Cheemeng Tan

Molecular crowding shapes gene expression in synthetic cellular nanosystems

Summary The integration of synthetic and cell-free biology has made tremendous strides towards creating artificial cellular nanosystems using concepts from solution-based chemistry: only the concentrations of reacting species modulate gene expression rates. However, it is known that macromolecular crowding, a key feature of natural cells, can dramatically influence biochemical kinetics by volume exclusion effects that reduce diffusion rates and enhance binding rates of macromolecules. Here, we demonstrate that macromolecular crowding can increase the robustness of gene expression through integrating synthetic cellular components of biological circuits and artificial cellular nanosystems. In addition, we reveal how ubiquitous cellular modules, including genetic components, a negative feedback loop, and the size of crowding molecules, can fine tune gene circuit response to molecular crowding. By bridging a key gap between artificial and living cells, our work has implications for efficient and robust control of both synthetic and natural cellular circuits.

Origin of bistability underlying mammalian cell cycle entry

Precise control of cell proliferation is fundamental to tissue homeostasis and differentiation. Mammalian cells commit to proliferation at the restriction point (R‐point). It has long been recognized that the R‐point is tightly regulated by the Rb–E2F signaling pathway. Our recent work has further demonstrated that this regulation is mediated by a bistable switch mechanism. Nevertheless, the essential regulatory features in the Rb–E2F pathway that create this switching property have not been defined. Here we analyzed a library of gene circuits comprising all possible link combinations in a simplified Rb–E2F network. We identified a minimal circuit that is able to generate robust, resettable bistability. This minimal circuit contains a feed‐forward loop coupled with a mutual‐inhibition feedback loop, which forms an AND‐gate control of the E2F activation. Underscoring its importance, experimental disruption of this circuit abolishes maintenance of the activated E2F state, supporting its importance for the bistability of the Rb–E2F system. Our findings suggested basic design principles for the robust control of the bistable cell cycle entry at the R‐point.

Image segmentation and dynamic lineage analysis in single-cell fluorescence microscopy

An increasingly common component of studies in synthetic and systems biology is analysis of dynamics of gene expression at the single‐cell level, a context that is heavily dependent on the use of time‐lapse movies. Extracting quantitative data on the single‐cell temporal dynamics from such movies remains a major challenge. Here, we describe novel methods for automating key steps in the analysis of single‐cell, fluorescent images—segmentation and lineage reconstruction—to recognize and track individual cells over time. The automated analysis iteratively combines a set of extended morphological methods for segmentation, and uses a neighborhood‐based scoring method for frame‐to‐frame lineage linking. Our studies with bacteria, budding yeast and human cells, demonstrate the portability and usability of these methods, whether using phase, bright field or fluorescent images. These examples also demonstrate the utility of our integrated approach in facilitating analyses of engineered and natural cellular networks in diverse settings. The automated methods are implemented in freely available, open‐source software.

Biology by design: reduction and synthesis of cellular components and behaviour

Biological research is experiencing an increasing focus on the application of knowledge rather than on its generation. Thanks to the increased understanding of cellular systems and technological advances, biologists are more frequently asking not only ‘how can I understand the structure and behaviour of this biological system?’, but also ‘how can I apply that knowledge to generate novel functions in different biological systems or in other contexts?’ Active pursuit of the latter has nurtured the emergence of synthetic biology. Here, we discuss the motivation behind, and foundational technologies enabling, the development of this nascent field. We examine some early successes and applications while highlighting the challenges involved. Finally, we consider future directions and mention non-scientific considerations that can influence the fields growth.

The inoculum effect and band-pass bacterial response to periodic antibiotic treatment.

The inoculum effect (IE) refers to the decreasing efficacy of an antibiotic with increasing bacterial density. It represents a unique strategy of antibiotic tolerance and it can complicate design of effective antibiotic treatment of bacterial infections. To gain insight into this phenomenon, we have analyzed responses of a lab strain of Escherichia coli to antibiotics that target the ribosome. We show that the IE can be explained by bistable inhibition of bacterial growth. A critical requirement for this bistability is sufficiently fast degradation of ribosomes, which can result from antibiotic‐induced heat‐shock response. Furthermore, antibiotics that elicit the IE can lead to ‘band‐pass’ response of bacterial growth to periodic antibiotic treatment: the treatment efficacy drastically diminishes at intermediate frequencies of treatment. Our proposed mechanism for the IE may be generally applicable to other bacterial species treated with antibiotics targeting the ribosomes.

Programming microbial population dynamics by engineered cell–cell communication

A major aim of synthetic biology is to program novel cellular behavior using engineered gene circuits. Early endeavors focused on building simple circuits that fulfill simple functions, such as logic gates, bistable toggle switches, and oscillators. These gene circuits have primarily focused on single‐cell behaviors since they operate intracellularly. Thus, they are often susceptible to cell–cell variations due to stochastic gene expression. Cell–cell communication offers an efficient strategy to coordinate cellular behavior at the population level. To this end, we review recent advances in engineering cell–cell communication to achieve reliable population dynamics, spanning from communication within single species to multispecies, from one‐way sender–receiver communication to two‐way communication in synthetic microbial ecosystems. These engineered systems serve as well‐defined model systems to better understand design principles of their naturally occurring counterparts and to facilitate novel biotechnology applications.

The engineering of artificial cellular nanosystems using synthetic biology approaches

Artificial cellular systems are minimal systems that mimic certain properties of natural cells, including signaling pathways, membranes, and metabolic pathways. These artificial cells (or protocells) can be constructed following a synthetic biology approach by assembling biomembranes, synthetic gene circuits, and cell-free expression systems. As artificial cells are built from bottom-up using minimal and a defined number of components, they are more amenable to predictive mathematical modeling and engineered controls when compared with natural cells. Indeed, artificial cells have been implemented as drug delivery machineries and in situ protein expression systems. Furthermore, artificial cells have been used as biomimetic systems to unveil new insights into functions of natural cells, which are otherwise difficult to investigate owing to their inherent complexity. It is our vision that the development of artificial cells would bring forth parallel advancements in synthetic biology, cell-free systems, and in vitro systems biology. For further resources related to this article, please visit the WIREs website. Conflict of interests: The authors declare that they have no competing financial interests.

Computation of Steady-State Probability Distributions in Stochastic Models of Cellular Networks

Cellular processes are “noisy”. In each cell, concentrations of molecules are subject to random fluctuations due to the small numbers of these molecules and to environmental perturbations. While noise varies with time, it is often measured at steady state, for example by flow cytometry. When interrogating aspects of a cellular network by such steady-state measurements of network components, a key need is to develop efficient methods to simulate and compute these distributions. We describe innovations in stochastic modeling coupled with approaches to this computational challenge: first, an approach to modeling intrinsic noise via solution of the chemical master equation, and second, a convolution technique to account for contributions of extrinsic noise. We show how these techniques can be combined in a streamlined procedure for evaluation of different sources of variability in a biochemical network. Evaluation and illustrations are given in analysis of two well-characterized synthetic gene circuits, as well as a signaling network underlying the mammalian cell cycle entry.

Synthetic microbial consortia enable rapid assembly of pure translation machinery

Assembly of recombinant multiprotein systems requires multiple culturing and purification steps that scale linearly with the number of constituent proteins. This problem is particularly pronounced in the preparation of the 34 proteins involved in transcription and translation systems, which are fundamental biochemistry tools for reconstitution of cellular pathways ex vivo. Here, we engineer synthetic microbial consortia consisting of between 15 and 34 Escherichia coli strains to assemble the 34 proteins in a single culturing, lysis, and purification procedure. The expression of these proteins is controlled by synthetic genetic modules to produce the proteins at the correct ratios. We show that the pure multiprotein system is functional and reproducible, and has low protein contaminants. We also demonstrate its application in the screening of synthetic promoters and protease inhibitors. Our work establishes a novel strategy for producing pure translation machinery, which may be extended to the production of other multiprotein systems.

Synthetic Biology: A Bridge between Artificial and Natural Cells

Artificial cells are simple cell-like entities that possess certain properties of natural cells. In general, artificial cells are constructed using three parts: (1) biological membranes that serve as protective barriers, while allowing communication between the cells and the environment; (2) transcription and translation machinery that synthesize proteins based on genetic sequences; and (3) genetic modules that control the dynamics of the whole cell. Artificial cells are minimal and well-defined systems that can be more easily engineered and controlled when compared to natural cells. Artificial cells can be used as biomimetic systems to study and understand natural dynamics of cells with minimal interference from cellular complexity. However, there remain significant gaps between artificial and natural cells. How much information can we encode into artificial cells? What is the minimal number of factors that are necessary to achieve robust functioning of artificial cells? Can artificial cells communicate with their environments efficiently? Can artificial cells replicate, divide or even evolve? Here, we review synthetic biological methods that could shrink the gaps between artificial and natural cells. The closure of these gaps will lead to advancement in synthetic biology, cellular biology and biomedical applications.