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.