A Synthetic Stopgap: Self-Assembly Systems & the Nanobiomedicine Frontier-Department of Biological Sciences - Carnegie Mellon University

Friday, October 31, 2008

A Synthetic Stopgap: Self-Assembly Systems & the Nanobiomedicine Frontier

The field of medicine has never before experienced a deluge of information flow this big: the emerging confluence of nanotechnology and biomedicine has produced a wealth of new information, applications, and a need for new, innovative, and more complex machine design. Plus, nanotechnology itself faces new difficulties. Everyday, medicine places new ideals for a sort of technological imperative, one that calls for increasingly complex nanobiostructures intended for cancer treatment, drug delivery, and gene therapy. However, the gap between 'living' and 'synthetic' nanostructure design continues to widen as new nanotechnology research proceeds to build machines in synthetic fashion and faces challenges in complexity when applied to the living body. Thus, an alarm needs to be raised for morphological understanding of biosystems before further engineering of nanostructures. Self-assembly biosystems provide such a way. Specifically, computational studies of self-assembly systems give this insight.

Once infection of a healthy bacterial cell is complete, a bacteriophage replicates its viral particles, and uses cellular machinery to translate identical viral capsid proteins. Suddenly, not acted on by outside forces, these capsid monomers then spontaneously self-assemble into icosahedral structures reminiscent of their brothers who infected the cell [3]. The phage bodies now house new viral DNA that upon lysis will inject, infect, replicate, translate, and self-assemble in a reproduction scenario the same as in the old cell but now in the distant environment of a new bacterium.

What causes these capsid monomers to act autonomously? How does a single monomer cooperate amongst others in a colloidal population to organize into more complex superstructures? Are these superstructures simply then the sum of their monomers, or can they behave functionally different, perhaps even acting as monomers themselves? If so, how do we make use of this fact to design novel synthetic biosystems?

For nanotechnology scientists, self-forming systems such as phage reproduction give insight into nature's in vivo infrastructure. All biosystems, such as phage capsid formations, have a basis in nanoscale structures. Inside the body, the cell combines these nanoscale units using weak molecular interactions: van der Waals forces, H-bonds, hydrophobic interactions, electrostatic dipoles, and enzymatic reactions that act as chaperones in formation. 

For years, these interactions were subjects of study for 'dry' in vitro nanotechnology which attempted to mimic natural properties using  synthetic materials. Through biologically inspired ideas, designers for nanotechnology created innovative nanostructures - carbon nanotubes, gold plated Quantom dots, and magnetic nanocrystals, for example, fueled a new biotechnology industry. Self-assembly systems were adopted as a beacon of inspiration for creating these structures. Studying self-assembly helped nanotechnologists design new materials for nanobiomedicine.

However, a fundamental limitation to creating these structures is synthetic (in vitro) environments and materials. Key to this problem is that 'dry' structural changes over time cannot be extrapolated to those of 'wet' biological systems. Whereas biological systems are capable of adapting to their environment, synthetic structures wear down over time and function only under specific conditions. thus, as medicine pushes for nanomachines to tunnel deeper and invasively interact in an increasingly precise fashion, current biologically-inspired machines fail. The primary reason for machine failure is the vast complexity difference between biological systems and nanostructures that mimic these systems [1].

Thus, nanobiomedicine has reached a frontier; we are at a precipice where biomedical inspirations such as targeted treatment of disease and drug delivery require increasingly complex systems of nanotechnology. Yet current self-assembled structures are limited by their synthetic simplicity [14]. However, by studying biological self-assembled systems more closely, it is possible to gain deeper insights into natural design of how monomeric units combine independently without an outside stimulating force.

Current Synthetic Materials

Before we turn to the approaches scientists take in studying 'living' self-assembly, it is important to first analyze current synthetic methods to generate self-forming nanostructures. The research in synthetic material design focuses largely on inducing changes in nanoscale monomers using interactions among subunits. These intermolecular contacts are the fundamental driving forces for self-assembly [1]. Each force alters the energetics of the surrounding environment, thereby driving the subunits to autonomously organize. Specifically, the main interactions are coulomb interactions, van der Waals forces, and short range repulsions between subunits. 

Coulomb interactions are forces created by charged particles; each subunit, when seen as a particle, carries a permanent charge. This is either positive or negative. These charged particles generate attraction or repulsion based on what charge they are. Like charges repel, and unlike charges attract. 

Van der Waals interactions are inter-molecular forces caused by spontaneous polarization between adjacent subunits. These include momentary attractions between separate atoms and diatomic free molecules. They are always of a negative charge. Like Coulomb forces, van der Waals forces function to change the energetics of the systems in self-assembly.

Finally, short range repulsions between monomers are forces that are created when two monomers are at a very close separation distance. as the distance between two subunits gets increasingly close, the repulsion force rises exponentially.

All three intermolecular energies, combined, induce self-assembly of monomers. This is clear when their energies are considered as a function of distance of separation for all three interactions. At a certain distance, equilibrium is reached between monomers. At equilibrium, there is little to no formation.

Self-assembly is initiated when the distance between two monomers gets closer than the equilibrium position. When this happens, there is exponential rise in energy from short range repulsions and Coulomb interactions, in addition to a decrease in van der Waals forces. The dramatic increases in electrostatic interactions combined with decreases in van der Waals forces allows for self-formation of polymers.

Thus, to create nanostructures, scientists work to change raw materials from a dispersed state to a condensed state. In the dispensed state, the individual monomers are farther away from their equilibrium distance, and thus their intermolecular forces do not interact with neighboring molecules. To induce a phase change, some parameter about the system has to be altered: the temperature increased, the pH lowered, or the electrolyte concentration increased. This phase transition is initiated using thermally or electrically induced fields [1] - which dictate the structure's rigidity, its stability, and its lifespan.

Knowing that structural changes occur with the phase transitions and knowing that careful changes to parameters can control the morphology of the resulting nanostructure, applications can take advantage of the different structural possibilities. One such application is targeted drug delivery. The use of self-assembled synthetic polymers is important to drug delivery. For example, poly(Nisopropylacrylamide) or (PNIPAAm) is one self-formed polymer that can bulge or shrivel in different phase changes. This is controlled by a critical temperature point - above which the PNIPAAm polymer chains collapse (the structure shrivels), and below which the chains are expanded (the structure swells). So, using this sensitivity to temperature difference, a drug can be delivered at a specific time inside the body. First the drug is loaded into the PNIPAAm gel, and invasively injected into the body. When the temperature rises inside the body above, the PNIPAAm structure shrivels and the drug is released into the blood stream. This is a step towards targeted release using self-assembled structures.

Synthetic targeted drug delivery systems are but one example in the nanotechnology arena where self-assembly is used as an inspiration for nanostructure design. Other applications include artificial muscles, reversible surfaces, separation membranes, enzyme immobilization, catalysis substrates, and chemical valves in the body. Plus, in the field of nanobiomedicine, self-forming nanostructures play a key role in non-invasive medicine. Diagnosis, for example, becomes increasingly molecular, where in rapid screening, networking, and measuring of chemical imbalances is done at a cell-to-cell scale. These nanosensors cut out guesswork in detection, and facilitate in exact diagnosis [2]. Furthermore, rapid topical delivery of active drug compounds can be attached to nanoparticles, where the nanoscale size of the liposome particles (self-assembled structures) allows for easy diffusion through membranes. For example, the Blood Brain Barrier (BBB), the body's natural protection of the brain from pathogens, is one place where nanoparticle drug delivery is effective. By being able to cross through the BBB membrane, nanoparticles can target areas of the brain once thought to be inaccessible. Companies like NanoPharm from Germany have made synthetic systems capable of this feat, allowing for brain anesthesia and other therapeutic applications. 

In this way, the confluence of medicine, nanotechnology, and other fields like computational biology has created a new "technological imperative' [7]. This imperative imposes a need for novel designs from nanostructures. There is a unique sense of control at new molecular level, and a growing need to engineer cures for disease [8]. Whereas once medicine was diagnostic, the need for new technology to raise the level of care has blurred the line between technology and therapeutic medicine.

For self-assembled nanostructure design, this technomedical imperative places an additional pressure to deliver new systems of higher complexity. Molecular medicine forces nanotechnology to create new materials from self-assembly that must change multiple phases, perform intelligent sensory functions, and respond to unique stimuli. Most importantly, these structures must adapt to different environments as they travel through the body. In fact, the main problem that self-assembly nanostructure science faces is mimicking the hierarchal order, function-driven assembly, and dynamic change of living systems. In living systems, materials are ordered on different scales other than the nanoscale [1]. This can be seen in the bone and muscle tissue systems. Next, every assembly is purposeful: the creation of viral phage structures, as discussed earlier, only happens at a specific time during infection. The phage envelopes dissolve after their purpose is done. Third, there is dynamic change and adaptability of structures. As the cell divides, for example, microtubules grow and shrink accordingly, and they themselves self-organize into spindles. Thus, it is obvious that living self-assembly is on a different complexity scale than current nanostructures. It is driven by a complicated phase transition space. Phase changes, as discussed before, are required for self-assembly to be initiated. For example, viral capsid protein formations go through numerous intermediaries and disassemblies before reaching their final state [9].

Current techniques, unable to include these critical components of living systems into synthetic machines, are in danger of failing to meet the demands of biomedical claims. Instead, the most they can provide is a synthetic stopgap: expedient measures to fuel a nanobiomedical industry. Truth be told, artificial self-assembly produces key technologies and sets up important technological platforms for emerging architecture. Key applications include nanosemiconductor electronics, solar cells, photo-catalysis, light emitting diodes, lasers, detectors, sensors, and bioimaging devices, all of which play an important part in the medical arena. But, these devices are just the tip of the surface when compared to the level of functionality of living systems. It is time, then, to study biological systems at a deeper level. This will give insights into the complex nature of living self-assembly, some of which can be used to engineer better synthetic counterparts.

Computational Studies

Through there are many ways to study biosystems, this article will finish with the main computational techniques used to model living systems. Specifically, the formation of viral capsid assembly is discussed. By far, computational methods are the most effective ways to study these systems. Whereas experimental approaches are risk-laden and sometimes not possible due to the scale of assembly [3], computational methods allow rapid techniques that can be reapplied with ease. The main method of study is simulations modeling.

The different simulation models each focus on certain parts of phage assembly, from kinetics of formation to non-symmetrical structures. One main method of simulation is the local rules method. This method of simulation is based on the theory that self-assembly in living systems happens from the bottom-up. Precisely, it says that each protein subunit, or monomer, has a set of local rules that is abides by in terms of its association with other subunits. In this way, viral capsid formation is driven by each monomer following the simple local rules [10]. The end product thus depends on which local rules are present in formation. Knowing these rules, computational biologists simulate viral capsid formations. They are able to alter parameters in the local rules that lead to different assembly intermediaries and that interact with each other [3], [11], [12].

Another model of study is the equilibrium dynamics model. In this model, it is proposed that the resulting phage structure is at equilibrium with the protein subunits, the assembly intermediates, and the viral payload itself. This suggests that the self-assembly of the subunits causes conformational changes in all three components, and disassembly results in a reversal of conformational changes. Thus, any change in interaction energies between the different subunits causes large morphological changes in the final structure [3], [13].

There are also many considerations when simulating phage assembly. One important consideration is random versus non-random assemble pathways. In the former, it is possible for the final assembly to occur in an unconstrained fashion. Under unconstrained models, it is possible for assembly intermediaries (subunits who have formed different parts of the final structure) to combine. However, under non-random constrained models, only individual subunits are allowed to add at each time interval. One by one, the final phage structure is formed. The speed of formation is different in each model. In fact, the constrained model is assumed in most simulation models [3].

This consideration of random versus non-random pathways shows a fundamental problem: the large number of possible pathways a simulation could take. Which one is right? Can we assume one set of pathways (such as the constrained pathways) over another (unconstrained)? This has implications in synthetic design. For example, if constrined models are correct, synthetic design cannot allow assembly intermediaries, and vice versa.

Thus, computational methods are a step towards the right direction when studying the complex binding patterns and phase changes in living systems. Considerations such as constrained versus unconstrained pathways will shape what models are used, which simulation techniques are employed and most importantly how synthetic design must continue. Computational simulations, therefore, function to reveal problems in structural prediction. Only when these problems are solved can synthetic design follow accordingly.

References:

[1] Zhang, Jin., Wang, Zhong-lin., Liu, Jun., Chen, Shaowei., Liu, gang-yu. Self-Assembled Nanostructures. New York: Klluwer Academic/Plenum Publishers, 2003.
[2] Malsch, Neelina. Biomedical Nanotechnology. Boca Raton: Taylor & Francis Group, 2005.
[3] Zhang, Tiequan., Schwartz, Russell. "Simulation Study of the Contribution of Oligomer/Oligomer Binding to Capsid Assembly Kinetics" Biophys J. 2006 January 1; 90(1): 57-64. Published online 2005 October 7. doi: 10.1529/biophysj.105.072207.
[4] http://www/in-pharmatechnologist.com/news/ng.asp?n=58523-nanotechnology-to-revolutionise
[5] http://findarticles.com/p/articles/mi_qa3938/is_200406/ai_n9417963/print
[6] http://findarticles.com/p/articles/mi_qa3938/is_200406/ai_n9417963/print
[7] Warner and Tighe. Major Problems in the History of American Medicine and Public Health. Boston: Houghton Mifflin Company, 2001. Pgs. 349-368
[8] Rose,Nikolas. The Politics of Life Itself. Princeton: Princeton University Press, 2007. Pg. 3
[9] T. Keff, C. Micheletti, and R. Twarock, "Master equation approach to the assembly of viral capsids, "J. Theor. Biol., vol. 242, pp. 713-721, 2006.
[10] Berger, B., P.W. Shor, L. Tucker-Kellogg, and J. King. 1994. Local rule-based theory of virus shell assembly. Proc. Natl. Acad. Sci. USA. 91:7732-7736.
[11] Schwartz, R., P.E. Prevelige,Jr., and B. Berger. 1998. Local Rules Modeling of Nucleation-Limited Virus Capsid Assembly (MIT-LCS-TM-584). MIT Laboratory for Computer Sciences, Cambridge, MA.
[12] Zhang, T., R. Rohlfs, and R. Schwartz. 2005. Implementation of a discrete event simulator for biological self-assembly systems. Proc. 2005 Winter Simulation Conf. In press.
[13] Zlotnick, A. 1994. To build a virus capsid: an equilibrium model of the self assembly of polyhedral protein complexes. J. Mol. Biol. 241: 59-67.
[14] Zhang, T., Kim, Woo Tae., Schwartz, Russell. "Investigating Scaling Effects on Virus Capsid-Like Self-Assembly Using Discrete Event Simulations" IEEE Transactions on Nanobioscience, Vol. 6, NO. 3, September 2007.

Graphics Reference:

http://www.csm.ornl.gov/SC98/viz/tuzun.jpg

Written by: Vamsee Pillalamarri,  M.S. in Computational Biology Candidate, B.S in Computational Biology (2008)

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