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Search indexed bioRxiv preprints in genomics, neuroscience, cell biology and bioinformatics. Read source abstracts and check manuscript versions; preprints are not peer reviewed.

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bioRxiv2014

Branching and oscillations in the epigenetic landscape of cell-fate determination

The well-known Waddingtons epigenetic landscape of cell-fate determination is not static but varies because of the dynamic gene regulation during development. However, existing mathematical models with few state variables and fixed parameters are inadequate in characterizing the temporal transformation of the landscape. Here we simulate a decision-switch model of gene regulation with more than two state variables and with time-varying repression among regulatory factors. We are able to demonstrate multi-lineage differentiation at different timescales that portrays the branching canals in Waddingtons illustration. We also present a repressilator-type system that activates suppressed genes via sustained oscillations in a flattened landscape, hence providing an alternative scheme for cellular reprogramming. The time-dependent parameters governed by gradient-based dynamics regulate cell differentiation, dedifferentiation and transdifferentiation. Our prediction integrates the theories of branching and structural oscillations in cell-fate determination, which reveals key temporal patterns of cell differentiation and associated diseases, such as cancer.

Systems Biology↗
bioRxiv2015

Core variability in substitution rates and the basal sequence characteristics of the human genome

Accurate knowledge on the core components of substitution rates is of vital importance to understand genome evolution and dynamics. By performing a single-genome and direct analysis of 39894 retrotransposon remnants, we reveal germline sequence-dependent nucleotide substitution rates that can be assigned to each position in the human genome. Benefiting from the data made available in such detail, we show that a simulated genome, generated by equilibrating a random DNA sequence solely using our rate constants, exhibits nucleotide organisation close to that in the human genome. We next generate the germline basal substitution propensity (BSP) profile of the human genome and show a decreased tendency of moieties with low BSP to undergo somatic mutations in many cancer types.

Evolutionary Biology↗
bioRxiv2018

Conformational regulation of proteolysis in the extracellular matrix receptor dystroglycan

The adhesion receptor dystroglycan provides a critical mechanical link between the extracellular matrix (ECM) and the actin cytoskeleton to help muscle cells withstand contraction and neural cells maintain the blood brain barrier. Disrupting the link is associated with diseases such as cancer and muscular dystrophy. Proteolysis of dystroglycan by Matrix Metalloproteases (MMPs) also breaks the mechanical anchor and is amplified in several pathogenic states. We use a combination of biochemical and cell-based assays to show that dystroglycan proteolysis is conformationally regulated by an extracellular, juxtamembrane \"proteolysis domain\", comprised of tandem Ig-like and SEA-like domains. The intact proteolysis domain is resistant to MMP cleavage, but structurally-disruptive muscular dystrophy-related mutations sensitize dystroglycan to proteolysis. Moreover, increased dystroglycan proteolysis correlates with faster cell migration, linking proteolysis to a disease-relevant cellular phenotype. Intriguingly, previously uncharacterized cancer-associated mutations that map to the proteolysis domain similarly lead to increases in proteolysis and rates of cell migration, potentially revealing a new pathogenic mechanism in cancer.

cell biology↗
bioRxiv2018

Assessment of a highly multiplexed RNA sequencing platform and comparison to existing high-throughput gene expression profiling techniques

The need to reduce per sample cost of RNA-seq profiling for scalable data generation has led to the emergence of highly multiplexed RNA-seq. These technologies utilize barcoding of cDNA sequences in order to combine samples into single sequencing lane to be separated during data processing. In this study, we report the performance of one such technique denoted as sparse full length sequencing (SFL), a ribosomal RNA depletion-based RNA sequencing approach that allows for the simultaneous sequencing of 96 samples and higher. We offer comparisons to well established single-sample techniques, including: full coverage Poly-A capture RNA-seq and microarray, as well as another low-cost highly multiplexed technique known as 3 digital gene expression (3DGE). Data was generated for a set of exposure experiments on immortalized human lung epithelial (AALE) cells in a two-by-two study design, in which samples received both genetic and chemical perturbations of known oncogenes/tumor suppressors and lung carcinogens. SFL demonstrated improved performance over 3DGE in terms of coverage, power to detect differential gene expression, and biological recapitulation of patterns of differential gene expression from in vivo lung cancer mutation signatures.

bioinformatics↗
bioRxiv2018

Proteotoxic stress-induced Nrf1 transcriptional program requires a functional TIP60 complex

In response to inhibition of the cellular proteasome, the transcription factor Nrf1 (also called NFE2L1) induces transcription of proteasome subunit genes resulting in the restoration of proteasome activity and thus enabling the cells to mitigate the proteotoxic stress. To identify novel regulators of Nrf1, we performed an RNA interference screen and discovered that the AAA+ ATPase RUVBL1 is necessary for its transcriptional activity. Given that RUVBL1 is part of different multi-subunit complexes that play key roles in transcription, we dissected this phenomenon further and found that the TIP60 chromatin regulatory complex is essential for Nrf1-dependent transcription of proteasome genes. Consistent with these observations, Nrf1, RUVBL1, and TIP60 proteins were co-recruited to the promoter regions of proteasome genes after proteasome inhibitor treatments. More importantly, depletion of RUVBL1 or TIP60 in various cancer cells sensitized them to cell death induced by proteasome inhibition. Our study provides a framework for manipulating the Nrf1-TIP60 axis to alter proteasome function in various human diseases including cancer.

molecular biology↗
bioRxiv2018

Hydroxymethylated-P16 Allele Is Transcription-Inactive

Background5-Methylcytosine can be oxidized into 5-hydroxymethylcytosine (5hmC) in the genome. Methylated-P16 (P16M) can be oxidized into completely hydroxymethylated-P16 (P16H) in human cancer and precancer cells. The aim of this study is to investigate the biological function of P16H.\n\nMethodsTrue P16M and P16H were analyzed using bisulfite/TAB-based assays. A ZFP-based P16-specific dioxygenase (P16-TET) was constructed and used to induce P16H. Cell proliferation and migration were determined with a series of biological analyses.\n\nResults(A) The 5hmCs were enriched in the antisense-strand of the P16 exon-1 in HCT116 and AGS cells containing methylated-P16 alleles (P16M). (B) P16-TET induced both P16H and P16 demethylation in H1299 and AGS cells and reactivated P16 expression. Notably, P16H was only detectable in the sorted P16-TET H1299 and AGS cells that did not show P16 expression. (C) P16-TET significantly inhibited the xenograft growth derived from H1299 cells in NOD-SCID mice, but did not inhibit the growth of P16-deleted A549 control cells. P16-siRNA knockdown could rescue P16-TET-inhibited cell migration.\n\nConclusionHydroxymethylated P16 alleles are transcriptionally inactive.\n\nAUTHOR SUMMARYIt is well known that 5-methylcytosine (5mC) in genomic DNA of mammalian cells can be oxidized into 5-hydroxymethylcytosine (5hmC) and other derivates by DNA dioxygenase TETs. While conversion of 5mC to 5hmC plays an important role in active DNA demethylation through further oxidations, a certain proportion of 5hmCs remain in the genome. Although it is supposed that occurrence of 5hmCs may contribute to the flexibility of chromatin and the protection of the bivalent promoters from hypermethylation, the direct effect of 5hmCs on gene transcription is unknown. In the present study, we engineered a zinc-finger protein-based P16-specific DNA dioxygenase and used it to induce P16 hydroxymethylation and demethylation in cancer cells. Our results demonstrate, for the first time, that the hydroxymethylated P16 alleles retain transcriptionally inactive. This is supported by our recent findings that mRNAs are always transcribed only from the unmethylated P16 strands, but not from the hydroxymethylated/methylated strands in HCT116 cells, and that the risks for malignant transformation are similar for patients with the P16 methylation-positive oral epithelial dysplasia with and without P16 hydroxymethylation in a prospective study.

molecular biology↗
bioRxiv2018

Miro-dependent mitochondrial pool of CENP-F and its farnesylated C-terminal domain are dispensable for normal development in mice

CENP-F is a large, microtubule-binding protein that regulates multiple cellular processes including chromosome segregation and mitochondrial trafficking at cytokinesis. This multiplicity of function is mediated through the binding of various partners, like Bub1 at the kinetochore and Miro at mito-chondria. Due to the multifunctionality of CENP-F, the cellular phenotypes observed upon its depletion are difficult to interpret and there is a need to genetically separate its different functions by preventing binding to selected partners. Here we engineer a CENP-F point-mutant that is deficient in Miro binding and thus is unable to localize to mitochondria, but retains other localizations. We introduced this mutation in cultured human cells using CRISPR/Cas9 and show it causes a defect in mitochondrial spreading similar to that observed upon Miro depletion. We further create a mouse model carrying this CENP-F variant, as well as truncated CENP-F mutants lacking the farnesylated C-terminus of the protein. Importantly, one of these truncations leads to [~]80% downregulation of CENP-F expression. We observe that, despite the phenotypes apparent in cultured cells, mutant mice develop normally. Taken together, these mice will serve as important models to study CENP-F biology at organismal level. In addition, because truncations of CENP-F in humans cause a lethal disease termed Stromme syndrome and because CENP-F is involved in cancer development, they might also be relevant disease models.

cell biology↗
bioRxiv2017

Watching the clock for 25 years in FlyClockbase: Variability in circadian clocks of Drosophila melanogaster as uncovered by biological model curation

Biological model curation provides new insights by integrating biological knowledge-fragments, assessing their uncertainty, and analyzing the reliability of potential interpretations. Here we integrate published results about circadian clocks in Drosophila melanogaster while exploring economies of scale in biological model curation. Clocks govern rhythms of gene-expression that impact fitness, health, cancer, memory, mental functions, and more. Human clock insights have been repeatedly pioneered in flies. Flies simplify investigating complex gene regulatory networks, which express proteins cyclically using environmentally entrained interlocking feedback loops that act as clocks. Simulations could simplify research further. We found that very few computational models test their quality directly against experimentally observed time series scattered in the literature. We designed FlyClockbase for integrating such scattered data to enable robust efficient access for biologists and modelers. To this end we have been defining data structures that simplify the construction and maintenance of Versioned Biological Information Resources (VBIRs) that prioritize simplicity, openness, and therefore maintainability. We aim to simplify the preservation of more raw data and relevant annotations from experiments in order to multiply the long-term value of wet-lab datasets for modelers interested in meta-analyses, parameter estimates, and hypothesis testing. Currently FlyClockbase contains over 400 wildtype time series of core circadian components systematically curated from 86 studies published between 1990 and 2015. Using FlyClockbase, we show that PERIOD protein amount peak time variance unexpectedly exceeds that of TIMELESS. We hypothesize that PERIODs exceedingly more complex phosphorylation rules are responsible. Variances of daily event times are easily confounded by errors. We improved result reliability by a human error analysis of our data handling; this revealed significance-degrading outliers, possibly violating a presumed absence of wildtype heterogeneity or lab evolution. Separate analyses revealed elevated stochasticity in PCR-based peak time variances; yet our reported core difference in peak time variances appears robust. Our study demonstrates how biological model curation enhances the understanding of circadian clocks. It also highlights diverse broader challenges that are likely to become recurrent themes if models in molecular systems biology aim to integrate all relevant knowledge. We developed a trans-disciplinary workflow, which demonstrates the importance of developing compilers for VBIRs with a more biology-friendly logic that is likely to greatly simplify biological model curation. Curation-limited grand challenges, including personalizing medicine, critically depend on such progress if they are indeed to integrate all relevant knowledge.\n\nGeneral Article SummaryCircadian clocks impact health and fitness by controlling daily rhythms of gene-expression through complex gene-regulatory networks. Deciphering how they work requires experimentally tracking changes in amounts of clock components. We designed FlyClockbase to simplify data-access for biologists and modelers, curating over 400 time series observed in wildtype fruit flies from 25 years of clock research. Substantial biological model curation was essential for identifying differences in peak time variance of the clock-proteins PERIOD and TIMELESS, which probably stem from differences in phosphorylation-network complexity.\n\nWe repeatedly encountered systemic limitations of contemporary data analysis strategies in our work on circadian clocks. Thus, we used it as an opportunity for composing a panoramic view of the broader challenges in biological model curation, which are likely to increase as biologists aim to integrate all existing expertise in order to address diverse grand challenges. We developed and tested a trans-disciplinary research workflow, which enables biologists and compiler-architects to define biology-friendly compilers for efficiently constructing and maintaining Versioned Biological Information Resources (VBIRs). We report insights gleaned from our practical clock research that are essential for defining a VBIRs infrastructure, which improves the efficiency of biological model curation to the point where it can be democratized.\n\nStatement of data availabilityStabilizing Versioned Variant of this file: QQv1r4_2017m07d14_Lion\n\nBefore final publication FlyClockbase will be at https://github.com/FlyClockbase For review purposes FlyClockbase QQv1r4 will be provided as a zip-archive in the uploaded Supplemental Material; it is also available upon request from L. Loewe.\n\nAbbreviationsTable 1: Molecular core clock components\n\nTable 2: Concepts for organizing FlyClockbase\n\nSupplemental MaterialAppendix: Supplemental Text and Tables (32 pages included in this file, QQv1v4)\n\nSupplemental Statistical Analysis (87 pages not included in this file, QQv1v4)\n\nR-Script zip file (>12K lines not included in this file, QQv1v4)\n\nFlyClockbase zip file (available upon request, QQv1v4)

genetics↗
bioRxiv2013

Investigating the relation between stochastic differentiation and homeostasis in intestinal crypts via multiscale modeling

Colorectal tumors originate and develop within intestinal crypts. Even though some of the essential phenomena that characterize crypt structure and dynamics have been effectively described in the past, the relation between the differentiation process and the overall crypt homeostasis is still partially understood. We here investigate this relation and other important biological phenomena by introducing a novel multiscale model that combines a morphological description of the crypt with a gene regulation model: the emergent dynamical behavior of the underlying gene regulatory network drives cell growth and differentiation processes, linking the two distinct spatio-temporal levels. The model relies on a few a priori assumptions, yet accounting for several key processes related to crypt functioning, such as: dynamic gene activation patterns, stochastic differentiation, signaling pathways ruling cell adhesion properties, cell displacement, cell growth, mitosis, apoptosis and the presence of biological noise.\n\nWe show that this modeling approach captures the major dynamical phenomena that characterize the regular physiology of crypts, such as cell sorting, coordinate migration, dynamic turnover, stem cell niche maintenance and clonal expansion. All in all, the model suggests that the process of stochastic differentiation might be sufficient to drive the crypt to homeostasis, under certain crypt configurations. Besides, our approach allows to make precise quantitative inferences that, when possible, were matched to the current biological knowledge and it permits to investigate the role of gene-level perturbations, with reference to cancer development. We also remark the theoretical framework is general and may applied to different tissues, organs or organisms.

Systems Biology↗
bioRxiv2017

Building Applications For Interactive Data Exploration In Systems Biology

As the systems biology community generates and collects data at an unprecedented rate, there is a growing need for interactive data exploration tools to explore the datasets. These tools need to combine advanced statistical analyses, relevant knowledge from biological databases, and interactive visualizations in an application with clear user interfaces. To answer specific research questions tools must provide specialized user interfaces and visualizations. While these are application-specific, the underlying components of a data analysis tool can be shared and reused later. Application developers can therefore compose applications of reusable services rather than implementing a single monolithic application from the ground up for each project.\n\nOur approach for developing data exploration applications in systems biology builds on the microservice architecture. Microservice architectures separates an application into smaller components that communicate using language-agnostic protocols. We show that this design is suitable in bioinformatics applications where applications often use different tools, written in different languages, by different research groups. Packaging each service in a software container enables re-use and sharing of key components between applications, reducing development, deployment, and maintenance time.\n\nWe demonstrate the viability of our approach through a web application, MIxT blood-tumor, for exploring and comparing transcriptional profiles from blood and tumor samples in breast cancer patients. The application integrates advanced statistical software, up-to-date information from biological databases, and modern data visualization libraries.\n\nThe web application for exploring transcriptional profiles, MIxT, is online at mixt-blood-tumor.bci.mcgill.ca and open-sourced at github.com/fjukstad/mixt. Packages to build the supporting microservices are open-sourced as a part of Kvik at github.com/fjukstad/kvik.

bioinformatics↗
bioRxiv2018

Nach is a novel ancestral subfamily of the CNC-bZIP transcription factors selected during evolution from the marine bacteria to human

All living organisms have undergone the evolutionary selection under the changing natural environments to survive as diverse life forms. All life processes including normal homeostatic development and growth into organismic bodies with distinct cellular identifications, as well as their adaptive responses to various intracellular and environmental stresses, are tightly controlled by signaling of transcriptional networks towards regulation of cognate genes by many different transcription factors. Amongst them, one of the most conserved is the basic-region leucine zipper (bZIP) family. They play vital roles essential for cell proliferation, differentiation and maintenance in complex multicellular organisms. Notably, an unresolved divergence on the evolution of bZIP proteins is addressed here. By a combination of bioinformatics with genomics and molecular biology, we have demonstrated that two of the most ancestral family members classified into BATF and Jun subgroups are originated from viruses, albeit expansion and diversification of the bZIP superfamily occur in different vertebrates. Interestingly, a specific ancestral subfamily of bZIP proteins is identified and also designated Nach (Nrf and CNC homology) on account of their highly conservativity with NF-E2 p45 subunit-related factors Nrf1/2. Further experimental evidence reveals that Nach1/2 from the marine bacteria exerts distinctive functions from Nrf1/2 in the transcriptional ability to regulate antioxidant response element (ARE)-driven cytoprotective genes. Collectively, an insight into Nach/CNC-bZIP proteins provides a better understanding of distinct biological functions between these factors selected during evolution from the marine bacteria to human.\n\nSignificanceWe identified the novel ancestral subfamily (i.e. Nach) of CNC-bZIP transcription factors with highly conservativity from marine bacteria to human. Combination of bioinformatics with genomics and molecular biology demonstrated that two of the most ancestral family members classified into BATF and Jun subgroups are originated from viruses. The Jun and CNC subfamilies also share a common origin of these bZIP proteins. Further experimental evidence reveals that Nach1/2 from the marine bacteria exerts nuance functions from human Nrf1/2 in the transcriptional ability to regulate antioxidant response element (ARE)-driven genes, responsible for the host cytoprotection against inflammation and cancer. Overall, this study is of multidisciplinary interests to provide a better understanding of distinct biological functions between Nach/CNC-bZIPs selected during evolution.

ecology↗
bioRxiv2018

Genome-wide analysis of RNA editing levels in human blood identified interactions with mRNA processing genes and suggested correlations with biological and drug-related variables

BackgroundA-to-I RNA editing is a co-/post-transcriptional modification catalyzed by ADAR enzymes, that deaminates Adenosines (A) into Inosines (I). Most of known editing events are located within inverted ALU repeats, but they also occur in coding sequences and may alter the function of encoded proteins. RNA editing contributes to generate transcriptomic diversity and it is found altered in cancer, autoimmune and neurological disorders. Emerging evidences indicate that editing process could be influenced by genetic variations, biological and environmental variables.\n\nResultsWe analyzed RNA editing levels in human blood using RNA-seq data from 459 healthy individuals and identified 2,079 sites consistently edited in this tissue. As expected, analysis of gene expression revealed that ADAR is the major contributor to editing on these sites, explaining [~]13% of observed variability. After removing ADAR effect, we found significant associations for 1,122 genes, mainly involved in RNA processing. These genes were significantly enriched in genes encoding proteins interacting with ADARs, including 276 potential ADARs interactors and 9 ADARs direct partners. In addition, our analysis revealed several factors potentially influencing RNA editing in blood, including cell composition, age, Body Mass Index, smoke and alcohol consumption. Finally, we identified genetic loci associated with editing levels, including known ADAR eQTLs and a small region on chromosome 7, containing LOC730338, a lincRNA gene that appears to modulate ADARs mRNA expression.\n\nConclusionsOur data provides a detailed picture of the most relevant RNA editing events and their variability in human blood, giving interesting insights on potential mechanisms behind this post-transcriptional modification and its regulation in this tissue.

genomics↗
bioRxiv2017

Signaling Pathway Activities Improve Prognosis for Breast Cancer

With the advent of high-throughput technologies for genome-wide expression profiling, a large number of methods have been proposed to discover gene-based signatures as biomarkers to guide cancer prognosis. However, it is often difficult to interpret the list of genes in a prognostic signature regarding the underlying biological processes responsible for disease progression or therapeutic response. A particularly interesting alternative to gene-based biomarkers is mechanistic biomarkers, derived from signaling pathway activities, which are known to play a key role in cancer progression and thus provide more informative insights into cellular functions involved in cancer mechanism. In this study, we demonstrate that pathway-level features, such as the activity of signaling circuits, outperform conventional gene-level features in prediction performance in breast cancer prognosis. We also show that the proposed classification scheme can even suggest, in addition to relevant signaling circuits related to disease outcome, a list of genes that do not code for signaling proteins whose contribution to cancer prognosis potentially supplements the mechanisms detected by pathway analysis.

bioinformatics↗
bioRxiv2018

Increased PHGDH expression uncouples hair follicle cycle progression and promotes inappropriate melanin accumulation

Copy number gain of the PHGDH gene that encodes the first enzyme of the serine biosynthesis pathway is found in some human cancers, including a subset of melanomas. In order to study the effect of increased PHGDH expression in tissues in vivo, we generated mice harboring a PHGDHtetO allele that allows tissue-specific, doxycycline-inducible PHGDH expression. Tissues and cells derived from PHGDHtetO mice exhibit increased serine biosynthesis. Histological examination of skin tissue from PHGDHtetO mice reveals the presence of melanin granules in anagen II hair follicles, despite the fact that melanin synthesis is normally closely coupled to the hair follicle cycle and does not begin until later in the cycle. This phenotype occurs in the absence of any global change in hair follicle cycle timing. The inappropriate presence of melanin early in the hair follicle cycle following PHGDH expression is also accompanied by increased melanocyte abundance in anagen II skin. Together, these data support a model in which PHGDH expression affects melanocyte proliferation and/or differentiation and may provide insight into how PHGDH expression impacts normal melanocyte biology to promote melanoma.\n\nSIGNIFICANCEThe significance behind copy number gain of PHGDH in human cancers is unclear. In this study, we generate a mouse model that mimics PHGDH gene copy number gain and characterize its effect on normal tissues. Increased PHGDH expression yields a phenotype of aberrant melanin production, which indicates that PHGDH expression may play a role in normal melanocyte biology. This result may provide insight into why PHGDH copy number gain is observed in melanoma more frequently than in most other tumor types.

developmental biology↗
bioRxiv2016

PhysiCell: an Open Source Physics-Based Cell Simulator for 3-D Multicellular Systems

Many multicellular systems problems can only be understood by studying how cells move, grow, divide, interact, and die. Tissue-scale dynamics emerge from systems of many interacting cells as they respond to and influence their microenvironment. The ideal \"virtual laboratory\" for such multicellular systems simulates both the biochemical microenvironment (the \"stage\") and many mechanically and biochemically interacting cells (the \"players\" upon the stage).\n\nPhysiCell--physics-based multicellular simulator--is an open source agent-based simulator that provides both the stage and the players for studying many interacting cells in dynamic tissue microenvironments. It builds upon a multi-substrate biotransport solver to link cell phenotype to multiple diffusing substrates and signaling factors. It includes biologically-driven sub-models for cell cycling, apoptosis, necrosis, solid and fluid volume changes, mechanics, and motility \"out of the box.\" The C++ code has minimal dependencies, making it simple to maintain and deploy across platforms. PhysiCell has been parallelized with OpenMP, and its performance scales linearly with the number of cells. Simulations up to 105-106 cells are feasible on quad-core desktop workstations; larger simulations are attainable on single HPC compute nodes.\n\nWe demonstrate PhysiCell by simulating the impact of necrotic core biomechanics, 3-D geometry, and stochasticity on the dynamics of hanging drop tumor spheroids and ductal carcinoma in situ (DCIS) of the breast. We demonstrate stochastic motility, chemical and contact-based interaction of multiple cell types, and the extensibility of PhysiCell with examples in synthetic multicellular systems (a \"cellular cargo delivery\" system, with application to anti-cancer treatments), cancer heterogeneity, and cancer immunology. PhysiCell is a powerful multicellular systems simulator that will be continually improved with new capabilities and performance improvements. It also represents a significant independent code base for replicating results from other simulation platforms. The PhysiCell source code, examples, documentation, and support are available under the BSD license at http://PhysiCell.MathCancer.org and http://PhysiCell.sf.net.\n\nAuthor SummaryThis paper introduces PhysiCell: an open source, agent-based modeling framework for 3-D multicellular simulations. It includes a standard library of sub-models for cell fluid and solid volume changes, cycle progression, apoptosis, necrosis, mechanics, and motility. PhysiCell is directly coupled to a biotransport solver to simulate many diffusing substrates and cell-secreted signals. Each cell can dynamically update its phenotype based on its microenvironmental conditions. Users can customize or replace the included sub-models.\n\nPhysiCell runs on a variety of platforms (Linux, OSX, and Windows) with few software dependencies. Its computational cost scales linearly in the number of cells. It is feasible to simulate 500,000 cells on quad-core desktop workstations, and millions of cells on single HPC compute nodes. We demonstrate PhysiCell by simulating the impact of necrotic core biomechanics, 3-D geometry, and stochasticity on hanging drop tumor spheroids (HDS) and ductal carcinoma in situ (DCIS) of the breast. We demonstrate contact- and chemokine-based interactions among multiple cell types with examples in synthetic multicellular bioengineering, cancer heterogeneity, and cancer immunology.\n\nWe developed PhysiCell to help the scientific community tackle multicellular systems biology problems involving many interacting cells in multi-substrate microenvironments. PhysiCell is also an independent, cross-platform codebase for replicating results from other simulators.

systems biology↗
bioRxiv2017

Differential Community Detection in Paired Biological Networks

MotivationBiological networks unravel the inherent structure of molecular interactions which can lead to discovery of driver genes and meaningful pathways especially in cancer context. Often due to gene mutations, the gene expression undergoes changes and the corresponding gene regulatory network sustains some amount of localized re-wiring. The ability to identify significant changes in the interaction patterns caused by the progression of the disease can lead to the revelation of novel relevant signatures.\n\nMethodsThe task of identifying differential sub-networks in paired biological networks (A:control,B:case) can be re-phrased as one of finding dense communities in a single noisy differential topological (DT) graph constructed by taking absolute difference between the topological graphs of A and B. In this paper, we propose a fast two-stage approach, namely Differential Community Detection (DCD), to identify differential sub-networks as differential communities in a de-noised version of the DT graph. In the first stage, we iteratively re-order the nodes of the DT graph to determine approximate block diagonals present in the DT adjacency matrix using neighbourhood information of the nodes and Jaccard similarity. In the second stage, the ordered DT adjacency matrix is traversed along the diagonal to remove all the edges associated with a node, if that node has no immediate edges within a window. We then apply community detection methods on this de-noised DT graph to discover differential sub-networks as communities.\n\nResultsOur proposed DCD approach can effectively locate differential sub-networks in several simulated paired random-geometric networks and various paired scale-free graphs with different power-law exponents. The DCD approach easily outperforms community detection methods applied on the original noisy DT graph and recent statistical techniques in simulation studies. We applied DCD method on two real datasets: a) Ovarian cancer dataset to discover differential DNA co-methylation sub-networks in patients and controls; b) Glioma cancer dataset to discover the difference between the regulatory networks of IDH-mutant and IDH-wild-type. We demonstrate the potential benefits of DCD for finding network-inferred bio-markers/pathways associated with a trait of interest.\n\nConclusionThe proposed DCD approach overcomes the limitations of previous statistical techniques and the issues associated with identifying differential sub-networks by use of community detection methods on the noisy DT graph. This is reflected in the superior performance of the DCD method with respect to various metrics like Precision, Accuracy, Kappa and Specificity. The code implementing proposed DCD method is available at https://sites.google.com/site/ raghvendramallmlresearcher/codes.

systems biology↗
bioRxiv2016

Long single-molecule reads can resolve the complexity of the Influenza virus composed of rare, closely related mutant variants

As a result of a high rate of mutations and recombination events, an RNA-virus exists as a heterogeneous \"swarm\". The ability of next-generation sequencing to produce massive quantities of genomic data inexpensively has allowed virologists to study the structure of viral populations from an infected host at an unprecedented resolution. However, high similarity and low frequency of the viral variants impose a huge challenge to assembly of individual full-length genomes. The long read length offered by a single-molecule sequencing technologies allows each mutant variant to be sequenced in a single pass. However, high error rate limits the ability to reconstruct heterogeneous viral population composed of rare, related mutant variants. In this paper, we present 2SNV, a method able to tolerate the high error-rate of the single-molecule protocol and reconstruct mutant variants. The proposed protocol is able to eliminate sequencing errors and reconstruct closely related viral mutant variants. 2SNV uses linkage between single nucleotide variations to efficiently distinguish them from read errors. To benchmark the sensitivity of 2SNV, we performed a single-molecule sequencing experiment on a sample containing a titrated level of known viral mutant variants.\n\nOur method is able to accurately reconstruct clone with frequency of 0.2% and distinguish clones that differed in only two nucleotides distantly located on the genome. 2SNV outperforms existing methods for full-length viral mutant reconstruction. With a high sensitivity and accuracy, 2SNV is anticipated to facilitate not only viral quasispecies reconstruction, but also other biological questions that require detection of rare haplotypes such as genetic diversity in cancer cell population, and monitoring B-cell and T-cell receptor repertoire. The open source implementation of 2SNV is freely available for download at http://alan.cs.gsu.edu/NGS/?q=content/2snv

Bioinformatics↗
bioRxiv2018

Stochastic temporal timing for intracellular events to cross dynamically fluctuating thresholds

Fractional killing, which is a significant impediment to successful chemotherapy, is observed even in a population of genetically identical cancer cells exposed to apoptosis-inducing agents. This phenomenon arises not from genetic mutation but from cell-to-cell variation in the activation timing and level of the proteins that regulate apoptosis. To understand the mechanism behind the phenomenon, we formulate complex fractional killing processes as a first-passage time (FPT) problem with a stochastically fluctuating boundary. Analytical calculations are performed for the FPT distribution in a toy model of stochastic p53 gene expression, where the cancer cell is killed only when the p53 expression level crosses an activity apoptotic threshold. Counterintuitively, we find that threshold fluctuations can effectively enhance cellular killing by significantly decreasing the mean time that the p53 protein reaches the threshold level for the first time. Moreover, faster fluctuations lead to the killing of more cells. These qualitative results imply that dynamic variability in threshold is an unneglectable stochastic source, and can be taken as a strategy for combating fractional killing of cancer cells.

systems biology↗