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Session A: (July 22 and July 23)	Session B: (July 24 and July 25)
Presentation Schedule for July 22, 6:00 pm – 8:00 pm A) 3DSIG B) BioVis C) Function D) HitSeq E) RegSys F) SysMod G) TransMed H) Special Session (SST01): Text Mining for Biology and Healthcare Presentation Schedule for July 23, 6:00 pm – 8:00 pm I) Bio-Ontologies J) CompMS K) EvolCompGen L) Microbiome M) NetBio	Presentation Schedule for July 24, 6:00 pm – 8:00 pm P) BOSC Q) CAMDA R) Education S) MLCSB T) RNA U) VarI V) General Computational Biology
Session A Poster Set-up and Dismantle Session A Posters set up: Monday, July 22 between 7:30 am - 10:00 am Session A Posters should be removed at 8:00 pm, Tuesday, July 23.	Session B Poster Set-up and Dismantle Session B Posters set up: Wednesday, July 24 between 7:30 am - 10:00 am Session B Posters should be removed at 2:00 pm, Thursday, July 25.

Short Abstract: Abnormal peristaltic contraction of the ureter smooth muscle (USM) causes the pathophysiological condition to the urinary system. The USM contractions are discretely initiated by the USM cell action potentials (APs). Therefore, the USM AP is a dynamic parameter to investigate the abnormal USM contractions. In the interest of figuring out the internal membrane ionic currents responsible for USM AP origination, this paper aimed at developing a computational model of the USM cell AP. From various published electrophysiological recordings, we listed all major USM cell ion channels associated with generating cellular electrical activities. Then, we constructed all ion channel models after extracting biophysical details from the documented voltage clamp experiments in guinea pig USM tissue. The individual ion channel model properties were validated by comparing the simulation results with the experimental voltage-clamp results. Then, all ion channels were integrated to generate the USM AP after introducing a current stimulus to a single cell model. This model reproduced USM AP successfully that replicates the experimental AP. This model also allows analyzing the ion channel implications at a different phase of the AP. In the future, this primary model can be further extended to explore new intracellular insights for abnormal USM contraction.

Short Abstract: Retinoblastoma (RB) is a childhood eye cancer, caused by loss of RB1 gene. It affects one or both the eyes, with a current global incidence of in 15,000 to 20,000 births. Chemotherapy, local therapy, and enucleation are the main ways in which RB is managed. However, these treatment strategies exhibit severe side effects, warranting a systems-level analysis of RB to predict novel diagnostics and safer therapeutics. Herein, we used mathematical modeling approach (i.e., constraint-based reconstruction and analysis) to identify and explain RB-specific survival strategies. While there was over-utilization of amino acids by RB for energy, there was under-utilization of cholesterol synthesis for preservation of redox potential. Variable synthesis of long-chain/very-long chain fatty acids was found to be classifying RB-subtypes. Further, our model-specific secretion profile was also found in RB1-depleted human retinal cells in vitro and suggests that novel biomarkers involved in lipid metabolism may be important. Finally, RB-specific synthetic lethals have been predicted as lipid and nucleoside transport proteins that can aid in novel drug target development.

Short Abstract: Microbial communities are ubiquitous in nature and impact human well-being in many ways. They close global elemental cycles, are harnessed in biotechnological applications such as biogas production, and play an important role in human health. To uncover the complex web of metabolic interactions in these systems, we introduce µbialSim (pronounced ‘microbialSim’), a novel numerical simulator that implements the dynamic Flux-Balance-Analysis approach. By employing a novel numerical integration scheme, our simulator can consider communities at their natural diversity, going beyond current simulator codes which are restricted to few species only. As an example, we apply µbialSim to the entirety of a model collection of 773 species of the human gut microbiome. We demonstrate how the predicted pattern of compound exchange and its dynamics can be analyzed as the community feeds on a western-diet substrate pulse. While quantitative predictions have to be interpreted in the light of the simulator’s current limitations – being restricted to metabolic interactions only – we envision µbialSim as a starting point for an extensive in silico characterization of community dynamics at an unprecedented level of detail and helping in elucidating general principles in microbial ecology, and as a tool for experimental design and the design of communities.

Short Abstract: The study of protein interactions is mainly accomplished with network-analysis. Although it’s a powerful tool, it fails in showing what the dynamic properties of these interactions are. Simulation tools help with this task but they are barely known in the area. Kappa is a modeling language based on rules and agents. Agents are particle-like entities which can bind and be modified according to rules. We expand Kappa models with PISKaS, a software based on KaSim v3.0 which is capable to extend the modeling bounds efficiently. Our implementation’s new features include spatiality, resource optimization, modeling flexibility, and statistical analysis. We can simulate volumes where agents travel among them using a parallel algorithm. Agents now have numeric internal-values, allowing rules to operate with them and even vary their reaction-rate. Several trajectories can run at once, which allows to optimize simulations based on previous results and extract some basic statistics. Nowadays, PISKaS can perform just as KaSim, with no significant differences in time or accuracy. Furthermore, internal-values allow agents to represent more complex entities than proteins, such as cells or humans, making our software suitable to perform social simulations. Partial economic support from FA9550−18−1−0438 AFOSR. AFB-170004 Fundacion Ciencia y Vida, ICM-Economia P09-022 CINV.

Short Abstract: Drug resistance is driven by mutations and dynamic plasticity deregulating pathway activities and regulatory programs of a heterogeneous tumour. We propose a model to simulate population dynamics of heterogeneous tumour cells with reversible drug resistance. Drug sensitivity of a tumour cell is determined by its internal states demarcated by coordinated activities of multiple interconnected pathways. Transitions between cellular states depend on the effects of drugs and regulatory relations between the pathways. We build a simple model to capture drug resistance characteristics of BRAF-mutant melanoma, where cellular states are determined by two mutually inhibitory – main and alternative – pathways. Cells with an activated main pathway are proliferative yet sensitive to the BRAF inhibitor, and cells with an activated alternative pathway are quiescent but resistant to the drug. We describe a dynamical process of tumour growth, and compare efficacy of three treatment strategies from simulated data: static treatments with constant dosages, periodic treatments with regular intermittent active phases and drug holidays, and treatments derived from optimal control theory (OCT). Periodic treatments outperform static treatments with a considerable margin, while treatments based on OCT outperform the best periodic treatment. Our results provide insights regarding optimal cancer treatment modalities for heterogeneous tumours.

Short Abstract: Genome-scale metabolic networks and transcriptomic data represent complementary sources of knowledge about an organism’s metabolism, yet their integration to achieve biological insight remains challenging. We investigate here condition-specific series of metabolic sub-networks constructed by successively removing genes from a comprehensive network. The optimal order of gene removal is deduced from transcriptomic data and the produced sub-networks are evaluated via a fitness function, which estimates their degree of alteration. We then consider how a gene set, i.e. a group of genes contributing to a common biological function, is depleted in different series of sub-networks to reveal the difference between experimental conditions. The method, named metaboGSE, was validated on public data for Yarrowia lipolytica. It was shown to produce GO terms of higher specificity compared to popular gene set enrichment methods like GSEA or topGO. Furthermore, metaboGSE permits identifying genes that are not necessarily differentially expressed, but nevertheless responsible for functional differences between conditions. We are currently investigating this aspect as part of a study about the early modifications leading to metaflammation in white adipose tissue of mice under high-fat diet. The metaboGSE R package is available at https://CRAN.R-project.org/package=metaboGSE.

Short Abstract: Computational models of biological systems are growing in complexity, approaching the whole-cell scale. Both modeling and simulation of such systems are far from trivial, yet, significant advances in this direction have already been performed. Current whole-cell models yield heterogeneous representations of cellular processes, each one being approached using established methods. Their integration is achieved by exchanging information between them from time to time. Although this approach proved to be useful, its organism-specificity makes it hard to scale and adapt to other organisms. Here, we present a homogeneous approach to model and simulate whole-cells where all cellular process are represented through their underlying biochemical reactions. Such a representation results in a map of all possible biochemical interactions between molecular entities of a cell in the form of a single biochemical network, naturally integrating cellular processes. We discuss the implications of such an approach on automated model generation, user-friendliness, parameter estimation, scalable simulation methods, and computational costs. We also present an example of the entire pipeline extending from model construction up to simulation and analysis using toy models. In addition, we present a biochemical network model of a whole real organism.

Short Abstract: Gut microbes have been shown to play an important role in human health and disease, affecting host physiology and homeostasis by secretion of small molecules.A few such circulating blood metabolites derived from gut microbes have been shown to influence mitochondrial function.Despite sharing the common evolutionary origin, there are no such studies so far which could identify metabolites affecting mitochondrial health on a large scale. Here,we have utilized constraint based model of mitochondria to screen such gut microbial metabolites.Out of 437 metabolites taking part in mitochondrial pathways,325 were common between metabolites produced by gut microbes and mitochondrial metabolites. The effect of these metabolites on mitochondrial function was tested using the metabolic model of mitochondria.We simulated hypoxic condition,a proxy to mitochondrial dysfunction by restricting the oxygen uptake from 19.8μM-5.0μM which resulted in decrease in ATP production from 102.7μM-30μM.In this condition, we simulated uptake of various gut microbial metabolites to identify which metabolites restore ATP levels to normal.Of the 127 metabolites tested so far,21 metabolites showed positive results and are being validated in-vitro. This is the first study of its kind that uses metabolic model to screen large number of metabolites that are poised to be a part of future mitochondrial targeted therapy

Short Abstract: Motivation: Metabolic flux balance analysis is a standard tool in analysing metabolic reaction rates compatible with measurements, steady-state and the metabolic reaction network stoichiometry. Flux analysis methods commonly place unrealistic assumptions on fluxes due to the convenience of formulating the problem as a linear programming model, and most methods ignore the notable uncertainty in flux estimates. Results: We introduce a novel paradigm of Bayesian metabolic flux analysis that models the reactions of the whole genome-scale cellular system in probabilistic terms, and can infer the full flux vector distribution of genome-scale metabolic systems based on exchange and intracellular (e.g. 13C) flux measurements, steady-state assumptions, and objective function assumptions. The Bayesian model couples all fluxes jointly together in a simple truncated multivariate posterior distribution, which reveals informative flux couplings. Our model is a plug-in replacement to conventional metabolic balance methods, such as flux balance analysis (FBA). Our experiments indicate that we can characterise the genome-scale flux covariances, reveal flux couplings, and determine more intracellular unobserved fluxes in C. acetobutylicum from 13C data than flux variability analysis. Availability: The COBRA compatible software is available at github.com/markusheinonen/bamfa Contact: markus.o.heinonen@aalto.fi

Short Abstract: Whole cell modeling is one of the grand scientific challenges in the 21st century. However, despite the accumulation of a huge amount of experimental data, the way of modeling is still a "black-box", where a model manually emerges without formalization of its procedure. Here, we present a novel framework for precise genome-scale simulation of bacteria, i.e. Escherichia coli, based on the genome sequence. To achieve the prediction of phenotype from genotype, this framework accepts a genome sequence instead of a mathematical model as an input and automatically annotates the given sequence utilizing information stored in multiple bioinformatic databases. Based on the annotation, it generates a dynamic, stochastic, and single-cell model consisting of multiple pathways, such as gene expression, signaling, and replication, involving more than 10 thousand reactions and 1 million agents. Additionally, the whole cell simulation enables us to predict omics profiles directly from a genome sequence without knowledge about the mathematical details of the model. The automated modeling can facilitate the construction and maintenance of large complex models against massive knowledge updated constantly and allow us to design a genome from scratch toward an era of synthetic cells.

Short Abstract: Biological aerosols may influence atmospheric processes that lead to rainfall; however, their constant presence makes it difficult to identify when and where they might play an important role. To investigate this, we utilized data on rainfall feedback, a phenomenon where relatively heavy rainfall has a measurable effect on subsequent rainfall. Previous studies suggest that rainfall feedback persistent over several weeks is due to environmental phenomena that involve microbial growth. Rain-induced increases in surface populations of microorganisms leads to an increase in cloud-active bioaerosols which have the capability to influence precipitation formation. In this study, we utilized a geographically weighted regression model to explore the underlying mechanisms of rainfall feedback in the context of the continental USA. We found that known predictors of microbial aerosol diversity and abundance and precipitation pattern, such as mean temperature, mean precipitation, and landcover composition, are potential determinants of rainfall feedback. Relationships between rainfall feedback and landcover type are spatially non-stationary: both the strength and the direction of the relationships differ over space. Our study also identified that certain landcover types are more favorable for positive rainfall feedback, and thus likely favorable to the production and emission of bioaerosols in non-limiting quantities.

Short Abstract: One of the most significant bottlenecks in metabolic-model development is the time required to add reactions to the network that were initially omitted due to incompleteness in the genome annotation. Algorithms have been developed to address this problem, called network gap filling, by choosing a minimal number of reactions from a universal reaction database such as MetaCyc, such that adding those reactions to the metabolic network enables the metabolic model to produce all biomass metabolites defined for the model. Our past studies have shown that reaction gap filling has a significant error rate. Here we present an enhanced taxonomic gap-filling algorithm with significantly improved performance. The algorithm assigns a lower cost to MetaCyc reactions that are found in other organisms within the same phylum as the organism being gap filled, on the assumption that different taxonomic groups are biased toward using different metabolic reactions. For example, when gap filling the Escherichia coli metabolic network, the algorithm assigns a lower cost to reactions found in other BioCyc databases for organisms in the same phylum (Proteobacteria) as E. coli. For gap filling an E. coli metabolic network containing randomly introduced gaps, gap filling accuracy increases from 87.8% to 97.3%.

Short Abstract: In the early stages of drug development, prediction of pharmacokinetic profiles of new chemical entities is essential to minimize the risks of potential withdrawals. The pharmacokinetic profile of a drug depends on various properties including solubility, intestinal absorption, plasma protein binding, metabolism, biliary excretion, distribution and renal excretion. Recently, computer-aided drug design using in silico models to predict ADMET parameters (absorption, distribution, metabolism, excretion, and toxicity) has attracted much attention. In this study, we introduce several prediction models for pharmacokinetic parameters, mainly the fraction unbound in plasma (fu,p), renal excretion (fe) and renal clearance (CLr) of drugs in human. fu,p is an important determinant of drug efficacy and the excretion of unchanged compounds by the kidney is a major route in drug elimination and these parameters play an important role in pharmacokinetics. In this study, prediction models for fu,p were created initially, additional models for fe and CLr in human were generated using machine learning methods such as Random Forest and Support Vector Machine with predicted values of fu,p as a descriptor. Our prediction system, consisting of fu,p, renal excretion and other models, is freely available to the public and it can be used in screening processes of drug discovery.

Short Abstract: Background: Clostridium difficile is a gram-positive spore-forming bacterium [1, 2]. Upon germination in the colon, it produces toxins that cause death of epithelial cells, severe colitis and potentially death [3]. Motivation: We built a mathematical model of immune responses to C.difficile generalizable to other gram-positive toxin-producing select agents potentially used as bioweapons, to determine the immunological mechanisms responsible for pathogenesis. Method: The ODE model has 58 equations involving 34 species at the interface of host-pathogen interactions and describes the dynamics of: i) bacterium and toxins (tcdA/tcdB, binary); ii) innate responses: type 3 innate lymphoid cells (ILC3s), macrophages, neutrophils, eosinophils, iii) cytokines: IL22, IL25, IL1β (IL1beta), and iv) T and B cells. The model was calibrated using data from a mouse C.difficile infection (CDI) model. Sensitivity analysis identified critical factors influencing outcome of infection. Results: A novel modeling prediction was that macrophage-derived IL1β enhanced the production of ILC3s which in turn controlled IL22 levels. Increased levels of colonic IL22 was beneficial by having a negative impact on epithelial cell damage during CDI. Conclusion: The results highlighted IL1β production as key factor in reducing epithelial cell damage during CDI. These findings will be validated experimentally using mouse models and human primary cells.

Short Abstract: We began an initiative “Development of a Drug Discovery Informatics System” supported by the Japan Agency for Medical Research and Development. The main aim of this initiative is to develop accurate prediction systems for DMPK (drug metabolism and pharmacokinetics), primarily targeting academic scientists. We collected pharmacokinetic and physicochemical parameters from ChEMBL. However, since ChEMBL compiles data obtained in different experimental conditions, we selected the data measured in compatible conditions and reformatted the results as appropriate for our prediction system. In addition to the public data, we have acquired experimental data under unified protocols. The in vitro data include solubility, distribution coefficient, metabolic stability, fraction unbound in plasma, fraction unbound in brain homogenate, and blood-to-plasma concentration ratio. The in vivo data include the drug concentrations in plasma and several tissues after oral or intravenous administration of the drug and pharmacokinetic parameters calculated therefrom. We stored these data to DruMAP database. We are currently developing prediction models for several pharmacokinetic parameters using these data. DruMAP also provides the ability to predict those parameters for user input compounds using our prediction models. DruMAP can be used for early DMPK studies and for candidate compound selection to accelerate novel drug development.

Short Abstract: Metabolic engineering is a common technique used to improve the production of target chemicals in microorganisms. There are already various published methods that identify potential metabolic engineering strategies such as gene and reaction knock-outs. However, most of the available approaches do not explicitly take into account that in some cases, the target chemical can be toxic for the microorganism itself, which might render the production unstable. We are currently developing a method that aims to identify knock-outs which increase the production of the target and which, at the same time, ensure that the microorganism keeps a high resistance against the toxic target. In a first step, our approach uses multi-objective linear optimization to find valid trade-offs between growth, target production and toxicity resistance against the target. Afterwards, potential knock-outs are enumerated and then ranked to choose the best candidates for a desired trade-off. The toxicity resistance is measured by the activity of a set of critical reactions that have to be known or identified experimentally as a prerequisite. To test our method, we are applying it to identify knock-outs for the production of ethanol in yeast.

Short Abstract: Pseudomonas aeruginosa (Pa) is a Gram-negative opportunistic pathogen. Its potential virulence, its intrinsic and acquired resistance, and its ability to cause primary health-care associated infections, motivate the research of pathogenicity of Pa. Based on the hypothesis that the absence of genes can potentially increase the pathogenicity of Pa, we aim to identify such genes and to validate their modulating role for the pathogen’s virulence. Whole-genome sequencing data of the Pa strain PA14 and a patient isolate associated with high mortality were used to identify single nucleotide polymorphisms (SNPs). The potential consequences of the genetic differences were analysed using a genome-scale metabolic network model of PA14 and information about virulence factors. Assuming SNP variants could cause the loss of function of a gene product, the SNP-affected genes were subsequently knocked out in the model, and its effect on virulence-associated reactions was monitored using flux balance analyses. Promising candidate genes were validated in the Galleria mellonella infection model using mutant Pa strains. First experiments suggest a modulating role of the urocanate hydratase gene in Pa by increasing the virulence when being absent. The insight into factors enhancing and modulating the virulence could be used to detect new targets for therapeutic approaches.

Short Abstract: Organisms are constantly acquiring information from the environmental. Information improves the decisions made by organisms, directly affecting their survival and reproductive success. Signaling pathways are the basic mechanisms used by cells to obtain information. They rely on reversible reactions for the binding of signals to receptors, the activation of molecules via allosteric regulation, and the binding of transcription factors to the DNA. However, reversible reactions are noisy, because of random fluctuations in the concentration and activity of molecules. Noise causes uncertainty about the information conveyed by transforming an input into a distribution of possible outputs. For this reason, it is commonly stated that noise reduces the capacity to acquire information. Interestingly, our results show that, under realistic biological conditions, reversible chemical reactions unavoidably produce non-minimal levels of noise for information acquisition. We study how this phenomenon affects the capacity of signaling pathways to acquire and transmit information. We show that the non-minimal levels of noise are transmitted from reversible reactions to the production of mRNA and protein. However, the strength of the binding of a reversible reaction modulates information acquisition and noise levels. Finally, we test our results using the nuclear receptor signaling pathway as an example.

Short Abstract: Detection of biomarker genes plays a crucial role in disease detection and treatment. These computational approaches enhance the insights derived from experiments and reduce the efforts of biologists and experimentalists. This is essentially achieved through prioritizing a set of genes with certain attributes. In this work, I show that understanding the regulatory mechanisms underlying stem cells helps to identify cancer biomarkers. We got inspired by the regulatory mechanisms of the pluripotency network in mouse embryonic stem cells to formulate the problem where a set of master regulatory genes in regulatory networks is identified with two combinatorial optimization problems namely as minimum dominating set and minimum connected dominating set in weakly and strongly connected components. We applied the developed methods to regulatory cancer networks to identify disease-associated genes and anti-cancer drug targets in breast cancer and hepatocellular carcinoma. As not all the nodes in the solutions are critical, I developed a new prioritization method named TopControl to ranks a set of candidate genes which are related to a certain disease. Moreover, this work shows that the topological features in regulatory networks surrounding differentially expressed genes are highly consistent in terms of using the output of several analysis tools.

Short Abstract: Intratumour heterogeneity characterizing cancer populations represent a key factor in fostering the disease progression. In particular, metabolic intratumour heterogeneity increases the repertoire of possible cellular responses to drugs and boosts the adaptive nature of cellular behaviors, hindering the identification of effective treatments. Unfortunately, current metabolomics technologies depict the average cell population behavior, but disregard both internal interactions and differences. To explore such metabolic heterogeneity, characterization of metabolic programs at the single-cell level must be used. In this regard, single-cell metabolomics is still at its infancy thus is less advanced than single-cell sequencing. To bridge this gap, we present a computational framework to characterize metabolism at the single cell level and possible metabolic interactions among cells, by integrating bulk metabolomics and single-cell transcriptomics data. Than, we exploit constraint-based modeling to simulate a set of replicates of a human metabolic network corresponding to interacting distinct cells of a given population. The integration of transcriptomics profiles of individual tumour cells isolated from lung adenocarcinoma and breast cancer patients allowed to compute single-cell fluxomes, to identify clusters of cells with different growth rates, and to point out the possible metabolic interactions among cells via exchange of metabolites by showing adherence to experimental evidences.

Short Abstract: Since the discovery of Treponema pallidum ssp. pallidum (Tp) as the etiologic agent of syphilis in 1905, still no vaccine is available, and the world is still burdened by syphilis, which in its early stages enhances the transmission of HIV. Continuous in vitro culture of Tp has still not been achieved, imposing a substantial roadblock to its experimental inspection, and even the sequencing of its genome did not yield an obvious solution to the cultivation problem. We present iSM161, a first manually curated draft reconstruction of the metabolic network in Tp towards a genome-scale metabolic model (GEM), comprising 161 genes (1039 predicted open reading frames), 239 reactions, and 277 metabolites. The model is still under development and steadily updated. For the reconstruction, COBRApy has been used, where subsystem information is added and parsed as SBML groups extension using libSBML. Using this reconstruction, we anticipate to gather new insights into the pathogen’s physiology and pathology, and in how this spirochete, which has earned the designation of “stealth pathogen,” succeeds in making a living and eluding human’s immune defenses as well as cultivation attempts. It is planned to make the model available to the community in SBML format.

Short Abstract: Objectives: Lenalidomide, an immunomodulatory agent, is approved for the treatment of multiple myeloma, del 5q myelodysplastic syndrome and mantle cell lymphoma. Lenalidomide causes a reversible block in neutrophil maturation. To investigate the dose and schedule that allows for neutrophil recovery, we developed an in silico model based on an in vitro assay. This model is applied to explore dosing regimens. Methods: A compartmental model was developed to represent the in vitro maturation assay [1]. Donor related parameters were fitted to DMSO treatment data and compound related parameters were fitted to the effect upon treatment with a concentration range of lenalidomide. Results: The proposed model quantitatively represents the in vitro neutropenia maturation system and the block in neutrophil maturation caused by lenalidomide. In silico predictions for neutrophil recovery after off-drug period were validated experimentally (predicted vs experimental data R2 = 0.985). Conclusions: An in silico model that represents an in vitro neutropenia assay was developed. Good parameter fit and validated predictions support the applicability of the model to explore dose and schedule of lenalidomide in silico and propose regimens that could minimize a key clinical toxicity of this compound. References: [1] Chiu et al., Br J Haematol 2019 Feb 14

Short Abstract: Metabolism and Expression models (ME-models) are a constraint-based modeling approach, which explicitly accounts for the cost of macromolecular biosynthesis. Explicitly representing these processes allows ME-models to investigate genotype-phenotype relationships with quantitative incorporation of '-omics' data. Common standards are a prerequisite for interoperability of systems biology tools. Novel approaches often require additional or changed data structures that existing standards can often not directly represent. ME-models are powerful tools, but a lack of standards for encoding and creation, and the increased complexity of these models prevented widespread use. SBMLme extends current model encoding standards and enables the representation of the ME-Model variant COBRAme in SBML. A prototype of the extension has been created in Java together with a standalone, bi-directional converter, between this extension and COBRAme's model storage format. The converter showed that SBMLme could fully and correctly encode a COBRAme model. The use of SBMLme enables sharing ME-models more efficiently and a wider variety of tools to access ME-models, promoting their propagation. SBMLme is a proof-of-concept towards an official SBML package for ME-models. SBMLme is freely available at https://github.com/draeger-lab/SBMLme (under MIT license).

Short Abstract: Cancer cells reprogram metabolism to support rapid proliferation and survival. Energy metabolism is particularly important for growth. A genome scale metabolic model (GSMM) is a mathematical formalization of metabolism which allows simulation and hypotheses testing of metabolic strategies using Constraint-Based Modeling. GSMMs are based on the accumulated knowledge of metabolic reactions and enzymes in the published literature where reactions are represented mathematically as a stoichiometric matrix. One of the widely used generic human metabolic models is Recon 2.2, containing 7785 reactions, 5324 metabolites and 1675 genes. The accuracy of predictions using GSMMs is dependent on accurate gene associations to reactions. Current GSMMs do not consider intracellular protein localization in annotating GPR relationships which may lead to false-positives. In this study, protein localization data obtained from 10 different studies were used to verify the intracellular location of each protein. Mismatches between protein localization and reaction location were corrected. The modified Recon 2.2 includes 8135 reactions, 5801 metabolites and 1756 genes, an addition of 5%, 10% and 5%, respectively. The improved model can predict energy productions correctly and perform more known metabolic tasks. Improving prediction accuracy of Recon 2.2 will facilitate identification of biomarkers and drug targets using contextualized GSMMs.

Short Abstract: Computational models of biological systems aid to understand how cellular properties and functions emerge from individual reactions. Model formalization requires accurate and scalable mapping of the available data. However, both scalability issues and data coverage challenge the development of large-scale models of signaling networks. Here, we present a mechanistically detailed model of the molecular network that controls the cell cycle in Saccharomyces cerevisiae. We use the reaction-contingency (rxncon) language to establish a knowledge base, enabling translation into a bipartite Boolean model (bBM). We use the bBM to evaluate the knowledge base and to predict genotype-to-phenotype relationships. Our model reproduces wildtype behavior on the level of macroscopic observables, and correctly predicts 62 out of 85 tested phenotypes. In the future, a similar effort may pave the way towards human whole-cell models.

Short Abstract: There are few large longitudinal microbiome studies, and fewer that include planned, annotated perturbations between sampling-points. Thus, there are few opportunities to employ data-driven computational analyses of perturbed microbial communities over time. Our novel computational system simulates the dynamics of microbial communities under perturbations, using genome-scale metabolic models (GEM). Perturbations include modifications to a) the nutrients available in the medium, allowing modelling of prebiotics; or b) the microorganisms present in the community, to model probiotics or pathogen infection. These simulations generate the quantity and types of information used as input to the MDPbiome system, which builds predictive models suggesting the perturbation(s) required to engineer microbial communities to a desired state. We demonstrate that this novel combination, called MDPbiomeGEM, is able to model the influence of prebiotic fiber and probiotic in the case of a Crohn’s disease microbiome. The output's recommended perturbation to recover from dysbiosis is to consume inulin, which promotes butyrate production to reach homeostasis, consistent with prior biomedical knowledge. Our system could also contribute to design (perturbed) microbial community dynamics experiments, potentially saving resources both in natural microbiome scenarios by optimizing sequencing sampling, or to optimize in-vitro culture formulations for generating performant synthetic microbial communities.

Short Abstract: Topography is one of many features of the microenvironment known to affect cell migration. This interaction is critical to many physiological and pathophysiological processes (e.g. wound repair and cancer metastasis) and is exploited for application in biomedicine (e.g. medical implants and tissue engineering scaffolds). Despite a large body of experiment-based literature establishing cell response to different topographic configurations, little is understood about how topographies influence migration behaviour. This work aims to use mathematical modelling within a systems biology framework to better understand the dynamics of topographically influenced migration. Based on an Ornstein-Uhlenbeck process, the model describes velocity-time evolution of an individual point cell undergoing resisted Brownian motion, extended to incorporate directional bias caused by physical surface gradients. Numerical simulations produce individual cell paths with average properties comparable to that measured from experimental migration on grooved topographies. Preliminary comparison between model and experimental data suggest grooved topographies stimulate changes to both intrinsic (kinesis) and directional (taxis) migration for different groove widths, implying considerable motile sensitivity to physical dimension (consistent with literature findings). The intention is to use the model to predict migration upon topographies for potential use as an implant surface or tissue scaffold.

Short Abstract: Modeling effect of experimental perturbations on the functioning of biological networks governing cell differentiation in health or disease can improve our capacity to achieve desired system behaviours (for example, reprogram the differentiation process). To this aim Boolean networks (BNs) enable modeling large biological networks offering the level of abstraction that can match the current knowledge and the measurement accuracy, but identifying BNs whose dynamics is compatible with the data is a heavy combinatorial problem. Some of the existing methods focus on the logical model inference from constraints resulting from the requirement of reachability between sequential temporal observations. However, this type of constraint is insufficient to represent differentiating trajectories, characterized by irreversible bifurcations into distinct final stable states. Here our contribution is to take these features into account, formalizing them in Answer Set Programming as trap spaces and non reachability constraints. We have tested the new constraint types on a toy example of neuron precursor differentiation model. The method allows identifying few hundreds BNs compatible with the data from hundreds of millions of possible BNs. Our approach can be readily applied to binarized bulk cell population molecular data, and to single-cell data after proper pre-processing steps.

Short Abstract: Cardiovascular-diseases are multifactorial and complex pathologies that cannot be described by reductionist approaches. In order to tackle this, we developed a logical modeling framework composed of three components. The first step is an expert-based curation related to the body of literature we call Prior Knowledge Network (PKN). The PKN is assembled from the existing knowledge and experimental evidence. It includes the relevant components for Cardiovascular-diseases as well as the regulations between them. As compared to databases that register facts and summarize them, we have encoded the logical rules of regulations, enabling the use of the PKN for modeling and simulation. The second step simulates the cellular decision process and identifies the phenotypes attained by the regulatory network. As the PKN is large a manual optimization would be time consuming. Therefore we use Optimusqual, a method that uses a genetic algorithm to find in the PKN the regulatory sub-graph that fit to a training-set. In the final step we simulate several in silico perturbations. That allows to evaluate the pertinence of our model and to make predictions and generate testable hypotheses about driver nodes able to switch the network from a disease to the healthy state and ultimately find interventional targets.

Short Abstract: In recent years, simulation and training of large kinetic multi-pathway models with hundreds to thousands of species and parameters has become increasingly tractable. These large kinetics models are often constructed with the aim to deepen our mechanistic understanding of signaling pathways. Yet, the high complexity of large kinetic models impedes our ability to understand signal transduction in the model and thus limits possibilities for mechanistic insight and hypothesis generation. Here we propose the use of model structure to provide high- and low-level descriptions of signaling dynamics. We exploit the abstraction of rule-based models to provide protein-level summaries of signaling dynamics. To study emergent properties of the model, we apply a combination of causal compression and hierarchical modularization to provide pathway-level summaries of signal transduction. We apply these methods to an ordinary differential equation model of adaptive resistance in melanoma (EGFR and ERK pathways, >1k state variables, >10k reactions). We trained the model on absolute proteomic and phospho-proteomic as well as time-resolved immunofluorescence data, both in dose-response to small molecule inhibitors. We illustrate how low- and high-level descriptions can be used to probe signaling dynamics in the trained model and provide simple explanations for the observe nonlinear dose-response data.

Short Abstract: Models of multi-cellular systems need to account not only for cellular molecular networks but also for cell-cell communication that altogether orchestrate the dynamics of the whole. We present EpiLog, a software tool implementing a logical modelling framework to handle pattern formation on epithelia [1]. Briefly, this framework defines a cellular automaton in which each cell carries a logical regulatory model whose input nodes embody cell receptors. Integration functions specify how these receptors are activated depending on signals from neighbouring cells (how many, at what distance). EpiLog defines a fixed grid of hexagonal cells, with parametrisable size and boundary conditions. To explore the validity of this fixed topological configuration, we consider different cell-cell communication networks and assess the resulting patterns of a simple lateral inhibition model. This study suggests that reasonable deviations from a hexagonal grid do not change much the characteristics of the resulting patterns. Furthermore, our study indicates that measures such as the number of shared neighbours between pairs of contacting cells and network regularity are relevant to qualify such deviations. [1] Varela PL et al. EpiLog: A software for the logical modelling of epithelial dynamics [version 2; peer review: 3 approved]. F1000Research 2019, 7:1145

Short Abstract: Receptor mediated signals are often propagated via a sequence of activating double phosphorylation events. The phosphorylation mechanism, which is commonly thought to be either distributive or processive, as well as potential positive and negative feedback loops, can strongly impact the response behavior. We try to pinpoint the mechanism and feedbacks needed to generate the ultrasensitive Hog1 response, a MAPK pathway in Saccharomyces cerevisiae. We generated an ODE model that summarizes current knowledge of the pathway. To provide information we collected various publicly available data sets and our own measurements of Hog1 activation in different conditions. These were used to fit and parameterize different model variants, encoding different mechanisms of Hog1 phosphorylation and putative feedback loops. The best fitting model incorporates a mixed phosphorylation mechanism that switches between a mainly distributive nature before and a more processive nature after activation. This change is induced by a Hog1 mediated positive feedback loop. In our simulations, this arrangement displays robustness to model perturbations, mediated by the more distributive mechanism, while the induced processivity is needed for quicker and total activation.

Short Abstract: Cellular senescence (CS) is a cell fate that arrests cell proliferation in response to numerous stresses most notably oncogenes such as RAS. The phenotypic transformations that occur in CS include cell cycle arrest, inflammatory responses and a complex metabolic shift. An emerging paradigm stipulates that senescent cells are major contributors to health and age-related illnesses, particularly cancer. As such, research on therapeutic strategies targeting senescence to treat cancer and improve healthspan has gained enormous momentum in recent years. The phenotypic and transcriptomic changes that occur in CS can be interpreted as transitions in a high-dimensional state space, where each dimension corresponds to a molecular species. These transitions are specified by the architecture of its underlying gene-regulatory network (GRN), which represents the possible molecular interactions encoded in the genome. In order to describe and predict the mechanisms governing CS, we applied the Sparse Identification of Nonlinear Dynamics algorithm (SINDy) in a high performance computing environment to datasets containing time-course gene expression data on cells undergoing senescence. Our proposed predictive modeling approach will provide a deeper understanding of cellular senescence and has the potential to unravel unknown vulnerabilities of senescent cells that may be exploited to promote healthspan.

Short Abstract: Elementary Flux Modes (EFMs) are an indispensable tool for constraint-based modelling and metabolic network analysis. However, systematic and automated visualization of EFMs, capable of integrating various data types is still a challenge. In this study, a semi-automated, customizable, MATLAB-based workflow was developed for graphically visualizing EFMs as a network of reactions, metabolites and genes. The workflow integrates COBRA and RAVEN toolboxes with the open-source tool Cytoscape and offers a platform for comprehensive EFM analysis, starting with EFM generation followed by visualization and data mapping. Once processed, network manipulations in Cytoscape were semi-automated using R along with application of the widely accepted SBGN layout, thus minimizing both time and user effort. The biological applicability of the workflow is demonstrated using EFMs generated from two genome-scale models, (1) a medium-sized E. coli model (iAF1260) and (2) a large-scale human model (Recon 2.2). Additionally, two different types of data, gene expression and reaction fluxes, were mapped onto the visualized EFMs, thereby illustrating that such integrated visualization can enable better understanding of the metabolic described by the EFMs.

Short Abstract: Background: Kinetic models are at the heart of system identification. A priori chosen rate functions may, however, be unfitting or too restrictive for complex or previously unanticipated regulation. Methods: We applied general purpose piecewise linear functions for stochastic system identification in one dimension using published flow cytometry data on E.coli and report on identification results for equilibrium state and dynamic time series. Results: In metabolic labelling experiments during yeast osmotic stress response, we find mRNA production and degradation to be strongly co-regulated. In addition, mRNA degradation appears overall uncorrelated with mRNA level. Comparison of different system identification approaches using semi-empirical synthetic data revealed the superiority of single-cell tracking for parameter identification. Generally, we find that even within restrictive error bounds for deviation from experimental data, the number of viable regulation types may be large. Indeed, distinct regulation can lead to similar expression behaviour over time. Conclusion: Our results demonstrate that molecule production and degradation rates may often differ from classical constant, linear or Michaelis–Menten type kinetics. (1) NPJ Syst Biol Appl. 2018 Apr 11; 4:15. doi: 10.1038/s41540-018-0049-0, PMID 29675268

Short Abstract: Systems biology integrates genomic profiles of specific cell types to generate functionally-testable hypotheses of lineage-specificity. Here we compare RNA expression, DNA methylation, chromatin accessibility, DNA binding proteins and histone modification profiles in seven different hematopoietic populations using a Bayesian non-parametric hierarchical latent-class mixed-effect model known as IDEAS to characterize epigenetic changes associated with hematopoietic differentiation. Previous hematopoietic epigenome segmentation studies have focused on histone modifications, chromatin accessibility and DNA binding protein profiles. DNA methylation has been shown to vary markedly in hematopoietic populations. Inclusion of DNA methylation in these segmentation studies increased the original 36-state model of regulatory interactions to 41 states. These new DNA methylation-related states were associated with repressive marks, active RNA transcription, and a novel state regulated by DNA methylation alone. Imputing epigenetic models on inputs systematically perturbed for hematopoietic populations resulted in epigenetic models of varying degrees of overlap, which were quantified and set in context with underlying biological processes. We furthermore leveraged these imputation-related differences to infer potential lineage-specific impacts on regulation. Our data show that methylation has a strong impact on functional genomic modeling and can be used to discern cell type specific epigenetic regulatory behavior by leveraging imputation for missing cell type data.

Short Abstract: Mathematical models of cancer pathways are built by mining the literature for relevant experimental observations or extracting information from pathway databases. As a consequence, these models generally do not capture the heterogeneity of tumors and their therapeutic responses. We present here a novel framework, PROFILE, to tailor logical models to particular biological samples such as patient tumors, compare the model simulations to individual clinical data and investigate therapeutic strategies. Our approach makes use of MaBoSS, a tool based on Monte-Carlo kinetic algorithm to perform stochastic simulations on logical models resulting in model state probabilities. This semi-quantitative framework allows to integrate mutation data, copy number alterations (CNA), and transcriptomics/proteomics into logical models. These personalized models are validated by comparing simulation outputs with patients’ clinical data (subtypes, survival) and then used for cell line-specific investigations regarding the effects of drug perturbations, allowing both verification of the theoretical behavior of the model and comparison with experimental drug sensitivities. Our approach aims to combine the mechanistic insights of logical modeling with multi-omics data integration to provide patient or cell line-relevant models that can serve as tools for analyzing therapeutic responses.

Short Abstract: Influenza remains a major threat to global health resulting in millions of severe infections and hundreds of thousands of deaths each year. The rapidly mutating nature and diverse strains of the influenza virus limit vaccine efficacy and highlight the necessity of novel thinking to produce more effective treatment options. Understanding the early innate immune response to infection is an essential component to this process. Despite considerable study into the dynamics of influenza infection, much is still unknown about the interplay between viral antagonism and the propagation of innate immune response across a cell population. Computational modeling provides an ability to measure this interplay with a real-time resolution that would be infeasible experimentally. We have devised a spatial, stochastic agent-based model of influenza virus infection of lung epithelium that tracks the spread of a viral infection and corresponding host cytokine response across a layer of epithelial cells. In order to fit our model, we apply in vitro infection time course data and single cell RNA sequencing data, including novel findings of paracrine signaling-induced IFNλ production. Our findings suggest this feed forward paracrine signaling loop can have a significant impact on the effectiveness of host immune response.

Short Abstract: Intervertebral disc (IVD) failure is closely related to low back pain, i.e. one of the biggest health-care burdens worldwide. Despite the extensive effort in IVD research, underlying mechanisms of IVD failure are unknown, which might be related to a lack of information about the behavior of IVD cells exposed to a multifactorial environment. This work aims at estimating cellular behavior within their corresponding micro-environment by developing a methodology that couples experimental cell-culture results with a systems biology approach. Therefore, Agent-based modelling (ABM) is introduced as a new technique in IVD research. Nutritional factors as well as inflammation are considered to estimate cell viability and mRNA expression of nucleus pulposus cells. Results are in good agreement with data from the literature, since qualitative cell viability prediction and mRNA expression fit well experimental data. The latter might furthermore provide unique explanation possibilities for surprising results from in-vitro cell culture experiments. Both results underline the utility of this new methodology to create cell regulation networks. Furthermore, provided results might be extended to multiscale models, including cell signaling or gene regulation networks to provide a deeper understanding of cellular interaction with the microenvironment.

Short Abstract: The Systems Biology Markup Language (SBML) is often used to represent biological network models in a standardized and interoperable file format. Searching the models and accessing relational information stored in them is an aspect that is not easily covered with a file-based approach. The aim of this project is to use the power of graph databases to make these biological models queryable, traverse pathways in them, and integrate multiple models into one unified graph representation. In an Extract-Map-Connect (EMC) process we extract the model information, map the enclosed entities to graph nodes using object-graph mapping (OGM), and add relationship information between the elements according to the SBML specification. Multiple models with matching identifier systems can be integrated in the same database by connecting them through common entities. In this process, additional information from online services, like the public KEGG API or the Systems Biology Ontology, can be fetched and added to the emerging Knowledge Graph. We store this Graph in a Neo4j database which offers sub-second query response times for retrieving model-spanning subnetworks and biologically relevant network-contexts. Graph algorithms like shortest path, target identification via breadth-first search as well as mapping of omics data are accessible through a RESTful API.

Short Abstract: Regulation of gene expression is essential for cell homeostasis and adaptation. This regulation relies on transcription factors and other proteins that trigger specific genetic programs. However, the complexity of this regulatory network precludes efforts to model gene regulation at genome-scale. In this work, we developed the Pleiades toolkit that is currently composed by Atlas, Pleione, and Sterope. Atlas reconstruct a Rule-Based Model (RBMs) from biological networks. These Rules are similar to chemical equations and Atlas interpret nodes as model components and edges as a set of reactions, depending on the encoded nature of the networks. After model reconstruction, Pleione parameterizes RBMs employing one of four stochastic simulation software and distribute calculations with subprocesses or SLURM, taking advantage of high-performance computers and computational clusters. Finally, Sterope performs a global sensitivity analysis of selected parameters, calculating the interference or contribution of one Rule to itself and the remaining Rules. We validate the Pleiades employing the Escherichia coli regulatory and metabolic networks retrieved from Ecocyc and expression data from the literature. The developed Toolkit allows assessing of the impact of modifications like gene copy number, operon architecture, and other common genetic modifications to understand bacterial physiology, disease, and eventually, engineering of those systems.

Short Abstract: Modelling microtubule dynamics in cells together with regulatory networks requires integrating the spatiotemporal evolution of regulators with stochastically growing and shrinking microtubules. In principle, such stochastic spatiotemporal models of cells can be simulated using the reaction-diffusion master equation (RDME)-type framework. However, no simulation software exists for RDME models with embedded filaments that, themselves, evolve according to a stochastic model. We therefore developed RDMEcpp, a high-performance, extensible, and cross-platform solution in C++ for simulating 1D-3D RDME-type models that require subvolume coordinate lookup at runtime, e.g., to determine concentrations along dynamic microtubules. RDMEcpp exhibits similar or better performance compared to the state-of-the art URDME on the MinD oscillation model from Escherichia coli. For 2D Xenopus laevis egg extract spindle autonucleation, we incorporate experimental nucleation angle measurements between microtubules as well as explicit microtubules; microtubules evolve according to a stochastic microtubule tip model, which determines the time until catastrophe occurs. Compared to previous deterministic partial differential equation models, the RDME model’s more mechanistically accurate microtubule nucleation and tip evolution lead to a more realistic predicted aster microtubule density . We anticipate future extensions of RDME models to include regulators of microtubule dynamics for detailed investigation of control mechanisms in vivo.

Short Abstract: Based on published gene expression datasets from directed evolution experiments on E. coli exposed to 10 antibiotics and iML1515 genome wide metabolic reconstruction model, we developed a library of treatment specific genome scale models. Metabolic network reconstruction was performed with the COBRA (iMAT) and CORDA methods. Interaction probabilities for 1753 TF-gene pairs with strong experimental evidence were computed using the E. coli M3D dataset (264 samples). We used PROM to construct integrated regulatory-metabolic networks and run TF knockout simulations. For each virtual TF knockout, the growth defect of the model, Δgrowth, due to knockout was computed. If Δgrowth is larger for the resistant model than the wildtype model, this TF may be considered as potential target for future antibiotic intervention. A total of 73 TFs associated with resistance to at least one antibiotic were identified. Their target genes engage predominantly in catalytic, binding and transporter activity and include known resistance associated genes like acrD, cyoC, folA, marA, tsx, ompF, and oppA. The TFs GadE, SoxR, and FliZ were predicted to be associated with resistance for most models. CORDA models were more responsive to TF knockout simulations.

Short Abstract: Quantitative Structure-Activity Relationship (QSAR) methods employ features of chemical compounds to model molecular properties such as activity against a target. QSAR models are important in drug discovery, for example in lead optimisation and virtual screening of molecular compounds. Recently, the need for models that are not only predictive but also interpretable has been highlighted. We report a methodology for QSAR modelling by combining elements of complex network analysis and piecewise linear regression. The algorithm, modSAR, employs a two-step procedure where first, compounds associated with a common target are represented as a network in terms of their structural similarity, revealing modules of similar chemical properties through network community detection. Second, a novel mixed integer programming optimisation model is used to subdivide every module into subsets (regions), each region modelled by an independent linear equation. The piecewise linear optimisation step involves determining an optimal molecular feature to separate data into regions as well as linear equations to predict the outcome variable in each region, and includes a regularisation term to prevent overfitting and implicitly selecting most informative features. Comparative analysis shows that modSAR offers similar predictive accuracy to popular algorithms, such as RF and SVM, while also being interpretable and mathematically descriptive.

Short Abstract: Protein tyrosine phosphatase 1B (PTP1B), an anti-diabetic molecular target, is a cytosolic phosphatase which plays an important role in the negative regulation of the insulin signaling pathway, so it is a key element in maintaining glucose homeostasis and in the molecular mechanism of insulin resistance, a relevant condition in the pathogenesis of diabetes and some other metabolic disorders. The identification of selective PTP1B inhibitors has increased considerably in recent years. Nevertheless, owing to the subcellular location and structural properties of the enzyme, the design of pharmaceutically acceptable inhibitors remains a challenge. With the aim to find an inhibitor targeting PTP1B, an extended search with the allosteric and active site of the protein through structure-based virtual screening (with AutoDock Vina and iDock) and molecular docking was carried out with crystal structures of PTP1B and 20410 chemical compounds from Zinc database. They were selected according to their origin (biogenic or not), FDA approval, human use, and commercial availability. Molecular dynamics simulation shows complex stability formed by PTP1B and irinotecan, which was the substance that presented best bind affinity to the enzyme (-37.0 Kcal/mol). Findings suggest that irinotecan as a potentially good PTP1B inhibitor. Future enzyme assays can be done to prove it.