Watch the clock-engineering biological systems to be on time

Synthetic tissue engineering: Programming multicellular self-organization by designing customized cell-cell communication

Cells communicate with each other to organize multicellular collective systems and assemble complex, elaborate tissue structures by themselves during development. Despite intensive biological studies, what kind of cell-cell communication can sufficiently drive self-organization of specific tissue architectures remain unclear. Thanks to recent advances on genetic engineering technologies, synthetic biologists start to build customized cell-cell communication with user-defined signal input and gene expression output to program multicellular behaviors using mammalian systems. This review article introduces how we can design and engineer customized cell-cell communication to program synthetic self-organizing multicellular structures. Creating tissue formation processes with synthetic genetic programs will help understanding of fundamental principles of how genetic programs drive tissue self-organization and provide new capabilities on tissue engineering for cell-based regenerative therapy applications.

Rational design of functional and tunable oscillating enzymatic networks

Life is sustained by complex systems operating far from equilibrium and consisting of a multitude of enzymatic reaction networks. The operating principles of biology's regulatory networks are known, but the in vitro assembly of out-of-equilibrium enzymatic reaction networks has proved challenging, limiting the development of synthetic systems showing autonomous behaviour. Here, we present a strategy for the rational design of programmable functional reaction networks that exhibit dynamic behaviour. We demonstrate that a network built around autoactivation and delayed negative feedback of the enzyme trypsin is capable of producing sustained oscillating concentrations of active trypsin for over 65 h. Other functions, such as amplification, analog-to-digital conversion and periodic control over equilibrium systems, are obtained by linking multiple network modules in microfluidic flow reactors. The methodology developed here provides a general framework to construct dissipative, tunable and robust (bio)chemical reaction networks.

Design principles of regulatory networks: searching for the molecular algorithms of the cell

A challenge in biology is to understand how complex molecular networks in the cell execute sophisticated regulatory functions. Here we explore the idea that there are common and general principles that link network structures to biological functions, principles that constrain the design solutions that evolution can converge upon for accomplishing a given cellular task. We describe approaches for classifying networks based on abstract architectures and functions, rather than on the specific molecular components of the networks. For any common regulatory task, can we define the space of all possible molecular solutions? Such inverse approaches might ultimately allow the assembly of a design table of core molecular algorithms that could serve as a guide for building synthetic networks and modulating disease networks.

Feedback-based, system-level properties of vertebrate-microbial interactions

Background

Improved characterization of infectious disease dynamics is required. To that end, three-dimensional (3D) data analysis of feedback-like processes may be considered.

Methods

To detect infectious disease data patterns, a systems biology (SB) and evolutionary biology (EB) approach was evaluated, which utilizes leukocyte data structures designed to diminish data variability and enhance discrimination. Using data collected from one avian and two mammalian (human and bovine) species infected with viral, parasite, or bacterial agents (both sensitive and resistant to antimicrobials), four data structures were explored: (i) counts or percentages of a single leukocyte type, such as lymphocytes, neutrophils, or macrophages (the classic approach), and three levels of the SB/EB approach, which assessed (ii) 2D, (iii) 3D, and (iv) multi-dimensional (rotating 3D) host-microbial interactions.

Results

In all studies, no classic data structure discriminated disease-positive (D+, or observations in which a microbe was isolated) from disease-negative (D–, or microbial-negative) groups: D+ and D– data distributions overlapped. In contrast, multi-dimensional analysis of indicators designed to possess desirable features, such as a single line of observations, displayed a continuous, circular data structure, whose abrupt inflections facilitated partitioning into subsets statistically significantly different from one another. In all studies, the 3D, SB/EB approach distinguished three (steady, positive, and negative) feedback phases, in which D– data characterized the steady state phase, and D+ data were found in the positive and negative phases. In humans, spatial patterns revealed false-negative observations and three malaria-positive data classes. In both humans and bovines, methicillin-resistant Staphylococcus aureus (MRSA) infections were discriminated from non-MRSA infections.

Conclusions

More information can be extracted, from the same data, provided that data are structured, their 3D relationships are considered, and well-conserved (feedback-like) functions are estimated. Patterns emerging from such structures may distinguish well-conserved from recently developed host-microbial interactions. Applications include diagnosis, error detection, and modeling.

Emerging biomedical applications of synthetic biology

Synthetic biology aims to create functional devices, systems and organisms with novel and useful functions on the basis of catalogued and standardized biological building blocks. Although they were initially constructed to elucidate the dynamics of simple processes, designed devices now contribute to the understanding of disease mechanisms, provide novel diagnostic tools, enable economic production of therapeutics and allow the design of novel strategies for the treatment of cancer, immune diseases and metabolic disorders, such as diabetes and gout, as well as a range of infectious diseases. In this Review, we cover the impact and potential of synthetic biology for biomedical applications.