distribution and high clustering coefficient - are shared by networks of distantly related organisms. Here we investigate the role of the topological features of the protein interaction network in promoting cell organization.
Methods
We have used a stochastic model, dubbed ProtNet representing a computer stylized cell to answer questions about the dynamic consequences of the topological properties of the static graphs representing protein interaction networks.
Results
By using a novel metrics of cell organization, we show that natural networks, differently from random networks, can promote cell self-organization. Furthermore the ensemble of protein complexes that forms in pseudocells, which self-organize according to the interaction rules of natural networks, are more robust to perturbations.
Conclusions
The analysis of the dynamic properties of networks with a variety of topological characteristics lead us to conclude that self organization is a consequence of the high clustering coefficient, whereas the scale free degree distribution has little influence on this property.
doi:10.1186/1752-0509-9-S3-S3
PMCID: PMC4464023  PMID: 26050708
23.  A versatile mathematical work-flow to explore how Cancer Stem Cell fate influences tumor progression 
BMC Systems Biology  2015;9(Suppl 3):S1.
Background
Nowadays multidisciplinary approaches combining mathematical models with experimental assays are becoming relevant for the study of biological systems. Indeed, in cancer research multidisciplinary approaches are successfully used to understand the crucial aspects implicated in tumor growth. In particular, the Cancer Stem Cell (CSC) biology represents an area particularly suited to be studied through multidisciplinary approaches, and modeling has significantly contributed to pinpoint the crucial aspects implicated in this theory.
More generally, to acquire new insights on a biological system it is necessary to have an accurate description of the phenomenon, such that making accurate predictions on its future behaviors becomes more likely. In this context, the identification of the parameters influencing model dynamics can be advantageous to increase model accuracy and to provide hints in designing wet experiments. Different techniques, ranging from statistical methods to analytical studies, have been developed. Their applications depend on case-specific aspects, such as the availability and quality of experimental data, and the dimension of the parameter space.
Results
The study of a new model on the CSC-based tumor progression has been the motivation to design a new work-flow that helps to characterize possible system dynamics and to identify those parameters influencing such behaviors. In detail, we extended our recent model on CSC-dynamics creating a new system capable of describing tumor growth during the different stages of cancer progression. Indeed, tumor cells appear to progress through lineage stages like those of normal tissues, being their division auto-regulated by internal feedback mechanisms. These new features have introduced some non-linearities in the model, making it more difficult to be studied by solely analytical techniques. Our new work-flow, based on statistical methods, was used to identify the parameters which influence the tumor growth. The effectiveness of the presented work-flow was firstly verified on two well known models and then applied to investigate our extended CSC model.
Conclusions
We propose a new work-flow to study in a practical and informative way complex systems, allowing an easy identification, interpretation, and visualization of the key model parameters. Our methodology is useful to investigate possible model behaviors and to establish factors driving model dynamics.
Analyzing our new CSC model guided by the proposed work-flow, we found that the deregulation of CSC asymmetric proliferation contributes to cancer initiation, in accordance with several experimental evidences. Specifically, model results indicated that the probability of CSC symmetric proliferation is responsible of a switching-like behavior which discriminates between tumorigenesis and unsustainable tumor growth.
doi:10.1186/1752-0509-9-S3-S1
PMCID: PMC4464028  PMID: 26050594
Non Linear Mathematical Models; Cancer Stem Cells; Parameter analysis
24.  A renewal model for the emergence of anomalous solute crowding in liposomes 
BMC Systems Biology  2015;9(Suppl 3):S7.
A fundamental evolutionary step in the onset of living cells is thought to be the spontaneous formation of lipid vesicles (liposomes) in the pre-biotic mixture. Even though it is well known that hydrophobic forces drive spontaneous liposome formation in aqueous solutions, how the components of the earliest biochemical pathways were trapped and concentrated in the forming vesicles is an issue that still needs to be clarified. In recent years, some authors carried out a set of experiments where a unexpectedly high amount of solutes were found in a small number of liposomes, spontaneously formed in aqueous solution. A great number of empty liposomes were found in the same experiments and the global observed behavior was that of a distribution of solute particles into liposomes in agreement with a inverse power-law function rather than with the expected Poisson distribution. The chemical and physical mechanisms leading to the observed "anomalous solute crowding" are still unclear, but the non-Poisson power-law behavior is associated with some cooperative behavior with strong non-linear interactions in the biochemical processes occurring in the solution. For tackling this issue we propose a model grounding on the Cox's theory of renewal point processes, which many authors consider to play a central role in the description of complex cooperative systems. Starting from two very basic hypotheses and the renewal assumption, we derive a model reproducing the behavior outlined above. In particular, we show that the assumption of a "cooperative" interaction between the solute molecules and the forming liposomes is sufficient for the emergence of the observed power-law behavior. Even though our approach does not provide experimental evidences of the chemical and physical bases of the solute crowding, it suggests promising directions for experimental research and it also provide a first theoretical prediction that could possibly be tested in future experimental investigations.
doi:10.1186/1752-0509-9-S3-S7
PMCID: PMC4464207  PMID: 26051120
protein crowding; anomalous solute crowding; super-concentration; renewal processes
25.  Modelling non-Markovian dynamics in biochemical reactions 
BMC Systems Biology  2015;9(Suppl 3):S8.
Background
Biochemical reactions are often modelled as discrete-state continuous-time stochastic processes evolving as memoryless Markov processes. However, in some cases, biochemical systems exhibit non-Markovian dynamics. We propose here a methodology for building stochastic simulation algorithms which model more precisely non-Markovian processes in some specific situations. Our methodology is based on Constraint Programming and is implemented by using Gecode, a state-of-the-art framework for constraint solving.
Results
Our technique allows us to randomly sample waiting times from probability density functions that not necessarily are distributed according to a negative exponential function. In this context, we discuss an important case-study in which the probability density function is inferred from single-molecule experiments that describe the distribution of the time intervals between two consecutive enzymatically catalysed reactions. Noticeably, this feature allows some types of enzyme reactions to be modelled as non-Markovian processes.
Conclusions
We show that our methodology makes it possible to obtain accurate models of enzymatic reactions that, in specific cases, fit experimental data better than the corresponding Markovian models.
doi:10.1186/1752-0509-9-S3-S8
PMCID: PMC4464211  PMID: 26051249
Non-Markovian dynamics; constraint programming; biochemical reactions

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