Synthetic biology milestones employing computational methods, as well as those that were built conceptually from computational paradigms. With the present focus on microbial engineering, many of these characteristics can be safely neglected; at mammalian levels of complexity, they render the behavior of synthetic systems difficult to predict a priori.
Although these challenges are manifold, they are not insurmountable. The answers may lie in systems biology. This computational discipline seeks to shift the basic molecular biology paradigm from isolation to coordination: from characterizing individual components of cell behavior to analyzing how these components function in tandem Kitano, a , b. Accordingly, systems bioengineers bring a diverse array of computational modeling techniques—drawing on mathematics, computer science, and engineering—to bear on questions of both mechanism and design at the cell and tissue levels Kitano, ; Alon, In doing so, the field provides computational tools to characterize behavioral patterns at the cellular level that will be the building blocks of more sophisticated synthetic design.
Systems biology approaches are particularly powerful in characterizing cell—cell interactions across scales, such as in capillary patterning and organ development, where the gene circuits approach in synthetic biology has proven limited in capturing adaptation, cellular heterogeneity and spatial hierarchy Yingling et al.
As such, many of the challenges to applying synthetic biology toward controlling mammalian tissue can be addressed in part by methods and techniques that are well-developed in systems bioengineering. Here, we discuss each of these roadblocks categorically, with the associated tools to address them. Characterizing population-level emergent behavior and cell—cell heterogeneity has long been recognized as a principal goal and challenge in synthetic biology Canton et al.
Traditional synthetic designs have assumed identical expression patterns across a cell population, as the standard biocircuit framework does not permit the simulation of cell behavioral variability Elowitz et al.
Metabolic flux analysis in systems biology of mammalian cells.
Moreover, the limited capacity for gene circuit models to characterize emergent behavior—defined formally as patterns that emerge from a myriad of relatively simple interactions—inhibits scale-dependent design Benner and Sismour, As a mammalian example, if intricate cerebral function results from the coordinated function of millions of individual neurons, any synthetic design applied to the brain must first require accurate simulations of how neural cell-level changes manifest on the cerebral tissue-level.
Deriving such scale-driven causal links from observed principles is non-trivial at best. One systems biology method to address the research challenge of emergence in biology is agent-based modeling. The approach has a simple premise: such systems exhibit emergent behavior that arises from the interactions between individual actors or agents and, consequently, would be impossible to know a priori Chandran et al. Fundamentally, this class of modeling method diverges from biocircuit models, which typically characterize fluctuations in state variables governed by differential relationships.
Supplanting the latter's top-down, intracellular perspective with the former's bottom-up, multi-scale viewpoint permits the simulation of heterogeneity while eliminating the need to derive inter-scale relationships beforehand Chandran et al. Notably, agent-based modeling encompasses a broad range of variations in implementation, rather than any specific algorithm or rule-set. Existing libraries, such as MASON, Repast, and Swarm, allow for the construction of multi-scale agent-based models atop adaptable frameworks, facilitating their use by synthetic biologists with limited prior exposure to the technique.
This methodology has been employed toward modeling brain capillary regeneration Long et al. Within synthetic biology specifically, agent-based models have also simulated tissue development, tissue formation, and microbial chemotaxis Endler et al. Similarly rule-based formalisms are also being applied to coarse-grain patterns in chemical-kinetic models Feret et al. Forecasting interactions and dynamics in protein and metabolic pathways is crucial for fine-tuned control of mammalian synthetic bioengineering.
Whereas traditional kinetic- and gene circuit-based methods use simplified pathways to represent these signaling dynamics, in many cases the relationships between molecules are highly non-linear Marcotte, and multiplex, i. Common, too, are linkages between parallel molecular pathways that each simultaneously affect the output of the other Jeong et al.
Several types of network models allow for better predictive simulation of these multiplex interactions. Graph methods, for example, are a class of models that represent pathway components as networked nodes, and graph-based approaches have been used to model cellular machinery including genes, proteins and other subcellular compartments Ma'ayan et al.
The interactions between components are drawn as edge connections between the relevant nodes Ma'ayan et al. Graph-based models vary in implementation to capture different kinds of molecular relationships e. In mammalian cells, as an example, researchers have had early success in characterizing the dynamics of key feedforward modules and motifs, helping to enable the circuit design of adaptive gene expression Bleris et al. One common type of acyclic graph method, known as Bayesian Network Analysis, is a form of directed statistical modeling designed to capture conditional dependencies between probabilistic events Pe'er, In a Bayesian network model, probabilities define the relationship between the current node and its predecessor or parent in a graph Alterovitz et al.
Markov models are another network-based technique that can provide a framework to describe molecular or cellular states and the weighted probability of transitioning between them. However, for gene or protein pathways with more complex topology—such as those examples offered above—cyclic graph models might be necessary, for which a variety of analytical tools and approaches are described by computational biologists in the literature particularly from research on neural networks Bianchini et al.
In parallel with the above techniques, another suite of computational methods permits not only the analysis of cellular pathways, but also directly facilitates their synthetic design. Known as evolutionary algorithms, these methods can predict state changes in the behavior of signaling pathways over time, through adaptation or random mutation, by modeling this rewiring directly Hallinan et al. Many alternative optimization techniques exist, e.
A unique advantage of evolutionary and optimization algorithms is their ability to A be applied broadly to many forms of models, including ODEs and rule-based simulations and B generate a diverse array of functional network topologies. Thus, far, synthetic biology research has largely omitted studies on cell shape. The few exceptions in the literature focus on morphological properties as reporters for specific signaling cascades or to control specific spatial features Yeh et al.
For instance, one recent work described controlled shape changes of synthetic yeast cells Tanaka and Yi, Another study scored filopodial and lamellipodial phenotypes as indicators for successful synthetic rewiring of Rho GTPase signaling Yeh et al. Despite the few studies in this area, cell morphology is often a characteristic of central importance to synthetic biology experiments.
For instance, synthetic systems seeking to modulate cell—cell interactions must necessarily account for morphological and spatial-dependent interactions between cells Ben-Ze'ev et al. These membrane adjacency and receptor localization are drivers of pathways like Delta-Notch signaling, in which a signaling cascade is triggered by the binding of two transmembrane proteins on adjacent cells Appel et al. Moreover, cell behavior—and at a higher scale, tissue functionality—is often predicated on geometry Haeuptle et al.
For example, optimizing a synthetic cell for metabolic filtration necessitates that its membrane surface area be maximized for nutrient exchange, such as through inward folds Gahan, Doing so requires leveraging computational modeling to predict three-dimensional shape response to changes in genetic circuit design. Examples of methods for geometrical-rendered modeling of cells include tensegrity models, Voronoi-based simulations, and molecular dynamics models. Though abstract in concept, computational models of tensegrity have been demonstrated to approximate cell shape and mechanics, providing a representation for simulating cell morphology in vitro Huang et al.
Tensegrity principles have been used to represent cytoskeletal elements, allowing for changes in these proteins induced by regulatory networks e. An alternative geometrical model is the Voronoi diagram, a mathematical concept of dividing space into distinct regions based on proximity to initial seed points. Voronoi diagrams provide a useful means of constraining complex cell shapes into adjacent spatial tessellations, a technique particularly useful to study patterning at the cell population- or tissue-level Schaller and Meyer-Hermann, ; Luengo-Oroz et al.
Lastly, molecular dynamics simulations of cell shape represent cells as collections of individual molecules in Newtonian motion, either abstractly as particles or concretely as cytoskeletal elements , to model an agglomerated cellular structure at high resolution—albeit at greater computational cost Rapaport, ; Pfaendtner et al.
Linking geometric-based models to gene network simulations offers the opportunity to guide synthetic biocircuit design in silico such that specific cell morphologies can be engineered. Previously, this method has led to a complete representation of osteocyte cytoskeleton dynamics Kardas et al. In conjunction, computational cell phenotyping enables changes in morphology to be quantitatively measured and tracked, such that the desired design can be achieved.
Phenotyping techniques couple high-fidelity cell imaging with processing metrics to parse shape information Chung et al. These shape metrics can facilitate the computer-aided design of synthetic networks. Perhaps the most significant research challenge in synthetic bioengineering is enabling the design of cellular systems that are robust to biological stochasticity Chopra and Kamma, ; Purnick and Weiss, Existing gene circuit models are largely deterministic, behaving in highly reproducible ways.
These models, as alluded to previously, present regulatory networks as homogeneous concentrations of molecules modulated by parameterized rate constants through coupled differential equations. Yet there exists increasing evidence that biological networks and intracellular behavior are innately stochastic Thattai and van Oudenaarden, Whereas noise effects are often assumed to be negligible at the population level, noise can play a significant role at the single-cell level, e. Furthermore, research indicates the phenomenon of noise propagation, in which cell-level stochasticity can accrue at the population-level to create emergent behavior that deviates substantially from the desired target, a phenomena recently documented in E.
Coli , leading to a loss of synchrony between cells Hooshangi et al. Such studies suggest that complex synthetic systems cannot be engineered without first accounting for stochasticity in the circuit design. Fortunately, there exist a wide variety of computational techniques to capture and predict this biological stochasticity at the systems level.
One specific approach, known as the Gillespie Algorithm, rejects the deterministic ODE approach of modeling chemical-kinetics in favor of stochastic representations of molecular interactions Gillespie, For synthetic biology applications, these reactions can be defined as discrete regulatory steps along a specific gene circuit, allowing the effects of noise along the circuit to be well-characterized.
The Gillespie algorithm belongs to a larger class of stochastic modeling techniques known as Monte Carlo methods, which can be adapted to suit the needs of a specific biocircuit design Athale, Monte Carlo methods, while varied in implementation, share the property of employing random simulations over many iterations to quantify properties of biological systems. Traditional synthetic biology designs are based on assumptions of biochemically homogenous cell interiors, but for gene circuit designs of higher complexity, this set of assumptions is unlikely to hold Agapakis et al.
Often, the spatial information associated with a protein or pathway inside the cell can influence the end-behavior of a molecular network Agapakis et al. In addition to variations in metabolic conditions e. Characterizing intracellular spatial dependences and molecular dynamics becomes particularly important in mammalian cells, for which fine spatial organization of regulatory pathways is commonplace. To this end, particle- and lattice-based computational techniques can be employed to model spatial systems within a synthetic cell Spicher et al.
Rather than simulate bulk flow, particle-based models track molecules separately and in discrete quantities Takahashi et al. In systems biology, such methods have already been applied toward characterizing single-cell gradient sensing in the presence of multiple competitive ligands Liou and Chen, Particle models could be similarly applicable to synthetic biology in engineering mammalian cells to function as fine-tuned hypoxic or nitric oxide sensors, in an effort to minimize effects of ischemic stroke—to name just one instance.
The complexity of particle models is mitigated by the availability of open source simulators, including E-Cell and ChemCell Klann and Koeppl, Many of these implementations also allow particle simulations to be combined with models of other classes. As an example, a spatial derivative of the Gillespie algorithm can integrate stochastic modeling with space-dependent computation Takahashi et al. Spatial modeling can also be performed using PDE models; examples include gene circuits defining chemical diffusion-mediated interactions between localized cell populations Song et al.
In other applications to synthetic biology, these spatial techniques have been combined with mechanistic models, such as kinetic RNA folding simulations, to provide fine-tuned control of gene expression along a specific component of a regulatory pathway Carothers et al. PDE formalisms offer relative simplicity of construction compared to other spatiotemporal methods, with the caveat of not being well-suited to highly heterogeneous spatial environments.
Until now, the overwhelming focus of research and progress in synthetic biology has been on prokaryotic cells: mostly bacteria commonly E. Coli and assorted microbes. This is a natural consequence of the knowledge gap described previously; prokaryotic cells are orders of magnitude simpler than eukaryotic ones—not to mention easier to manipulate. However, if the ultimate goal of the discipline is to uncover novel therapeutic targets and treatments in biomedicine, such strict characterization of non-mammalian systems will restrain our ability to advance human health.
In the end, we must learn to walk. To do so means confronting the complexity that the in vivo mammalian system brings. Methods already employed in systems biology to characterize this complexity can open up the boundaries of modern medicine. As an example, it is not difficult to imagine a future where computational models enable the design of synthetic neural progenitor cells programmed to promote recovery post-ischemic stroke. To foster an era of personalized medicine, this potential could revolutionize the manner in which we approach tissue engineering: cells grown en masse , and then programmed to meet the specific needs of the patient.
Moreover, such customizable cells would permit targeted regeneration to a degree that simple stem cell treatments cannot achieve. Such innovations, while distant, are attainable, but they necessitate the coupling of systems approaches with synthetic biology. Bringing the sister disciplines of synthetic and systems biology closer together could recast the gene circuit paradigm, and enhance our ability to engineer and program cells for applications across energy, computing and biomedicine. Leveraging a computational toolkit refined by systems biologists for the last half-century offers a unique catalyst that to help pave the future of synthetic biology.
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
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