..:: Víctor Sojo :: UCL-CoMPLEX ::..

About UCL-CoMPLEX and my research here

MBA: after the week in Plymouth, we created a poster, marine biology-inspired. You can download mine by clicking here.

Title: When is Gene Import Advantageous?
Supervisors: Prof. Andrew Pomiankowski, Dr. Nick Lane, Prof. Rob Seymour
Subject: Evolution, methematical biology, computational biology

For my first Case Presentation (CP1) project I studied the conditions under which Horizontal Gene Transfer (HGT) may be favoured in prokaryotes. HGT is a process by which prokaryotes acquire genetic material from the environment into the cytoplasm, later to be added to or recombined into the genome, as opposed to digested as food. It may come in one of three flavours:

  • Transduction, whereby viral material is parasitically inserted into the prokaryotic cell.
  • Conjugation, a famous process in which plasmids (full small chromosomes) are copied from one cell into a neighbouring other, some times (but not necessarily) providing an advantage to the receiving cell.
  • Transformation, the only of the three processes that is under direct control of the cell, and thus the one we wanted to study here.

I started by developing a set of equations taking into account import, excrection, & mutation rates, probability of picking up a "good" or "bad" gene at random from the gene pool, cost of having genes in general, advantage of having good genes, and other relevant factors.

From there I built a discrete simulation model that allowed evaluating different outcomes in a wide variety of conditions. You can play with a large number of parameters to see the effects of Natural Selection, Genetic Drift, and HGT itself. Click below if you want to try the demo!

Run the CP1 demo Download CP1 report (PDF)

Title: Comparing models of reinforcement learning in aversive contexts
Supervisor: Prof. Peter Dayan
Subject: Neuroscience, Computational Neuroscience, aversive learning

Learning is one of the most fascinating phenomena that animals are capable of. The exact mechanisms operating when we learn have been under scrutiny for over a century, and it is now clear that the picture is inevitably complex.

For my CP2 I focused on aversive learning, the type that a healthy animal experiences when exposed repeatedly to a painful or otherwise aversive stimulus, and in particular when this is preceded by a neutral stimulus that the animal soon comes to associate with the truly negative one.

Thus, the animal learns to avoid the aversive stimulus, something that has been termed the Conditioned Avoidance Response (CAR).

It was of interest to compare algorithms that might be operating in the brain of an animal when the CAR is acquired. Several such algorithms have been developed, and I used three of them to both generate behaviour and then fit to each of the three to compare the likelihood that the behaviour had been generated by such an algorithm. This was used as a starting point to design experiments that, given a true live animal, could aid in discerning which of the possible algorithms might be operating in its brain while acquiring a CAR.

Download CP2 report (PDF)

Title: Computational Modelling of the human P2Y1 receptor
Supervisors: Prof. Peter Coveney, Dr. Andrea Townsend-Nicholson
Subject: Structural Biology, Computational Chemistry, Drug Design

Obesity is currently considered by the WHO as one of the most serious upcoming threats to public health worldwide. It is now over twice as prevalent than it was in 1980, the number of overweight adults is nearing 2 billion, a third of which are obese, and 65% of the World's population is estimated to inhabit countries in which overweight kills more people than underweight does!

Weight loss is therefore far from a cosmetic desire or a problem of "rich" countries, so in this project I worked on the computational modelling of the human hP2Y1 target, a protein that interacts with a potential drug target for obesity, the related P2Y11 receptor.

hP2Y1 belongs to large group called the G-protein-coupled receptors, Seven-transmembrane-helix receptors, or heptahelical receptors. As the name suggests, they are large transmembrane protein receptors formed by seven helices that cross the membrane from the intracellular to the extracellular regions.

In my project, I created a homology model of the receptor by comparison to the human adenosine A2A receptor, whose structure has been ellucidated and is freely available in the PDB. I then proceded to build a full model of a membrane bilayer system, with water, ions, and all, and let it run in a molecular dynamics simulation during various microseconds.

Several refinements were made, comparisons and re-modelling to other structures, changes to the charges of susceptible aminoacids, and so on, to obtain a stable structure. Finally, a stable model of the hP2Y1 receptor in a membrane was obtained, one that may be used for further modelling of its interactions with HP2Y11.

Download CP3 report (PDF)

Title: Evolutionary constraints of non-coding material in the fission yeasts
Supervisors: Dr. Daniel Jeffares, Prof. Jürg Bähler
Subject: Molecular evolution, bioinformatics, evolution

The Central Dogma of Molecular Biology, by which DNA makes RNA makes protein, served as a useful theoretical tool for many years. Much of biology can still be understood in this way, but there are many more outcomes for the information stored in genetic material besides proteins.

Canonical RNAs which function structurally as such, frequently in combination with other proteins, include ribosomal, transfer, small nucleolar, and small nuclear RNAs (rRNAs, tRNAs, snoRNAs, and snRNAs, respectively).

For this project, we are interested in a further type of non-coding RNA which, for lack of a better name, are termed non-canonical RNAs or simply non-coding RNAs (ncRNAs).

I have been studying the selective constraints of these non-canonical non-coding RNAs, trying to detect which of them seem to be conserved across the four species of the fission yeasts (Schizosaccharomyces pombe, S. octosporus, S. japonicus, and S. chryophylus), the hypothesis being that those ncRNAs that are more highly conserved should be exerting some sort of function in the cells, with hopes of guiding future experimental research to classify these ncRNAs within known or perhaps new classes.

Download Summer Project report (PDF)

I'm currently working on the Summer Project. Please check back after September 2012 for the PDF.

About me

Undergrad - Chemistry

I obtained a Licenciate degree (a 5-year BSc by research) in Chemistry from the Central University of Venezuela (UCV), the country's oldest and largest university.

My research topic was wet lab-based. I extracted compounds from several amazonian plants, and then evaluated their ability to inhibit Glucose-6-phosphatase, a crucial enzyme in the metabolism of carbohydrates and thus a potential target for treating diabetes and related conditions.

While at UCV, I either formally took or audited many elective subjects from the School of Biology, including Evolution, Biochemistry, Cell Biology, Molecular Genetics, Population Genetics, Population Ecology, and Animal Behaviour.

First Masters - Computer Science

I later pursued and completed a 3-year Masters by Research in Computer Science & Software Engineering, also at UCV.
For my research dissertation I developed an integrative web-based environment for Computational Chemistry.

Second Masters - Biological Modelling

I will soon be completing a Masters of Research (MRes) in Modelling Biological Complexity, at University College London (UCL), in the UK.
This has been done at CoMPLEX, an innovative centre for interdisciplinary research at UCL combining skills from the mathematical, physical, and computational sciences to tackle a diversity of problems from all the breadth of biology.
You can read more about my research at CoMPLEX in the tabs below.

In no particular order...

  • Evolutionary biology
  • The origin of life
  • The origin of essentially anything in biology
  • Animal behaviour
  • Human evolution
  • Computational (bio)chemistry & Structural Biology
  • Bioinformatics & Computational Biology

In no particular order... and with absolutely no level of expertise, even casual, in any of them...

  • Music & dancing
  • Travelling & languages
  • Cooking & wine
  • History & Politics
  • Education & New technologies for it
  • Rock climbing & Mountaineering
  • Drawing & animation