Monday, 31 October 2016

ARTICLE: The maths of mitochondrial DNA

Evolution of Cell-to-Cell Variability in Stochastic, Controlled, Heteroplasmic mtDNA Populations
IG Johnston, NS Jones
The American Journal of Human Genetics 99 (5), 1150-1162 (2016)
  • Vital populations of mtDNA are constantly evolving in our cells in response to random influences and control from the nucleus: we build a general mathematical theory describing this poorly-understood process and show that it predicts a wide range of existing experimental outcomes and gives us lots of new insights into biology and disease
Mitochondrial DNA (mtDNA) contains instructions for building important cellular machines. We have populations of mtDNA inside each of our cells -- almost like a population of animals in an ecosystem. Indeed, mitochondria were originally independent organisms, that billions of years ago were engulfed by our ancestor's cells and survived -- so the picture of mtDNA as a population of critters living inside our cells has evolutionary precedent! MtDNA molecules replicate and degrade in our cells in response to signals passed back and forth between mitochondria and the nucleus (the cell's "control tower"). Describing the behaviour of these population given the random, noisy environment of the cell, the fact that cells divide, and the complicated nuclear signals governing mtDNA populations, is challenging. At the same time, experiments looking in detail at mtDNA inside cells are difficult -- so predictive theoretical descriptions of these populations are highly valuable.

Why should we care about these cellular populations? MtDNA can become mutated, wrecking the instructions for building machines. If a high enough proportion of mtDNAs in a cell are mutated, our cells struggle and we get diseases. It only takes a few cells exceeding this "threshold" to cause problems -- so understanding the cell-to-cell distribution of mtDNA is medically important (as well as biologically fascinating). Simple mathematical approaches typically describe only average behaviours -- we need to describe the variability in mtDNA populations too. And for that, we need to account for the random effects that influence them.
​In our cells, signals from the "control tower" nucleus lead to the replication (orange) and degradation (purple) of mtDNA. These processes affect mtDNA populations that may contain normal (blue) and mutant (red) molecules. Our mathematical approach -- extending work addressing a similar but simpler system -- describes how the total number of machines, and the proportion of mutants, is likely to behave and change with time and as cells divide.
In the past, we have used a branch of maths called stochastic processes to answer questions about the random behaviour of mtDNA populations. But these previous approaches cannot account for the "control tower" -- the nucleus' control of mtDNA. To address this, we've developed a mathematical tradeoff -- we make a particular assumption (which we show not to be unreasonable) and in exchange are able to derive a wealth of results about mtDNA behaviour under all sorts of different nuclear control signals. Technically, we use a rather magical-sounding tool called "Van Kampen's system size expansion" to approximate mtDNA behaviour, then explore how the resulting equations behave as time progresses and cells divide.

Our approach shows that the cell-to-cell variability in heteroplasmy (the potentially damaging proportion of mutants in a cell) generally increases with time, and surprisingly does so in the same way regardless of how the control tower signals the population. We're able to update a decades-old and commonly-used expression (often called the Wright formula) for describing heteroplasmy variance, so that the formula, instead of being rather abstract and hard to interpret, is directly linked to real biological quantities. We also show that control tower attempts to decrease mutant mtDNA can induce more variability in the remaining "normal" mtDNA population. We link these and other results to biological applications, and show that our approach unifies and generalises many previous models and treatments of mtDNA -- providing a consistent and powerful theoretical platform with which to understand cellular mtDNA populations. The article is in the American Journal of Human Genetics here and a preprint version can be viewed here. Iain

Friday, 28 October 2016

ARTICLE: Random number seed

Variability in seeds: biological, ecological, and agricultural implications 
J Mitchell, IG Johnston, GW Bassel
Journal of Experimental Botany, erw397 (2016) 
  • Natural variability across scales, from the molecular to the environmental, means that individual seeds behave differently; we explore the challenges this variability poses for agriculture and food security, and how modern science can help address these challenges.
Seeds feed the world. Whether eaten themselves, or allowed to develop into crop plants which are then consumed by humans or livestock, seeds are the fundamental starting point for agriculture. But each seed has a different story. Throughout millions of years of evolution, plants have evolved to -- forgive the pun -- "hedge" their bets from one generation to the next. A parent plant cannot completely predict the environmental conditions that its offspring will face, so it induces variability in the seeds it produces. If some seeds are better at surviving in environment A and some are better in environment B, the plant has a way of ensuring its genes will survive regardless of whether the environment is A-like or B-like in future.

This bet-hedging is a sensible evolutionary strategy when environments are unpredictable. But modern agriculture makes environments much more predictable than the wild situations plants have been exposed to throughout evolutionary history. Now bet-hedging becomes a bad thing -- if we know the environment will always be C, energy spent ensuring that seeds survive in environments A and B is wasted, reducing potential yields.

Understanding and controlling the variability within populations of seeds thus has huge implications for agriculture. Variability inherent within populations of seeds, in addition to differences in the environments that seeds experience, means that, for example, seed lots germinate asynchronously (some quickly, some slowly or not at all). This leads to non-uniform and sub-optimal crop production, allows pests to enter fields, and challenges our ability to plan agricultural strategies. If we could control seed variability, these problems would be diminished, with a host of positive consequences for food security.

A given set of seeds will vary in their behaviour due to influences on many scales, from random molecular processes within cells to large-scale environmental stimuli. As a result, important features like germination propensity vary across seed lots (perhaps taking a broad distribution like that illustrated here), posing a challenge to agriculture and food security, which scientific understanding can mitigate.

In a new review, we survey our current understanding of the sources of variability in seeds, and its biological and agricultural implications. Processes across many scales induce variability in seed behaviour, from random cell biological interactions (like we've written about before!), through seed position in a parent plant, to large-scale environmental differences. We particularly focus on germination, an aspect of seed behaviour of crucial biological and agronomic importance, which takes place when a "developmental switch" in a seed is flipped. We discuss the genetic and molecular players that modern science has discovered to influence this decision to germinate in seeds, and describe the challenges in furthering our understanding of this vital question -- and how cool new tech, and maths, can help us make new progress! The review is in the Journal of Experimental Botany here. Iain

Wednesday, 5 October 2016

ARTICLE: European region is the most sceptical on vaccine safety

The State of Vaccine Confidence 2016: Global Insights Through a 67-Country Survey
Heidi J Larson, Alexandre de Figueiredo, Zhao Xiahong, William S Schulz, Pierre Verger, Iain G Johnston, Alex R Cook, Nick S Jones
EBioMedicine 12, 295-301 (2016)
  • How people view vaccines has a direct influence on the spread and impact of diseases; we use the largest-ever global survey of vaccine opinions to explore where and why people have issues with immunisation programmes.
Monitoring trust in immunisation programmes is essential if we are to identify areas and socioeconomic groups that are prone to vaccine-scepticism, and also if we are to forecast these levels of mistrust. Identification of vaccine-sceptic groups is especially important as clustering of non-vaccinators in social networks can serve to disproportionately lower the required vaccination levels for collective (or herd) immunity. To investigate these regions and socioeconomic groups, we performed a large-scale, data-driven study on attitudes towards vaccination. The survey — which we believe to be the largest on attitudes to vaccinations to date with responses from 67,000 people from 67 countries — was conducted by WIN Gallup International Association and probed respondents’ vaccine views by asking them to rate their agreement with the following statements: “vaccines are important for children to have”; “overall I think vaccines are safe”; “overall I think vaccines are effective”; and “vaccines are compatible with my religious beliefs”.

Our results show that attitudes vary by country, socioeconomic group, and between survey questions (where respondents are more likely to agree that vaccines are important than safe). Vaccine-safety related sentiment is particularly low in the European region, which has seven of the ten least confident countries, including France, where 41% of respondents disagree that vaccines are safe. Interestingly, the oldest age group — who may have been more exposed to the havoc that vaccine-preventable diseases can cause — hold more positive views on vaccines than the young, highlighting the association between perceived danger and pro-vaccine views. Education also plays a role. Individuals with higher levels of education are more likely to view vaccines as important and effective, but higher levels of education appear not to influence views on vaccine safety.

Our study, "The State of Vaccine Confidence 2016: Global Insights Through a 67-Country Survey" can be read for free in the journal EBioMedicine with a commentary here. You can find other treatments in Science magazine, New Scientist, Financial Times, Le Monde and Scientific American. Sadly our work also appeared in the Daily Mail. Alex, Iain, and Nick.

Saturday, 10 September 2016

ARTICLE: Migration, mothers, mitochondria, and medicine

mtDNA diversity in human populations highlights the merit of haplotype matching in gene therapies

EC Røyrvik, JP Burgstaller, IG Johnston
Molecular Human Reproduction 22 (11), 809-817 (2016)
  • The diversity of mtDNA in modern human populations may pose a challenge to gene therapies that aim to prevent the inheritance of deadly mtDNA disease; we use population and census data, and large-scale mtDNA sequence data, to assess this risk and suggest strategies to combat it.
Some mothers carry disease-causing mutations in their mitochondrial DNA (mtDNA), which can be passed on to their children. Amazing cutting-edge therapies are designed to avoid the inheritance of mutant mtDNA, by endowing a child with mtDNA from another woman (let's say Wilma) -- with no dangerous mutations -- instead of the mother's (let's say Miranda's) mtDNA. However, due to technical challenges in the implementation of these therapies, a small amount of the mother's mtDNA may remain in the child. If that initially small amount can become amplified -- say Miranda's mtDNA proliferates more quickly than Wilma's -- it may come to dominate cells in the child. Then the disease which the therapy attempted to avoid may become manifest -- as we've written about before

We have previously found, in mice, that the more different two mtDNA types are, the more likely one is to dominate over another. So if Miranda and Wilma have very different mtDNA, there's a good chance Miranda's might become amplified. But, although these effects are dramatic in natural mouse populations, we don't really know how likely this "winning" and "losing" was between human mtDNAs (as we'd see in the above therapies). Say Matilda and Wilma both come from London. How different will their mtDNA types likely be? And so, what is the risk that Matilda's mtDNA will beat Wilma's, potentially complicating therapies?

Human mtDNA varies by geography -- women from different parts of the world belong to different mtDNA "haplogroups". Some haplogroups are themselves very diverse, and some less so; haplogroups also differ from each other by varying degrees. So we needed to address two questions: (1) what are the likely mtDNA groups of women taken from a given region (say, Birmingham); and (2) how genetically different are two mtDNAs taken from these groups?
(left) Due to the history and evolution of human populations, some mtDNA types -- denoted here by letters -- are historically more common in different world regions. (right) Our analysis of large-scale sequence data tells us how genetically different two mtDNAs from randomly-sampled women from different ancestral backgrounds are likely to be (circle size). The more different, the more likely the therapies involving that pair of women will experience difficulties.

To answer these, we retrieved (from the NCBI database) over 7000 human mtDNA sequences, as well as information about the mtDNA makeup of pre-industrial different regions around the world, and census information about the UK's, London's, and Birmingham's ethnic makeup. We used this information to estimate the mtDNA makeup of modern human populations -- which have become highly mixed through migration in recent times. Using these estimates, we then simulated thousands of Matilda-Wilma pairings in specific regions around the world (including the UK, London, and Birmingham). We recorded the genetic differences between these simulated pairs of mtDNAs to see how different we may expect women from different regions to be. The results have just appeared in Molecular Human Reproduction here; a similar, pre-peer-review version can be viewed for free here.

We found that the size of genetic differences likely to arise when sampling pairs women from modern populations was around 20-80 SNPs (single nucleotide polymorphisms -- specific molecular differences in mtDNA). This level of difference was enough to lead to substantial segregation bias in mouse models, suggesting that unprincipled choice of Wilmas from the general population could be problematic. These large differences are in large part due to modern population mixing, with substantial mixing of African and Asian mtDNA in modern UK cities contributing to the diversity. We showed that "haplotype matching" -- checking that Wilma is genetically similar to Matilda -- decreases these differences and so decreases the likelihood of problems with therapies. We also created a preliminary chart to help this process, showing which human haplotypes are genetically similar to others -- hopefully this will both help scientific understanding and therapeutic implementation in this field. Iain and Ellen

Friday, 2 September 2016

ARTICLE: Controlling the control of our cellular power stations

Modulating mitochondrial quality in disease transmission: towards enabling mitochondrial DNA disease carriers to have healthy children

Alan Diot, Eszter Dombi, Tiffany Lodge, Chunyan Liao, Karl Morten, Janet Carver, Dagan Wells, Tim Child, Iain G Johnston, Suzannah Williams, Joanna Poulton
Biochem Soc Trans (in press) (2016)
  • Dysfunctional mitochondria are recycled by the cell in a process that helps avoid disease; we summarise extending and provide new information about this process, and show -- agreeing with our mathematical theory -- that it can be modulated with drug treatments, providing potentially new therapeutic avenues.
Mitochondria -- a focus of our research -- are "power stations" in our cells that produce the energy we need to live. Like the power stations we build, mitochondria contain machines that work to produce this energy. They also contain the genetic "instructions" on how to build these machines, in the form of mitochondrial DNA (mtDNA). MtDNA can become mutated, spoiling these instructions, giving rise to dysfunctional machines and causing problems in our cells. Thankfully, our cells have systems that helps remove these mutant mtDNAs and recycle the bad machines that they've produced. One example is "mitophagy" (from mito-(chondria) and -phagy (eating)), as we've written about before.

Mitophagy uses "autophagosomes" to remove mtDNA from the cell, but it's hard to observe and measure: our understanding of the process, and how we may influence it to address diseases, is limited. In a recent paper, we summarise current understanding of mitophagy, particularly during early development (of importance for the inheritance of mtDNA diseases). As experiments and models explore the process in more detail, different types of mitophagy (progressing through different pathways) have been identified, as have fascinating "surges" of mitophagy at different developmental stages. In a new paper in Biochemical Society Transactions we discuss how these individual results are helping to build an overall picture of how mtDNA populations are controlled by cells.

Figure: single-cell microscopy determines how many autophagosomes (green), potentially recycling dysfunctional mitochondria, exist in cells during development. Drug treatments (lower row) can influence this number, potentially allowing us to control cellular mtDNA populations.

We also present some interesting preliminary results that may help us better understand, and control, mitophagy. Very soon after fertilisation, as an egg cell starts to divide, it seems that the amount of mtDNA in the growing embryo may decrease, rather more than previously reported. The experimental team, centred on Alan Diot, explored how many autophagosomes existed within cells during this process, and also showed that post-fertilisation treatment with drugs can affect the number of autophagosomes and hence the mtDNA populations in dividing cells (see figure). We've previously shown using mathematical modelling that decreasing mtDNA content may help avoid the inheritance of mtDNA diseases -- these new results highlight the feasibility of these potential new therapeutic strategies to address mtDNA disease inheritance. Iain

Wednesday, 31 August 2016

ARTICLE: Understanding the strength and correlates of immunisation programmes

Forecasted trends in vaccination coverage and correlations with socioeconomic factors: a global time-series analysis over 30 years

  •  Lack of trust in vaccines results in preventable illness and death all over the world; we use tools from statistics and large-scale socio-economic data to explore which features of a country "prime" it for weakened vaccine coverage, identifying factors which may help policymakers address vaccine confidence issues.
Childhood vaccinations are vital for the protection of children against dreadful diseases such as measles, polio, and diphtheria. In addition to providing personal protection, vaccines can also suppress epidemic outbreaks if a sufficiently large proportion of the population has immunity status – this “herd immunity” is important for society as many individuals are unable to vaccinate for medical reasons. Over the past half a century, public health organisations have made concerted efforts to vaccinate every child worldwide. However, notwithstanding the substantial improvements to vaccine coverage rates across the globe over the past few decades, there are still millions of unvaccinated children worldwide. The majority of these children live in countries where large numbers of the populations live in deprived, rural regions with poor access to healthcare. However, a number of children are denied vaccines because of parental attitudes and beliefs (which are often influenced by the media, religious groups, or anti-vaccination groups) – such hesitancy has been responsible for recent outbreaks in developing (e.g. Nigeria, Pakistan, Afghanistan) and developed (e.g. USA, UK) countries alike. Monitoring vaccine coverage rates, summarising recent vaccination behaviours, and understanding the factors which drive vaccination behaviour are thus key to our understanding vaccine acceptance, and can allow immunisation programmes to be more effectively tailored.
To understand these pertinent issues, we used machine learning tools on publicly-available vaccination and socioeconomic data (which can be found here and on the World Health Organization’s websites). We used Gaussian process regression to forecast vaccine coverage rates and used the predictive distributions over forecasted coverage rates to introduce a quantitative marker summarising a country’s recent vaccination trends and variability:  this summary is termed the Vaccine Performance Index. Parameterisations of this index can then be used to identify countries which are likely (over next few years) to have vaccine coverage rates far from those required for herd immunity levels or that are displaying worrying declines in rates and to assess which countries will miss immunisation goals set by global public health bodies. We find that these poorly-performing countries were mostly located in South-East Asia and sub-Saharan Africa though, surprisingly, a handful of European countries also perform poorly.

To investigate the factors associated with vaccination coverage, we sought links between socioeconomic factors with vaccine coverage and found that countries with higher levels of births attended by skilled health staff, gross domestic product, government health spending, and higher education levels have higher vaccination coverage levels (though these results are region-dependent).

Our vaccine performance index could aid policy makers’ assessments of the strength and resilience of immunisation programmes. Further,  identification of socioeconomic correlates of vaccine coverage points to factors to address to improve vaccination coverage. You can read further in our freely available paper – which is in collaboration with the London School of Hygiene and Tropical Medicine (Heidi Larson and David Smith) and IIT Delhi (Sumeet Agarwal) – in the open-access journal Lancet Global Health under the title “Forecasted trends in vaccination coverage and correlations with socioeconomic factors: a global time-series analysis over 30 years” and there is another free article unpacking it under the title "Global Trends in Vaccination Coverage". Alex, Iain, Nick.

Thursday, 18 February 2016

ARTICLE: Who keeps the plans for our power stations?

Evolutionary Inference across Eukaryotes Identifies Specific Pressures Favoring Mitochondrial Gene Retention

IG Johnston, BP Williams
Cell Systems 2 (2), 101-111 (2016)

  • Why some genes are retained in mitochondria, where they are prone to disease-causing mutation, is a much-debated evolutionary question: we use new and generalisable maths and statistics to harness a large volume of sequence data and find the features of genes that predict the patterns of mitochondrial evolution that we observe.

Billions of years ago, a single-celled organism that would become our ancestor engulfed another smaller single-celled organism. The engulfed cell was probably intended to be lunch, but for reasons that remain mysterious (though recently explored here), it remained intact within our ancestor. It produced valuable chemicals that our ancestor could make use of, and was protected within the larger cell. This started a mutually beneficial relationship that evolved over billions of years to give rise to our situation today -- we are the descendants of the big cell, and our mitochondria are the descendants of the small, engulfed cell. 

As they were once independent organisms, mitochondria possess their own genomes (mitochondrial DNA, or mtDNA, which we've written about before). However, unlike the genomes of independent single-celled organisms like bacteria, mtDNA has only a handful of genes: why? Over evolutionary time, the majority of genes have either vanished from mtDNA or been transferred to the nucleus of the host cell. The reasons for transferring these genes to the nucleus are quite well understood; the nucleus is a safer environment for genes, less prone to mutation, and has several other evolutionary advantages.  But, given that transfer to the nucleus is possible, and genes in mtDNA are susceptible to mutation and damage (often giving rise to devastating diseases, which we study and try to prevent), why have mitochondria retained any genes at all? 

This question has been asked for decades, but until recently we lacked the data and the mathematical language to answer it quantitatively. Scientists energetically debate several different hypotheses: our approach attempts to let the data speak for itself without any preconceived ideas about which hypotheses are most likely. To this end, we built a mathematical model encompassing the evolutionary history of organisms with mitochondria, and a powerful statistical framework to amalgamate all the data that has been collected in recent years -- thousands of mitochondrial genomes from organisms from plants to protists (and humans) -- and harness it to compare the many disputed hypotheses addressing this question. 

Our mathematical approach allows us to "rewind the tape of evolution" and explore how mitochondrial genes have evolved. We're looking at Complex I -- an important protein complex involved in respiration -- over time, and watching the number of its subunits encoded in mitochondrial DNA (coloured black) decrease over evolutionary time, according to rules which we identify. The skyscrapers in the background are part of a graph describing how more mtDNA genes are lost over evolutionary history.

In a new paper in Cell Systems here (free here) we found several features that are most related to whether a gene is retained in mtDNA. Before discussing what they were, note that this picture -- several different features each with some influence -- explains and justifies the existing scientific debate. If hypothesis X and hypothesis Y both represent parts of the underlying "truth", then scientists advocating X alone and scientists advocating Y alone are neither completely wrong nor necessarily at odds -- everyone's partly right and the truth lies in the combination of the two arguments. 

The features that predict mtDNA gene retention are how central a gene's product is in its protein complex, the hydrophobicity of the protein the gene encodes, and the proportion of G's and C's in the gene's sequence. This suggests that genes are retained in mtDNA:  
(a) To allow local control of mitochondrial machinery (individual mitochondria can be controlled in response to cellular demands, rather than having to apply changes to the entire cellular population of mitochondria at once).  
(b) To prevent hydrophobic proteins ending up in the wrong place in the cell (if encoded by the far-away nucleus, these proteins may not be able to reach or enter the mitochondrion). 
(c and most speculatively) Because they are capable of withstanding the damaging environment of the mitochondria (GC-rich DNA and RNA is chemically more robust than GC-poor molecules). 

We found that the combination of the features we identified also predicted the success of experiments where scientists have attempted to mimic evolution and artificially transfer genes from the mitochondrion to the nucleus. Our results, as well as addressing a central mystery of evolutionary biology, thus also have the potential to inform synthetic biology approaches to tailor the genetics and bioenergetics of organisms. One final but important point is that the mathematical and statistical machinery we built for this project is highly generalisable and an efficient way of harnessing large sets of data about evolutionary and progressive processes -- we hope to use it to explore lots of other questions, including figuring out the pathways of disease progression and suggesting personalised medicine strategies in the clinic. Iain and Ben

Sunday, 14 February 2016

ARTICLE: Happy Valentine's Day!

Endless love: On the termination of a playground number game

  • A well-known playground game aims to compute a "love score" between two players based on the letters in their names and demonstrates surprisingly rich mathematical behavior; we use some back-of-the-envelope maths and computer simulation to find the highest-scoring names, situations in which the game never ends, and general rules underlying its behaviour.

The "Love Calculator" game is played in playgrounds and online across the world. It computes a playful "love compatibility" by first writing down the counts of l's, o's, v's, e's and s's that appear in two partners' names, then repeatedly adding numbers written next to each other until a percentage score is found -- "Alice loves Bob 54%!" (11010 -> 2111 -> 322 -> 54, as in the picture).

Our light-hearted research project has used maths and computer simulation to explore how the game behaves with different names -- including pairs of all the most common childrens' names in the UK -- and in different languages. "Endless love" -- when the game gets stuck in a loop, or keeps expanding forever -- often occurs between names with high letter counts (like Reese Witherspoon and Calvin Harris).

The project found that any point in the game can be described as a point in a mathematical "space", and that each step in the game moves a point in different ways, like different pieces on a chessboard. While individual outcomes are hard to predict, the average behaviour shows patterns that are repeated over games. The space contains a "cliff"; if moves carry a game over the cliff, it will continue forever.

(left) Alice and Bob playing the "loves" game. The number of l's, o's, v's, e's, and s's in their names give the first string of numbers. Adding neighbouring numbers produces the next strings, until we arrive at the 54% score. But the game never stops for some name combinations. (right) A mathematical picture of the game. w is the length of a string of numbers; m is the sum of the numbers in the string (so 11010 would have w = 5 and m = 3). The arrows show how w and m change on average as a game progresses. The blue region moves left toward an m = 2 final score; the red region keeps growing (or looping), never reaching a final score, and leading to "endless love". 

Different patterns of the "loves" letters give different expected scores. Among the most common childrens' names in the UK, Connor has the highest expected score of 67%, with Evie, Holly, Lola, Molly and Olivia also scoring highly. Names with no "loves" letters -- from Adam to Ryan -- have the lowest expected score of 26%. The most successful names have a middling number of "loves" letters, between 2 and 6. Pairs of o's, several l's, and an absence of some other letters seem to be the key to success -- though a full theory of which patterns give which scores remains elusive.

"Endless love: On the termination of a playground number game" is due to appear in Recreational Mathematics Magazine and is available here. Iain

Wednesday, 3 February 2016

Mitochondrial / math bio conferences

Here's a very rough list of some mathematical biology and mitochondrial conferences that look interesting in 2016!

Generally: Some good listings for math bio conferences; some more

European Conf Mathematical Theoretical Biol 2016Nottingham Jul 11-15 Feb 14
Biomath 2016Bulgaria Jun 19-25 GONE
Int Conf Math BiolPrague Mar 30-31 Jan 31 GONE
Midwest Math bioWisconsin May 21-21Feb 19
Pacific Symposium biocomputingHawaii Jan 4-8 2017??
Algorithms for computational biologyTrujillo Spain Jun 21-23 Feb 2
International symposium bioinf research applicationsMinsk Belarus Jun 5-8 Feb 15
European confernece computational biologyHague Sep 3-7 Mar 29
Systems Biology of Mammalian CellsMunich Apr 6-8 Feb 15
Math mdoelling biology medicineCuba Jun 8-17 Feb 28
Comput Methods Sys BioCambridge Sep 21-23 ??
Applications of Mathamtics to Nonlinear SciencesNepal May 26-29 Feb 28
Information Probability Inference in Systesm BiologyAustria May 18-20 Mar 31
BAMM Biology and Medicine through MathsVirginia May 20-20 Mar 1
Foundations of systems biol in engineeringGermany Oct 9-12 Mar 20
Prac Appl Comp Biol BioinfSeville Jun 1-3 Feb 5

Generally: Some mito confs ;some more

Mitochondrial MedicineCambridge May 4-6 Mar 2
Mitochondrial MedicineSeattle Jun 15-18 ???
CSH Asia: MitochondriaChina Oct 12-16 Aug 21
GRC Mitos and ChlorosVermont Jun 19-24 May 22

Evolution, energetics and noise in the press!

Here are a few times our work on evolution, energetics and noise has appeared in the science and/or popular press, or has otherwise been in the public eye.

The evolution of C4 photosynthesis and design of efficient crops (blog article here)
The Scientist; Science Daily; BANG! Science; eLife; Imperial College; University of Cambridge; UK Plant Sciences Federation;; Innovations Report; e! Science News; Science Newsline 

Evolution of mtDNA gene content (blog article here)
Science (magazine); Science (news); Science's #1 favourite 2016 news article; The Scientist; Cell SystemsEurekAlert; Science 2.0;; Science Daily; MedIndia; University of Birmingham

MtDNA segregation and potential issues with gene therapies (blog article here)
UK HFEA (review here); Science Daily; Imperial College; Health canal; Medical Xpress

Potential issues with gene therapies in real human populations (blog article here)
UK HFEA review 2016

Worldwide vaccine confidence (blog article here)
World Economic Forum, La Vanguardia, Le Monde, International Business Times, Scientific American, Fox News, Vox, Yahoo News, New Scientist, Science, Daily Mirror, Daily Mail 

Caladis, a probabilistic calculator (blog article here)
Nature; Biophysical Journal

"Endless love" and the behaviour of a playground game (blog article here)
University of Birmingham

Mitochondrial "pulsing" in plants (blog article here)
Recommended by F1000
cpYFP responds to pH, not superoxide (blog article here)
Science Daily; Health Medicine Network; EurekAlert; Science 2.0; Health Canal; Medical Xpress; Bioportfolio

Polyomino self-assembly (blog article here)
Our polyominoes made an appearance at the "calcuLate" Science Museum Lates in Nov 2015; simulation website
Videos here; here

Polyominoes modelling proteins (blog article here) 
One of the most-cited articles in Interface for 2014
Virus self-assembly (blog article here)  

Journal front cover and 2010 highlight; video here

Friday, 29 January 2016

ARTICLE: Go green -- recycle mitochondria

A novel quantitative assay of mitophagy: Combining high content fluorescence microscopy and mitochondrial DNA load to quantify mitophagy and identify novel pharmacological tools against pathogenic heteroplasmic mtDNA

  • Mitophagy degrades mitochondria, and likely plays important roles in the cell's responses to mitochondrial disease, but is hard to measure and thus poorly understood: we propose new ways of measuring mitophagy and use them to explore drugs that may help change damaged mitochondrial populations
Mitochondria, as we've written about before, are important entities in our cells that produce energy and take part in many other vital processes. Mitochondrial DNA (mtDNA), inherited from our mothers, contains instructions on how to build important mitochondrial machinery. MtDNA is sometimes mutated, leading to problems with our mitochondria. How do our cells cope?

Mitophagy (from mito-(chondria) and -phagy (eating)) is a process by which cells degrade and recycle mitochondria, allowing dysfunctional mitochondria to be removed and replaced. Mitophagy is one of a number of cellular mechanisms that maintain a healthy population of mitochondria, and appears to play a central role in determining the inheritance and evolution of mtDNA over our lifetimes. However, our understanding of mitophagy is limited because it is hard to observe.

In a recent and epically-titled paper in Pharmacological Research here, we explore two different approaches for measuring mitophagy in cells. The first is physical. We used chemicals to make mitochondria glow red, and autophagosomes (the cellular machines responsible for the degradation of mitochondria) glow green. We then used a microscope to examine large numbers of cells and recorded how often red (mitochondria) and green (autophagosomes) were seen together, which we took to imply that mitophagy may be occurring. We confirmed that various drugs and chemicals known to affect mitophagy had the expected effects on this estimate of mitophagy, and that perturbing ATG7 (an essential part of the autophagic machinery) sustantially reduced our observed mitophagy levels.

We also subjected cells to stress by growing them with a less plentiful supply of energy. We found that this energy stress increased the amount of mitophagy (perhaps as cells struggle to make the very best of their mitochondrial populations). We also found that mitophagy broadly decreased in cells from older people, and was increased in cells from people carrying an mtDNA disease (negatively affecting mitochondrial functionality).

The second approach is genetic. In cells from patients with mtDNA disease, some mtDNA is normal and some is mutated -- we used genetic tools to measure the proportion of mutant mtDNA in cells. We observed that when we stressed patients' cells, levels of mutant mtDNA decreased while our physically observed measure of mitophagy increased, supporting a picture in which mitophagy removes dysfunctional mitochondria when energy output is of central importance. We also found evidence for undirected mitophagy, where mtDNA copy number is depleted with no preference for mutant or wildtype.

Observing the colocalisation of autophagosomes (green) and mitochondria (red), as well as the proportion of mutant mtDNA (white stars), allows a bilateral characterisation of mitophagy. The patterns of changes in these observations tell us about how drug treatments and different environments change mitochondrial populations.

The physical and genetic approaches give us two largely independent means to estimate mitophagy, placing our understanding of this vital process on a solid analytical foundation. We used these tools to assess the effects of various drugs on mitophagy, allowing us to characterise the effects of drugs like metformin (inhibiting mitophagy) and phenanthroline (inducing undirected mitophagy) in unprecedented detail and facilitating more precise statements about their utility in clinical contexts. Iain

Wednesday, 27 January 2016

ARTICLE: Warburg Ensemble

Monitoring Intracellular Oxygen Concentration: Implications for Hypoxia Studies and Real-time Oxygen Monitoring

  • Cancer cells vary in how they produce their energy: we make progress understanding this variability, which may eventually help scientists design better therapies.
Cells can produce energy through several processes. We'll consider two – process "O" (for "oxidative phosphorylation"), and process "G" (for "glycolysis"). "O" uses oxygen, and harnesses the cell's mitochondria to produce energy. "G" does not use oxygen and produces energy without directly using mitochondria.

Healthy cells use both “O” and “G”, but cancer cells are often observed to rely on "G" much more. The shift away from "O+G" towards just "G" in cancer is often called the "Warburg effect", after Otto Warburg, who wrote about the shift in the 1950s. It remains unclear, however, whether the Warburg effect applies to all cancer cells under all conditions, or if different cells and different environments experience different shifts. This is important because understanding how cancer cells get their energy -- and, more generally, what changes occur in cancer cells compared to healthy cells -- may allow us to design therapies that challenge cancer cells while leaving healthy cells undamaged.

We used some fancy modern technology (focussed around the MitoXpress-Intra probe) to measure the difference between oxygen levels within a cell and oxygen levels in the cell's environment. We developed a mathematical way of producing "calibration curves", directly linking the observed MitoXpress behaviour to oxygen concentrations. If cells are using "G" alone, these levels are similar, as no oxygen is being consumed by the cells. If cells are also using "O", oxygen levels within cells should be rather lower than in their environment.

We found that two different cancer cell lines (with the rather jargon-y names "RD" and "U87MG") behaved surprisingly differently. When grown on glucose, U87MG looks quite "G", with oxygen levels within cells similar to those in the environment (e.g. 17.1% in cells, 18% outside). RD looks much more "O+G", with substantial differences between in-cell and outside-cell oxygen levels (e.g 13.2% in cells, 18% outside). Importantly, these findings were reproduced across a range of environmental oxygen levels (18% to 5%), modelling the range of conditions that cancer cells experience in tumours in the body. The two cancer cell lines thus seem to produce their energy in rather different ways, underlining that the Warburg effect is not an invariant across all cancers, and that treatments may be improved by taking this into account. We also showed that treating a different cancer cell line ("786-0") with phenformin, a drug inhibiting mitochondria, shifts cells away from "O+G" to "G", and that this shift can be monitored in real time with MitoXpress.

Different cancer cell lines (U87MG and RD) produce energy through different pathways, engaging more “G” (glycolysis) or “O” (oxidative phosphorylation). “O” uses oxygen (O2), lowering oxygen levels in cells compared to their environment. The different balance of “G” and “O” in different cases is important for understanding the heterogeneity of cancer.

Our paper appears in a book with the catchy title "Oxygen Transport to Tissue XXXVII", associated with the journal Advances in Experimental Medicine and Biology. You can get a sneak peek here and we'll update with a link when possible. Iain

ARTICLE: Generations of generating functions in dividing cells

Closed-form stochastic solutions for non-equilibrium dynamics and inheritance of cellular components over many cell divisions

  • Populations of important machines in our cells behave quite randomly: we build a mathematical framework to better understand these populations (which has already helped us understand the inheritance of mtDNA disease)
Cell biology is a unpredictable world, as we've written about before. The important machines in our cells replicate and degrade in processes that can be described as random; and when cells divide, the partitioning of these machines between the resulting cells also looks random. The number of machines we have in our cells is important, but how can we work with numbers in this unpredictable environment?

In our cells, machines are produced (red), replicate (orange), and degrade (purple) randomly with time, as well as being randomly partitioned when cells split and divide (blue). Our mathematical approach describes how the total number of machines is likely to behave and change with time and as cells divide.

Tools called "generating functions" are useful in this situation. A generating function is a mathematical function (like G(z) = z2, but generally more complicated) that encodes all the information about a random system. To find the generating function for a particular system, one needs to consider all the random things that can happen to change the state of that system, write them down in an equation (the "master equation") describing them all together, then use a mathematical trick to push that equation into a different mathematical space, where it is easier to solve. If that "transformed" equation can be solved, the result is the generating function, from which we can then get all the information we could want about a random system: the behaviour of its mean and variance, the probability of making any observation at any time, and so on.

We've gone through this mathematical process for a set of systems where individual cellular machines can be produced, replicated, and degraded randomly, and split at cell divisions in a variety of different ways. The generating functions we obtain allow us to follow this random cellular behaviour in new detail. We can make probabilistic statements about any aspect of the system at any time and after any number of cell divisions, instead of relying on assumptions that the system has somehow reached an equilibrium, or restricting ourselves to a single or small number of divisions. We've applied this tool to questions about the random dynamics of mitochondrial DNA (which we're very interested in! And this work connects explicitly with our recent eLife paper) in cells that divide (like our cells) or "bud" (like yeast cells), but the approach is very general and we hope it will allow progress in many more biological situations. You can read about this, free, here in the Proceedings of the Royal Society A. Iain and Nick [blog article also here]