Data-driven modelling of biological multi-scale processes

Biological processes involve a variety of spatial and temporal scales. A holistic understanding of many biological processes therefore requires multi-scale models which capture the relevant properties on all these scales. In this manuscript we review mathematical modelling approaches used to describe the individual spatial scales and how they are integrated into holistic models. We discuss the relation between spatial and temporal scales and the implication of that on multi-scale modelling. Based upon this overview over state-of-the-art modelling approaches, we formulate key challenges in mathematical and computational modelling of biological multi-scale and multi-physics processes. In particular, we considered the availability of analysis tools for multi-scale models and model-based multi-scale data integration. We provide a compact review of methods for model-based data integration and model-based hypothesis testing. Furthermore, novel approaches and recent trends are discussed, including computation time reduction using reduced order and surrogate models, which contribute to the solution of inference problems. We conclude the manuscript by providing a few ideas for the development of tailored multi-scale inference methods.Comment: This manuscript will appear in the Journal of Coupled Systems and Multiscale Dynamics (American Scientific Publishers

A synthetic Escherichia coli predator–prey ecosystem

We have constructed a synthetic ecosystem consisting of two Escherichia coli populations, which communicate bi-directionally through quorum sensing and regulate each other's gene expression and survival via engineered gene circuits. Our synthetic ecosystem resembles canonical predator–prey systems in terms of logic and dynamics. The predator cells kill the prey by inducing expression of a killer protein in the prey, while the prey rescue the predators by eliciting expression of an antidote protein in the predator. Extinction, coexistence and oscillatory dynamics of the predator and prey populations are possible depending on the operating conditions as experimentally validated by long-term culturing of the system in microchemostats. A simple mathematical model is developed to capture these system dynamics. Coherent interplay between experiments and mathematical analysis enables exploration of the dynamics of interacting populations in a predictable manner

ISOpureR: an R implementation of a computational purification algorithm of mixed tumour profiles

Background Tumour samples containing distinct sub-populations of cancer and normal cells present challenges in the development of reproducible biomarkers, as these biomarkers are based on bulk signals from mixed tumour profiles. ISOpure is the only mRNA computational purification method to date that does not require a paired tumour-normal sample, provides a personalized cancer profile for each patient, and has been tested on clinical data. Replacing mixed tumour profiles with ISOpure-preprocessed cancer profiles led to better prognostic gene signatures for lung and prostate cancer. Results To simplify the integration of ISOpure into standard R-based bioinformatics analysis pipelines, the algorithm has been implemented as an R package. The ISOpureR package performs analogously to the original code in estimating the fraction of cancer cells and the patient cancer mRNA abundance profile from tumour samples in four cancer datasets. Conclusions The ISOpureR package estimates the fraction of cancer cells and personalized patient cancer mRNA abundance profile from a mixed tumour profile. This open-source R implementation enables integration into existing computational pipelines, as well as easy testing, modification and extension of the model.Prostate Cancer CanadaMovember Foundation (Grant RS2014-01

Control of asymmetric Hopfield networks and application to cancer attractors

The asymmetric Hopfield model is used to simulate signaling dynamics in gene/transcription factor networks. The model allows for a direct mapping of a gene expression pattern into attractor states. We analyze different control strategies aiming at disrupting attractor patterns using selective local fields representing therapeutic interventions. The control strategies are based on the identification of signaling $bottlenecks$, which are single nodes or strongly connected clusters of nodes that have a large impact on the signaling. We provide a theorem with bounds on the minimum number of nodes that guarantee controllability of bottlenecks consisting of strongly connected components. The control strategies are applied to the identification of sets of proteins that, when inhibited, selectively disrupt the signaling of cancer cells while preserving the signaling of normal cells. We use an experimentally validated non-specific network and a specific B cell interactome reconstructed from gene expression data to model cancer signaling in lung and B cells, respectively. This model could help in the rational design of novel robust therapeutic interventions based on our increasing knowledge of complex gene signaling networks

A genetically encoded reporter of synaptic activity in vivo

To image synaptic activity within neural circuits, we tethered the genetically encoded calcium indicator (GECI) GCaMP2 to synaptic vesicles by fusion to synaptophysin. The resulting reporter, SyGCaMP2, detected the electrical activity of neurons with two advantages over existing cytoplasmic GECIs: it identified the locations of synapses and had a linear response over a wider range of spike frequencies. Simulations and experimental measurements indicated that linearity arises because SyGCaMP2 samples the brief calcium transient passing through the presynaptic compartment close to voltage-sensitive calcium channels rather than changes in bulk calcium concentration. In vivo imaging in zebrafish demonstrated that SyGCaMP2 can assess electrical activity in conventional synapses of spiking neurons in the optic tectum and graded voltage signals transmitted by ribbon synapses of retinal bipolar cells. Localizing a GECI to synaptic terminals provides a strategy for monitoring activity across large groups of neurons at the level of individual synapses

A Shift in Central Metabolism Accompanies Virulence Activation in Pseudomonas aeruginosa.

The availability of energy has significant impact on cell physiology. However, the role of cellular metabolism in bacterial pathogenesis is not understood. We investigated the dynamics of central metabolism during virulence induction by surface sensing and quorum sensing in early-stage biofilms of the multidrug-resistant bacterium Pseudomonas aeruginosa We established a metabolic profile for P. aeruginosa using fluorescence lifetime imaging microscopy (FLIM), which reports the activity of NADH in live cells. We identified a critical growth transition period during which virulence is activated. We performed FLIM measurements and direct measurements of NADH and NAD+ concentrations during this period. Here, planktonic (low-virulence) and surface-attached (virulence-activated) populations diverged into distinct metabolic states, with the surface-attached population exhibiting FLIM lifetimes that were associated with lower levels of enzyme-bound NADH and decreasing total NAD(H) production. We inhibited virulence by perturbing central metabolism using citrate and pyruvate, which further decreased the enzyme-bound NADH fraction and total NAD(H) production and suggested the involvement of the glyoxylate pathway in virulence activation in surface-attached populations. In addition, we induced virulence at an earlier time using the electron transport chain oxidase inhibitor antimycin A. Our results demonstrate the use of FLIM to noninvasively measure NADH dynamics in biofilms and suggest a model in which a metabolic rearrangement accompanies the virulence activation period.IMPORTANCE The rise of antibiotic resistance requires the development of new strategies to combat bacterial infection and pathogenesis. A major direction has been the development of drugs that broadly target virulence. However, few targets have been identified due to the species-specific nature of many virulence regulators. The lack of a virulence regulator that is conserved across species has presented a further challenge to the development of therapeutics. Here, we identify that NADH activity has an important role in the induction of virulence in the pathogen P. aeruginosa This finding, coupled with the ubiquity of NADH in bacterial pathogens, opens up the possibility of targeting enzymes that process NADH as a potential broad antivirulence approach

Data based identification and prediction of nonlinear and complex dynamical systems

We thank Dr. R. Yang (formerly at ASU), Dr. R.-Q. Su (formerly at ASU), and Mr. Zhesi Shen for their contributions to a number of original papers on which this Review is partly based. This work was supported by ARO under Grant No. W911NF-14-1-0504. W.-X. Wang was also supported by NSFC under Grants No. 61573064 and No. 61074116, as well as by the Fundamental Research Funds for the Central Universities, Beijing Nova Programme.Peer reviewedPostprin