Research Activities

The research program in the Multi-scale Systems Engineering Laboratory is directed at obtaining a fundamental understanding of key issues in the areas of (i) Product and Process Systems Engineering, and (ii) Bioinformatics and Computational Biology. As such, our work lies at the interface of chemical engineering, applied mathematics/operations research, computer science, computational chemistry and molecular biology. The unified thrust of our research is to address fundamental problems and application areas through detailed mathematical modeling at the microscopic, mesoscopic and/or macroscopic level, rigorous optimization theory and algorithms, and large-scale computations on high performance clusters.

Product and Process Design, Synthesis and Discovery

In this area, we aim at developing systematically new products and processes which meet the specified performance criteria of (i) minimum cost or maximum profit, (ii) energy efficiency, and (iii) good operability with respect to flexibility, controllability, reliability, safety, and environmental regulations. Our approach is based on a mixed-integer nonlinear optimization framework where discrete and continuous decisions are modeled explicitly. Current research work focuses on (a) simulation, design, synthesis,and optimization of novel hybrid energy processes for the conversion of biomass, coal, and natural gas to chemicals and transportation fuels; and (b) discovery of zeolites for shape selective separation and catalysis.

Selected References

Review Articles

  1. Floudas, C. A.; Elia, J. A.; Baliban, R. C. Hybrid and single feedstock energy processes for liquid transportation fuels: A critical review. Computers & Chemical Engineering 2012, 41 (6), 24-51.

Process Synthesis

  1. Floudas, C. A.; Ciric, A. R.; Grossmann, I. E. Automatic Synthesis of Optimum Heat Exchanger Network Configurations. AIChE Journal 1986, 32, 276.
  2. Floudas, C. A.; Ciric, A. R. Strategies for Overcoming Uncertainties In Heat Exchanger Network Synthesis. Computers and Chemical Engineering 1989, 13 (10), 1133-1152.
  3. Ciric, A. R.; Floudas, C. A. Application of the Simultaneous Match-Network Optimization Approach to the Pseudo-Pinch Problem. Computers and Chemical Engineering 1990, 14 (3), 241-250.
  4. Kokossis, A. C.; Floudas, C. A. Optimization of Complex Reactor Networks: I-Isothermal Operation. Chemical Engineering Science 1990, 43 (3), 595-614.
  5. Kokossis, A. C.; Floudas, C. A. Synthesis of Isothermal Reactor-Separator-Recycle Systems. Chemical Engineering Science 1991, 46 (5/6), 1361-1383.
  6. Ciric, A. R.; Floudas, C. A. Heat Exchanger Network Synthesis Without Decomposition. Computers and Chemical Engineering 1991, 15 (6), 385-396.
  7. Paules, G. E. IV; Floudas, C. A. Stochastic Programming In Process Synthesis : A two-Stage Model with MINLP Recourse for Multiperiod Heat-Integrated Distillation Sequences. Computers and Chemical Engineering 1992, 16 (3), 189-210.
  8. Aggarwal, A.; Floudas, C. A. Synthesis of Heat Integrated Nonsharp Distillation Sequences. Computers and Chemical Engineering 1992, 16 (2), 89-108.
  9. Schweiger, C. A.; Floudas, C. A. Optimization Framework for the Synthesis of Chemical Reactor Networks. Industrial & Engineering Chemistry Research 1999, 38, 744-766.

Zeolites for Shape Selective Separation and Catalysis

  1. Gounaris, C. E.; Floudas, C. A.; Wei, J. Rational Design of Shape Selective Separation and Catalysis: I. Concepts and Analysis. Chemical Engineering Science 2006, 61 (24), 7933-7948.
  2. Gounaris, C. E.; Wei, J.; Floudas, C. A. Rational Design of Shape Selective Separation and Catalysis: II. Mathematical Model and Computational Studies. Chemical Engineering Science 2006, 61 (24), 7949-7962.
  3. Ranjan, R.; Thust, S.; Gounaris, C. E.; Woo, M.; Floudas, C. A.; Keitz, M. V.; Valentas, K. J.; Wei, J.; Tsapatsis, M. Adsorption of fermentation inhibitors from lignocellulosic biomass hydrolyzates for improved ethanol yield and value-added product recovery. Microporous Mesoporous Materials 2009, 122 (1-3), 143-148.
  4. Wei, J.; Floudas, C. A.; Gounaris, C. E. Search engines for shape selectivity. Catalysis Letters 2009, 133 (1-2), 234-241.
  5. Gounaris, C. E.; Wei, J.; Floudas, C. A. Rational design of shape selective separations and catalysis: Lattice relaxation, locally optimal conformations and effective aperture size. AIChE J. 2010, 56 (3), 611-632.
  6. First, E. L.; Gounaris, C. E.; Wei, J.; Floudas, C. A. Computational characterization of zeolite porous networks: an automated approach. Physical Chemistry Chemical Physics 2011, 13, 17339-17358.
  7. First, E. L.; Gounaris, C. E.; Floudas, C. A. Stereochemically Consistent Reaction Mapping and Identification of Multiple Reaction Mechanisms through Integer Linear Optimization. Journal of Chemical Information and Modeling 2012, 52 (1), 84-92.
  8. First, E. L.; Floudas, C. A. MOFomics: Computational pore characterization of metal–organic frameworks. Microporous and Mesoporous Materials 2013, 165, 32-39.
  9. First, E. L.; Gounaris, C. E.; Floudas, C. A. Predictive Framework for Shape-Selective Separations in Three-Dimensional Zeolites and Metal–Organic Frameworks. Langmuir 2013, 29 (18), 5599-5608.
  10. Hasan, M. M. F.; First, E. L.; Floudas, C. A. Cost-effective CO2 capture based on in silico screening of zeolites and process optimization. Phys. Chem. Chem. Phys. 2013, 15 (40), 17601-17618.
  11. Gounaris, C. E; First, E. L; Floudas, C. A Estimation of diffusion anisotropy in microporous crystalline materials and optimization of crystal orientation in membranes. The Journal of Chemical Physics 2013, 139 (12), 124703.
  12. First, E. L.; Hasan, M. M. F.; Floudas, C. A. Discovery of novel zeolites for natural gas purification through combined material screening and process optimization. AIChE Journal 2014, 60 (5), 1767-1785.

Hybrid Energy Processes

  1. Baliban, R. C.; Elia, J. A.; Floudas, C. A. Toward novel hybrid biomass, coal, and natural gas processes for satisfying current transportation fuel demands, 1: Process alternatives, gasification modeling, process simulation, and economic analysis. Industrial & Engineering Chemistry Research 2010, 49, 7343-7370.
  2. Elia, J. A.; Baliban, R, C.; Floudas, C. A. Toward novel hybrid biomass, coal, and natural gas processes for satisfying current transportation fuel demands, 2: Simultaneous heat and power integration. Industrial & Engineering Chemistry Research 2010, 49, 7371-7388.
  3. Elia, J. A.; Baliban, R. C.; Xiao, X.; Floudas, C. A. Optimal energy supply network determination and life cycle analysis for hybrid coal, biomass and natural gas to liquid (CBGTL) plants using carbon-based hydrogen production. Computers and Chemical Engineering 2011, 35, 1399-1430.
  4. Baliban, R. C.; Elia, J. A.; Floudas, C. A. Optimization framework for the simultaneous process synthesis, heat and power integration of a thermochemical hybrid biomass, coal, and natural gas facility. Computers and Chemical Engineering 2011, 35, 1647-1690.
  5. Baliban, R. C.; Elia, J. A.; Floudas, C. A. Simultaneous process synthesis, heat, power, and water integration of thermochemical hybrid biomass, coal, and natural gas facilities. Computers & Chemical Engineering 2012, 37, 297-327.
  6. Baliban, R. C; Elia, J. A; Misener, R.; Floudas, C. A Global optimization of a MINLP process synthesis model for thermochemical based conversion of hybrid coal, biomass, and natural gas to liquid fuels. Computers & Chemical Engineering 2012, 42, 64-86.
  7. Baliban, R. C.; Elia, J. A.; Weekman, V.; Floudas, C. A. Process synthesis of hybrid coal, biomass, and natural gas to liquids via Fischer–Tropsch synthesis, ZSM-5 catalytic conversion, methanol synthesis, methanol-to-gasoline, and methanol-to-olefins/distillate technologies. Computers & Chemical Engineering 2012, 47 (12), 29-56.
  8. Hasan, M. M. F.; Baliban, R. C.; Elia, J. A.; Floudas, C. A. Modeling, Simulation, and Optimization of Postcombustion CO2 Capture for Variable Feed Concentration and Flow Rate. 1. Chemical Absorption and Membrane Processes. Industrial & Engineering Chemistry Research 2012, 51 (48), 15642-15664.
  9. Hasan, M. M. F.; Baliban, R. C.; Elia, J. A.; Floudas, C. A. Modeling, Simulation, and Optimization of Postcombustion CO2 Capture for Variable Feed Concentration and Flow Rate. 2. Pressure Swing Adsorption and Vacuum Swing Adsorption Processes. Industrial & Engineering Chemistry Research 2012, 51 (48), 15665-15682.
  10. Baliban, R. C.; Elia, J. A.; Floudas, C. A. Biomass to liquid transportation fuels (BTL) systems: process synthesis and global optimization framework. Energy Environ. Sci. 2013, 6 (1), 267-287.
  11. Baliban, R. C.; Elia, J. A.; Floudas, C. A. Biomass and Natural Gas to Liquid Transportation Fuels: Process Synthesis, Global Optimization, and Topology Analysis. Industrial & Engineering Chemistry Research 2013, 52 (9), 3381-3406.
  12. Baliban, R. C.; Elia, J. A.; Floudas, C. A.; Gurau, B.; Weingarten, M. B.; Klotz, S. D. Hardwood Biomass to Gasoline, Diesel, and Jet Fuel: 1. Process Synthesis and Global Optimization of a Thermochemical Refinery. Energy & Fuels 2013, 27 (8), 4302-4324.
  13. Baliban, R. C.; Elia, J. A.; Floudas, C. A.; Xiao, X.; Zhang, Z.; Li, J.; Cao, H.; Ma, J.; Qiao, Y.; Hu, X. Thermochemical Conversion of Duckweed Biomass to Gasoline, Diesel, and Jet Fuel: Process Synthesis and Global Optimization. Industrial & Engineering Chemistry Research 2013, 52 (33), 11436-11450.
  14. Niziolek, A. M; Onel, O.; Elia, J. A.; Baliban, R. C.; Xiao, X.; Floudas, C. A Coal and Biomass to Liquid Transportation Fuels: Process Synthesis and Global Optimization Strategies. Industrial & Engineering Chemistry Research 2014, 53 (44), 17002-17025.
  15. Onel, O.; Niziolek, A. M.; Hasan, M. M. F.; Floudas, C. A. Municipal solid waste to liquid transportation fuels - Part I: Mathematical modeling of a municipal solid waste gasifier. Computers & Chemical Engineering 2014, 71 (0), 636-647.
  16. Onel, O.; Niziolek, A. M.; Elia, J. A.; Baliban, R. C.; Floudas, C. A. Biomass and Natural Gas to Liquid Transportation Fuels and Olefins (BGTL+C2_C4): Process Synthesis and Global Optimization. Industrial & Engineering Chemistry Research 2015, 54 (1), 359-385.
  17. Niziolek, A. M; Onel, O.; Hasan, M. M. F.; Floudas, C. A Municipal solid waste to liquid transportation fuels - Part II: Process synthesis and global optimization strategies. Computers & Chemical Engineering 2015, 74 (0), 184-203.
  18. Niziolek, A. M.; Onel, O.; Elia, J. A.; Baliban, R. C.; Floudas, C. A. Coproduction of liquid transportation fuels and C6_C8 aromatics from biomass and natural gas. AIChE Journal 2015, 61 (3), 831-856.

Product and Process Operations: Planning and Scheduling under Uncertainty

In this area, our primary objective in Scheduling and Planning is to investigate, refine, and apply effective combinatorial optimization models based on our novel continuous-time framework for short term scheduling of batch, semi-continuous and continuous processes. The thrust of our approach is to develop a unified framework that addresses intermediate due dates and demands, establishes the trade-offs in the design, synthesis and scheduling of multipurpose batch plants, and is directly applicable to large-scale manufacturing processes. Current research focuses on (a) new methods for short and medium-term scheduling of manufacturing processes, (b) short-term and medium-term scheduling under uncertainty in the processing times, product demands, and cost coefficients, (c) new methods that reduce the integrality gap for combinatorial models in scheduling and planning, (d) planning of batch and continuous processes, (e) integration of planning and scheduling, and (f) planning and scheduling under uncertainty.

Selected References

Review Articles

  1. Floudas, C. A.; Lin, X. Continuous-Time versus Discrete-Time Approaches for Scheduling of Chemical Processes: A Review. Computers and Chemical Engineering 2004, 28 (11), 2109-2129.
  2. Floudas, C. A.; Lin, X. Mixed Integer Linear Programming in Process Scheduling: Modeling, Algorithms, and Applications. Annals of Operations Research 2005, 139 (1), 131-162.
  3. Verderame, P. M.; Elia, J. A.; Li, J.; Floudas, C. A. Planning and scheduling under uncertainty: A review across multiple sectors. Industrial & Engineering Chemistry Research 2010, 49, 3993-4017.
  4. Elia, J. A.; Floudas, C. A. Energy Supply Chain Optimization of Hybrid Feedstock Processes: A Review. Annual Review of Chemical and Biomolecular Engineering 2014, 5 (1), 147-179.

Scheduling

  1. Ierapetritou, M. G.; Floudas, C. A. Short-term Scheduling: New Mathematical Models vs. Algorithmic Improvements. Computers and Chemical Engineering 1998, 22, S419-S426.
  2. Ierapetritou, M. G.; Floudas, C. A. Effective Continuous-Time Formulation for Short-Term Scheduling. 1. Multipurpose Batch Processes. Industrial & Engineering Chemistry Research 1998, 37, 4341-4359.
  3. Ierapetritou, M. G.; Floudas, C. A. Effective Continuous-Time Formulation for Short-Term Scheduling. 2. Continuous and Semicontinuous Processes. Industrial & Engineering Chemistry Research 1998, 37, 4360-4374.
  4. Ierapetritou, M. G.; Hene, T. S.; Floudas, C. A. Effective Continuous-Time Formulation for Short Term Scheduling. 3. Multiple Intermediate Due Dates. Industrial & Engineering Chemistry Research 1999, 38 (9), 3446-3461.
  5. Ierapetritou, M. G.; Hene, T. S.; Floudas, C. A. Short-Term Scheduling of Batch Plants with Multiple Intermediate Due Dates. Computers and Chemical Engineering 1999, 23, S515-518.
  6. Lin, X.; Floudas, C. A. Design, Synthesis and Scheduling of Multipurpose Batch Plants via an Effective Continuous-Time Formulation. Computers and Chemical Engineering 2001, 25, 665-674.
  7. Lin, X.; Floudas, C. A.; Modi, S.; Juhasz, N. M. Continuous-Time Optimization Approach for Medium-Range Production Scheduling of a Multiproduct Batch Plant. Industrial & Engineering Chemistry Research 2002, 41, 3884-3906.
  8. Lin, X.; Chajakis, E. D.; Floudas, C. A. Scheduling of Tanker Lightering via a Novel Continuous-Time Optimization Framework. Industrial & Engineering Chemistry Research 2003, 42, 4441-4451.
  9. Janak, S. L.; Lin, X.; Floudas, C. A. Enhanced Continuous-Time Unit-Specific Event Based Formulation for Short-Term Scheduling of Multipurpose Batch Processes: Resource Constraints and Mixed Storage Policies. Industrial & Engineering Chemistry Research 2004, 43, 2516-2533.
  10. Janak, S. L.; Floudas, C. A.; Kallrath, J.; Vormbrock, N. Production Scheduling of a Large-Scale Industrial Batch Plant. I. Short-Term and Medium-Term Scheduling. Industrial & Engineering Chemistry Research 2006, 45 (25), 8234-8252.
  11. Janak, S. L.; Floudas, C. A.; Kallrath, J.; Vormbrock, N. Production Scheduling of a Large-Scale Industrial Batch Plant. II. Reactive Scheduling. Industrial & Engineering Chemistry Research 2006, 45 (25), 8253-8269.
  12. Shaik, M. A.; Janak, S. L.; Floudas, C. A. Continuous-Time Models for Short-Term Scheduling of Multipurpose Batch Plants: A Comparative Study. Industrial & Engineering Chemistry Research 2006, 45 (18), 6190-6209.
  13. Shaik, M. A.; Floudas, C. A. An Improved Unit-Specific-Event based Continuous-Time Model for Short-Term Scheduling of Continuous Processes: Rigorous Treatment of Storage Requirements. Industrial & Engineering Chemistry Research 2007, 46, 1764-1779.
  14. Janak, S. L.; Floudas, C. A. Improving Unit-Specific Event Based Continuous-Time Approaches for Batch Processes: Integrality Gap and Task Splitting. Computers and Chemical Engineering 2008, 32 (4-5), 913-955.
  15. Shaik, M. A.; Floudas, C. A. Unit-Specific-Event based Continuous-Time Approach for Short-Term Scheduling of Batch Plants using RTN Framework. Computers and Chemical Engineering 2008, 32, 260-274.
  16. Shaik, M. A.; Floudas, C. A.; Kallrath, J.; Pitz, H. J. Production Scheduling of a Large-Scale Industrial Continuous Plant: Short-Term and Medium-Term Scheduling. Computers and Chemical Engineering 2009, 33 (3), 670-686.
  17. Shaik, M. A.; Floudas, C. A. Novel Unified Modeling Approach for Short-term Scheduling. Industrial & Engineering Chemistry Research 2009, 48, 2947-2964.
  18. Li, J.; Floudas, C. A. Optimal event point determination for short term scheduling of multipurpose batch plants via unit-specific event-based continuous-time approaches. Industrial & Engineering Chemistry Research 2010, 49, 7446-7469.
  19. Li, J.; Misener, R.; Floudas, C. A. Continuous-time modeling and global optimization approach for scheduling of crude oil operations. AIChE Journal 2012, 58 (1), 205-226.
  20. Tang, Q.; Li, J.; Floudas, C. A.; Deng, M.; Yan, Y.; Xi, Z.; Chen, P.; Kong, J. Optimization framework for process scheduling of operation-dependent automobile assembly lines. Optimization Letters 2012, 6 (4), 797-824.
  21. Li, J.; Xiao, X.; Tang, Q.; Floudas, C. A. Production Scheduling of a Large-Scale Steelmaking Continuous Casting Process via Unit-Specific Event-Based Continuous-Time Models: Short-Term and Medium-Term Scheduling. Industrial & Engineering Chemistry Research 2012, 51 (21), 7300-7319.

Planning

  1. Lin, X.; Floudas, C. A. A Novel Continuous-Time Modeling and Optimization Framework for Well Platform Planning Problems. Optimization and Engineering 2003, 4, 65-95.
  2. Floudas, C. A.; Lin, X. Continuous-Time versus Discrete-Time Approaches for Scheduling of Chemical Processes: A Review. Computers and Chemical Engineering 2004, 28 (11), 2109-2129.
  3. Floudas, C. A.; Lin, X. Mixed Integer Linear Programming in Process Scheduling: Modeling, Algorithms, and Applications. Annals of Operations Research 2005, 139 (1), 131-162.
  4. Verderame, P. M.; Floudas, C. A. Integrated Operational Planning and Medium-Term Scheduling of a Large-Scale Industrial Batch Plants. Industrial & Engineering Chemistry Research 2008, 47, 4845-4860.
  5. Verderame, P. M.; Floudas, C. A. Operational Planning Framework for Multisite Production and Distribution Networks. Computers & Chemical Engineering 2009, 33 (5), 1036-1050.
  6. Verderame, P. M.; Elia, J. A.; Li, J.; Floudas, C. A. Planning and scheduling under uncertainty: A review across multiple sectors. Industrial & Engineering Chemistry Research 2010, 49, 3993-4017.
  7. Elia, J. A.; Floudas, C. A. Energy Supply Chain Optimization of Hybrid Feedstock Processes: A Review. Annual Review of Chemical and Biomolecular Engineering 2014, 5 (1), 147-179.
  8. Elia, J. A; Li, Ji; Floudas, C. A Strategic planning optimization for natural gas to liquid transportation fuel (GTL) systems. Computers & Chemical Engineering 2015, 72 (0), 109-125.

Planning and Scheduling under Uncertainty

  1. Ierapetritou, M.; Pistikopoulos, E. N.; Floudas, C. A. Operational Planning Under Uncertainty. Computers and Chemical Engineering 1993, 18, S553-S557.
  2. Harding, S. T.; Floudas, C. A. Global Optimization in Multiproduct and Multipurpose Batch Design Under Uncertainty. Industrial & Engineering Chemistry Research 1997, 36, 1644-1664.
  3. Lin, X.; Janak, S. L.; Floudas, C. A. A New Robust Optimization Approach for Scheduling under Uncertainty: I. Bounded Uncertainty. Computers and Chemical Engineering 2004, 28, 1069-1085.
  4. Janak, S. L.; Lin, X.; Floudas, C. A. A New Robust Optimization Approach for Scheduling under Uncertainty: II. Uncertainty with Known Probability Distribution. Computers and Chemical Engineering 2007, 31, 171-195.
  5. Verderame, P. M.; Floudas, C. A. Operational planning of large scale industrial batch plants under demand due date and amount uncertainty: I. Robust optimization framework. Industrial & Engineering Chemistry Research 2009, 48 (15), 7214-7231.
  6. Verderame, P. M.; Floudas, C. A. Operational planning of large scale industrial batch plants under demand due date and amount uncertainty: II. Conditional value at risk framework. Industrial & Engineering Chemistry Research 2010, 49 (1), 260-275.
  7. Verderame, P. M.; Floudas, C. A. Integration of operational planning and medium-term scheduling for large-scale industrial batch plants under demand and processing time uncertainty. Industrial & Engineering Chemistry Research 2010, 49, 4948-4965.
  8. Verderame, P.; Floudas, C. A. Multisite planning under demand and transportation time uncertainty: Robust optimization and conditional value at risk frameworks. Industrial & Engineering Chemistry Research 2010, 50 (9), 4959-4982.
  9. Li, Z.; Ding, R.; Floudas, C. A. A Comparative Theoretical and Computational Study on Robust Counterpart Optimization: I. Robust Linear Optimization and Robust Mixed Integer Linear Optimization. Industrial & Engineering Chemistry Research 2011, 50, 10567-10603.
  10. Li, J.; Verderame, P. M.; Floudas, C. A. Operational Planning of Large-Scale Continuous Processes: Deterministic Planning Model and Robust Optimization for Demand Amount and Due Date Uncertainty. Industrial & Engineering Chemistry Research 2012, 51 (11), 4347-4362.
  11. Li, Z.; Tang, Q.; Floudas, C. A. A Comparative Theoretical and Computational Study on Robust Counterpart Optimization: II. Probabilistic Guarantees on Constraint Satisfaction. Industrial & Engineering Chemistry Research 2012, 51 (19), 6769-6788.
  12. Li, J.; Misener, R.; Floudas, C. A. Scheduling of crude oil operations under demand uncertainty: A robust optimization framework coupled with global optimization. AIChE Journal 2012, 58 (8), 2373-2396.
  13. Gounaris, C. E.; Wiesemann, W.; Floudas, C. A. The Robust Capacitated Vehicle Routing Problem Under Demand Uncertainty. Operations Research 2013, 61, 677-693.
  14. Ye, Y.; Li, J.; Li, Z.; Tang, Q.; Xiao, X.; Floudas, C. A. Robust optimization and stochastic programming approaches for medium-term production scheduling of a large-scale steelmaking continuous casting process under demand uncertainty. Computers & Chemical Engineering 2014, 66, 165-185.
  15. Li, Z.; Floudas, C. A. A Comparative Theoretical and Computational Study on Robust Counterpart Optimization: III. Improving the Quality of Robust Solutions. Industrial & Engineering Chemistry Research 2014, 53 (33), 13112-13124.
  16. Li, Z.; Floudas, C. A. Optimal scenario reduction framework based on distance of uncertainty distribution and output performance: I. Single reduction via mixed integer linear optimization. Computers & Chemical Engineering 2014, 70 (0), 50-66.

Hybrid Energy Supply Chain

  1. Elia, J. A.; Baliban, R. C.; Floudas, C. A. Nationwide energy supply chain analysis for hybrid feedstock processes with significant CO2 emissions reduction. AIChE Journal 2012, 58 (7), 2142-2154.
  2. Elia, J. A.; Baliban, R. C.; Floudas, C. A.; Gurau, B.; Weingarten, M. B.; Klotz, S. D. Hardwood Biomass to Gasoline, Diesel, and Jet Fuel: 2. Supply Chain Optimization Framework for a Network of Thermochemical Refineries. Energy & Fuels 2013, 27 (8), 4325-4352.
  3. Elia, J. A.; Baliban, R. C.; Floudas, C. A. Nationwide, Regional, and Statewide Energy Supply Chain Optimization for Natural Gas to Liquid Transportation Fuel (GTL) Systems. Industrial & Engineering Chemistry Research 2014, 53 (13), 5366-5397.
  4. Hasan, M. M. F.; Boukouvala, F.; First, E. L.; Floudas, C. A. Nationwide, Regional, and Statewide CO2 Capture, Utilization, and Sequestration Supply Chain Network Optimization. Industrial & Engineering Chemistry Research 2014, 53 (18), 7489-7506.

Generalized Assignment Problems

  1. Janak, S. L.; Taylor, M.; Floudas, C. A.; Burka, M.; Mountziaris, T. J. A Novel and Effective Integer Optimization Approach for the NSF Panel Assignment Problem: A Mutli-Resource and Preference-Constrained Generalized Assignment Problem. Industrial & Engineering Chemistry Research 2006, 45 (1), 258-265.

Discrete-Continuous Nonlinear Optimization

Modeling product and process synthesis problems, as well as fundamental problems in metabolic engineering and secondary structure prediction in protein folding results in mixed-integer linear and nonlinear optimization formulations. We have studied modeling and algorithmic issues in approaches based on the principles of Generalized Benders Decomposition, developed the framework MINOPT, and applied the resulting methodologies to the synthesis of separation systems, heat exchanger networks, reactor based systems, mass exchange networks, and analysis and synthesis of metabolic networks (see the graduate textbook Nonlinear Mixed-Integer Optimization by Floudas, 1995). Current emphasis is on investigating new methods for MINLP problems that are based on continuous representations.

Selected References

Books, Monographs

  1. Floudas, C. A. Nonlinear and Mixed-Integer Optimization: Fundamentals and Applications; Oxford University Press, 1995.

Chapters in Books

  1. Adjiman, C. S.; Schweiger, C. A.; Floudas, C. A. Mixed-Integer Nonlinear Optimization in Process Synthesis. In Handbook of Combinatorial Optimization; Du, D.-Z., Pardalos, P. M., Eds.; Springer, 1998.
  2. Adjiman, C. S.; Schweiger, C. A.; Floudas, C. A. Nonlinear and Mixed-Integer Optimization in Chemical Process Network Systems. In Network Design: Connectivity and Facilities Location; Pardalos, P. M., Du, D.-Z., Eds.; DIMACS Series in Discrete Mathematics and Theoretical Computer Science, Vol. 40; American Mathematical Society, 1998; pp 429-452.
  3. Floudas, C. A. Mixed-Integer Nonlinear Optimization. In Handbook of Applied Optimization; Resende, M., Pardalos, P. M., Eds.; Oxford University Press, 2001; p 451.
  4. Schweiger, C. A.; Floudas, C. A. The MINOPT Modeling Language. In Modeling Languages; Kallrath, J., Ed.; Kluwer Academic Publishers, 2003.

Journals

  1. Hatzimanikatis, V.; Floudas, C. A.; Bailey, J. E. Analysis and Design of Metabolic Reaction Networks via Mixed-Integer Linear Optimization. AIChE J. 1996, 42, 1277-1292.
  2. Hatzimanikatis, V.; Floudas, C. A.; Bailey, J. E. Optimization of Regulatory Architectures in Metabolic Reaction Networks. Biotechnology and Bioengineering 1996, 52, 485-500.
  3. Ierapetritou, M. G.; Floudas, C. A.; Vasantharajan, S.; Cullick, A. S. Optimal Location of Vertical Wells: A Decomposition Approach. AIChE Journal 1999, 45, 844-859.
  4. Schweiger, C. A.; Floudas, C. A. Synthesis of Optimal Chemical Reactor Networks. Computers and Chemical Engineering 1999, S47-50.
  5. Adjiman, C. S.; Androulakis, I. P.; Floudas, C. A. Global Optimization of Mixed Integer Nonlinear Problems. AIChE Journal 2000, 46, 1769-1797.

Deterministic Global Optimization

A plethora of the most important problems in science and engineering, ranging from the atomistic domain to large-scale, process-level representations, are described mathematically by functions characterized by the existence of multiple minima and maxima, as well as first-, second-, and higher-order saddle points. The area of Global Optimization is concerned with theoretical, algorithmic and computational advances to address the computation and characterization of global minima and maxima, determine valid lower and upper bounds on the global minima and maxima, and address the enclosure of all solutions of nonlinear constrained systems of equations. The textbook Deterministic Global Optimization, (Floudas, 2000) povides a unified and insightful treatment of global optimization. Current research work focuses on theoretical and algorithmic studies of (a) novel deterministic global optimization methods for mixed-integer nonlinear optimization problems, (b) new improved classes of convex underestimators for general nonlinear constrained problems, (c) hybrid deterministic global optimization methods, (d) new approaches for convex underestimation using piecewise-linear relaxations, (e) large scale standard, generalized, and extended pooling applications, and (f) packing and nesting of arbitrary shapes.

Selected References

Books, Monographs

  1. Floudas, C. A.; Pardalos, P. M. A Collection of Test Problems for Constrained Global Optimization Algorithms; Lecture Notes in Computer Science 455; Springer-Verlag, 1990.
  2. Floudas, C. A.; Pardalos, P. M.; Adjiman, C. S.; Esposito, W. R.; Gümüş, Z.; Harding, S. T.; Klepeis, J. L.; Meyer, C. A.; Schweiger, C. A. Handbook of Test Problems for Local and Global Optimization; Kluwer Academic Publishers, 1999.
  3. Floudas, C. A. Deterministic Global Optimization: Theory, Methods and Applications; Kluwer Academic Publishers, 2000.
  4. Floudas, C. A., Pardalos, P. M., Eds. Encyclopedia of Optimization, 2nd ed.; Springer, 2008.
  5. Rassias, T. M., Floudas, C. A., Butenko, S., Eds. Optimization in Science and Engineering; Springer, 2014.

Review Articles

  1. Floudas, C. A. Recent Advances in Global Optimization for Process Synthesis, Design and Control: Enclosure of All Solutions. Computers and Chemical Engineering 1999, S963-973.
  2. Floudas, C. A.; Akrotirianakis, I. G.; Caratzoulas, S.; Meyer, C. A.; Kallrath, J. Global Optimization in the 21st Century: Advances and Challenges. Computers and Chemical Engineering 2005, 29 (6), 1185-1202.
  3. Floudas, C. A. Research Challenges, Opportunities and Synergism in Systems Engineering and Computational Biology. AIChE Journal 2005, 51 (7), 1872-1884.
  4. Floudas, C. A.; Gounaris, C. E. A Review of Recent Advances In Global Optimization. J. of Global Optimization 2009, 45, 3-38.
  5. Misener, R.; Floudas, C. A. Global optimization of mixed-integer models with quadratic and signomial functions: a review. Applied and Computational Mathematics 2012, 11, 317-336.

Theory and Methods

Biconvex, Bilinear, Polynomial: GOP, GOS
  1. Floudas, C. A.; Aggarwal, A.; Ciric, A. R. Global Optimum Search for Nonconvex NLP and MINLP Problems. Computers and Chemical Engineering 1989, 13 (10), 1117-1132.
  2. Floudas, C. A.; Visweswaran, V. A Global Optimization Algorithm (GOP) for Certain Classes of Nonconvex NLPs : I. Theory. Computers and Chemical Engineering 1990, 14 (12), 1397-1417.
  3. Visweswaran, V.; Floudas, C. A. A Global Optimization Algorithm (GOP) for Certain Classes of Nonconvex NLPs : II. Applications of Theory and Test Problems. Computers and Chemical Engineering 1990, 14 (12), 1417-1434.
  4. Visweswaran, V.; Floudas, C. A. Unconstrained and Constrained Global Optimization of Polynomial Functions in One Variable. Journal of Global Optimization 1992, 2 (1), 73-99.
  5. Floudas, C. A.; Visweswaran, V. A Primal-Relaxed Dual Global Optimization Approach. Journal of Optimization 1993, Theory (and its Applications), 187-225.
  6. Visweswaran, V.; Floudas, C. A. New Properties and Computational Improvement of the GOP Algorithm For Problems With Quadratic Objective Function and Constraints. Journal of Global Optimization 1993, 3 (4), 439-462.
  7. Liu, W. B.; Floudas, C. A. A Remark on the GOP Algorithm for Global Optimization. Journal of Global Optimization 1993, 3 (4), 519-521.
  8. Liu, W. B.; Floudas, C. A. Convergence of the GOP Algorithm for a Large Class of Smooth Optimization Problems. Journal of Global Optimization 1995, 6, 207.
  9. Liu, W. B.; Floudas, C. A. A Generalized Primal-Relaxed Dual Approach for Global Optimization. Journal of Optimization Theory and its Applications 1996, 90, 417-434.
Twice Continuously Differentiable NLPs: αBB, P_αBB, G_αBB, Augmented Lagrangian
  1. Androulakis, I. P.; Maranas, C. D.; Floudas, C. A. αBB: A Global Optimization Method for General Constrained Nonconvex Problems. Journal of Global Optimization 1995, 7 (4), 337-363.
  2. Maranas, C. D.; Floudas, C. A. Finding All Solutions of Nonlinearly Constrained Systems of Equations. Journal of Global Optimization 1995, 7 (2), 143-182.
  3. Adjiman, C. S.; Androulakis, I. P.; Maranas, C. D.; Floudas, C. A. A Global Optimization Method αBB for Process Design. Computers and Chemical Engineering 1996, 20, S419-424.
  4. Adjiman, C. S.; Floudas, C. A. Rigorous Convex Underestimators for General Twice-Differentiable Problems. Journal of Global Optimization 1996, 9, 23-40.
  5. Adjiman, C. S.; Dallwig, S.; Floudas, C. A.; Neumaier, A. A Global Optimization Method, αBB, for General Twice-Differentiable Constrained NLPs - I. Theoretical Advances. Computers and Chemical Engineering 1998, 22, 1137-1158.
  6. Adjiman, C. S.; Androulakis, I. P.; Floudas, C. A. A Global Optimization Method, αBB, for General Twice-Differentiable Constrained NLPs - II. Implementation and Computational Results. Computers and Chemical Engineering 1998, 22, 1159-1179.
  7. Hertz, D.; Adjiman, C. S.; Floudas, C. A. Two results on bounding the roots of interval polynomials. Computers and Chemical Engineering 1999, 23, 1333-1339.
  8. Akrotirianakis, I. G.; Floudas, C. A. A New Class of Improved Convex Underestimators for Twice Continuously Differentiable Constrained NLPs. Journal of Global Optimization 2004, 30 (4), 367-390.
  9. Akrotirianakis, I. G.; Floudas, C. A. Computational Experience with a New Class of Convex Underestimators: Box Constrained NLP Problems. Journal of Global Optimization 2004, 29 (3), 249-264.
  10. Meyer, C. A.; Floudas, C. A. Convex underestimation of twice continuously differentiable functions by piecewise quadratic perturbation: Spline αBB underestimators. Journal of Global Optimization 2005, 32 (2), 221-258.
  11. Floudas, C. A.; Kreinovich, V. On the Functional Form of Convex Underestimators for Twice Continuously Differentiable Functions. Optimization Letters 2007, 1 (2), 187-192.
  12. Birgin, E. G.; Floudas, C. A.; Martinez, J. M. Global Minimization using an Augmented Lagrangian Method with Variable Lower-Level Constraints. Mathematical Programming, Ser. A 2010, 125, 139-162.
  13. Skjäl, A.; Westerlund, T.; Misener, R.; Floudas, C. A. A Generalization of the Classical αBB Convex Underestimation via Diagonal and Nondiagonal Quadratic Terms. Journal of Optimization Theory and Applications 2012, 154 (2), 462-490.
Twice Continuously Differentiable MINLPs: Smin_αBB, Gmin_αBB
  1. Adjiman, C. S.; Androulakis, I. P.; Floudas, C. A. Global Optimization of Mixed Integer Nonlinear Problems. AIChE Journal 2000, 46, 1769-1797.
Generalized Geometric Programming
  1. Li, H.-L.; Tsai, J.-F.; Floudas, C. A. Convex Underestimation for Posynomial Functions of Positive Variables. Optimization Letters 2008, 2 (3), 333-340.
  2. Lu, H.-C.; Li, H.-L.; Gounaris, C. E.; Floudas, C. A. Convex Relaxation for Solving Posynomial Programs. Journal of Global Optimization 2010, 46, 147-154.
Convex Envelopes and Relaxations
  1. Meyer, C. A.; Floudas, C. A. Convex Hull of Trilinear Monomials with Mixed Sign Domains. Journal of Global Optimization 2004, 29 (2), 125-155.
  2. Caratzoulas, S.; Floudas, C. A. Trigonometric Convex Underestimator for the Base Functions in Fourier Space. Journal of Optimization Theory and Applications 2004, 124 (2), 339-362.
  3. Meyer, C. A.; Floudas, C. A. Convex Envelopes for Edge-Concave Functions. Mathematical Programming 2005, 103 (2), 207-224.
  4. Gounaris, C. E.; Floudas, C. A. Tight Convex Underestimators for C2-Continuous Problems: I. Univariate Functions. J. Global Optimization 2008, 42, 51-67.
  5. Gounaris, C. E.; Floudas, C. A. Tight Convex Underestimators for C2-Continuous Problems: II. Multivariate Functions. J. Global Optimization 2008, 42, 69-89.
  6. Gounaris, C. E.; Floudas, C. A. Convexity of Products of Univariate Functions and Convexification Transformations for Geometric Programming. J. Opt. Theory and App. 2008, 138 (1), 407-427.
  7. Gounaris, C. E.; Misener, R.; Floudas, C. A. Computational comparison of piecewise-linear relaxations for pooling problems. Industrial & Engineering Chemistry Research 2009, 48 (12), 5742-5766.
  8. Misener, R.; Floudas, C. A. Piecewise-linear approximations of multidimensional functions. Journal of Optimization Theory and its Applications 2010, 145, 120-147.
  9. Misener, R.; Floudas, C. A. Global optimization of mixed-integer quadratically-constrained quadratic programs (MIQCQP) through piecewise-linear and edge-concave relaxations. Mathematical Programming 2012, 136, 155-182.
  10. Misener, R.; Floudas, C. A. GloMIQO: Global mixed-integer quadratic optimizer. Journal of Global Optimization 2013, 57 (1), 3-50.
  11. Misener, R.; Floudas, C. A. A Framework for Globally Optimizing Mixed-Integer Signomial Programs. Journal of Optimization Theory and Applications 2014, 161 (3), 905-932.
  12. Misener, R.; Floudas, ChristodoulosA. ANTIGONE: Algorithms for coNTinuous / Integer Global Optimization of Nonlinear Equations. Journal of Global Optimization 2014, 59 (2-3), 503-526.
  13. Misener, R.; Smadbeck, J. B.; Floudas, C. A. Dynamically generated cutting planes for mixed-integer quadratically constrained quadratic programs and their incorporation into GloMIQO 2. Optimization Methods and Software 2015, 30 (1), 215-249.
Bilevel Optimization
  1. Gümüş, Z. H.; Floudas, C. A. Global Optimization of Nonlinear Bilevel Programming Problems. Journal of Global Optimization 2001, 20, 1-31.
  2. Gümüş, Z. H.; Floudas, C. A. Global Optimization of Mixed-Integer Bilevel Programming Problems. Journal of Computational Management and Optimization 2005, 2 (3), 181-212.
Grey-Box Optimization
  1. Meyer, C. A.; Floudas, C. A.; Neumaier, A. Global Optimization with Non-Factorable Constraints. Industrial & Engineering Chemistry Research 2002, 41, 6413-6424.
Semi-Infinite Optimization
  1. Floudas, C. A.; Stein, O. The Adaptive Convexification Algorithm: A Feasible Point Method for Semi-Infinite Programming. SIAM Journal of Optimization 2007, 18 (4), 1187-1208.

Application Domains

Thermodynamics
  1. Maranas, C. D.; Floudas, C. A. A Global Optimization Approach for Lennard-Jones Microclusters. Journal of Chemical Physics 1992, 97 (10), 7667-7678.
  2. Maranas, C. D.; Floudas, C. A. Global Optimization for Molecular Conformation Problems. Annals of Operations Research 1993, 42, 85-117.
  3. Maranas, C. D.; Floudas, C. A. Global Minimum Potential Energy Conformations of Small Molecules. Journal of Global Optimization 1994, 4 (2), 135-170.
  4. Maranas, C. D.; Floudas, C. A. A Deterministic Global Optimization Approach for Molecular Structure Determination. Journal of Chemical Physics 1994, 100 (2), 15 January.
  5. McDonald, C. M.; Floudas, C. A. Decomposition Based and Branch and Bound Global Optimization Approaches for the Phase Equilibrium Problem. J. of Global Optimization 1994, 5, 205-251.
  6. McDonald, C. M.; Floudas, C. A. Global Optimization for the Phase and Chemical Equilibrium Problem: Application to the NRTL Equation. Computers and Chemical Engineering 1995, 19 (11), 1111-1141.
  7. McDonald, C. M.; Floudas, C. A. Global Optimization for the Phase Stability Problem. AIChE J. 1995, 41 (7), 1798-1814.
  8. McDonald, C. M.; Floudas, C. A. Global Optimization and Analysis for the Gibbs Free Energy Function for the UNIFAC, Wilson, and ASOG Equations. Industrial & Engineering Chemistry Research 1995, 34, 1674-1687.
  9. Maranas, C. D.; McDonald, C. M.; Harding, S. T.; Floudas, C. A. Locating All Azeotropes in Homogeneous Azeotropic Systems. Computers and Chemical Engineering 1996, 20, S413-418.
  10. McDonald, C. M.; Floudas, C. A. GLOPEQ: A New Computational Tool for the Phase and Chemical Equilibrium Problem. Computers and Chemical Engineering 1997, 21, 1-23.
  11. Harding, S. T.; Maranas, C. D.; McDonald, C. M.; Floudas, C. A. Locating All Homogeneous Azeotropes in Multicomponent Mixtures. Industrial & Engineering Chemistry Research 1997, 36, 160-178.
  12. Harding, S. T.; Floudas, C. A. Phase Stability with Cubic Equations of State: A Global Optimization Approach. AIChE Journal 2000, 46 (7), 1422-1440.
  13. Harding, S. T.; Floudas, C. A. Locating Heterogeneous and Reactive Azeotropes. Industrial & Engineering Chemistry Research 2000, 39 (6), 1576-1595.
Parameter Estimation and Optimal Control
  1. Esposito, W. R.; Floudas, C. A. Parameter Estimation in Nonlinear Algebraic Models via Global Optimization. Computers and Chemical Engineering 1998, 22, S213-S220.
  2. Esposito, W. R.; Floudas, C. A. Global Optimization in Parameter Estimation of Nonlinear Algebraic Models via the Error-In-Variables Approach. Industrial & Engineering Chemistry Research 1998, 37, 1841-1858.
  3. Esposito, W. R.; Floudas, C. A. Global Optimization for the Parameter Estimation of Differential-Algebraic Systems. Industrial & Engineering Chemistry Research 2000, 39 (5), 1291-1310.
  4. Esposito, W. R.; Floudas, C. A. Deterministic Global Optimization in Nonlinear Optimal Control Problems. Journal of Global Optimization 2000, 17, 97-126.
  5. Esposito, W. R.; Floudas, C. A. Deterministic Global Optimization in Isothermal Reactor Network Synthesis. Journal of Global Optimization 2002, 22, 59-95.
Pooling Problems
  1. Floudas, C. A.; Aggarwal, A. A Decomposition Approach for Global Optimum Search In The Pooling Problem. Operations Research Journal On Computing 1990, 2 (3), 225-234.
  2. Meyer, C.; Floudas, C. A. Global Optimization of a Combinatorially Complex Generalized Pooling Problem. AIChE Journal 2006, 52 (3), 1027-1037.
  3. Misener, R.; Gounaris, C. E.; Floudas, C. A. Global Optimization of Gas Lifting Operations: A Comparative Study of Piecewise Linear Formulations. Industrial & Engineering Chemistry Research 2009, 48 (13), 6098-6104.
  4. Misener, R.; Floudas, C. A. Advances for the pooling problem: Modeling, global optimization and computational studies. Appl. Comput. Math. 2009, 8, 3-22.
  5. Misener, R.; Gounaris, C. E.; Floudas, C. A. Mathematical modeling and global optimization of large scale extended pooling problems with the EPA complex emissions contraints. Computers and Chemical Engineering 2010, 34, 1432-1456.
  6. Misener, R.; Floudas, C. A. Global optimization of large-scale generalized pooling problems: Quadratically constrained MINLP models. Industrial & Engineering Chemistry Research 2010, 49, 5424-5438.
  7. Misener, R.; Thompson, J. P.; Floudas, C. A. APOGEE: Global Optimization of Standard, Generalized, and Extended Pooling Problems via Linear and Logarithmic Partitioning Schemes. Comput. Chem. Eng. 2011, 35, 876-892.
Process Synthesis, Design, and Uncertainty
  1. Harding, S. T.; Floudas, C. A. Global Optimization in Multiproduct and Multipurpose Batch Design Under Uncertainty. Industrial & Engineering Chemistry Research 1997, 36, 1644-1664.
  2. Adjiman, C. S.; Androulakis, I. P.; Floudas, C. A. Global Optimization of MINLP Problems in Process Synthesis and Design. Computers and Chemical Engineering 1997, 21, S445-S450.
  3. Floudas, C. A.; Gümüş, Z. H.; Ierapetritou, M. G. Global Optimization in Design Under Uncertainty: Feasibility Test and Flexibility Index Problems. Industrial & Engineering Chemistry Research 2001, 40, 4267-4282.
  4. Lin, X.; Floudas, C. A.; Kallrath, J. Global Solution Approach for a Nonconvex MINLP Problem in Product Portfolio Optimization. Journal of Global Optimization 2005, 32, 417-431.
  5. Baliban, R. C; Elia, J. A; Misener, R.; Floudas, C. A Global optimization of a MINLP process synthesis model for thermochemical based conversion of hybrid coal, biomass, and natural gas to liquid fuels. Computers & Chemical Engineering 2012, 42, 64-86.
Finance
  1. Maranas, C. D.; Androulakis, I. P.; Floudas, C. A.; Berger, A. J.; Mulvey, J. M. Solving Stochastic Control Problems in Finance via Global Optimization. Journal of Economics, Dynamics and Control 1997, 21, 1405-1425.

Bioinformatics and Computational Biology

The overiding theme is to improve our fundamental understanding on the Sequence to Structure to Function roadmap. The genomics revolution has generated major challenges and opportunities for systems approaches in bioinformatics and computational genomics. The essential completion of several genome projects, including that of the human genome, provided a detailed map from the gene sequences to the protein sequences. The overwhelmingly large number of generated protein sequences makes protein structure prediction from the amino acid sequence of paramount importance. The elucidation of the protein structures provides information on the type of fold, the type of packing, the residues that are exposed to solvent, the residues that are buried to the core, the highly conserved residues, the candidate residues for mutations, as well as the shape and electrostatic properties of the fold. Such elements provide the basis for the development of approaches for the location of active sites, the determination of structural and functional motifs, the study of protein-protein, protein-ligand complexes and protein-DNA interactions, the design of new inhibitors, and drug discovery through target selection, lead discovery and optimization. Better understanding of the protein-ligand and protein-DNA interactions will provide important information on addressing key topology related questions in both the cellular metabolic and signal transduction networks.

The thrust of our approach is the unique combination of detailed atomistic level modeling including state of the art solvation methods with rigorous deterministic global optimization methods, mixed-integer optimization, molecular dynamics, and free energy calculations. Current research focuses on (a) protein structure prediction from first principles, (b) de novo protein design, and (c) proteomics via quantitative mass spectrometry.

Selected References

Review Articles

  1. Floudas, C. A.; Fung, H. K.; McAllister, S. R.; Monnigmann, M.; Rajgaria, R. Advances in Protein Structure Prediction and De Novo Protein Design: A Review. Chemical Engineering Science 2005, 61, 966-988.
  2. Floudas, C. A. Research Challenges, Opportunities and Synergism in Systems Engineering and Computational Biology. AIChE Journal 2005, 51 (7), 1872-1884.
  3. Floudas, C. A. Computational Methods in Protein Structure Prediction. Biotechnology and Bioengineering 2007, 97, 207-213.
  4. Fung, H. K.; Welsh, W. J.; Floudas, C. A. Computational De Novo Peptide and Protein Design: Rigid Templates versus Flexible Templates. Industrial & Engineering Chemistry Research 2008, 47, 993-1001.
  5. Bellows, M. L.; Floudas, C. A. Computational methods for de novo protein design and its applications to the human immunodeficiency virus 1, purine nucleoside phosphorylase, ubiquitin specific protease 7 and histone demethylases. Current Drug Targets 2010, 11 (3), 264-278.
  6. Khoury, G. A.; Smadbeck, J.; Kieslich, C. A.; Floudas, C. A. Protein folding and de novo protein design for biotechnological applications. Trends in Biotechnology 2014, 32 (2), 99-109.
  7. Guzman, Y. A; Sakellari, D.; Arsenakis, M.; Floudas, C. A Proteomics for the discovery of biomarkers and diagnosis of periodontitis: a critical review. Expert Review of Proteomics 2014, 11 (1), 31-41.

Chapters in Books

  1. Floudas, C. A.; Klepeis, J. L.; Pardalos, P. M. Global Optimization Approaches in Protein Folding and Peptide Docking. In Mathematical Support for Molecular Biology; Farach-Colton, M., Roberts, F. S., Vingron, M., Waterman, M., Eds.; DIMACS Series in Discrete Mathematics and Theoretical Computer Science, Vol. 47; American Mathematical Society, 1999; pp 141-171.

Protein Folding

Structure Prediction
  1. Androulakis, I. P.; Maranas, C. D.; Floudas, C. A. Prediction of Oligopeptide Conformations via Deterministic Global Optimization. Journal of Global Optimization 1997, 11, 1-34.
  2. Klepeis, J. L.; Androulakis, I. P.; Ierapetritou, M. G.; Floudas, C. A. Predicting Solvated Peptide Conformations via Global Minimization of Energetic Atom-to-Atom Interactions. Computers and Chemical Engineering 1998, 22, 765-788.
  3. Klepeis, J. L.; Floudas, C. A. A Comparative Study of Global Minimum Energy Conformations of Hydrated Peptides. Journal of Computational Chemistry 1999, 20, 636-654.
  4. Klepeis, J. L.; Floudas, C. A. Free Energy Calculations for Peptides via Deterministic Global Optimization. Journal of Chemical Physics 1999, 110, 7491-7512.
  5. Klepeis, J. L.; Schafroth, H. D.; Westerberg, K. M.; Floudas, C. A. Deterministic Global Optimization and Ab Initio Approaches for the Structure Prediction of Polypeptides, Dynamics of Protein Folding and Protein-Protein Interactions. Advances in Chemical Physics 2002, 120, 265-457.
  6. Klepeis, J. L.; Floudas, C. A. Ab Initio Prediction of Helical Segments in Polypeptides. Journal of Computational Chemistry 2002, 23, 245-266.
  7. Klepeis, J. L.; Floudas, C. A. Prediction of Beta-Sheet Topology and Disulfide Bridges in Polypeptides. Journal of Computational Chemistry 2003, 24, 191-208.
  8. Klepeis, J. L.; Floudas, C. A. Ab Initio Tertiary Structure Prediction of Proteins. Journal of Global Optimization 2003, 25, 113-140.
  9. Klepeis, J. L.; Pieja, M.; Floudas, C. A. A New Class of Hybrid Global Optimization Algorithms for Peptide Structure Prediction: Integrated Hybrids. Computer Physics Communications 2003, 151, 121-140.
  10. Klepeis, J. L.; Pieja, M.; Floudas, C. A. A New Class of Hybrid Global Optimization Algorithms for Peptide Structure Prediction: Alternating Hybrids and Application fo Met-Enkephalin and Melittin. Biophysical Journal 2003, 84, 869-882.
  11. Klepeis, J. L.; Floudas, C. A. ASTRO-FOLD: a combinatorial and global optimization framework for ab initio prediction of three-dimensional structures of proteins from the amino acid sequence. Biophysical Journal 2003, 85, 2119-2146.
  12. Klepeis, J. L.; Wei, Y.; Hecht, M. H.; Floudas, C. A. Ab Initio Prediction of the 3-Dimensional Structure of a De Novo Designed Protein: A Double Blind Case Study. Proteins 2005, 58, 560-570.
  13. Klepeis, J. L.; Floudas, C. A. Analysis and Prediction of Loop Segments in Protein Structures. Computers and Chemical Engineering 2005, 29, 423-436.
  14. Monnigmann, M.; Floudas, C. A. Protein Loop Structure Prediction with Flexible Stem Geometries. Proteins 2005, 61, 748-762.
  15. McAllister, S. R.; Mickus, B. E.; Klepeis, J. L.; Floudas, C. A. A Novel Approach for Alpha-Helical Topology Prediction in Globular Proteins: Generation of Interhelical Restraints. Proteins 2006, 65, 930-952.
  16. McAllister, S. R.; Floudas, C. A. Alpha-Helical Topology Prediction and Generation of Distance Restraints in Membrane Proteins. Biophysical Journal 2008, 95 (11), 5281-5295.
  17. McAllister, S. R.; Floudas, C. A. Enhanced Bounding Techniques to Reduce the Protein Conformational Search Space. Optimization Methods and Software 2009, 24 (4-5), 837-855.
  18. Rajgaria, R.; McAllister, S. R.; Floudas, C. A. Towards Accurate Residue-Residue Contact Prediction for Alpha Helical Proteins via Integer Linear Optimization. Proteins 2009, 74 (4), 929-947.
  19. Subramani, A.; DiMaggio, P. A.; Floudas, C. A. Selecting high quality protein structures from diverse conformational ensembles. Biophysical J. 2009, 97, 1728-1736.
  20. McAllister, S. R.; Floudas, C. A. An Improved Hybrid Global Optimization Method for Protein Tertiary Structure Prediction. Computational Optimization and Applications 2010, 45, 377-413.
  21. Rajgaria, R.; Wei, Y.; Floudas, C. A. Contact prediction for beta and alpha-beta proteins using integer linear optimization and its impact on the first principles 3d structure prediction method ASTRO-FOLD. Proteins 2010, 78, 1825-1846.
  22. Wei, Y.; Floudas, C. A. Enhanced inter-helical residue contact prediction in transmembrane proteins. Chemical Engineering Science 2011, 66, 4356-4369.
  23. Subramani, A.; Wei, Y.; Floudas, C. A. ASTRO-FOLD 2.0: An enhanced framework for protein structure prediction. AIChE Journal 2012, 58 (5), 1619-1637.
  24. Subramani, A.; Floudas, C. A. Structure Prediction of Loops with Fixed and Flexible Stems. The Journal of Physical Chemistry B 2012, 116 (23), 6670-6682.
  25. Khoury, G. A.; Tamamis, P.; Pinnaduwage, N.; Smadbeck, J.; Kieslich, C. A.; Floudas, C. A. Princeton_TIGRESS: Protein geometry refinement using simulations and support vector machines. Proteins: Structure, Function, and Bioinformatics 2014, 82 (5), 794-814.
  26. Khoury, G. A.; Liwo, A.; Khatib, F.; Zhou, H.; Chopra, G.; Bacardit, J.; Bortot, L. O.; Faccioli, R. A.; Deng, X.; He, Yi; Krupa, P.; Li, J.; Mozolewska, M. A.; Sieradzan, A. K.; Smadbeck, J.; Wirecki, T.; Cooper, S.; Flatten, J.; Xu, K.; Baker, D.; Cheng, J.; Delbem, A. C. B.; Floudas, C. A.; Keasar, C.; Levitt, M.; Popović, Z.; Scheraga, H. A.; Skolnick, J.; Crivelli, S. N.; Players, F. WeFold: A coopetition for protein structure prediction. Proteins: Structure, Function, and Bioinformatics 2014, 82 (9), 1850-1868.
Dynamics
  1. Westerberg, K. M.; Floudas, C. A. Locating All Transition States and Studying Reaction Pathways of Potential Energy Surfaces. Journal of Chemical Physics 1999, 110, 9259-9296.
  2. Westerberg, K. M.; Floudas, C. A. Dynamics of Peptide Folding: Transition States and Reaction Pathways of Solvated and Unsolvated Tetra-Alanine. Journal of Global Optimization 1999, 15, 261-297.
Force Fields
  1. Loose, C.; Klepeis, J. L.; Floudas, C. A. A New Pairwise Folding Potential Based on Improved Decoy Generation and Side-Chain Packing. Proteins 2004, 54, 303-314.
  2. Rajgaria, R.; McAllister, S. R.; Floudas, C. A. A Novel High Resolution Cα-Cα Distance Dependent Force Field Based on a High Quality Decoy Set. Proteins 2006, 65 (3), 726-741.
  3. Rajgaria, R.; McAllister, S. R.; Floudas, C. A. Distance Dependent Centroid to Centroid Force Fields using High Resolution Decoys. Proteins 2008, 70, 950-970.
  4. Khoury, G. A.; Thompson, J. P.; Smadbeck, J.; Kieslich, C. A.; Floudas, C. A. Forcefield_PTM: Ab Initio Charge and AMBER Forcefield Parameters for Frequently Occurring Post-Translational Modifications. Journal of Chemical Theory and Computation 2013, 9 (12), 5653-5674.
  5. Khoury, G. A; Bhatia, N.; Floudas, C. A Hydration free energies calculated using the AMBER ff03 charge model for natural and unnatural amino acids and multiple water models. Computers & Chemical Engineering 2014, 71 (0), 745-752.
  6. Khoury, G. A.; Smadbeck, J.; Tamamis, P.; Vandris, A. C.; Kieslich, C. A.; Floudas, C. A. Forcefield_NCAA: Ab Initio Charge Parameters to Aid in the Discovery and Design of Therapeutic Proteins and Peptides with Unnatural Amino Acids and Their Application to Complement Inhibitors of the Compstatin Family. ACS Synthetic Biology 2014, 3 (12), 855-869.
NMR Structure Refinement
  1. Klepeis, J. L.; Floudas, C. A.; Morikis, D.; Lambris, J. D. Predicting Peptide Structures Using NMR Data and Deterministic Global Optimization. Journal of Computational Chemistry 1999, 20 (13), 1354-1370.
Sequence Alignment
  1. McAllister, S. R.; Rajgaria, R.; Floudas, C. A. Global Pairwise Sequence Alignment Through Mixed-Integer Linear Programming: A Template-Free Approach. Optimization Methods and Software 2007, 22 (1), 127-144.
  2. McAllister, S. R.; Rajgaria, R.; Floudas, C. A. A Path Selection Approach to Global Pairwise Sequence Alignment using Integer Linear Optimization. Optimization 2008, 57, 101-111.

De Novo Protein Design

  1. Klepeis, J. L.; Floudas, C. A.; Morikis, D.; Tsokos, C. G.; Argyropoulos, E.; Spruce, L.; Lambris, J. D. Integrated Computational and Experimenal Approach for Lead Optimization and Design of Compstatin Variants with Improved Activity. Journal of the American Chemical Society 2003, 125 (28), 8422-8423.
  2. Klepeis, J. L.; Floudas, C. A.; Morikis, D.; Lambris, J. D. Design of Peptide Analogs with Improved Activity using a Novel de novo Protein Design Approach. Industrial & Engineering Chemistry Research 2004, 43, 3817-3826.
  3. Morikis, D.; Soulika, A. M.; Mallik, B.; Klepeis, J. L.; Floudas, C. A.; Lambris, J. D. Improvement of the anti-C3 activity of complement using rational and combinatorial approaches. Biochemical Society Transactions 2004, 32, 28-32.
  4. Fung, H. K.; Rao, S.; Floudas, C. A.; Prokopyev, O.; Pardalos, P. M.; Rendl, F. Computational Comparison Studies of Quadratic Assignment Like Formulations for the In Silico Sequence Selection Problem in De Novo Protein Design. Journal of Combinatorial Optimization 2005, 10, 41-60.
  5. Morikis, D.; Floudas, C. A.; Lambris, J. D. Structure-based integrative computational and experimental approach for the optimization of drug design. Lecture Notes in Computer Science 2005, 3515, 680-688.
  6. Fung, H. K.; Taylor, M. S.; Floudas, C. A. Novel Formulations for the Sequence Selection Problem in de Novo Protein Design with Flexible Templates. Optimization Methods and Software 2007, 22 (1), 51-71.
  7. Fung, H. K.; Floudas, C. A; Taylor, M. S.; Zhang, L.; Morikis, D. Towards Full Sequence De Novo Protein Design with Flexible Templates for Human Beta-Defensin-2. Biophysical J. 2008, 94, 584-599.
  8. Taylor, M. S.; Fung, H. K.; Rajgaria, R.; Filizola, M.; Weinstein, H.; Floudas, C. A. Mutations Affecting the Oligomerization Interface of G-Protein Coupled Receptors Revealed by a Novel De Novo Protein Design Framework. Biophysical J. 2008, 94, 2470-2481.
  9. Sun, J.; Abdeljabbar, D. M.; Clarke, N.; Bellows, M. L.; Floudas, C. A.; Link, A. J. Reconstitution and Engineering of Apoptotic Protein Interactions on the Bacterial Cell Surface. J. Molecular Biology 2009, 394, 297-305.
  10. Bellows, M. L.; Fung, H. K.; Taylor, M. S.; Floudas, C. A.; Lopez de Victoria, A.; Morikis, D. New Compstatin Variants Through Two De Novo Protein Design Frameworks. Biophysical Journal 2010, 98 (10), 2337-2346.
  11. Tamamis, P.; Morikis, D.; Floudas, C. A.; Archontis, G. Species specificity of the complement inhibitor compstatin investigated by all-atom molecular dynamics simulations. Proteins 2010, 78 (12), 2655-2667.
  12. Bellows, M. L.; Taylor, M. S.; Cole, P. A.; Shen, L.; Siliciano, R. F.; Fung, H. K.; Floudas, C. A. Discovery of entry inhibitors for HIV-1 via a novel de novo protein design framework. Biophysical J. 2010, 99, 3445-3453.
  13. Pan, S. J.; Cheung, W. L.; Fung, H. K.; Floudas, C. A.; Link, A. J. Computational design of the lasso peptide antibiotic microcin J25. Protein Engineering, Design & Selection 2011, 24, 275-282.
  14. Lopez de Victoria, A.; Gorham Jr., R. D.; Bellows-Peterson, M. L.; Ling, J.; D., Lo D; Floudas, C. A.; Morikis, D. A new generation of potent complement inhibitors of the compstatin family. Chemical Biology and Drug Design 2011, 77, 431-440.
  15. Tamamis, P.; Pierou, P.; Mytidou, C.; Floudas, C. A.; Morikis, D.; Archontis, G. Design of a modified mouse protein with ligand binding properties of its human analog by molecular dynamics simulations: The case of C3 inhibition by compstatin. Proteins: Structure, Function, and Bioinformatics 2011, 79 (11), 3166-3179.
  16. Tamamis, P.; López de Victoria, A.; Gorham, R. D.; Bellows-Peterson, M. L.; Pierou, P.; Floudas, C. A.; Morikis, D.; Archontis, G. Molecular Dynamics in Drug Design: New Generations of Compstatin Analogs. Chemical Biology & Drug Design 2012, 79 (5), 703-718.
  17. Bellows-Peterson, M. L.; Fung, Ho Ki; Floudas, C. A.; Kieslich, C. A.; Zhang, Li; Morikis, D.; Wareham, K. J.; Monk, P. N.; Hawksworth, O. A.; Woodruff, T. M. De Novo Peptide Design with C3a Receptor Agonist and Antagonist Activities: Theoretical Predictions and Experimental Validation. Journal of Medicinal Chemistry 2012, 55 (9), 4159-4168.
  18. Smadbeck, J.; Peterson, M. B.; Khoury, G. A.; Taylor, M. A.; Floudas, C. A. Protein WISDOM: a Workbench for In Silico De novo Design of BioMolecules. Journal of Visualized Experiments 2013, e50476, 1-25.
  19. Gorham Jr., R. D.; Forest, D. L; Tamamis, P.; Victoria, A. L. d; Kraszni, M.; Kieslich, C. A; Banna, C. D; Bellows-Peterson, M. L; Larive, C. K; Floudas, C. A; Archontis, G.; Johnson, L. V; Morikis, D. Novel compstatin family peptides inhibit complement activation by drusen-like deposits in human retinal pigmented epithelial cell cultures. Experimental Eye Research 2013, 116, 96-108.
  20. Smadbeck, J.; Peterson, M. B.; Zee, B. M.; Garapaty, S.; Mago, A.; Lee, C.; Giannis, A.; Trojer, P.; A., Garcia. B.; Floudas, C. A. De Novo Peptide Design and Experimental Validation of Histone Methyltransferase Inhibitors. PLoS ONE 2014, 9 (2), e90095.
  21. Smadbeck, J.; Chan, K. H.; Khoury, G. A.; Xue, Bo; Robinson, R. C.; Hauser, C. A. E.; Floudas, C. A. De Novo Design and Experimental Characterization of Ultrashort Self-Associating Peptides. PLoS Comput Biol 2014, 10 (7), e1003718.
  22. Halai, R.; Bellows-Peterson, M. L; Branchett, W.; Smadbeck, J.; Kieslich, C. A; Croker, D. E; Cooper, M. A; Morikis, D.; Woodruff, T. M; Floudas, C. A; Monk, P. N Derivation of ligands for the complement C3a receptor from the C-terminus of C5a. European journal of pharmacology 2014, 745, 176-181.
  23. Gorham, R. D.; Forest, D. L.; Khoury, G. A.; Smadbeck, J.; Beecher, C. N.; Healy, E. D.; Tamamis, P.; Archontis, G.; Larive, C. K.; Floudas, C. A.; Radeke, M. J.; Johnson, L. V.; Morikis, D. New Compstatin Peptides Containing N-Terminal Extensions and Non-Natural Amino Acids Exhibit Potent Complement Inhibition and Improved Solubility Characteristics. Journal of Medicinal Chemistry 2015, 58 (2), 814-826.

Proteomics

De Novo and Hybrid Peptide Identification
  1. DiMaggio, P. A.; Floudas, C. A. A mixed-integer optimization framework for de novo peptide identification. AIChE Journal 2007, 53 (1), 160-173.
  2. DiMaggio, P. A.; Floudas, C. A. De Novo Peptide Identification via Tandem Mass Spectrometry and Integer Linear Optimization. Analytical Chemistry 2007, 79, 1433-1466.
  3. DiMaggio, P. A.; Floudas, C. A.; Lu, B.; Yates, J. R. A Hybrid Method for Peptide Identification using Integer Linear Optimization, Local Database Search, and QTOF or OrbiTrap Tandem Mass Spectrometry. J. Proteome Res. 2008, 7, 1584-1593.
Post-Translationally Modified Peptide Identification
  1. Young, N. L.; DiMaggio, P. A.; Plazas-Mayorca, M. D.; Baliban, R. C.; Floudas, C. A.; Garcia, B. A. High-throughput characterization of combinatorial histone codes. Molecular & Cellular Proteomics 2009, 8 (10), 2266-2284.
  2. DiMaggio, P. A.; Young, N. L.; Baliban, R. C.; Garcia, B. A.; Floudas, C. A. A mixed integer linear optimization framework for the identification and quantification of targeted post-translational modifications of highly modified proteins using multiplexed electron transfer dissociation tandem mass spectrometry. Molecular & Cellular Proteomics 2009, 8 (11), 2527-2543.
  3. Baliban, R. C.; DiMaggio, P. A.; Young, N. L.; Plazas-Mayorca, M. D.; Garcia, B. A.; Floudas, C. A. A novel approach for untargeted post-translational modification identification using integer linear optimization and tandem mass spectrometry. Molecular and Cellular Proteomics 2010, 9 (5), 764-779.
  4. Young, N. L.; Plazas-Mayorca, M. D.; DiMaggio, P. A.; Flaniken, I. Z.; J., B. A; Mishra, N.; LeRoy, G.; Floudas, C. A.; Garcia, B. A. Collective mass spectrometry approaches reveal broad and combinatorial modification of high mobility group protein A1a. J. American Society for Mass Spectrometry 2010, 21, 960-970.
  5. Khoury, G. A.; Baliban, R. C.; Floudas, C. A. Proteome-wide post-translational modification statistics: frequency analysis and curation of the swiss-prot database. Scientific Reports 2011, 1 (90), 1-5.
  6. Torrente, M. P.; Zee, B. M.; Young, N. L.; Baliban, R. C.; LeRoy, G.; Floudas, C. A.; Hake, S. B.; Garcia, B. A. Proteomic Interrogation of Human Chromatin. PLoS ONE 2011, 6 (9), e24747.
  7. Baliban, R. C.; Sakellari, D.; Li, Z.; DiMaggio, P. A.; Garcia, B. A.; Floudas, C. A. Novel protein identification methods for biomarker discovery via a proteomic analysis of periodontally healthy and diseased gingival crevicular fluid samples. Journal of Clinical Periodontology 2012, 39 (3), 203-212.
  8. Baliban, R. C.; Sakellari, D.; Li, Z.; DiMaggio, P. A.; Garcia, B. A.; Floudas, C. A. Novel protein identification methods for biomarker discovery via a proteomic analysis of periodontally healthy and diseased gingival crevicular fluid samples. Journal of Clinical Periodontology 2012, 39 (3), 203-212.
  9. Baliban, R. C.; DiMaggio, P. A.; Plazas-Mayorca, M. D.; Garcia, B. A.; Floudas, C. A. PILOT_PROTEIN: Identification of Unmodified and Modified Proteins via High-Resolution Mass Spectrometry and Mixed-Integer Linear Optimization. Journal of Proteome Research 2012, 11 (9), 4615-4629.
  10. Baliban, R. C.; Sakellari, D.; Li, Z.; Guzman, Y. A.; Garcia, B. A.; Floudas, C. A. Discovery of biomarker combinations that predict periodontal health or disease with high accuracy from GCF samples based on high-throughput proteomic analysis and mixed-integer linear optimization. Journal of Clinical Periodontology 2013, 40 (2), 131-139.
  11. Baliban, R. C.; Sakellari, D.; Li, Z.; Guzman, Y. A.; Garcia, B. A.; Floudas, C. A. Discovery of biomarker combinations that predict periodontal health or disease with high accuracy from GCF samples based on high-throughput proteomic analysis and mixed-integer linear optimization. Journal of Clinical Periodontology 2013, 40 (2), 131-139.

Protein–Protein Interactions

  1. Androulakis, I. P.; Nayak, N. N.; Monos, D. S.; Ierapetritou, M. G.; Floudas, C. A. A Predictive Method for Evaluation of Peptide Binding in Pocket-1 of HLA-DRB1 via Global Minimization of Energy Interactions. Proteins: Structure, Function, and Genetics 1997, 29 (1), 87-102.
  2. Schafroth, H. D.; Floudas, C. A. Predicting peptide binding to MHC pockets via molecular modeling, implicit solvation, and global optimization. Proteins 2004, 54, 534-556.
  3. Tamamis, P.; Floudas, C. A. Molecular Recognition of CXCR4 by a Dual Tropic HIV-1 gp120 V3 Loop. Biophysical Journal 2013, 105 (6), 1502-1514.
  4. Tamamis, P.; Floudas, C. A. Molecular Recognition of CCR5 by an HIV-1 gp120 V3 Loop. PLoS ONE 2014, 9 (4), e95767.
  5. Tamamis, P.; Floudas, C. A. Elucidating a Key Component of Cancer Metastasis: CXCL12 (SDF-1α) Binding to CXCR4. Journal of Chemical Information and Modeling 2014, 54 (4), 1174-1188.
  6. Tamamis, P.; Floudas, C. A Elucidating a Key Anti-HIV-1 and Cancer-Associated Axis: The Structure of CCL5 (Rantes) in Complex with CCR5. Scientific Reports 2014, 4 (5447), 1-9.

Metabolic and Signal Transduction Networks

  1. Hatzimanikatis, V.; Floudas, C. A.; Bailey, J. E. Analysis and Design of Metabolic Reaction Networks via Mixed-Integer Linear Optimization. AIChE J. 1996, 42, 1277-1292.
  2. Hatzimanikatis, V.; Floudas, C. A.; Bailey, J. E. Optimization of Regulatory Architectures in Metabolic Reaction Networks. Biotechnology and Bioengineering 1996, 52, 485-500.
  3. Lin, X.; Floudas, C. A.; Wang, Y.; Broach, J. R. Theoretical and Computational Studies of the Glucose Signaling Pathways in Yeast Using Global Gene Expression Data. Biotechnology and Bioengineering 2003, 84, 864-886.

Clustering and Biclustering

  1. Tan, M. P.; Broach, J. R.; Floudas, C. A. A Novel Clustering Approach and Prediction of Optimal Number of Clusters: Global Optimum Search with Enhanced Positioning. J. Global Optimization 2007, 39, 323-346.
  2. Tan, M. P.; Broach, J. R.; Floudas, C. A. Evaluation of Normalization and Pre-Clustering Issues in a Novel Clustering Approach: Global Optimum Search with Enhanced Positioning. Journal of Bioinformatics and Computational Biology 2007, 5 (4), 895-913.
  3. DiMaggio, P. A.; McAllister, S. R.; Floudas, C. A.; Feng, X. J.; Rabinowitz, J. D.; Rabitz, H. A. Biclustering via Optimal Re-ordering of Data Matrices in Systems Biology: Rigorous Methods and Comparative Studies. BMC Bioinformatics 2008, 9, 458.
  4. McAllister, S. R.; DiMaggio, P. A; Floudas, C. A.; Feng, X. J.; Rabinowitz, J. D.; Rabitz, H. A. Descriptor-Free Molecular Discovery in Large Libraries by Adaptive Substituent Reordering. Bioorganic and Medicinal Chemistry Letters 2008, 18 (22), 5967-5970.
  5. DiMaggio, P. A.; McAllister, S. R.; Floudas, C. A.; Feng, X. J.; Rabinowitz, J. D.; Rabitz, H. A. Optimal Methods for Re-ordering Data Matrices in Systems Biology and Drug Discovery Applications. Biophysical Reviews and Letters 2008, 3, 19-42.
  6. Tan, M. P.; Smith, E. R.; Broach, J. R.; Floudas, C. A. Microarray Data Mining: A Novel Optimization-Based Approach to Uncover Biologically Coherent Structures. BMC Bioinformatics 2008, 9, 268-283.
  7. McAllister, S. R.; DiMaggio, P. A.; Floudas, C. A. Mathematical Modeling and Efficient Optimization Methods for the Distance-Dependent Rearrangement Clustering Problem. J. of Global Optimization 2009, 45, 111-129.
  8. DiMaggio, P. A.; McAllister, S. R.; Floudas, C. A.; Feng, X. J.; Rabinowitz, J. D.; Rabitz, H. A. A network flow model for biclustering via optimal re-ordering of data matrices. J. of Global Optimization 2010, 47 (3), 343-354.
  9. DiMaggio, P. A.; McAllister, S. R.; Floudas, C. A.; Feng, X. J.; Rabinowitz, J. D.; Rabitz, H. A. Enhancing Molecular Discovery Using Descriptor-Free Rearrangement Clustering Techniques for Sparse Data Sets. AIChE J. 2010, 56 (2), 405-418.
  10. DiMaggio, P. A.; Subramani, A.; Judson, R. S.; Floudas, C. A. A novel framework for predicting in vivo toxicities from in vitro data using optimal methods for dense and sparse matrix clustering and logistic regression. Toxicological Sciences 2010, 118 (1), 251-265.
  11. Gounaris, C. E.; Rajendran, K.; Kevrekidis, I. G. ,; Floudas, C. A. Generation of Networks with Prescribed Degree-Dependent Clustering. Optim. Lett. 2011, 5, 435-451.