Computer-Aided Whole-Cell Design: Taking a Holistic Approach by Integrating Synthetic With Systems Biology.

Lucia Marucci,Elibio Rech,Christopher Woods,Stefan Andreas Hoffmann,Richard Seabrook,Claire Grierson,Matteo Barberis,Paul R Race,Joshua Rees-Garbutt,Jonathan Karr,Sophie Landon,William Shaw,Oliver Ray,Miguel De Souza Andrade

doi:10.3389/fbioe.2020.00942

Abstract

Computer-aided design (CAD) for synthetic biology promises to accelerate the rational and robust engineering of biological systems. It requires both detailed and quantitative mathematical and experimental models of the processes to (re)design biology, and software and tools for genetic engineering and DNA assembly. Ultimately, the increased precision in the design phase will have a dramatic impact on the production of designer cells and organisms with bespoke functions and increased modularity. CAD strategies require quantitative models of cells that can capture multiscale processes and link genotypes to phenotypes. Here, we present a perspective on how whole-cell, multiscale models could transform design-build-test-learn cycles in synthetic biology. We show how these models could significantly aid in the design and learn phases while reducing experimental testing by presenting case studies spanning from genome minimization to cell-free systems. We also discuss several challenges for the realization of our vision. The possibility to describe and build whole-cells in silico offers an opportunity to develop increasingly automatized, precise and accessible CAD tools and strategies.

Highlights

Whole-cell models (WCMs) are state-of-the-art Systems Biology formalisms: they aim at representing and integrating all cellular functions within a unique computational framework, enabling a holistic, and quantitative understanding of cell biology (Tomita, 2001; Karr et al, 2015a)
While simplified models for specific cellular functions were first developed over 30 years ago [e.g., gene expression regulation (McAdams and Arkin, 1997), signaling (Morton-Firth and Bray, 1998) and metabolic pathways (Cornish-Bowden and Hofmeyr, 1991), cell growth (Shu and Shuler, 1989) and the cell cycle (Goldbeter, 1991; Tyson, 1991; Novak and Tyson, 1993)], the first WCM, the E-Cell model, was only derived in the 1990s for Mycoplasma genitalium, which has the smallest genome among freely living organisms (Tomita et al, 1999)
We have shown that WCMs are likely to be instrumental to inform design-build-test cycles across synthetic biology applications

Summary

INTRODUCTION

Whole-cell models (WCMs) are state-of-the-art Systems Biology formalisms: they aim at representing and integrating all cellular functions within a unique computational framework, enabling a holistic, and quantitative understanding of cell biology (Tomita, 2001; Karr et al, 2015a). Multiscale frameworks coupling networks of differing granularity are being developed, by identifying the relevant regulations occurring among common network nodes and through the use of different mathematical formalisms (van der Zee and Barberis, 2019) These and other strategies are being developed to integrate networks of cellular functional modules (Prescott et al, 2015). Together with the identification of networks underlying the cell’s autonomous oscillations, these strategies can rationalize the proper timing of offspring generation accounted by WCMs. Designing synthetic gene networks by modeling and integrating them within WCM formalisms [as in Purcell et al (2013)] could be critical to investigate how gene expression correlates with codon usage, explore possible cell burden effects (Borkowski et al, 2016), and predict modularity of synthetic gene networks and tools to modulate gene expression across different chassis (Way et al, 2014; Pedone et al, 2019; Gomide et al, 2020)

Design and Engineering of Reduced Genomes

DISCUSSION