DSPX software user in lab in front of an Äkta


Your DownStream Process eXecuted in silico

scroll for more

DSPX - Your downstream process executed in silico

DSPX is a comprehensive software for simulating downstream processes in silico. DSPX relies on the simulation of mechanistic models, describing the underlying phenomena to explain and predict complexemergent behavior seen during processes, enabling a holistic, mechanistic insight and therefore prediction of the entire downstream train. This simulation suite enables any computer to become a bespoke, digital, process development laboratory, tailored to a specific use through the modular design.

GoSilico - the world leader in downstream process simulation

DSPX is developed by GoSilico, the leading force in mechanistic modeling for downstream processing. With its chromatography simulation software ChromX, GoSilico has provided solutions for seven of the ten largest biopharma companies, as well as a wealth of experience supporting SME’s to derive process understanding. It is this familiarity of bringing powerful, complex methods from the realm of academia to industrial labs through training, consulting and developing intuitive software that has led to the development of DSPX.

ChromX is now a part DSPX

Depiction of the transition of ChromX to DSPX

With the launch of DSPX, our broadly established chromatography simulation software ChromX is now available as a part of a bigger, more comprehensive DSP simulation suite. When designing DSPX, our vision was to develop ChromX into a straightforward and intuitively understandable simulation tool  and from there roll out simulation to the entire downstream process chain. DSPX is therefore designed in a modular way, and all unit operations can easily be combined together. Usability was the other key driving force when designing DSPX. Thereby, you can design the simulation toolbox you need to simulate your specific downstream process application. To improve usability, DSPX comes with auxiliary toolboxes, which facilitate lab integration, collaboration within a team, compliance and much more.

Customer voices

Iglesias Gonzales Maria Bayer testimonial portrait
Based on our experience, mechanistic models contribute significantly in the generation of process understanding, leading to a reduction of experimental work, enabling process optimization and supporting process characterization. ChromX enables the setup of mechanistic models in an attractive short time frame without the need to spend time on how partial differential equations are solved. Once the model is set up, the user interface is friendly for end-user process developers.
Dr. María Iglesias González

Technology Expert - Process Design and Optimization


Bayer AG

Juergen Hubbuch portrait testimonial
Over the past few decades, science has laid thefoundation for computer-guided process development. We've done our job. Now it's up to you to take advantage of the scientific achievements and go in silico.
Prof. Dr. Jürgen Hubbuch

Biomolecular Separation Engineering (MAB)



Yuyi Shen, Bolt Biotherapeutics
The reason we chose to do this modeling with ChromX is that we saw the true benefits of it. It’s not a showcase. It’s not just beautiful, futuristic technology – it’s practical, it’s applicable to process development, and the results matter.
Dr. Yuyi Shen

Associate Director Process Development and Manufacturing


Bolt Biotherapeutics

Benner Steven Merck portrait testimonial
Mechanistic chromatography modeling can be a powerful tool for process development. Not only does it accelerate development time by reducing the experimental workload, but it also significantly improves process understanding. I believe that chromatography modeling is gaining enough momentum in the industry that it will become a staple of downstream process development in coming years.
Dr. Steven Benner

Associate Principal Scientist



Simone Dimartino University of Edinburgh
We believe in educating the next generation of engineers ready to tackle industry relevant challenges. ChromX is the perfect tool to achieve this goal in bioprocessing: a friendly user interface to foster student’s engagement, full access to all model equations to help reinforce their understanding, and complete flexibility to cover any case study in chromatography simulation and optimisation!
Dr. Simone Dimartino

Senior Lecturer in Chemical Engineering: Bioprocess Engineering


School of Engineering | University of Edinburgh

Rensselaer Polytechnic Institute Todd Przybycien
Our group has used ChromX as a tool to support exploration of column operation scenarios, mass transport analyses and new isotherm development as part of our chromatographic bioseparations research. It’s convenient, it’s well-documented, it’s well-supported, and – most importantly for fundamental research – it’s flexible. ChromX allows us to focus on the separation science questions we want to answer without having to re-invent rigorous numerical algorithms for column chromatography simulations.
Prof. Dr. Todd M. Przybycien

Chemical and Biological Engineering


Rensselaer Polytechnic Institute (RPI)

Save time, reduce costs

DSPX allows you to replace your wet lab experiments with cheap and fast simulations. Once calibrated, all further experiments can be performed in silico – enabling process optimization, robustness studies, worst-case analyses and much more.

Process understanding

DSPX performs predictive simulations based upon mechanistic models. These models are based on fundamental laws of nature, such as fluid dynamics, kinetics and thermodynamics. Using such models, a profound mechanistic understanding of processes is gained, and it thereby fulfils regulatory demands on Quality by Design.

Knowledge management

Process knowledge improves from early to late process development phases. Mechanistic models offer a simple, yet clear language to express this knowledge in terms of mathematical equations. With DSPX, it is easy to share and manage process knowledge within your team.

A streamlined workflow to set the standard for Good Modeling Practice

DSPX follows an intuitive navigation and model building workflow from project definition to application. The main navigation reflects this: In the section ‘project’, you can set the scope of your project, see a dashboard of project activities, collaborators and utilized data resources. In the section ‘components’, you can define all of your experimental real-world assets like system components, buffers and molecules.

Then you can specify the mechanistic model you want to utilize to simulate your process. Next, you can calibrate your model and identify unknown parameters from experimental data. Once calibrated, you’re ready to apply your model to simulate your actual downstream process, e.g. sample a parameter range, optimize a process or perform single experiments in silico.

Depiction of DSPX general workflow

Plug-and-play: Component-centric data structure

Columns, resins, molecules and even methods are treated as individual items. All information regarding such an item is stored as digital representations of the respective real world component. All of these items are interchangeable and can be added to a project and optimized. This allows information to be stored easily and reused, while enabling comprehensive development, the fast combination of process parts and simple process alterations.

Screenshot of DSPX component centric data structure allowing plug and play

Better together: Collaborative model development

Two female coworkers working collaboratively with DSPX discussing about data on a screen

The DSPX Collaboration toolbox enables you to work on a project while being connected within a team. Multiple users can contribute to the same project, while a version management system gives you a full overview of who made which changes when.

Shared databases which contain system and model components, e.g. sensors, molecules, resins or columns, further help to provide colleagues with accessible process knowledge. Such a database can be saved on a flash drive, a local folder or even within a cloud. However, a database does not have to be project specific: Databases can be shared throughout various projects and serve as a central knowledge base, containing all previously simulated system and model components. Project-independent databases thereby enable straightforward knowledge management across time, distance and amongst different teams.

User-friendly and intuitive: Interactive help and software tutorials

Depiction of the DSPX help section

All DSPX features and functionalities are comprehensively described in the interactive DSPX Help and the DSPX User’s Guide. By clicking on the Help icon in the main menu of DSPX, the interactive help section is opened. DSPX will then show all the relevant information regarding the specific section of the navigation you are currently in. The help texts serve as a general guideline on what the user can do in this section. At the same time, they provide a short overview of the background. In case you are interested in more detailed information, the help texts directly refer to the respective chapters of this manual. Using the search button further assists you in finding related content in the manual. Additionally, detailed software tutorials lead you through typical DSPX applications step-by-step and enable a steep learning curve.

Sampling and optimization in DSPX: Let the algorithms work

DSPX provides the option of testing a multitude of combinations of different process conditions such as pH, load density and salt concentration with its sampling functionalities. Here, the user has the choice of different automatic sampling strategies. Besides automatically generating samples, DSPX can also import parameter sets from Excel™ files.

DSPX further provides advanced optimization algorithms specialized in maximizing yield, purity and other objectives by varying process parameters. Simply state the desired objective function and parameter ranges and DSPX does the rest. This is realized with the help of several different heuristic and deterministic optimization algorithms, including gradient descent, adaptive simulated annealing and genetic algorithms. Due to its flexible nature, DSPX allows the use of any of these algorithms for both model calibration and application.

Latin hypercube sampling

Latin hypercube

Latin hypercube sampling (LHS) provides an evenly distributed set of process conditions, optimized for distance between points. This strategy provides the best granularity of the sampling results given the number of sampled points.

Parameter sweep sampling

Parameter sweep

Parameter sweep sampling is based on a regular multidimensional lattice of parameter sets. This option is helpful if the effect of a single parameter is to be studied under different process conditions.

Normally distributed sampling

Normal distribution

Gaussian normally distributed sampling is extremely useful for robustness studies. The user simply states the confidence intervals for several process conditions, for example the setpoint plus and minus the accuracy to which the parameter is adjustable, and DSPX provides a probabilistic representation of the resulting process outcomes such as yield and purity.


If you are interested in using DSPX for commercial purposes, please fill in the contact form below. We’ll get in contact with you to plan the individual implementation in your organization. If you’d like more information about the DSPX license model, special features and pricing or if you’d like to receive a free trial version of DSPX, please don’t hesitate to contact us.

    Commercial purposeAcademic DSPX license

    With pressing the submit button I declare that I have read and understood GoSilico's Privacy Policy and consent to the collection, use and disclosure of my personal data by GoSilico for the purpose of contacting me via email.

    I furthermore allow GoSilico to contact me via its email newsletter.