Stochastic Biological System-of-Systems Modelling for iPSC Culture

Large-scale manufacturing of induced pluripotent stem cells (iPSCs) is essential for cell therapies and regenerative medicines. Yet, iPSCs form large cell aggregates in suspension bioreactors, resulting in insufficient nutrient supply and extra metabolic waste build-up for the cells located at core. Since subtle changes in micro-environment can lead to cell stress and heterogeneous cell population, a novel Biological System-of-Systems (Bio-SoS) framework is proposed to characterize cell-to-cell interactions, spatial heterogeneity, and cell response to micro-environmental variation. Building on stochastic metabolic reaction network, aggregation kinetics, and reaction-diffusion mechanisms, the Bio-SoS model can quantify the impact of factors (i.e., aggregate size) on cell product health and quality heterogeneity, accounting for causal interdependencies at individual cell, aggregate, and cell population levels. This framework can accurately predict iPSC culture conditions for both monolayer and aggregate cultures, where these predictions can be leveraged to ensure the control of culture processes for successful cell growth and expansion.Comment: 36 pages, 10 figure

The Effect of Proteolytic Queues on Antibiotic Tolerance and Persistence Cells Population in Escherichia Coli

A major contributing factor to the abundance of antibiotic-resistant microorganisms and failed antibiotic treatment is survival due to antibiotic tolerance and persistence. Antibiotic tolerance is a widespread phenomenon that enables cells to survive treatment without carrying a resistance gene. This phenomenon renders antibiotic treatments less effective and facilitates antibiotic resistance. We are particularly interested in proteases, responsible for degradation of proteins, because of their known relationship to tolerance and persistence. Here, we examine the effects of proteases and antibiotic survival using queueing theory, in which one type of customer competes for processing by servers, that has traditionally been applied to systems such as computer networks and call centers. The biological queueing theory principally assumes that there are limited processing resources in a cell. Using synthetic systems engineered to form proteolytic queues, we can now examine tolerance/persistence in a new manner. In this work, we demonstrated in E. coli that the overproducing of protein engineered to be digested by the protease ClpXP can form a proteolytic queue, and this queue results in an increase in antibiotic tolerance ~80 and ~60 fold with ampicillin and ciprofloxacin, respectively. The proteolytic queue had no apparent effect on bacterial persistence levels. Furthermore, we showed that the queueing at the other two major proteases, ClpAP and Lon, have a slight effect on tolerant cell population

Exploring Heterogeneous Phenotypes in Response to Stress

This work combines traditional microbiology with bioinformatic and synthetic biology approaches to study antibiotic tolerance. Antibiotic tolerance is a widespread phenomenon that facilitates antibiotic resistance and decreases the effectiveness of antibiotic treatment. Tolerance is distinct from antibiotic resistance, because tolerance is short term survival and typically results from phenotypic variations rather than genetic variation. The molecular mechanisms underlying tolerance are varied and debated in the literature. I have explored two intracellular processes related to tolerance, toxin-antitoxin (TA) systems (Chapter 2) and proteases (Chapter 4). Specifically, I focus on the ratio of antitoxin-to-toxin in type II TA systems, because type II TA systems must be regulated in such a way that antitoxins are more prevalent than their toxins. Our analysis of RNA-sequencing and ribosome profiling data demonstrates that most type II TA systems in E. coli are regulated at the translational level, while others rely on various combinations of transcriptional and post-transcriptional regulation. Before publishing this article, researchers often cited transcriptional regulation as the primary method of regulating TA systems. Studying antibiotic tolerance and other subpopulations necessitates the ability to study single-cell dynamics in the context of the whole population. To facilitate single-cell analysis, we have developed single-cell tracking software that leverages machine learning to identify cells. The software then tracks the cell based on this classification and returns data on cell size, location, division and fluorescence. The software provides the means of quantifying cell behavior before and after antibiotic treatment. One such system we would like to apply this software to is our work on proteolytic queueing and antibiotic tolerance. Proteases are responsible for protein degradation and, as such, regulate many cellular functions. To better identify the role proteases play in persistence, we used proteolytic queueing to interfere with proteolytic activity. We found that interfering with degradation at the protease ClpXP increases antibiotic tolerance ~80 and ~60 fold in an E. coli population treated with ampicillin and ciprofloxacin, respectively. I used stochastic modeling to support our results, and we have experimentally determined that altering the expression of the synthetic system affects the level of tolerance in the population. I am currently using next-generation sequencing to identify the systems being affected by the queue

Ribosome traffic on mRNAs maps to gene ontology : genome-wide quantification of translation initiation rates and polysome size regulation

Peer reviewedPublisher PD

Isocost Lines Describe the Cellular Economy of Genetic Circuits

Genetic circuits in living cells share transcriptional and translational resources that are available in limited amounts. This leads to unexpected couplings among seemingly unconnected modules, which result in poorly predictable circuit behavior. In this study, we determine these interdependencies between products of different genes by characterizing the economy of how transcriptional and translational resources are allocated to the production of proteins in genetic circuits. We discover that, when expressed from the same plasmid, the combinations of attainable protein concentrations are constrained by a linear relationship, which can be interpreted as an isocost line, a concept used in microeconomics. We created a library of circuits with two reporter genes, one constitutive and the other inducible in the same plasmid, without a regulatory path between them. In agreement with the model predictions, experiments reveal that the isocost line rotates when changing the ribosome binding site strength of the inducible gene and shifts when modifying the plasmid copy number. These results demonstrate that isocost lines can be employed to predict how genetic circuits become coupled when sharing resources and provide design guidelines for minimizing the effects of such couplings.United States. Air Force Office of Scientific Research (Grant FA9550-14-1-0060)United States. Defense Advanced Research Projects Agency (Contract W911NF-12-1-0540)National Institutes of Health (U.S.) (Grant P50 GM098792

Autotrophic Stoichiometry Emerging from Optimality and Variable Co-limitation

Autotrophic organisms reveal an astounding flexibility in their elemental stoichiometry, with potentially major implications on biogeochemical cycles and ecological functioning. Notwithstanding, stoichiometric regulation, and co-limitation by multiple resources in autotrophs were in the past often described by heuristic formulations. In this study, we present a mechanistic model of autotroph growth, which features two major improvements over the existing schemes. First, we introduce the concept of metabolic network independence that defines the degree of phase-locking between accessory machines. Network independence is in particular suggested to be proportional to protein synthesis capability as quantified by variable intracellular N:C. Consequently, the degree of co-limitation becomes variable, contrasting with the dichotomous debate on the use of Liebig's law or the product rule, standing for constantly low and high co-limitation, respectively. Second, we resolve dynamic protein partitioning to light harvesting, carboxylation processes, and to an arbitrary number of nutrient acquisition machineries, as well as instantaneous activity regulation of nutrient uptake. For all regulatory processes we assume growth rate optimality, here extended by an explicit consideration of indirect feed-back effects. The combination of network independence and optimal regulation displays unprecedented skill in reproducing rich stoichiometric patterns collected from a large number of published chemostat experiments. This high skill indicates (1) that the current paradigm of fixed co-limitation is a critical short-coming of conventional models, and (2) that stoichiometric flexibility in autotrophs possibly reflects an optimality strategy. Numerical experiments furthermore show that regulatory mechanisms homogenize the effect of multiple stressors. Extended optimality alleviates the effect of the most limiting resource(s) while down-regulating machineries for the less limiting ones, which induces an ubiquitous response surface of growth rate over ambient resource levels. Our approach constitutes a basis for improved mechanistic understanding and modeling of acclimative processes in autotrophic organisms. It hence may serve future experimental and theoretical investigations on the role of those processes in aquatic and terrestrial ecosystems