In our laboratory we mainly perform single cell genomics analysis of cells. We use cell culture experiments, and RNA expression analysis. RNAseq, ATACseq, proteomics, and PCR techniques are examples of techniques used. This analysis, generating and reading parts of living cells, is coupled with perturbations such as shRNAi, CRISPR/cas9, and live cell imaging for monitoring. To make sense of this we develop and use bioinformatics analysis, mathematical modeling, theory, construction of data-bases, and design of software (R-scripts, GitHub). We use cell-lines, primary cell, and clinical samples.

In our laboratory we come from different walks in life and around the globe. We have different professional backgrounds ranging across the natural, engineering, computational, and medical sciences.

Now, why are we doing this? Clearly, biomedical and computational applications are indeed useful and constitute an important yardstick for our work. Yet, our fundamental motivation can be traced back to the quest of understanding some principal aspects of the universe.

We believe that the existence of living systems entails cues to fundamental properties of matter, not readily distilled from fundamental laws such as Schrödinger´s or Maxwell´s equations. Evolution is a remarkable algorithm producing nested molecular systems capable of decision-making and acquisition of knowledge of and in response to the external world. Such intriguing systems, ranging from single cells to organisms, provide a glimpse of what can be achieved by exploiting specific combinatorial rearrangements of parts derived from the visible 5% of matter in our universe.

The impact of unlocking the workings underpinning such enigmatic and in part unexpected systems, formulated using a blend of fundamental mathematical, biological, and algorithmic language, is only limited by our own imagination. In practice we have, since the days of Watson-Crick and Turing, witnessed the rise of a new chapter in evolution. The script enables humanity with powerful tools supporting the design of novel techniques and machines for decomposing biological systems into parts, industrial production of devices physically instantiating the mathematics and architecture of computation, eventually setting the stage at the horizon for human crafting and reengineering of general-purpose synthetic reprogrammable bio-logic-computing systems.

Our lab is grounded in the observation that the innovation of and existence of cells in nature is arguable the fundamental unit of all matter that we perceive as alive on earth. This is the rationale guiding us to focus our work on single cells and systems of cells. Yet, our quantitative understanding how cells work as molecular computational machines, enabling the realization of different cell-types from the same genome, supporting molecular transitions or reprogramming between what we currently refer to as different cell-types is at best phenomenological. In short, we do not have a periodic system or a transition table encoding the rules if any. We cannot yet read or manipulate the instructions encoded somehow in the DNA. Deciphering the collective behavior of cells in terms of their physical properties, or collective dynamics of molecules within a cell for that matter, remains a grand challenge for any analysis of a living system. To wit, we can´t compute the emergence of an embryo, even less so creating one from first principles. Paraphrasing Feynman, what we cannot create, we don't understand.

Our systems approach is characterized by a combination of carefully designed experiments (differentiation, cellular reprogramming, genomics), generation, extraction, and analysis of large data-sets (computable databases), data analytics (bioinformatics, computational biology) & machine intelligence (machine learning, neural networks), and theory (dynamical systems, information theory, applied mathematics). In our daunting fundamental and curiosity driven quest, we encounter numerous and publish some solutions to what appears to be more mundane problems, yet linked to our overall vision, but in practice impeding our journey. In our day-to-day activity, we find practical results for medicine (biomarkers, mechanisms), analytics for complex data (causal discovery) and commercial innovations (algorithms and software).

In our view, combining single cell genomics analysis, advanced computation, and machine intelligence set the stage for cracking cellular codes, their implementation, and their execution supporting adaptive systems equipped with learning capabilities. At the end of the day, in analogy to material design, we like to ultimately read, write, compute, and design living molecular systems at will. At that juncture the community may fuel something akin to a Cambrian Explosion.