• slidebg1
    Welcome to the Salis Lab at Penn State University
    We develop predictive biophysical models and design algorithms
    to rationally engineer synthetic genetic systems and organisms
    for Synthetic Biology and Metabolic Engineering applications
  • slidebg1
    Welcome to the Salis Lab at Penn State University
    We engineer bacteria, yeast, and algae to add new metabolic and sensing capabilities.
    Our software has designed over 100,000 synthetic DNA sequences for biotech researchers around the world.
    Photo credit: ChiamYu Ng

Fundamental Research Questions

The Biophysics of Gene Expression and Regulation

An organism's genes control its cellular fate: metabolism, self-replication, development, pathogenesis, and disease. Gene expression levels are controlled by multiple layers of biomolecular interactions with its genetic template. With so many coupled interactions, it remains a challenge to predict how DNA mutations affect gene expression levels.

In the Salis Lab, we develop and experimentally validate biophysical models that predict an organism's gene expression levels from its DNA sequence. Each model is highly reductionist, focusing on specific biomolecular interactions that control transcription rate, translation rate, or their regulation. We formulate low-parameter models using statistical thermodynamics and kinetics, parameterize them using rationally designed series of experiments, and validate their predictions across hundreds of experiments using diverse DNA sequence sets. We then create user-friendly web interfaces so that the Life Science community can use our biophysical models of gene expression for their own studies.

Design and Optimization of Synthetic Genetic Systems

The functions of simple micro-organisms are ultimately determined by their DNA sequences. By redesigning those sequences, we can engineer micro-organisms to become distributed sensor networks, active signal processing agents, chemically computing decision-makers, and nano-sized chemical factories. In practice, there are an astronomical number of design choices (different DNA sequences), but only a relatively few will yield a successfully engineered micro-organism.

In the Salis Lab, we develop computational algorithms that allow researchers to rationally design and efficiently optimize the DNA sequences of engineered micro-organisms. Our algorithms design very specific DNA sequences that yield well-predicted gene expression levels inside the cell, enabling researchers to combine and control multiple genes together in a reliable fashion. We use our algorithms to increase recombinant protein expression, design sensors for diverse chemicals, efficiently optimize metabolic pathways, and program sophisticated decision-making genetic circuits.

"Engineering Super Microbes to Save Us"

Prof. Salis connects our everyday consumerism to the global energy and chemicals industry. What will it take to build a prosperous biorenewable chemicals industry, ridding our society of its dependence on petroleum? Can Super Microbes Save Us? A public lecture in the seminar series "Strategies for Survival on Planet Earth", hosted by Penn State University's College of Eberly Sciences.

"Clone Less, Know More"

Prof. Salis describes the recent applications of his lab's research in a globally tele-cast webinar entitled "Clone Less, Know More: Efficient Expression Optimization of Proteins and Pathways", hosted by GenScript USA. This webinar covers the computational design of synthetic DNA to systematically optimize expression levels in one- or multi-protein genetic systems, while performing the fewest number of experimental measurements. Because it's costly and honestly quite boring to screen combinatorial libraries. So let's use our brains instead!

Synthetic Biology and Metabolic Engineering Applications

Engineering Cellular Sensors

Micro-organisms can be engineered to detect and respond to specific chemicals with applications in medical diagnostics, CBE detection, and environmental remediation. Using a biophysical model of riboswitches (RNA-based sensors), we have rationally designed and engineered cell sensors for drugs, metabolites, toxins, explosives, hormones, and neurotransmitters. We have also engineered natural sensors that employ transcriptional regulation to sense important chemicals.

Engineering Genetic Circuits

Genetic circuits combine multiple gene regulators to perform signal processing and decision-making. We use our biophysical models to rationally engineer synthetic genetic circuits with desired input-output transformations. We have created a series of signal amplification circuits to enhance the dynamic ranges of our cellular sensors. We have also developed new approaches to experimentally measure the intrinsic binding affinities of transcription factors, which becomes necessary to predict, control, and optimize their gene regulatory behaviors.

Engineering Metabolic Networks and Pathways

We rationally engineer and introduce new metabolic pathways into organisms and efficiently optimize their biosynthetic capacities. We have engineered a 3-enzyme terpenoid biosynthesis pathway to demonstrate our new approach to pathway optimization; a 5-enzyme Entner-Doudoroff pathway to rapidly regenerate the essential cofactor NADPH; and a 6-enzyme furfural catabolic pathway to remove a toxic microbial inhibitor found in lignocellulosic feedstock. Our design algorithms allow new researchers to quickly design, build, and optimize metabolic pathways with a high rate of success.

Engineering Synthetic Genomes

As our engineered genetic systems have grown in size, we now routinely integrate them into the organism's genome to ensure their stability and self-replication. We've combined our rational design methods with the latest genome engineering techniques -- MAGE and CRISPR/Cas9 -- to snip, slice, and prune an organism's genome with desired knock-up and knock-down of its gene expression levels. Say goodbye to plasmids!

The Promoter Calculator

Predicts the transcription initiation rates of sigma70 promoters in bacteria.

Designs synthetic promoters for targeted transcription initiation rates.

The RBS Calculator v2.0

Predicts the translation initiation rates of natural bacterial mRNA sequences (validated in many bacterial hosts).

Designs synthetic ribosome binding sites for targeted translation initiation rates.

The RBS Library Calculator

Designs the smallest size library of ribosome binding sites to uniformly vary a bacterial protein's expression level.

Designs maximally informative genetic system variants for efficiently finding a system's optimal protein expression levels.

The Operon Calculator

Predicts the expression levels of proteins within a multi-cistronic operon (transcription rates, translation rates, and mRNA decay rate).

Designs multi-cistronic operons with desired protein expression levels.

The Synthesis Success Calculator

Predicts when a DNA fragment will be rapidly synthesized using DNA synthesis (to avoid surprises).

The Non-Repetitive Parts Calculator

Designs large toolboxes of highly non-repetitive genetic parts from specified constraints.

The ELSA Calculator

Designs Extra-Long sgRNA Arrays (ELSAs) to co-express up to 20 CRISPR RNAs at the same time without triggering genetic instability.

Advancing the Engineering Science of Synthetic Biology

Synthetic Biology has a Scaling Problem.

When we engineer an organism, there are an astronomical number of design choices (different DNA sequences), but only a tiny fraction will produce a high-performance, economically viable organism. Modern Synthetic Biology requires model-based predictions and sophisticated computational design methodologies to build only the most optimal designs.

The Salis Lab is at the fore-front, building a modern Synthetic Biology engineering discipline that expands the breadth of applications and eliminates trial-and-error.
Let's build it together. -Howard Salis

Who Uses Our Models and Algorithms?


Registered Users


Sequences Designed






Industrial Licensees


Undergraduate Courses

Meet the Lab

  • Howard's picture

    Prof. Howard Salis

    / Principal Investigator
    salis at! psu dot! edu

  • Erin's picture

    Erin Essington

    / Graduate Student
    exe5132 at! psu dot! edu

    Erin is building and characterizing novel genetic circuits inside soil bacteria to detect and respond to toxic chemicals.

  • Harry's picture

    Harry Adamson

    / Graduate Student
    hea5043 at! psu dot! edu

    Harry is engineering aquatic organisms with new capabilities to mitigate climate change.

  • James's picture

    James McLellan

    / Graduate Student
    jrm6978 at! psu dot! edu

    James is engineering new types of genetic circuits to detect high-dimensional patterns, carry out biocomputation, and control cellular functions.

  • Lindsey's picture

    Lindsey Bell

    / Graduate Student
    llb5404 at! psu dot! edu

    Lindsey is developing a new type of continuous-flow bioreactor to grow large quantities of algae biomass and sequester large amounts of atmospheric carbon dioxide.

Lab Alumni

  • Manish's picture

    Dr. Manish Kushwaha

    Research Scientist
    The Micalis Institute

  • Iman's picture

    Dr. Iman Farasat

    Ph.D. Chemical Engineering, 2015
    Associate Director Biologics, Janssen Pharmaceuticals

  • Amin's picture

    Dr. Amin Espah Borujeni

    Ph.D. Chemical Engineering, 2016
    Postdoctoral Scholar, MIT

  • Tian's picture

    Dr. Tian Tian

    Ph.D. Biological Engineering, 2016
    Senior Scientist, Inscripta

  • ChiamYu's picture

    Dr. Chiam Yu Ng

    Ph.D. Chemical Engineering, 2017
    Solutions Engineer, Google X

  • Long's picture

    Dr. Long Chen

    Ph.D. Biological Engineering, 2019
    Researcher, Tianjin Academy of Agricultural Sciences

    Sean Halper

    Ph.D. Chemical Engineering, 2019
    Chemical Engineer, U.S. Army CCDC Army Research Laboratory

  • Alex's picture

    Alexander Reis

    Ph.D. Chemical Engineering, 2020
    Scientist, Manifold Bio

    Grace Vezeau

    Ph.D. Biological Engineering, 2021
    Scientist, Ultivue

  • Daniel's picture

    Daniel Cetnar

    Ph.D. Chemical Engineering, 2021
    Scientist, Bristol Myers Squibb

  • Travis's picture

    Travis La Fleur

    Ph.D. Chemical Engineering, 2023
    Scientist, Moderna

  • Ayaan's picture

    Ayaan Hossain

    Ph.D. Integrated Bioinformatics and Genomics
    Scientist, Tessera Therapeutics

  • Morgan's picture

    Morgan Roggenbaum

    M.S. Biological Engineering
    Doctoral Student, University of Washington

Lab Photos and Videos


Aren't blue light trans-illuminators great? And your DNA is A-OK!


Tian admires her stack of agar plates.


Is this engineering or artistry? Or both!


Iman designs a genetic system using our algorithms.


A Lab Outing to Hershey Park in the Dark.

This was a fun one!

Our Publications

Vezeau, G.E., L.R. Gadila, and H.M. Salis. Automated design of protein-binding riboswitches for sensing human biomarkers in a cell-free expression system. Nature Communications v14(1), p2416 (2023).


La Fleur, T.L., Hossain, A. and H.M. Salis. Automated Model-Predictive Design of Synthetic Promoters to Control Transcriptional Profiles in Bacteria. Nature Communications v13(1), p5159 (2022).


Vezeau, G.E. and H.M. Salis. Tuning Cell-free Composition Controls the Time-delay, Dynamics, and Productivity of TX-TL Expression. ACS Synthetic Biology v10(10), p2508–2519 (2021)


Korwar, A.M., Hossain, A., Lee, T.J., Shay, A.E., Basrur, V., Conlon, K., Smith, P.B., Carlson, B.A., Salis, H.M., Patterson, A.D. and K.S. Prabhu. Selenium-dependent metabolic reprogramming during inflammation and resolution. Journal of Biological Chemistry 296, p100410 (2021)


Manzano, I., Taylor, N., Csordas, M., Vezeau, G.E., Salis, H.M., and A.L. Zydney. "Purification of Cas9—RNA complexes by ultrafiltration." Biotechnology progress 37(2), p3104 (2021)


Cetnar, D.P and H.M. Salis. Systematic Quantification of Sequence and Structural Determinants Controlling mRNA stability in Bacterial Operons. ACS Synthetic Biology, 10(2), p318-332 (2021)


Hossain, A., Lopez, E., Halper, S.M., Cetnar, D.P., Reis, A.C., Strickland, D., Klavins, E., and H.M. Salis. Automated design of thousands of nonrepetitive parts for engineering stable genetic systems. Nature biotechnology, 38(12), p1466-1475 (2020)


Halper, S.M., Hossain A., and H.M. Salis. The Synthesis Success Calculator: Predicting the rapid synthesis of DNA fragments with machine learning. ACS Synthetic Biology, 9(7), p1563-1571 (2020)


Leiby, N., Hossain, A., and Salis, H.M. EPiC Series in Computing. Convolutional neural net learns promoter sequence features driving transcription strength. In Proceedings of the 12th International Conference (Vol. 70, pp. 163-172). (2020)


Reis, A.C., Halper, S.M., Vezeau, G.E., Cetnar, D.P., Hossain, A., Clauer, P.R., and H.M. Salis. Simultaneous repression of multiple bacterial genes using nonrepetitive extra-long sgRNA arrays. Nature biotechnology, 37(11), 1294-1301 (2019).


Leistra, A.N., Gelderman, G., Sowa, S.W., Moon-Walker, A., Salis, H.M., and L.M. Contreras. A Canonical Biophysical Model of the CsrA Global Regulator Suggests Flexible Regulator-Target Interactions. Scientific reports, 8(1), 1-19 (2018).


Reis, A. C and H.M. Salis. An Automated Model Test System for Systematic Development and Improvement of Gene Expression Models. ACS Synthetic Biology, 9(11), p3145-3156 (2020)


Halper S.M., D. Cetnar, and H.M. Salis. An Automated Pipeline for Engineering Many-Enzyme Pathways: Computational Sequence Design, Pathway Expression-Flux Mapping, and Scalable Pathway Optimization. Synthetic Metabolic Pathways within the book series Methods in Molecular Biology, v1671. 2017.


Espah Borujeni A., D. Cetnar, I. Farasat, A. Smith, N. Lundgren and H.M. Salis. Precise Quantification of Translation Inhibition by mRNA structures that Overlap with the Ribosomal Footprint in N-terminal Coding Sequences. Nucleic Acids Research. 2017.


Espah Borujeni A. and H.M. Salis. Translation Initiation is Controlled by RNA Folding Kinetics via a Ribosome Drafting Mechanism. Journal of the American Chemical Society. 2016.


Farasat I. and H.M. Salis. A Biophysical Model of CRISPR/Cas9 Activity for Rational Design of Genome Editing and Gene Regulation. PLoS Computational Biology. 2016


Breakthrough Article: Espah Borujeni A., D.M. Mishler, J. Wang, W. Huso, and H.M. Salis. Automated Physics-based Design of Synthetic Riboswitches from Diverse RNA Aptamers. Nucleic Acids Research. 2016


Valerie Soo, Michael McAnulty, Arti Tripathi, Fayin Zhu, Limin Zhang, Emmanuel Hatzakis, Philip Smith, Saumya Agrawal, Hadi Nazem-Bokaee, Saratram Gopalakrishnan, Howard Salis, James Ferry, Costas Maranas, Andrew Patterson and Thomas Wood. Reversing Methanogenesis to Capture Methane for Liquid Biofuel Precursors. Microbial Cell Factories. 2016


Tian T. and H.M. Salis. A Predictive Biophysical Model of Translational Coupling to Coordinate and Control Protein Expression in Bacterial Operons. Nucleic Acids Research. 2015


Kushwaha M. and H.M. Salis. A Portable Expression Resource for Engineering Cross-species Genetic Circuits and Pathways. Nature Communications. 2015


Ng C.Y., I. Farasat, C. Maranas, and H.M. Salis. Rational Design of a Synthetic Entner-Doudoroff pathway for Improved and Controllable NADPH Regeneration. Metabolic Engineering. 2015


Farasat, I., Kushwaha M., Collens J., Easterbrook M., Guido M., and Salis H.M. Efficient search, mapping, and optimization of multi-protein genetic systems in diverse bacteria. Molecular Systems Biology. 2014


Espah Borujeni, A., Channarasappa A.S., and Salis H.M. Translation Rate is Controlled by Coupled Trade-offs between Site Accessibility, Selective RNA unfolding and Sliding at Upstream Standby Sites. Nucleic Acids Research. 2013


Salis, H.M. The Ribosome Binding Site Calculator, Methods in Enzymology. 2011.


Salis, H.M., Mirsky, E.A. and Voigt, C.A. Automated Design of Synthetic Ribosome Binding Sites to Control Protein Expression. Nature Biotechnology. 2009


Tabor, J.J., Salis, H.M., Simpson, Z.B., Chevalier, A.A., Levskaya, A., Marcotte, E., Voigt, C.A., and Ellington, A.D. A Synthetic Genetic Edge Detection Program. Cell. 2009


Groban, E.S., Clarke, E.J., Salis, H.M, Miller, S.M., and Voigt, C.A. Kinetic Buffering of Crosstalk between Bacterial Two-component Sensors. Journal of Molecular Biology. 2009


Salis H.M., Tamsir A., and Voigt C.A. Engineering Bacterial Signals and Sensors, Bacterial Sensing and Signaling. 2009


Temme, K., Salis, H., Tullman-Ercek, D. Levskaya, A., Hong, S-H., and Voigt, C. A. Induction and Relaxation Dynamics of the Regulatory Network Controlling the Type III Secretion System Encoded within Salmonella Pathogenicity Island 1. Journal of Molecular Biology. 2008


H. Salis and Y. Kaznessis. Computer Aided Design of Modular Protein Devices: Logical AND Gene Activation. Physical Biology. 2006


H. Salis, V. Sotiropoulos, and Y. Kaznessis. Multiscale Hy3S: Hybrid Stochastic Simulation for Supercomputers. BMC Bioinformatics. 2006.


L. Tuttle, H. Salis, J. Tomshine, and Y. Kaznessis. Model-Driven Designs of an Oscillating Gene Network. Biophysical Journal. 2005.


H. Salis, and Y. Kaznessis. An Equation-free Probabilistic Steady State Approximation: Dynamic Application to the Stochastic Simulation of Biochemical Reaction Networks. Journal of Chemical Physics. 2005


H. Salis, and Y. Kaznessis. Accurate Hybrid Stochastic Simulation of a System of Coupled Chemical or Biochemical Reactions. Journal of Chemical Physics. 2005


H. Salis, and Y. Kaznessis. Numerical Simulation of Stochastic Gene Circuits, Computers in Chemical Engineering. 2005


H.M. Salis. Simulation of Stochastic Chemical Systems: Applications in the Design and Construction of Synthetic Gene Networks, Ph.D. Thesis. Chemical Engineering, University of Minnesota. 2007.

Resident Education at Penn State University

BE 302: Transport Processes for Biological Systems

A junior-level required course. The fundamentals of fluid mechanics, heat transfer, and mass transfer are applied to biological systems at scales ranging from microbial to ecological. This course includes a weekly 2-hour lab. Past lecture notes are available here.

ChE 340: Introduction to Biomolecular Engineering

A junior/senior-level required course. Design principles for engineering biological systems are introduced, with a focus on biotechnology and pharmaceutical applications. This course covers the engineering of proteins, metabolism, and genetic circuits using kinetics, thermodynamics, bioinformatics, and genetic engineering techniques.

ChE 410: Mass Transfer and Separations

A senior-level required course. Introduction to principles and applications of mass transfer with a focus on the design of equilibrium staged and continuously contacting separation processes.

BE 297 | ChE 297: Introduction to Synthetic Biology and Genetic Engineering

An undergraduate elective course. An introductory course on designing and modeling small genetic systems -- sensors, regulators, and enzymes -- that reprogram an organism's behavior towards making cellular decisions and manufacturing chemical products.

BE 597 | ChE 597: Synthetic Biology. Programming Life.

A graduate-level elective course. An in-depth course on designing and modeling synthetic genetic circuits to carry out Boolean decision-making, analog feedback control, and programmed decision-making. Several literature examples are discussed. Stochastic and deterministic modeling approaches are reviewed and applied to example systems.

Undergraduate Research Projects

  • International Genetically Engineered Machine Contest (iGEM). 2010-present.
  • NSF Research Experience for Undergraduates: Chemical Energy Storage and Conversion. 2010-2013.
  • NSF Research Experience for Undergraduates: Biologically Inspired Catalytic Materials. 2014-present.

Join the Salis Lab

We are currently recruiting graduate students from the Biological Engineering and Chemical Engineering departments. Please apply to their respective graduate programs, and contact Howard for more information on research topics.