Systems Biology: The synergistic interplay between biology and mathematics

Phenotype switching in a global method for agent-based models of biological tissue

Agent-based models (ABMs) are an increasingly important tool for understanding the complexities presented by phenotypic and spatial heterogeneity in biological tissue. The resolution a modeler can achieve in these regards is unrivaled by other approaches. However, this comes at a steep computational cost limiting either the scale of such models or the ability to explore, parameterize, analyze, and apply them. When the models involve molecular-level dynamics, especially cell-specific dynamics, the limitations are compounded. We have developed a global method for solving these computationally expensive dynamics significantly decreases the computational time without altering the behavior of the system. Here, we extend this method to the case where cells can switch phenotypes in response to signals in the microenvironment. We find that the global method in this context preserves the temporal population dynamics and the spatial arrangements of the cells while requiring markedly less simulation time. We thus add a tool for efficiently simulating ABMs that captures key facets of the molecular and cellular dynamics in heterogeneous tissue.

A global method for fast simulations of molecular dynamics in multiscale agent-based models of biological tissues

Agent-based models (ABMs) are a natural platform for capturing the multiple time and spatial scales in biological processes. However, these models are computationally expensive, especially when including molecular-level effects. The traditional approach to simulating this type of multiscale ABM is to solve a system of ordinary differential equations for the molecular events per cell. This significantly adds to the computational cost of simulations as the number of agents grows, which contributes to many ABMs being limited to around10 5 cells. We propose an approach that requires the same computational time independent of the number of agents. This speeds up the entire simulation by orders of magnitude, allowing for more thorough explorations of ABMs with even larger numbers of agents. We use two systems to show that the new method strongly agrees with the traditionally used approach. This computational strategy can be applied to a wide range of biological investigations.

On Valve Interstitial Cell Signaling: The Link Between Multiscale Mechanics and Mechanobiology

Heart valves function in one of the most mechanically demanding environments in the body to ensure unidirectional blood flow. The resident valve interstitial cells respond to this mechanical environment and maintain the structure and integrity of the heart valve tissues to preserve homeostasis. While the mechanics of organ-tissue-cell heart valve function has progressed, the intracellular signaling network downstream of mechanical stimuli has not been fully elucidated. This has hindered efforts to both understand heart valve mechanobiology and rationally identify drug targets for treating valve disease. In the present work, we review the current literature on VIC mechanobiology and then propose mechanistic mathematical modeling of the mechanically-stimulated VIC signaling response to comprehend the coupling between VIC mechanobiology and valve mechanics.

An agent-based model to investigate microbial initiation of Alzheimer's via the olfactory system

BACKGROUND

Alzheimer's disease (AD) is a degenerative brain disease. A novel agent-based modelling framework was developed in NetLogo 3D to provide fundamental insights into the potential mechanisms by which a microbe (eg. Chlamydia pneumoniae) may play a role in late-onset AD. The objective of our initial model is to simulate one possible spatial and temporal pathway of bacterial propagation via the olfactory system, which may then lead to AD symptoms. The model maps the bacteria infecting cells from the nasal cavity and the olfactory epithelium, through the olfactory bulb and into the olfactory cortex and hippocampus regions of the brain.

RESULTS

Based on the set of biological rules, simulated randomized infection by the microbe led to the formation of beta-amyloid (Aβ) plaque and neurofibrillary (NF) tangles as well as caused immune responses. Our initial simulations demonstrated that breathing in C. pneumoniae can result in infection propagation and significant buildup of Aβ plaque and NF tangles in the olfactory cortex and hippocampus. Our model also indicated how mucosal and neural immunity can play a significant role in the pathway considered. Lower immunities, correlated with elderly individuals, had quicker and more Aβ plaque and NF tangle formation counts. In contrast, higher immunities, correlated with younger individuals, demonstrated little to no such formation.

CONCLUSION

The modelling framework provides an organized visual representation of how AD progression may occur via the olfactory system to better understand disease pathogenesis. The model confirms current conclusions in available research but can be easily adjusted to match future evidence and be used by researchers for their own individual purposes. The goal of our initial model is to ultimately guide further hypothesis refinement and experimental testing to better understand the dynamic system interactions present in the etiology and pathogenesis of AD.

Using Systems Biology and Mathematical Modeling Approaches in the Discovery of Therapeutic Targets for Spinal Muscular Atrophy

Systems biology uses a combination of experimental and mathematical approaches to investigate the complex and dynamic interactions with a given system or biological process. Systems biology integrates genetics, signal transduction, biochemistry and cell biology with mathematical modeling. It can be used to identify novel pathways implicated in diseases as well as to understand the mechanisms by which a specific gene is regulated. This review describes the development of mathematical models for the regulation of an endogenous modifier gene, SMN2, in spinal muscular atrophy-an early-onset motor neuron disease that is a leading genetic cause of infant mortality worldwide-by cAMP signaling. These mathematical models not only can aid in understanding how SMN2 expression is regulated but they can also be used to examine the best ways to manipulate cAMP signaling to maximally increase SMN2 expression. These models will lead to the development of therapeutic strategies for treating SMA. This systems biology approach can also be applied to other neurological diseases, particularly those in which a disease-causing gene or a modifier gene has been identified.

Novel systems modeling methodology in comparative microbial metabolomics: identifying key enzymes and metabolites implicated in autism spectrum disorders

Autism spectrum disorders are a group of mental illnesses highly correlated with gastrointestinal dysfunction. Recent studies have shown that there may be one or more microbial “fingerprints” in terms of the composition characterizing individuals with autism, which could be used for diagnostic purposes. This paper proposes a computational approach whereby metagenomes characteristic of “healthy” and autistic individuals are artificially constructed via genomic information, analyzed for the enzymes coded within, and then these enzymes are compared in detail. This is a text mining application. A custom-designed online application was built and used for the comparative metabolomics study and made publically available. Several of the enzyme-catalyzing reactions involved with the amino acid glutamate were curiously missing from the “autism” microbiome and were coded within almost every organism included in the “control” microbiome. Interestingly, there exists a leading hypothesis regarding autism and glutamate involving a neurological excitation/inhibition imbalance; but the association with this study is unclear. The results included data on the transsulfuration and transmethylation pathways, involved with oxidative stress, also of importance to autism. The results from this study are in alignment with leading hypotheses in the field, which is impressive, considering the purely in silico nature of this study. The present study provides new insight into the complex metabolic interactions underlying autism, and this novel methodology has potential to be useful for developing new hypotheses. However, limitations include sparse genome data availability and conflicting literature experimental data. We believe our software tool and methodology has potential for having great utility as data become more available, comprehensive and reliable.

Systems biology investigation of cAMP modulation to increase SMN levels for the treatment of spinal muscular atrophy

Spinal muscular atrophy (SMA), a leading genetic cause of infant death worldwide, is an autosomal recessive disorder caused by the loss of SMN1 (survival motor neuron 1), which encodes the protein SMN. The loss of SMN1 causes a deficiency in SMN protein levels leading to motor neuron cell death in the anterior horn of the spinal cord. SMN2, however, can also produce some functional SMN to partially compensate for loss of SMN1 in SMA suggesting increasing transcription of SMN2 as a potential therapy to treat patients with SMA. A cAMP response element was identified on the SMN2 promoter, implicating cAMP activation as a step in the transcription of SMN2. Therefore, we investigated the effects of modulating the cAMP signaling cascade on SMN production in vitro and in silico. SMA patient fibroblasts were treated with the cAMP signaling modulators rolipram, salbutamol, dbcAMP, epinephrine and forskolin. All of the modulators tested were able to increase gem formation, a marker for SMN protein in the nucleus, in a dose-dependent manner. We then derived two possible mathematical models simulating the regulation of SMN2 expression by cAMP signaling. Both models fit well with our experimental data. In silico treatment of SMA fibroblasts simultaneously with two different cAMP modulators resulted in an additive increase in gem formation. This study shows how a systems biology approach can be used to develop potential therapeutic targets for treating SMA.