ABOUT


Pouya Tavousi is currently a postdoctoral researcher at the University of Connecticut. He received his Bachelor’s degree in Aerospace Engineering in 2010 from Sharif University of Technology in Tehran, Iran. He then moved to US and received his PhD in Mechanical Engineering in 2016 from the University of Connecticut. Throughout his research career, he has focused on the development of state-of-the-art algorithmic approaches for addressing one of the most challenging science problems, namely characterization and understanding the behavior of nanoscale biological systems as well as design, fabrication and manipulation of such systems. He has developed new techniques and computational and physical tools for studying nanosystems and the related manufacturing processes.




PROJECTS



Assemble And Match

Rational drug design is the process of finding new medication that can activate or inhibit the biofunction of a target molecule by binding to it and forming a molecular complex. Here, shape and charge complementarities between drug and target are key. To help find effective drug molecules out of a huge pool of possibilities, physical and computer aided tools have been developed. Former offers a tangible experience of the molecular interactions yet lacks measurement and evaluation capabilities. Latter enables accurate and fast evaluations, but does not deliver the interactive tangible experience of physical models.
Assemble-And-Match is a novel hybrid model that enhance and combines the unique features of the two categories. Assemble-And-Match works based on fabrication of customized molecular fragments using the developed software and a 3D printer. Fragments are hinged to each other in different combinations and form flexible peptide chains, conformable to tertiary structures, to fit in the binding pocket of a (3D printed) target molecule. Through embedded measurement marks, the molecular model is reconstructed in silico and its properties are evaluated. Assemble-And-Match tool is expected to enable combination of visuospatial perception with in silico computational power to aid research and education in drug design. The findings of this project have been reported in an article in the Journal of Scientific Reports. Source code request and any questions should be addressed to Pouya Tavous at pouya.tavousi@uconn.edu.



Net Interaction Calculator

Many physical simulations aim at evaluating the net interaction between two rigid bodies, resulting from the cumulative effect of pairwise interactions between their constituents. This is manifested particularly in biomolecular applications such as hierarchical protein folding instances where the interaction between almost rigid domains directly influences the folding pathway, the interaction between macromolecules for drug design purposes, self-assembly of nanoparticles for drug design and drug delivery, and design of smart materials and bio-sensors. In general, the brute force approach requires quadratic (in terms of the number of particles) number of pairwise evaluation operations for any relative pose of the two bodies, unless simplifying assumptions lead to a collapse of the computational complexity. Our solution is to approximate the pairwise interaction function using a linear predictor function, in which the basis functions have separated forms, i.e. the variables that describe local geometries of the two rigid bodies and the ones that reflect the relative pose between them are split in each basis function. Doing so replaces the quadratic number of interaction evaluations for each relative pose with a one-time quadratic computation of a set of characteristic parameters at a preprocessing step, plus constant number of pose function evaluations at each pose, where this constant is determined by the required accuracy of approximation as well as the efficiency of the used approximation method. We show that the standard deviation of the error for the net interaction is linearly (in terms of number of particles) proportional to the regression error, if the regression errors are from a normal distribution. Our results show that proper balance of the tradeoff between accuracy and speed-up yields an approximation which is computationally superior to other existing methods while maintaining reasonable precision. The findings of this project have been reported in an article in the Journal of PLOS ONE. Source code request and any questions should be addressed to Pouya Tavous at pouya.tavousi@uconn.edu.



Analog Method For Fast Match Assessment

Rational structure based drug design aims at identifying ligand molecules that bind to the active site of a target molecule with high affinity (low binding free energy), to promote or inhibit certain biofunctions. Thus, it is absolutely essential that one can evaluate such affinity for the predicted molecular complexes in order to design drugs effectively. A key observation is that, binding affinity is proportional to the geometric fit between the two molecules. Having a way to assess the quality of the fit enables one to rank the quality of potential drug solutions. Other than experimental methods that are associated with excessive time, labor and cost, several in silico methods have been developed in this regard. However, a main challenge of any computation-based method is that, no matter how efficient the technique is, the trade-off between accuracy and speed is inevitable. Therefore, given today's existing computational power, one or both is often compromised. In this paper, we propose a novel analog approach, to address the aforementioned limitation of computation-based algorithms by simply taking advantage of Kirchhoff's circuit laws. Ligand and receptor are represented with 3D printed molecular models that account for the flexibility of the ligand. Upon the contact between the ligand and the receptor, an electrical current will be produced that is proportional to the number of representative contact points between the two scaled up molecular models. The affinity between the two molecules is then assessed by identifying the number of representative contact points obtainable from the measured total electrical current. The simple yet accurate proposed technique, in combination with our previously developed model, Assemble-And-Match, can be a breakthrough in development of tools for drug design. Furthermore, the proposed technique can be more broadly practiced in any application that involves assessing the quality of geometric match between two physical objects. The findings of this project have been reported in an preprint manuscript submitted to the Journal of PLOS ONE. Source code request and any questions should be addressed to Pouya Tavous at pouya.tavousi@uconn.edu.



Systematic Design and Analysis of Artificial Molecular Machines

Designing and building artificial nanomachines with prescribed functions remains a challenge, partly because the design principles developed by nature through evolution are not well understood and, hence, not applicable to engineered nanomachines. Thus, in the absence of a systematic approach, most known attempts have followed ad hoc trial and error design processes. We propose the first systematic and generic approach to designing, analyzing, and controlling manufacturable molecular machines with prescribed mobility and function built from a finite but extendable number of available "primitives." Our framework allows the systematic exploration of the design space of irreducibly simple nanomachines, built with prescribed motion specification by combining available nanocomponents into systems having constrained, and consequently controllable, motions. We show that the proposed framework has allowed us to discover and verify a molecule whose mobility (degree of freedom) is one, and that our experiments exhibit the type and range of motion predicted by our simulations. Enhancing such a structure into functional nanomachines by exploiting and controlling their motions individually or as part of an ensemble could galvanize development of the multitude of engineering, scientific, medical, and consumer applications that can benefit from artificial nanomachines. The findings of this project have been reported in Pouya Tavousi's PhD dissertation. Source code request and any questions should be addressed to Pouya Tavous at pouya.tavousi@uconn.edu.



Synthesizing Functional Mechanisms From a Link Soup

The synthesis of functional molecular linkages is constrained by difficulties in fabricating nanolinks of arbitrary shapes and sizes. Thus, classical mechanism synthesis methods, which assume the ability to manufacture any designed links, cannot provide a systematic process for assembling such linkages. We propose a new approach to building functional mechanisms with prescribed mobility by using only elements from a predefined “link soup.” First, we enumerate an exhaustive set of topologies, while employing divide-and-conquer algorithms to control the generation and elimination of redundant topologies. Then, we construct the linkage arrangements for each valid topology. Finally, we output a set of feasible geometries through a positional analysis step that minimizes the error associated with closure of the loops in the linkage while avoiding geometric interference. The proposed systematic approach outputs the ATLAS of candidate mechanisms, which can be further processed for downstream applications. The resulting synthesis procedure is the first of its kind that is capable of synthesizing functional linkages with prescribed mobility constructed from a soup of primitive entities. The findings of this project have been reported in an article in the Journal of Mechanical Design. Source code request and any questions should be addressed to Pouya Tavous at pouya.tavousi@uconn.edu



Protofold II: Enhanced Model and Implementation for Kinetostatic Protein Folding

A reliable prediction of three-dimensional (3D) protein structures from sequence data remains a big challenge due to both theoretical and computational difficulties. We have previously shown that our kinetostatic compliance method (KCM) implemented into the Protofold package can overcome some of the key difficulties faced by other de novo structure prediction methods, such as the very small time steps required by the molecular dynamics (MD) approaches or the very large number of samples needed by the Monte Carlo (MC) sampling techniques. In this paper, we improve the free energy formulation used in Protofold by including the typically underrated entropic effects, imparted due to differences in hydrophobicity of the chemical groups, which dominate the folding of most water-soluble proteins. In addition to the model enhancement, we revisit the numerical implementation by redesigning the algorithms and introducing efficient data structures that reduce the expected complexity from quadratic to linear. Moreover, we develop and optimize parallel implementations of the algorithms on both central and graphics processing units (CPU/GPU) achieving speed-ups up to two orders of magnitude on the GPU. Our simulations are consistent with the general behavior observed in the folding process in aqueous solvent, confirming the effectiveness of model improvements. We report on the folding process at multiple levels, namely, the formation of secondary structural elements and tertiary interactions between secondary elements or across larger domains. We also observe significant enhancements in running times that make the folding simulation tractable for large molecules. The findings of this project have been reported in an article in the Journal of Nanotechnology in Engineering and Medicine. Source code request and any questions should be addressed to Pouya Tavous at pouya.tavousi@uconn.edu



Tumor Control Index as a new tool to assess tumor growth in experimental animals

Measurement of tumor diameters, tumor volumes, or area under the curve has been traditionally used to quantitate and compare tumor growth curves in immune competent as well as immune-compromised mice and rats. Here, using tumor growth data from a large number of mice challenged with live tumor cells, we describe the use of a new composite parameter, Tumor Control Index (TCI) as an alternative method to do the same. This index, comprised of three distinct values, the Tumor Inhibition Score, Tumor Rejection Score, and Tumor Stability Score, provides a complete picture of nearly every aspect of tumor growth in large numbers of animals, can be deduced automatically from tumor diameter or volume data, and can be used to compare several groups of animals in different experiments. This automatically derivable index also corresponds neatly to the use of complete and partial responses and tumor stability data generated in human tumors, and can be used to assess the efficacy of interventions to be used in clinical studies. The findings of this project have been reported in an article in the Journal of Immunological Methods. Source code request and any questions should be addressed to Pouya Tavous at pouya.tavousi@uconn.edu



A novel crowdsourcing platform for microelectronics counterfeit defect detection

Disguising non-authentic electronic parts as otherwise, so called as electronic counterfeiting, continues to inflict significant damages on government, industry and society. This calls for finding effective ways to identify counterfeits. The current approaches involve acquisition of 2D and 3D images of the alleged part using a spectrum of microscopy tools, followed by having them assessed by a group of subject matter experts. This approach, nevertheless, entails two important shortcomings. First, the intensive computations needed for visualization, processing and analysis of the large microscopy data is not affordable by all. Second, due to lack of an objective measure for most classes of counterfeit, many defects are overlooked and even in some cases, they are falsely identified. Our proposed solution provides a collaborative platform to acquire assessments from a larger group of experts, towards forming a collective insight and minimizing overlooking of defects. Our first-of-its-kind web-based crowdsourcing platform can be leveraged for 3D visualization of microscopy data without imposing any computational load on the users, as well as collaborative analysis by collecting information from each user. Further, the collected information is compiled in a data bank, which serves as a valuable source for developing quantified measures and for training automated defect classification algorithms. The findings of this project have been reported in an article in the Journal of Microelectronics Reliability. Source code request and any questions should be addressed to Pouya Tavous at pouya.tavousi@uconn.edu



Investigation of Heat and Mass Transfer in Freeze Drying of Cartridges/Dual Chamber Syringes

Freeze drying is the process of removing water from biodegradable pharmaceutical products by means of sublimation. Conventionally, a vial containing the solution, is taken to subzero temperatures (~40 C), and then vacuum and heat are applied to promote the sublimation. The nal product is a cake that must be reconstituted, by adding diluent from a separate container, to get prepared for administration. Alternatively, one can use a dual chamber syringe which has a chamber for the cake product and one for the diluent. Through a similar procedure, the solution will be converted to a cake product in the associated chamber. However, differently from the case of vials, reconstitution occurs only using the diluent inside the other chamber and no additional container will be needed for this purpose. This has two main advantages. First, fewer steps are required for preparation and injection when compared to traditional vial/syringe combinations, with no need for multiple needles or transfers devices. Second, the all-in-one design allows precise dosing and helps to minimize reconstitution errors. Despite theses advantages, DCS containers are not yet widely used, partly because no robust models yet exist that can accurately describe the freeze drying phenomena in these containers. One may attempt to leverage the existing heat and mass transfer models developed for vials. However, due to the different geometry of cartridges and vials, it is critical that signi cant modi cations are made to these models before they can be applied. In this project, we focus on the investigation of heat and mass transfer of a simple existing holder system for freeze drying in cartridges and dual chamber syringes, aimed at investigating whether vial model can be adopted for dual chamber syringes. Following that, inspired by the observations made from the thermal attributes of the existing holder system, we will report the design of a new holder system. The new holder system, by design, lends itself better to being modeled which facilitates prediction of drying rate and product properties through the new version of the LyoCalculator software. It also features novel and sophisticated wight monitoring pieces of apparatus to facilitate data acquisition practices. Design and source code request and any questions should be addressed to Pouya Tavous at pouya.tavousi@uconn.edu