A low-cost, open-source evolutionary bioreactor and its educational use

Abstract

Recently, a concerted effort has been made to study the evolution of drug resistance in organisms at increasingly smaller time scales and in a high-throughput manner. One effective approach is through the use of customized bioreactors -devices that can continuously culture bacteria and monitor this growth in real time. These devices can be technically challenging and expensive to implement for scientists, let alone students or teachers who seek an innovative and intuitive way of studying evolution. We seek to provide a flexible and open source automated continuous culture device framework for the academic setting to study biological concepts such as population dynamics and evolution; a framework that is capable of replicating the functionality of many prominent and expensive bioreactors in the market today. Within the educational environment, our goal is to foster interaction and interest between the engineering and biological fields by allowing teachers and students to build their own systems and design experiments on the proposed open framework. We present a continuous culture device October 10, 2019 1/25 designed for bacterial culture that is easily and inexpensively constructed, lends itself to evolution experiments, and can be used both in the academic and educational environments. Author summaryThe continuous monitoring of population growth in the presence of cytotoxic selective pressures can reveal new insights into resistance development and corresponding susceptibilities. Bioreactors have been proposed to accomplish this task yet are costly and without formal build instructions or software. In this article, we present a framework for a bioreactor, called the EVolutionary biorEactor (EVE), that will enable users to economically implement hardware and create circuits through diagrams.Hardware communicates with open-source software, written in Python, to create a flexible yet fully featured bioreactor that incorporates many modes of operation. A single EVE controls many Culture Units simultaneously, each with the capability of running its own experiment or a replicate of the same experiment to measure stochastic differences during evolution. While currently built for bacterial culture, this framework can be adapted in many ways from continuous mammalian cell culture to the measurement of multi-population dynamics in various environments. In the educational setting, this easy-to-implement framework will enable educators to use a hands-on approach to evolution in their lessons and extracurricular activities. For scientists, we propose this open framework as a tool that can be used and modified to investigate new areas in population dynamics and evolution. Introduction 1Continuous microbial culture is a powerful tool for observing and directing evolution in 2 both research and industrial settings. Recently a shift towards open-source science has 3 brought about a new wave of Do-It-Yourself, customized continuous culture devices to 4 accommodate a wide variety of experimental designs across several research are...

Inferring Density-Dependent Population Dynamics Mechanisms through Rate Disambiguation for Logistic Birth-Death Processes

“…. , T s − 1}, the difference t s j+1 − t s j is equal to ∆t, which is independent of s and j, which is consistent with the format of the dataset produced from the EVolutionary biorEactor (EVE) experiments in our laboratory (Gopalakrishnan et al 2020). In our simulation, for all time series s = 1, .…”

“…For the present study, we confine our investigation to simulated data because of several factors. First, generating large ensembles of cell population trajectories is expensive, although high-throughput methods continue to accelerate the pace of data generation (Gopalakrishnan et al 2020). In a typical bioreactor, the data available are optical density time series, rather than direct cell number measurements.…”

“…Note that we use the notation X to represent data for the continuous random cell number under a Gaussian approximation, as discussed in Section 3.2. We use τ -leaping simulation so that for all the time series indices s ∈ {1,…, S } and all the time point indices j ∈ {0,…, T s − 1}, the difference is equal to Δ t , which is independent of s and j , which is consistent with the format of the dataset produced from the EVolutionary biorEactor (EVE) experiments in our laboratory (Gopalakrishnan et al 2020). In our simulation, for all time series s = 1, …, S , we choose to be equal to t 0 and T s to be equal to T so that each time series has the same number of data points as the others.…”

Experimental evolution in morbidostat reveals converging genomic trajectories on the path to triclosan resistance

“…Realization of the advantages of automated (computercontrolled) continuous culturing over manual serial dilution methods of experimental evolution [6] was enabled by the recently enhanced accessibility of 3D printing technology and programmable microcontrollers (such as Arduino and Raspberry Pi). One of the rapidly emerging custom-built continuous culturing devices [7][8][9][10], termed 'morbidostat' , is a modification of the chemostat in which, instead of nutrient supply, the culture density is controlled by selective drug pressure [11,12]. This approach enables gradual genetic adaptation of evolving bacterial populations to higher concentrations of an antibiotic and generally recapitulates the knowledge of resistance mechanisms from previous studies [11,13,14].…”

In situ characterisation and manipulation of biological systems with Chi.Bio

“…This can be partially achieved using continuous culture devices such as a turbidostat, which dilutes cells during growth to maintain a constant optical density (OD). In recent years, a number of turbidostat platforms have been developed and are beginning to find widespread applications in systems, synthetic, and evolutionary biology [11][12][13][14][15][16][17][18][19]. There has also been significant development of open-source platforms for optogenetics [20,21], which have been used to implement real-time measurement [22] and feedback control [23] and to demonstrate orthogonal regulation of multiplexed optogenetic regulators [24].…”