Engineering and modeling of multicellular morphologies and patterns

Mathematical and In Silico Analysis of Synthetic Inhibitory Circuits That Program Self-Organizing Multicellular Structures

Bottom-up approaches are becoming increasingly popular for studying multicellular self-organization and development. In contrast to the classic top-down approach, where parts of the organization/developmental process are broken to understand the process, the goal is to build the process to understand it. For example, synthetic circuits have been built to understand how cell-cell communication and differential adhesion can drive multicellular development. The majority of current bottom-up efforts focus on using activatory circuits to engineer and understand development, but efforts with inhibitory circuits have been minimal. Yet, inhibitory circuits are ubiquitous and vital to native developmental processes. Thus, inhibitory circuits are a crucial yet poorly studied facet of bottom-up multicellular development. To demonstrate the potential of inhibitory circuits for building and developing multicellular structures, several synthetic inhibitory circuits that combine engineered cell-cell communication and differential adhesion were designed, and then examined for synthetic development capability using a previously validated in silico framework. These designed inhibitory circuits can build a variety of patterned, self-organized structures and even morphological oscillations. These results support that inhibitory circuits can be powerful tools for building, studying, and understanding developmental processes.

Automatic design of gene regulatory mechanisms for spatial pattern formation

Gene regulatory mechanisms (GRMs) control the formation of spatial and temporal expression patterns that can serve as regulatory signals for the development of complex shapes. Synthetic developmental biology aims to engineer such genetic circuits for understanding and producing desired multicellular spatial patterns. However, designing synthetic GRMs for complex, multi-dimensional spatial patterns is a current challenge due to the nonlinear interactions and feedback loops in genetic circuits. Here we present a methodology to automatically design GRMs that can produce any given two-dimensional spatial pattern. The proposed approach uses two orthogonal morphogen gradients acting as positional information signals in a multicellular tissue area or culture, which constitutes a continuous field of engineered cells implementing the same designed GRM. To efficiently design both the circuit network and the interaction mechanisms-including the number of genes necessary for the formation of the target spatial pattern-we developed an automated algorithm based on high-performance evolutionary computation. The tolerance of the algorithm can be configured to design GRMs that are either simple to produce approximate patterns or complex to produce precise patterns. We demonstrate the approach by automatically designing GRMs that can produce a diverse set of synthetic spatial expression patterns by interpreting just two orthogonal morphogen gradients. The proposed framework offers a versatile approach to systematically design and discover complex genetic circuits producing spatial patterns.

Pseudomonas putida as a synthetic biology chassis and a metabolic engineering platform

The soil bacterium Pseudomonas putida, especially the KT2440 strain, is increasingly being utilized as a host for biotransformations of both industrial and environmental interest. The foundations of such performance include its robust redox metabolism, ability to tolerate a wide range of physicochemical stresses, rapid growth, versatile metabolism, nonpathogenic nature, and the availability of molecular tools for advanced genetic programming. These attributes have been leveraged for hosting engineered pathways for production of valuable chemicals or degradation/valorization of environmental pollutants. This has in turn pushed the boundaries of conventional enzymology toward previously unexplored reactions in nature. Furthermore, modifications to the physical properties of the cells have been made to enhance their catalytic performance. These advancements establish P. putida as bona fide chassis for synthetic biology, on par with more traditional metabolic engineering platforms.

Design and mathematical analysis of activating transcriptional amplifiers that enable modular temporal control in synthetic juxtacrine circuits

The ability to control mammalian cells such that they self-organize or enact therapeutic effects as desired has incredible implications. Not only would it further our understanding of native processes such as development and the immune response, but it would also have powerful applications in medical fields such as regenerative medicine and immunotherapy. This control is typically obtained by synthetic circuits that use synthetic receptors, but control remains incomplete. The synthetic juxtacrine receptors (SJRs) are widely used as they are fully modular and enable spatial control, but they have limited gene expression amplification and temporal control. As these are integral facets to cell control, I therefore designed transcription factor based amplifiers that amplify gene expression and enable unidirectional temporal control by prolonging duration of target gene expression. Using a validated in silico framework for SJR signaling, I combined these amplifiers with SJRs and show that these SJR amplifier circuits can direct spatiotemporal patterning and improve the quality of self-organization. I then show that these circuits can improve chimeric antigen receptor (CAR) T cell tumor killing against various heterogenous antigen expression tumors. These amplifiers are flexible tools that improve control over SJR based circuits with both basic and therapeutic applications.

Automatic design of gene regulatory mechanisms for spatial pattern formation

Synthetic developmental biology aims to engineer gene regulatory mechanisms (GRMs) for understanding and producing desired multicellular patterns and shapes. However, designing GRMs for spatial patterns is a current challenge due to the nonlinear interactions and feedback loops in genetic circuits. Here we present a methodology to automatically design GRMs that can produce any given spatial pattern. The proposed approach uses two orthogonal morphogen gradients acting as positional information signals in a multicellular tissue area or culture, which constitutes a continuous field of engineered cells implementing the same designed GRM. To efficiently design both the circuit network and the interaction mechanisms-including the number of genes necessary for the formation of the target pattern-we developed an automated algorithm based on high-performance evolutionary computation. The tolerance of the algorithm can be configured to design GRMs that are either simple to produce approximate patterns or complex to produce precise patterns. We demonstrate the approach by automatically designing GRMs that can produce a diverse set of synthetic spatial expression patterns by interpreting just two orthogonal morphogen gradients. The proposed framework offers a versatile approach to systematically design and discover pattern-producing genetic circuits.

Sequential Control of Cellular Interactions Using Dynamic DNA Displacement

Intercellular interactions play a significant role in various complex biological processes, and their dysregulation promotes disease progression. To reveal the mechanisms of intercellular interactions without destroying basic life processes, it is necessary to mimic multicellular behaviors in vitro. However, the precise control of multicellular systems remains technically challenging owing to dynamic interactions. Here, we used DNA as a molecular lock and key to sequentially assemble and disassemble different cell clusters in a programmed way, regulating intercellular interactions. Tagging the surface of live cells with cholesterol-modified DNA enabled dynamical intercellular assemblies. By consecutively adding corresponding metaphorical locks (attaching DNA strands) and keys (detaching DNA strands), clusters of different cells could be sequentially formed. This strategy improved the capability of natural killer NK-92 cells to target tumor cells, improving the antitumor therapy efficacy. Our suggested approach allows dynamic regulation of intercellular interactions in complex cell systems and increases understanding of intercellular communication networks.

4-bit adhesion logic enables universal multicellular interface patterning

Multicellular systems, from bacterial biofilms to human organs, form interfaces (or boundaries) between different cell collectives to spatially organize versatile functions^1,2. The evolution of sufficiently descriptive genetic toolkits probably triggered the explosion of complex multicellular life and patterning^3,4. Synthetic biology aims to engineer multicellular systems for practical applications and to serve as a build-to-understand methodology for natural systems^5–8. However, our ability to engineer multicellular interface patterns^2,9 is still very limited, as synthetic cell–cell adhesion toolkits and suitable patterning algorithms are underdeveloped^5,7,10–13. Here we introduce a synthetic cell–cell adhesin logic with swarming bacteria and establish the precise engineering, predictive modelling and algorithmic programming of multicellular interface patterns. We demonstrate interface generation through a swarming adhesion mechanism, quantitative control over interface geometry and adhesion-mediated analogues of developmental organizers and morphogen fields. Using tiling and four-colour-mapping concepts, we identify algorithms for creating universal target patterns. This synthetic 4-bit adhesion logic advances practical applications such as human-readable molecular diagnostics, spatial fluid control on biological surfaces and programmable self-growing materials^5–8,14. Notably, a minimal set of just four adhesins represents 4 bits of information that suffice to program universal tessellation patterns, implying a low critical threshold for the evolution and engineering of complex multicellular systems^3,5.

A synthetic cell-cell adhesion logic using swarming E. coli with 4 bits of information is introduced, enabling the programming of interfaces that combine to form universal tessellation patterns over a large scale.

Inference of dynamic spatial GRN models with multi-GPU evolutionary computation

Reverse engineering mechanistic gene regulatory network (GRN) models with a specific dynamic spatial behavior is an inverse problem without analytical solutions in general. Instead, heuristic machine learning algorithms have been proposed to infer the structure and parameters of a system of equations able to recapitulate a given gene expression pattern. However, these algorithms are computationally intensive as they need to simulate millions of candidate models, which limits their applicability and requires high computational resources. Graphics processing unit (GPU) computing is an affordable alternative for accelerating large-scale scientific computation, yet no method is currently available to exploit GPU technology for the reverse engineering of mechanistic GRNs from spatial phenotypes. Here we present an efficient methodology to parallelize evolutionary algorithms using GPU computing for the inference of mechanistic GRNs that can develop a given gene expression pattern in a multicellular tissue area or cell culture. The proposed approach is based on multi-CPU threads running the lightweight crossover, mutation and selection operators and launching GPU kernels asynchronously. Kernels can run in parallel in a single or multiple GPUs and each kernel simulates and scores the error of a model using the thread parallelism of the GPU. We tested this methodology for the inference of spatiotemporal mechanistic gene regulatory networks (GRNs)-including topology and parameters-that can develop a given 2D gene expression pattern. The results show a 700-fold speedup with respect to a single CPU implementation. This approach can streamline the extraction of knowledge from biological and medical datasets and accelerate the automatic design of GRNs for synthetic biology applications.

Nonlinear delay differential equations and their application to modeling biological network motifs

Biological regulatory systems, such as cell signaling networks, nervous systems and ecological webs, consist of complex dynamical interactions among many components. Network motif models focus on small sub-networks to provide quantitative insight into overall behavior. However, such models often overlook time delays either inherent to biological processes or associated with multi-step interactions. Here we systematically examine explicit-delay versions of the most common network motifs via delay differential equation (DDE) models, both analytically and numerically. We find many broadly applicable results, including parameter reduction versus canonical ordinary differential equation (ODE) models, analytical relations for converting between ODE and DDE models, criteria for when delays may be ignored, a complete phase space for autoregulation, universal behaviors of feedforward loops, a unified Hill-function logic framework, and conditions for oscillations and chaos. We conclude that explicit-delay modeling simplifies the phenomenology of many biological networks and may aid in discovering new functional motifs.

Network motif models focus on small sub-networks in biological systems to quantitatively describe overall behavior but they often overlook time delays. Here, the authors systematically examine the most common network motifs via delay differential equations (DDE), often leading to more concise descriptions.

Assembly Pathway Selection with DNA Reaction Circuits for Programming Multiple Cell-Cell Interactions

The manipulation of cell-cell interactions promotes the study of multicellular behavior, but it remains a great challenge for programming multicellular assembly in complex reaction pathways with multiple cell types. Here we report a DNA reaction circuit-based approach to cell-surface engineering for the programmable regulation of multiple cell-cell interactions. The DNA circuits are designed on the basis of a stem-loop-integrated DNA hairpin motif, which has the capability of programming diverse molecular self-assembly and disassembly pathways by sequential allosteric activation. Modifying the cell surface with such DNA reaction circuits allows for performing programmable chemical functions on cell membranes and the control of multicellular self-assembly with selectivity. We demonstrate the selective control of targeting the capability of natural killer (NK) cells to two types of tumor cells, which show selectively enhanced cell-specific adaptive immunotherapy efficacy. We hope that our method provides new ideas for the programmable control of multiple cell-cell interactions in complex reaction pathways and potentially promotes the development of cell immunotherapy.